diff --git a/docs/_downloads/032d653a4f5a9c1ec32b9fc7c989ffe1/seq2seq_translation_tutorial.ipynb b/docs/_downloads/032d653a4f5a9c1ec32b9fc7c989ffe1/seq2seq_translation_tutorial.ipynb
index a5bf39b3d..3cd3765f9 100644
--- a/docs/_downloads/032d653a4f5a9c1ec32b9fc7c989ffe1/seq2seq_translation_tutorial.ipynb
+++ b/docs/_downloads/032d653a4f5a9c1ec32b9fc7c989ffe1/seq2seq_translation_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP: Sequence to Sequence \ub124\ud2b8\uc6cc\ud06c\uc640 Attention\uc744 \uc774\uc6a9\ud55c \ubc88\uc5ed\n********************************************************************************\n**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_\n  **\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 \"\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP\"\uc758 \uc138\ubc88\uc9f8\uc774\uc790 \ub9c8\uc9c0\ub9c9 \ud3b8\uc73c\ub85c, NLP \ubaa8\ub378\ub9c1 \uc791\uc5c5\uc744\n\uc704\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc5d0 \uc0ac\uc6a9\ud560 \uc790\uccb4 \ud074\ub798\uc2a4\uc640 \ud568\uc218\ub4e4\uc744 \uc791\uc131\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ub9c8\uce5c \ub4a4\uc5d0\ub294 `torchtext` \uac00 \uc5b4\ub5bb\uac8c \uc9c0\uae08\uae4c\uc9c0\uc758 \ud29c\ud1a0\ub9ac\uc5bc\ub4e4\uc5d0\uc11c\uc758\n\uc804\ucc98\ub9ac \uacfc\uc815\uc744 \ub2e4\ub8e8\ub294\uc9c0\ub97c \uc774\ud6c4 \ud29c\ud1a0\ub9ac\uc5bc\ub4e4\uc5d0\uc11c \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\ub294 \uc2e0\uacbd\ub9dd\uc774 \ubd88\uc5b4\ub97c \uc601\uc5b4\ub85c \ubc88\uc5ed\ud558\ub3c4\ub85d \uac00\ub974\uce60 \uc608\uc815\uc785\ub2c8\ub2e4.\n\n::\n\n    [KEY: > input, = target, < output]\n\n    > il est en train de peindre un tableau .\n    = he is painting a picture .\n    < he is painting a picture .\n\n    > pourquoi ne pas essayer ce vin delicieux ?\n    = why not try that delicious wine ?\n    < why not try that delicious wine ?\n\n    > elle n est pas poete mais romanciere .\n    = she is not a poet but a novelist .\n    < she not not a poet but a novelist .\n\n    > vous etes trop maigre .\n    = you re too skinny .\n    < you re all alone .\n\n... \uc131\uacf5\uc728\uc740 \ubcc0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ud558\ub098\uc758 \uc2dc\ud000\uc2a4\ub97c \ub2e4\ub978 \uc2dc\ud000\uc2a4\ub85c \ubc14\uafb8\ub294 \ub450\uac1c\uc758 RNN\uc774 \ud568\uaed8 \ub3d9\uc791\ud558\ub294\n`sequence to sequence network <https://arxiv.org/abs/1409.3215>`__ \uc758 \uac04\ub2e8\ud558\uc9c0\ub9cc \uac15\ub825\ud55c \uc544\uc774\ub514\uc5b4\uac00\n\uc774\uac83(\ubc88\uc5ed)\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc778\ucf54\ub354 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc785\ub825 \uc2dc\ud000\uc2a4\ub97c \ubca1\ud130\ub85c \uc555\ucd95\ud558\uace0,\n\ub514\ucf54\ub354 \ub124\ud2b8\uc6cc\ud06c\ub294 \ud574\ub2f9 \ubca1\ud130\ub97c \uc0c8\ub85c\uc6b4 \uc2dc\ud000\uc2a4\ub85c \ud3bc\uce69\ub2c8\ub2e4.\n\n.. figure:: /_static/img/seq-seq-images/seq2seq.png\n   :alt:\n\n\uc774 \ubaa8\ub378\uc744 \uac1c\uc120\ud558\uae30 \uc704\ud574 `Attention Mechanism <https://arxiv.org/abs/1409.0473>`__ \uc744\n\uc0ac\uc6a9\ud558\uba74 \ub514\ucf54\ub354\uac00 \uc785\ub825 \uc2dc\ud000\uc2a4\uc758 \ud2b9\uc815 \ubc94\uc704\uc5d0 \uc9d1\uc911\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.\n\n**\ucd94\ucc9c \uc790\ub8cc:**\n\n\ucd5c\uc18c\ud55c Pytorch\ub97c \uc124\uce58\ud588\uace0, Python\uc744 \uc54c\uace0, Tensor\ub97c \uc774\ud574\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.:\n\n-  http://pytorch.org/ \uc124\uce58 \uc548\ub0b4\ub97c \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/deep_learning_60min_blitz` \uc77c\ubc18\uc801\uc778 PyTorch \uc2dc\uc791\uc744 \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/pytorch_with_examples` \ub113\uace0 \uae4a\uc740 \ud1b5\ucc30\uc744 \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/former_torchies_tutorial` \uc774\uc804 Lua Torch \uc0ac\uc6a9\uc790\ub97c \uc704\ud55c \uc790\ub8cc\n\n\nSequence to Sequence \ub124\ud2b8\uc6cc\ud06c\uc640 \ub3d9\uc791 \ubc29\ubc95\uc5d0 \uad00\ud574\uc11c \uc544\ub294 \uac83\uc740 \uc720\uc6a9\ud569\ub2c8\ub2e4:\n\n-  `Learning Phrase Representations using RNN Encoder-Decoder for\n   Statistical Machine Translation <https://arxiv.org/abs/1406.1078>`__\n-  `Sequence to Sequence Learning with Neural\n   Networks <https://arxiv.org/abs/1409.3215>`__\n-  `Neural Machine Translation by Jointly Learning to Align and\n   Translate <https://arxiv.org/abs/1409.0473>`__\n-  `A Neural Conversational Model <https://arxiv.org/abs/1506.05869>`__\n\n\uc774\uc804 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0 \uc788\ub294\n:doc:`/intermediate/char_rnn_classification_tutorial`\n\uc640 :doc:`/intermediate/char_rnn_generation_tutorial` \ub294\n\uac01\uac01 \uc778\ucf54\ub354, \ub514\ucf54\ub354 \ubaa8\ub378\uacfc \ube44\uc2b7\ud55c \ucee8\uc13c\uc744 \uac00\uc9c0\uae30 \ub54c\ubb38\uc5d0 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.\n\n\ucd94\uac00\ub85c \uc774 \ud1a0\ud53d\ub4e4\uc744 \ub2e4\ub8e8\ub294 \ub17c\ubb38\uc744 \uc77d\uc5b4 \ubcf4\uc2ed\uc2dc\uc624:\n\n-  `Learning Phrase Representations using RNN Encoder-Decoder for\n   Statistical Machine Translation <https://arxiv.org/abs/1406.1078>`__\n-  `Sequence to Sequence Learning with Neural\n   Networks <https://arxiv.org/abs/1409.3215>`__\n-  `Neural Machine Translation by Jointly Learning to Align and\n   Translate <https://arxiv.org/abs/1409.0473>`__\n-  `A Neural Conversational Model <https://arxiv.org/abs/1506.05869>`__\n\n\n**\uc694\uad6c \uc0ac\ud56d**\n\n"
+        "\n\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP: Sequence to Sequence \ub124\ud2b8\uc6cc\ud06c\uc640 Attention\uc744 \uc774\uc6a9\ud55c \ubc88\uc5ed\n********************************************************************************\n**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_\n  **\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 \"\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP\"\uc758 \uc138\ubc88\uc9f8\uc774\uc790 \ub9c8\uc9c0\ub9c9 \ud3b8\uc73c\ub85c, NLP \ubaa8\ub378\ub9c1 \uc791\uc5c5\uc744\n\uc704\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc5d0 \uc0ac\uc6a9\ud560 \uc790\uccb4 \ud074\ub798\uc2a4\uc640 \ud568\uc218\ub4e4\uc744 \uc791\uc131\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ub9c8\uce5c \ub4a4\uc5d0\ub294 `torchtext` \uac00 \uc5b4\ub5bb\uac8c \uc9c0\uae08\uae4c\uc9c0\uc758 \ud29c\ud1a0\ub9ac\uc5bc\ub4e4\uc5d0\uc11c\uc758\n\uc804\ucc98\ub9ac \uacfc\uc815\uc744 \ub2e4\ub8e8\ub294\uc9c0\ub97c \uc774\ud6c4 \ud29c\ud1a0\ub9ac\uc5bc\ub4e4\uc5d0\uc11c \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\ub294 \uc2e0\uacbd\ub9dd\uc774 \ubd88\uc5b4\ub97c \uc601\uc5b4\ub85c \ubc88\uc5ed\ud558\ub3c4\ub85d \uac00\ub974\uce60 \uc608\uc815\uc785\ub2c8\ub2e4.\n\n::\n\n    [KEY: > input, = target, < output]\n\n    > il est en train de peindre un tableau .\n    = he is painting a picture .\n    < he is painting a picture .\n\n    > pourquoi ne pas essayer ce vin delicieux ?\n    = why not try that delicious wine ?\n    < why not try that delicious wine ?\n\n    > elle n est pas poete mais romanciere .\n    = she is not a poet but a novelist .\n    < she not not a poet but a novelist .\n\n    > vous etes trop maigre .\n    = you re too skinny .\n    < you re all alone .\n\n... \uc131\uacf5\uc728\uc740 \ubcc0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ud558\ub098\uc758 \uc2dc\ud000\uc2a4\ub97c \ub2e4\ub978 \uc2dc\ud000\uc2a4\ub85c \ubc14\uafb8\ub294 \ub450\uac1c\uc758 RNN\uc774 \ud568\uaed8 \ub3d9\uc791\ud558\ub294\n`sequence to sequence network <https://arxiv.org/abs/1409.3215>`__ \uc758 \uac04\ub2e8\ud558\uc9c0\ub9cc \uac15\ub825\ud55c \uc544\uc774\ub514\uc5b4\uac00\n\uc774\uac83(\ubc88\uc5ed)\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4. \uc778\ucf54\ub354 \ub124\ud2b8\uc6cc\ud06c\ub294 \uc785\ub825 \uc2dc\ud000\uc2a4\ub97c \ubca1\ud130\ub85c \uc555\ucd95\ud558\uace0,\n\ub514\ucf54\ub354 \ub124\ud2b8\uc6cc\ud06c\ub294 \ud574\ub2f9 \ubca1\ud130\ub97c \uc0c8\ub85c\uc6b4 \uc2dc\ud000\uc2a4\ub85c \ud3bc\uce69\ub2c8\ub2e4.\n\n.. figure:: /_static/img/seq-seq-images/seq2seq.png\n   :alt:\n\n\uc774 \ubaa8\ub378\uc744 \uac1c\uc120\ud558\uae30 \uc704\ud574 `Attention Mechanism <https://arxiv.org/abs/1409.0473>`__ \uc744\n\uc0ac\uc6a9\ud558\uba74 \ub514\ucf54\ub354\uac00 \uc785\ub825 \uc2dc\ud000\uc2a4\uc758 \ud2b9\uc815 \ubc94\uc704\uc5d0 \uc9d1\uc911\ud560 \uc218 \uc788\ub3c4\ub85d \ud569\ub2c8\ub2e4.\n\n**\ucd94\ucc9c \uc790\ub8cc:**\n\n\ucd5c\uc18c\ud55c Pytorch\ub97c \uc124\uce58\ud588\uace0, Python\uc744 \uc54c\uace0, Tensor\ub97c \uc774\ud574\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.:\n\n-  http://pytorch.org/ \uc124\uce58 \uc548\ub0b4\ub97c \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/deep_learning_60min_blitz` \uc77c\ubc18\uc801\uc778 PyTorch \uc2dc\uc791\uc744 \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/pytorch_with_examples` \ub113\uace0 \uae4a\uc740 \ud1b5\ucc30\uc744 \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/former_torchies_tutorial` \uc774\uc804 Lua Torch \uc0ac\uc6a9\uc790\ub97c \uc704\ud55c \uc790\ub8cc\n\n\nSequence to Sequence \ub124\ud2b8\uc6cc\ud06c\uc640 \ub3d9\uc791 \ubc29\ubc95\uc5d0 \uad00\ud574\uc11c \uc544\ub294 \uac83\uc740 \uc720\uc6a9\ud569\ub2c8\ub2e4:\n\n-  `Learning Phrase Representations using RNN Encoder-Decoder for\n   Statistical Machine Translation <https://arxiv.org/abs/1406.1078>`__\n-  `Sequence to Sequence Learning with Neural\n   Networks <https://arxiv.org/abs/1409.3215>`__\n-  `Neural Machine Translation by Jointly Learning to Align and\n   Translate <https://arxiv.org/abs/1409.0473>`__\n-  `A Neural Conversational Model <https://arxiv.org/abs/1506.05869>`__\n\n\uc774\uc804 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0 \uc788\ub294\n:doc:`/intermediate/char_rnn_classification_tutorial`\n\uc640 :doc:`/intermediate/char_rnn_generation_tutorial` \ub294\n\uac01\uac01 \uc778\ucf54\ub354, \ub514\ucf54\ub354 \ubaa8\ub378\uacfc \ube44\uc2b7\ud55c \ucee8\uc13c\uc744 \uac00\uc9c0\uae30 \ub54c\ubb38\uc5d0 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.\n\n\ucd94\uac00\ub85c \uc774 \ud1a0\ud53d\ub4e4\uc744 \ub2e4\ub8e8\ub294 \ub17c\ubb38\uc744 \uc77d\uc5b4 \ubcf4\uc2ed\uc2dc\uc624:\n\n-  `Learning Phrase Representations using RNN Encoder-Decoder for\n   Statistical Machine Translation <https://arxiv.org/abs/1406.1078>`__\n-  `Sequence to Sequence Learning with Neural\n   Networks <https://arxiv.org/abs/1409.3215>`__\n-  `Neural Machine Translation by Jointly Learning to Align and\n   Translate <https://arxiv.org/abs/1409.0473>`__\n-  `A Neural Conversational Model <https://arxiv.org/abs/1506.05869>`__\n\n\n**\uc694\uad6c \uc0ac\ud56d**\n"
       ]
     },
     {
@@ -423,7 +423,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/03a48646520c277662581e858e680809/model_parallel_tutorial.ipynb b/docs/_downloads/03a48646520c277662581e858e680809/model_parallel_tutorial.ipynb
index cb7448ebd..85ae0e229 100644
--- a/docs/_downloads/03a48646520c277662581e858e680809/model_parallel_tutorial.ipynb
+++ b/docs/_downloads/03a48646520c277662581e858e680809/model_parallel_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\ub2e8\uc77c \uba38\uc2e0\uc744 \uc774\uc6a9\ud55c \ubaa8\ub378 \ubcd1\ub82c\ud654 \uc2e4\uc2b5 \uc608\uc81c\n===================================================\n**\uc800\uc790** : `Shen Li <https://mrshenli.github.io/>`_\n**\ubc88\uc5ed** : `\uc548\uc0c1\uc900 <https://github.com/Justin-A>`_\n\n\ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\ub294 \ubd84\uc0b0 \ud559\uc2b5 \uae30\uc220\uc5d0 \ubc94\uc6a9\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \n\uc774\uc804 \ud29c\ud1a0\ub9ac\uc5bc\ub4e4\uc5d0\uc11c\ub294 'DataParallel' `<https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html>`_\n\uc5ec\ub7ec GPU\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud559\uc2b5 \uc2dc\ud0ac \ub54c \uc5b4\ub5bb\uac8c DataParallel\uc744 \uc0ac\uc6a9\ud558\ub294\uc9c0\uc5d0 \ub300\ud574\uc11c \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4.\n\uc774 \ubc29\ubc95\uc740 \uac01 GPU\uc5d0 \uc785\ub825 \ub370\uc774\ud130\ub97c \ubd80\ubd84\uc801\uc73c\ub85c \ud560\ub2f9\ud558\uace0 \ub3d9\uc77c\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ubcf5\uc81c\ud558\uc5ec \uc774\uc6a9\ud558\ub294 \ubc29\uc2dd\uc774\uc5c8\uc2b5\ub2c8\ub2e4. \n\uc774 \ubc29\ubc95\uc740 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc0c1\ub2f9\ud788 \ube60\ub974\uac8c \ud559\uc2b5\uc2dc\ud0ac \uc218 \uc788\ub294 \uc7a5\uc810\uc774 \uc788\uc9c0\ub9cc, \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc774 \ub108\ubb34 \ucee4\uc11c \ub2e8\uc77c GPU\uc5d0 \ud560\ub2f9\uc774 \ub418\uc9c0 \uc54a\ub294 \uacbd\uc6b0\uc5d0\ub294 \ub3d9\uc791\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ``\ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac`` \uac00 \uc544\ub2cc **\ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac** \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ubc29\ubc95\uc744 \uc18c\uac1c\ud569\ub2c8\ub2e4.\n\uac01 GPU\uc5d0 \ubaa8\ub378 \uc804\uccb4\ub97c \ubcf5\uc81c\ud558\ub294 \uac83\uc774 \uc544\ub2cc, \ud558\ub098\uc758 \ubaa8\ub378\uc744 \uc5ec\ub7ec GPU\uc5d0 \ubd84\ud560\ud558\uc5ec \ud560\ub2f9\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.\n\uad6c\uccb4\uc801\uc73c\ub85c, 10\uac1c\uc758 \uce35\uc73c\ub85c \uad6c\uc131\ub41c ``m`` \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc5d0 \ub300\ud574\uc11c ``\ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac`` \ubc29\ubc95\uc740 10\uac1c\uc758 \uce35\uc744 \uc804\ubd80 \ubcf5\uc81c\ud558\uc5ec \uac01 GPU\uc5d0 \ud560\ub2f9\ud558\uc5ec \ucc98\ub9ac\ud558\uc9c0\ub9cc,\n\uc774\uc640 \ubc18\ub300\ub85c 2\uac1c\uc758 GPU\uc5d0 \ubaa8\ub378\uc744 \ubcd1\ub82c \ucc98\ub9ac\ud55c\ub2e4\uba74, \uac01 GPU\uc5d0 5\uac1c\uc758 \uce35\uc529 \uac01\uac01 \ud560\ub2f9\ud558\uc5ec \ud638\uc2a4\ud305\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\uc758 \uc804\ubc18\uc801\uc778 \uc544\uc774\ub514\uc5b4\ub294 \ubaa8\ub378\uc758 \uc11c\ube0c \ub124\ud2b8\uc6cc\ud06c\ub4e4\uc744 \uac01\uac01 \ub2e4\ub978 GPU\uc5d0 \ud560\ub2f9\ud558\uace0, \n\uac01 \uc7a5\ube44 \ubcc4\ub85c \uc21c\uc804\ud30c\ub97c \uc9c4\ud589\ud558\uc5ec \uacc4\uc0b0\ub418\ub294 \ucd9c\ub825\uac12\ub4e4\uc744 \uac01 \uc7a5\ube44 \uac04 \uacf5\uc720\ud558\uc5ec \uc774\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \n\uc774\uc6a9\ud558\uace0\uc790 \ud558\ub294 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ubd80\ubd84\uc801\uc73c\ub85c \uac01 GPU\uc5d0 \ud560\ub2f9\ud558\ub294 \uac83\uc774\uae30 \ub54c\ubb38\uc5d0, \uc5ec\ub7ec GPU\ub97c \uc774\uc6a9\ud558\uc5ec \ub354 \ud070 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud560\ub2f9\ud558\uace0 \ud559\uc2b5\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc740 \uac70\ub300\ud55c \ubaa8\ub378\uc744 \uc81c\ud55c\ub41c \uc218\uc758 GPU\uc5d0 \ubd84\ud560\ud558\uc5ec \ud560\ub2f9\ud558\uc9c0 \uc54a\uace0, \uadf8 \ub300\uc2e0, \ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\uc758 \uc544\uc774\ub514\uc5b4\ub97c \uc774\ud574\ud558\ub294 \ubaa9\uc801\uc73c\ub85c \uc791\uc131\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \n\ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\uc758 \uc544\uc774\ub514\uc5b4\ub97c \ud65c\uc6a9\ud558\uc5ec \uc2e4\uc81c \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0 \uc801\uc6a9\ud558\ub294 \uac83\uc740 \uc5ec\ub7ec\ubd84\uc758 \ubaab\uc785\ub2c8\ub2e4.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>\uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc5ec\ub7ec \uc11c\ubc84\ub97c \uc774\uc6a9\ud558\uc5ec \ud559\uc2b5\uc2dc\ud0a4\ub294 \ubcd1\ub82c \ud559\uc2b5 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c\ub294 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ucc38\uace0\ud558\uc138\uc694.\n   `\ubd84\uc0b0 \ud504\ub808\uc784\uc6cc\ud06c RPC \uc2dc\uc791\ud574\ubcf4\uae30 <rpc_tutorial.html>`__</p></div>\n\nBasic Usage\n-----------\n\n"
+        "\n\ub2e8\uc77c \uba38\uc2e0\uc744 \uc774\uc6a9\ud55c \ubaa8\ub378 \ubcd1\ub82c\ud654 \uc2e4\uc2b5 \uc608\uc81c\n===================================================\n**\uc800\uc790** : `Shen Li <https://mrshenli.github.io/>`_\n**\ubc88\uc5ed** : `\uc548\uc0c1\uc900 <https://github.com/Justin-A>`_\n\n\ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\ub294 \ubd84\uc0b0 \ud559\uc2b5 \uae30\uc220\uc5d0 \ubc94\uc6a9\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\uc804 \ud29c\ud1a0\ub9ac\uc5bc\ub4e4\uc5d0\uc11c\ub294 'DataParallel' `<https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html>`_\n\uc5ec\ub7ec GPU\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud559\uc2b5 \uc2dc\ud0ac \ub54c \uc5b4\ub5bb\uac8c DataParallel\uc744 \uc0ac\uc6a9\ud558\ub294\uc9c0\uc5d0 \ub300\ud574\uc11c \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4.\n\uc774 \ubc29\ubc95\uc740 \uac01 GPU\uc5d0 \uc785\ub825 \ub370\uc774\ud130\ub97c \ubd80\ubd84\uc801\uc73c\ub85c \ud560\ub2f9\ud558\uace0 \ub3d9\uc77c\ud55c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ubcf5\uc81c\ud558\uc5ec \uc774\uc6a9\ud558\ub294 \ubc29\uc2dd\uc774\uc5c8\uc2b5\ub2c8\ub2e4.\n\uc774 \ubc29\ubc95\uc740 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc0c1\ub2f9\ud788 \ube60\ub974\uac8c \ud559\uc2b5\uc2dc\ud0ac \uc218 \uc788\ub294 \uc7a5\uc810\uc774 \uc788\uc9c0\ub9cc, \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc774 \ub108\ubb34 \ucee4\uc11c \ub2e8\uc77c GPU\uc5d0 \ud560\ub2f9\uc774 \ub418\uc9c0 \uc54a\ub294 \uacbd\uc6b0\uc5d0\ub294 \ub3d9\uc791\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ``\ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac`` \uac00 \uc544\ub2cc **\ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac** \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ubc29\ubc95\uc744 \uc18c\uac1c\ud569\ub2c8\ub2e4.\n\uac01 GPU\uc5d0 \ubaa8\ub378 \uc804\uccb4\ub97c \ubcf5\uc81c\ud558\ub294 \uac83\uc774 \uc544\ub2cc, \ud558\ub098\uc758 \ubaa8\ub378\uc744 \uc5ec\ub7ec GPU\uc5d0 \ubd84\ud560\ud558\uc5ec \ud560\ub2f9\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.\n\uad6c\uccb4\uc801\uc73c\ub85c, 10\uac1c\uc758 \uce35\uc73c\ub85c \uad6c\uc131\ub41c ``m`` \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc5d0 \ub300\ud574\uc11c ``\ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac`` \ubc29\ubc95\uc740 10\uac1c\uc758 \uce35\uc744 \uc804\ubd80 \ubcf5\uc81c\ud558\uc5ec \uac01 GPU\uc5d0 \ud560\ub2f9\ud558\uc5ec \ucc98\ub9ac\ud558\uc9c0\ub9cc,\n\uc774\uc640 \ubc18\ub300\ub85c 2\uac1c\uc758 GPU\uc5d0 \ubaa8\ub378\uc744 \ubcd1\ub82c \ucc98\ub9ac\ud55c\ub2e4\uba74, \uac01 GPU\uc5d0 5\uac1c\uc758 \uce35\uc529 \uac01\uac01 \ud560\ub2f9\ud558\uc5ec \ud638\uc2a4\ud305\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\uc758 \uc804\ubc18\uc801\uc778 \uc544\uc774\ub514\uc5b4\ub294 \ubaa8\ub378\uc758 \uc11c\ube0c \ub124\ud2b8\uc6cc\ud06c\ub4e4\uc744 \uac01\uac01 \ub2e4\ub978 GPU\uc5d0 \ud560\ub2f9\ud558\uace0,\n\uac01 \uc7a5\ube44 \ubcc4\ub85c \uc21c\uc804\ud30c\ub97c \uc9c4\ud589\ud558\uc5ec \uacc4\uc0b0\ub418\ub294 \ucd9c\ub825\uac12\ub4e4\uc744 \uac01 \uc7a5\ube44 \uac04 \uacf5\uc720\ud558\uc5ec \uc774\uc6a9\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\uc774\uc6a9\ud558\uace0\uc790 \ud558\ub294 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ubd80\ubd84\uc801\uc73c\ub85c \uac01 GPU\uc5d0 \ud560\ub2f9\ud558\ub294 \uac83\uc774\uae30 \ub54c\ubb38\uc5d0, \uc5ec\ub7ec GPU\ub97c \uc774\uc6a9\ud558\uc5ec \ub354 \ud070 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud560\ub2f9\ud558\uace0 \ud559\uc2b5\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc740 \uac70\ub300\ud55c \ubaa8\ub378\uc744 \uc81c\ud55c\ub41c \uc218\uc758 GPU\uc5d0 \ubd84\ud560\ud558\uc5ec \ud560\ub2f9\ud558\uc9c0 \uc54a\uace0, \uadf8 \ub300\uc2e0, \ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\uc758 \uc544\uc774\ub514\uc5b4\ub97c \uc774\ud574\ud558\ub294 \ubaa9\uc801\uc73c\ub85c \uc791\uc131\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n\ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\uc758 \uc544\uc774\ub514\uc5b4\ub97c \ud65c\uc6a9\ud558\uc5ec \uc2e4\uc81c \uc5b4\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0 \uc801\uc6a9\ud558\ub294 \uac83\uc740 \uc5ec\ub7ec\ubd84\uc758 \ubaab\uc785\ub2c8\ub2e4.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>\uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc5ec\ub7ec \uc11c\ubc84\ub97c \uc774\uc6a9\ud558\uc5ec \ud559\uc2b5\uc2dc\ud0a4\ub294 \ubcd1\ub82c \ud559\uc2b5 \ubc29\ubc95\uc5d0 \ub300\ud574\uc11c\ub294 \ub2e4\uc74c \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ucc38\uace0\ud558\uc138\uc694.\n   `\ubd84\uc0b0 \ud504\ub808\uc784\uc6cc\ud06c RPC \uc2dc\uc791\ud574\ubcf4\uae30 <rpc_tutorial.html>`__</p></div>\n\nBasic Usage\n-----------\n"
       ]
     },
     {
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n\nclass ToyModel(nn.Module):\n    def __init__(self):\n        super(ToyModel, self).__init__()\n        self.net1 = torch.nn.Linear(10, 10).to('cuda:0') # \uccab \ubc88\uc9f8 \uce35\uc744 \uccab \ubc88\uc9f8 GPU\uc5d0 \ud560\ub2f9\n        self.relu = torch.nn.ReLU()\n        self.net2 = torch.nn.Linear(10, 5).to('cuda:1')  # \ub450 \ubc88\uc9f8 \uce35\uc744 \ub450 \ubc88\uc9f8 GPU\uc5d0 \ud560\ub2f9\n\n    def forward(self, x):\n        x = self.relu(self.net1(x.to('cuda:0'))) \n        return self.net2(x.to('cuda:1')) # \uccab \ubc88\uc9f8 \uce35\uc758 \uc0b0\ucd9c\ubb3c\uc744 \ub450 \ubc88\uc9f8 GPU\uc5d0 \ud560\ub2f9\ud558\uc5ec \uc9c4\ud589"
+        "import torch\nimport torch.nn as nn\nimport torch.optim as optim\n\n\nclass ToyModel(nn.Module):\n    def __init__(self):\n        super(ToyModel, self).__init__()\n        self.net1 = torch.nn.Linear(10, 10).to('cuda:0') # \uccab \ubc88\uc9f8 \uce35\uc744 \uccab \ubc88\uc9f8 GPU\uc5d0 \ud560\ub2f9\n        self.relu = torch.nn.ReLU()\n        self.net2 = torch.nn.Linear(10, 5).to('cuda:1')  # \ub450 \ubc88\uc9f8 \uce35\uc744 \ub450 \ubc88\uc9f8 GPU\uc5d0 \ud560\ub2f9\n\n    def forward(self, x):\n        x = self.relu(self.net1(x.to('cuda:0')))\n        return self.net2(x.to('cuda:1')) # \uccab \ubc88\uc9f8 \uce35\uc758 \uc0b0\ucd9c\ubb3c\uc744 \ub450 \ubc88\uc9f8 GPU\uc5d0 \ud560\ub2f9\ud558\uc5ec \uc9c4\ud589"
       ]
     },
     {
@@ -58,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uae30\uc874\uc5d0 \uc874\uc7ac\ud558\ub294 \ubaa8\ub4c8\uc5d0 \ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac \uc801\uc6a9\ud574\ubcf4\uae30\n----------------------------------------\n\n\uae30\uc874\uc5d0 \ub2e8\uc77c GPU\uc5d0 \uc874\uc7ac\ud558\ub294 \ubaa8\ub4c8\uc744 \uc5ec\ub7ec GPU\uc5d0 \ud560\ub2f9\ud558\ub294 \uac83\uc740  \ub2e8\uc9c0 \uba87 \uc904\uc758 \ucf54\ub4dc\ub97c \uc218\uc815\ud558\ub294 \uac83\uc73c\ub85c\ub3c4 \uc27d\uac8c \uac00\ub2a5\ud569\ub2c8\ub2e4. \n\uc544\ub798\uc5d0 \uc788\ub294 \ucf54\ub4dc\ub4e4\uc740 ResNet50 \ubaa8\ub378\uc744 \ubd84\ud560\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \uc544\uc774\ub514\uc5b4\ub294, \uae30\uc874\uc5d0 \uc874\uc7ac\ud558\ub294 ResNet \ubaa8\ub4c8\uc744 \uc0c1\uc18d\ubc1b\uc544 \uc124\uacc4\ud560 \ub54c, 2\uac1c\uc758 GPU\uc5d0 \uce35\uc744 \ub098\ub204\uc5b4 \uc124\uacc4\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4. \n\uadf8 \ud6c4, 2\uac1c GPU\uc5d0\uc11c \uacc4\uc0b0\ub418\ub294 \uc911\uac04 \uc0b0\ucd9c\ubb3c \ud150\uc11c\uac12\uc744 \uc801\uc808\ud788 \ubc30\uce58\ud558\uae30 \uc704\ud5e4 \uc21c\uc804\ud30c \uba54\uc18c\ub4dc\ub97c \uc218\uc815\ud569\ub2c8\ub2e4.\n\n"
+        "\uae30\uc874\uc5d0 \uc874\uc7ac\ud558\ub294 \ubaa8\ub4c8\uc5d0 \ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac \uc801\uc6a9\ud574\ubcf4\uae30\n---------------------------------------------------\n\n\uae30\uc874\uc5d0 \ub2e8\uc77c GPU\uc5d0 \uc874\uc7ac\ud558\ub294 \ubaa8\ub4c8\uc744 \uc5ec\ub7ec GPU\uc5d0 \ud560\ub2f9\ud558\ub294 \uac83\uc740  \ub2e8\uc9c0 \uba87 \uc904\uc758 \ucf54\ub4dc\ub97c \uc218\uc815\ud558\ub294 \uac83\uc73c\ub85c\ub3c4 \uc27d\uac8c \uac00\ub2a5\ud569\ub2c8\ub2e4.\n\uc544\ub798\uc5d0 \uc788\ub294 \ucf54\ub4dc\ub4e4\uc740 ResNet50 \ubaa8\ub378\uc744 \ubd84\ud560\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \uc544\uc774\ub514\uc5b4\ub294, \uae30\uc874\uc5d0 \uc874\uc7ac\ud558\ub294 ResNet \ubaa8\ub4c8\uc744 \uc0c1\uc18d\ubc1b\uc544 \uc124\uacc4\ud560 \ub54c, 2\uac1c\uc758 GPU\uc5d0 \uce35\uc744 \ub098\ub204\uc5b4 \uc124\uacc4\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4.\n\uadf8 \ud6c4, 2\uac1c GPU\uc5d0\uc11c \uacc4\uc0b0\ub418\ub294 \uc911\uac04 \uc0b0\ucd9c\ubb3c \ud150\uc11c\uac12\uc744 \uc801\uc808\ud788 \ubc30\uce58\ud558\uae30 \uc704\ud5e4 \uc21c\uc804\ud30c \uba54\uc18c\ub4dc\ub97c \uc218\uc815\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -76,7 +76,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc704\uc758 \uc608\uc81c\uc5d0\uc11c\ub294 \ub2e8\uc77c GPU\uc5d0 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud560\ub2f9\ud558\uc5ec \ud559\uc2b5\uc2dc\ud0a4\uae30\uc5d0\ub294 \ubaa8\ub378 \ud06c\uae30\uac00 \ub108\ubb34 \ud074 \ub54c \ubc1c\uc0dd\ud558\ub294 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \n\ud558\uc9c0\ub9cc, \uc5ec\ub7ec\ubd84\uc740 \ub2e8\uc77c GPU\ub97c \uc774\uc6a9\ud560 \ub54c\ubcf4\ub2e4 \ud559\uc2b5 \uacfc\uc815\uc774 \uc624\ub798\uac78\ub9ac\uba70, \uc774\ub294 \uc5ec\ub7ec\ubd84\ub4e4\uc774 \uc774\ubbf8 \uc54c\uace0 \uc788\ub294 \ub0b4\uc6a9\uc774\uc5c8\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \n\uadf8 \uc774\uc720\ub294, \ub450 \uac1c\uc758 GPU\uac00 \ub3d9\uc2dc\uc5d0 \uacc4\uc0b0\ud558\ub294 \uac83\uc774 \uc544\ub2c8\ub77c 1\uac1c\uc758 GPU\ub294 \uacc4\uc0b0\ud558\uc9c0 \uc54a\uace0 \ub300\uae30\ud558\uace0 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.\n\ub610\ud55c, \ub450 \ubc88\uc9f8 \uce35 (layer2)\uc774 \ud560\ub2f9\ub41c \uccab \ubc88\uc9f8 GPU\uc5d0\uc11c \uacc4\uc0b0\ub41c \uacb0\uacfc\ub97c \uc138 \ubc88\uc9f8 \uce35 (layer3)\uc774 \ud560\ub2f9\ub41c \ub450 \ubc88\uc9f8 GPU\ub85c \ud150\uc11c\uac12\uc744 \ubcf5\uc0ac\ud558\uae30 \ub54c\ubb38\uc5d0 \uacc4\uc0b0 \uacfc\uc815\uc774 \ub354 \uae38\uc5b4\uc9c0\uac8c \ub429\ub2c8\ub2e4.\n\n\ucf54\ub4dc \uc2e4\ud589 \uc2dc\uac04\uc744 \uc815\ub7c9\uc801\uc73c\ub85c \uc0b4\ud3b4\ubcf4\uae30 \uc704\ud574 \uc2e4\ud5d8\uc744 \ud558\ub098 \ud574\ubd05\uc2dc\ub2e4. \uc785\ub825 \ud150\uc11c\uac12\uacfc \ub808\uc774\ube14\uac12\uc744 \ub79c\ub364\uc73c\ub85c \uc124\uc815\ud55c \ud6c4, \n\uc774\ubbf8 \uc874\uc7ac\ud558\ub294 torchvision.models.reset50() \uacfc, \ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\ub97c \uc9c4\ud589\ud55c ``ModelParallelResNet50`` \uc744 \ud1b5\ud574 \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4.\n\ud559\uc2b5 \uc9c4\ud589\uc744 \uc644\ub8cc\ud55c \ud6c4, \ub450 \ubaa8\ub378\ub4e4\uc740 \ub79c\ub364\uc73c\ub85c \uc0dd\uc131\ub41c \ub370\uc774\ud130\ub85c \ud559\uc2b5\uc744 \uc9c4\ud589\ud588\uae30 \ub54c\ubb38\uc5d0 \uc2e4\uc6a9\uc801\uc778 \uc608\uce21\uc744 \ud558\uc9c4 \ubabb\ud558\uc9c0\ub9cc, \ud559\uc2b5 \uc9c4\ud589 \uc2dc\uac04\uc744 \uc2e4\uc6a9\uc801\uc73c\ub85c \ube44\uad50\ud558\uc5ec \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\uc704\uc758 \uc608\uc81c\uc5d0\uc11c\ub294 \ub2e8\uc77c GPU\uc5d0 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud560\ub2f9\ud558\uc5ec \ud559\uc2b5\uc2dc\ud0a4\uae30\uc5d0\ub294 \ubaa8\ub378 \ud06c\uae30\uac00 \ub108\ubb34 \ud074 \ub54c \ubc1c\uc0dd\ud558\ub294 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.\n\ud558\uc9c0\ub9cc, \uc5ec\ub7ec\ubd84\uc740 \ub2e8\uc77c GPU\ub97c \uc774\uc6a9\ud560 \ub54c\ubcf4\ub2e4 \ud559\uc2b5 \uacfc\uc815\uc774 \uc624\ub798\uac78\ub9ac\uba70, \uc774\ub294 \uc5ec\ub7ec\ubd84\ub4e4\uc774 \uc774\ubbf8 \uc54c\uace0 \uc788\ub294 \ub0b4\uc6a9\uc774\uc5c8\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uadf8 \uc774\uc720\ub294, \ub450 \uac1c\uc758 GPU\uac00 \ub3d9\uc2dc\uc5d0 \uacc4\uc0b0\ud558\ub294 \uac83\uc774 \uc544\ub2c8\ub77c 1\uac1c\uc758 GPU\ub294 \uacc4\uc0b0\ud558\uc9c0 \uc54a\uace0 \ub300\uae30\ud558\uace0 \uc788\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.\n\ub610\ud55c, \ub450 \ubc88\uc9f8 \uce35 (layer2)\uc774 \ud560\ub2f9\ub41c \uccab \ubc88\uc9f8 GPU\uc5d0\uc11c \uacc4\uc0b0\ub41c \uacb0\uacfc\ub97c \uc138 \ubc88\uc9f8 \uce35 (layer3)\uc774 \ud560\ub2f9\ub41c \ub450 \ubc88\uc9f8 GPU\ub85c \ud150\uc11c\uac12\uc744 \ubcf5\uc0ac\ud558\uae30 \ub54c\ubb38\uc5d0 \uacc4\uc0b0 \uacfc\uc815\uc774 \ub354 \uae38\uc5b4\uc9c0\uac8c \ub429\ub2c8\ub2e4.\n\n\ucf54\ub4dc \uc2e4\ud589 \uc2dc\uac04\uc744 \uc815\ub7c9\uc801\uc73c\ub85c \uc0b4\ud3b4\ubcf4\uae30 \uc704\ud574 \uc2e4\ud5d8\uc744 \ud558\ub098 \ud574\ubd05\uc2dc\ub2e4. \uc785\ub825 \ud150\uc11c\uac12\uacfc \ub808\uc774\ube14\uac12\uc744 \ub79c\ub364\uc73c\ub85c \uc124\uc815\ud55c \ud6c4,\n\uc774\ubbf8 \uc874\uc7ac\ud558\ub294 torchvision.models.reset50() \uacfc, \ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\ub97c \uc9c4\ud589\ud55c ``ModelParallelResNet50`` \uc744 \ud1b5\ud574 \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4.\n\ud559\uc2b5 \uc9c4\ud589\uc744 \uc644\ub8cc\ud55c \ud6c4, \ub450 \ubaa8\ub378\ub4e4\uc740 \ub79c\ub364\uc73c\ub85c \uc0dd\uc131\ub41c \ub370\uc774\ud130\ub85c \ud559\uc2b5\uc744 \uc9c4\ud589\ud588\uae30 \ub54c\ubb38\uc5d0 \uc2e4\uc6a9\uc801\uc778 \uc608\uce21\uc744 \ud558\uc9c4 \ubabb\ud558\uc9c0\ub9cc, \ud559\uc2b5 \uc9c4\ud589 \uc2dc\uac04\uc744 \uc2e4\uc6a9\uc801\uc73c\ub85c \ube44\uad50\ud558\uc5ec \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -94,7 +94,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc704\uc5d0\uc11c \uc815\uc758\ud55c ``train(model)`` \uba54\uc18c\ub4dc\ub294 nn.MSELoss (Mean Squared Error ; \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28) \ub85c \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud558\uc5ec \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \n\uadf8\ub9ac\uace0, ``optim.SGD`` \uba54\uc18c\ub4dc\ub294 \ucd5c\uc801\ud654 \ubc29\uc2dd\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc704 \ubc29\uc2dd\uc740 128 * 128 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\uac00 120\uac1c\ub85c \uad6c\uc131\ub41c \ubc30\uce58 \ub370\uc774\ud130\uac00 3\uac1c \uc874\uc7ac\ud558\ub294 \uc0c1\ud669\uc744 \ubaa8\ubc29\ud558\uae30 \uc704\ud574 \ub79c\ub364\uc73c\ub85c \uc0dd\uc131\ud558\uc600\uc2b5\ub2c8\ub2e4. \n\uadf8\ub9ac\uace0\ub098\uc11c, \uc6b0\ub9ac\ub294 ``timeit`` \uc744 \uc774\uc6a9\ud558\uc5ec ``train(model)`` \uba54\uc18c\ub4dc\ub97c 10\ud68c \uc2e4\ud589\ud558\uc5ec \ud559\uc2b5\uc744 \uc9c4\ud589\ud558\uace0, \ud559\uc2b5 \uc2e4\ud589 \uc2dc\uac04\uc5d0 \ub300\ud574\uc11c \ud45c\uc900 \ud3b8\ucc28\uac12\uc744 \ubc18\uc601\ud558\ub294 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uc5ec \uc800\uc7a5\ud569\ub2c8\ub2e4.\n\n"
+        "\uc704\uc5d0\uc11c \uc815\uc758\ud55c ``train(model)`` \uba54\uc18c\ub4dc\ub294 nn.MSELoss (Mean Squared Error ; \ud3c9\uade0 \uc81c\uacf1 \uc624\ucc28) \ub85c \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud558\uc5ec \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0, ``optim.SGD`` \uba54\uc18c\ub4dc\ub294 \ucd5c\uc801\ud654 \ubc29\uc2dd\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. \uc704 \ubc29\uc2dd\uc740 128 * 128 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\uac00 120\uac1c\ub85c \uad6c\uc131\ub41c \ubc30\uce58 \ub370\uc774\ud130\uac00 3\uac1c \uc874\uc7ac\ud558\ub294 \uc0c1\ud669\uc744 \ubaa8\ubc29\ud558\uae30 \uc704\ud574 \ub79c\ub364\uc73c\ub85c \uc0dd\uc131\ud558\uc600\uc2b5\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0\ub098\uc11c, \uc6b0\ub9ac\ub294 ``timeit`` \uc744 \uc774\uc6a9\ud558\uc5ec ``train(model)`` \uba54\uc18c\ub4dc\ub97c 10\ud68c \uc2e4\ud589\ud558\uc5ec \ud559\uc2b5\uc744 \uc9c4\ud589\ud558\uace0, \ud559\uc2b5 \uc2e4\ud589 \uc2dc\uac04\uc5d0 \ub300\ud574\uc11c \ud45c\uc900 \ud3b8\ucc28\uac12\uc744 \ubc18\uc601\ud558\ub294 \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud558\uc5ec \uc800\uc7a5\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -123,14 +123,14 @@
       },
       "outputs": [],
       "source": [
-        "# \uc2e4\ud5d8 \uacb0\uacfc, \ubaa8\ub378 \ubcd1\ub82c \ucca0\ub9ac\ud558\uc5ec \ud559\uc2b5\ud558\ub294 \uc2dc\uac04\uc774 \ub2e8\uc77c GPU\ub85c \ud559\uc2b5\ud558\ub294 \uc2dc\uac04\ubcf4\ub2e4 \uc57d 7% ``4.02/3.75-1=7%``\uc815\ub3c4\n# \uc624\ub798 \uac78\ub9ac\ub294 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ubbc0\ub85c, \uc21c\uc804\ud30c\uc640 \uc5ed\uc804\ud30c\ub97c \uc9c4\ud589\ud558\uba74\uc11c GPU \uac04 \ud150\uc11c\uac12\ub4e4\uc774\n# \ubcf5\uc81c\ub418\uc5b4 \uc774\uc6a9\ud558\ub294 \uc2dc\uac04\uc774 \uc57d 7%\uc815\ub3c4 \uc18c\uc694\ub418\ub294 \uac83\uc73c\ub85c \uacb0\ub860\uc9c0\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud559\uc2b5\ud558\ub294 \uacfc\uc815 \uc18d\uc5d0\uc11c\n# 2\uac1c\uc758 GPU \uc911 1\uac1c\uc758 GPU\uac00 \uacc4\uc0b0\ud558\uc9c0 \uc54a\uace0 \ub300\uae30\ud558\uace0 \uc788\uae30 \ub54c\ubb38\uc5d0, \uc774\ub97c \ud574\uacb0\ud558\uc5ec \n# \ud559\uc2b5 \uc2dc\uac04\uc744 \ube60\ub974\uac8c \uac1c\uc120\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740, \ud559\uc2b5 \ub2e8\uc704\uc778 \ubbf8\ub2c8 \ubc30\uce58 1\uac1c\uc758 \ub370\uc774\ud130\ub97c\n# 2\uac1c\ub85c \ubd84\ud560\ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc0dd\uc131\ud558\uc5ec, \ubd84\ud560\ub41c \uccab \ubc88\uc9f8 \ub370\uc774\ud130\uac00 \uccab \ubc88\uc9f8 \uce35\uc744 \ud1b5\uacfc\ud558\uc5ec \ub450 \ubc88\uc9f8 \uce35\uc73c\ub85c\n# \ubcf5\uc81c\ub418\uace0, \ub450 \ubc88\uc9f8 \uce35\uc744 \ud1b5\uacfc\ud560 \ub54c, \ub450\ubc88\uc7ac\ub85c \ubd84\ud560\ub41c \ub370\uc774\ud130\uac00 \uccab \ubc88\uca30 \uce35\uc744 \ud1b5\ud574 \uacc4\uc0b0\ub418\ub294 \ubc29\uc2dd\uc73c\ub85c \uc124\uc815\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n# \uc774\ub7ec\ud55c \ubc29\ubc95\uc744 \ud1b5\ud574\uc11c 2\uac1c\uc758 GPU\uac00 2\uac1c\ub85c \ubd84\ud560\ub41c \ub370\uc774\ud130\ub97c \ub3d9\uc2dc\uc5d0 \ucc98\ub9ac\ud560 \uc218 \uc788\uc73c\uba70 \ud559\uc2b5 \uc2dc\uac04\uc744 \ub2e8\ucd95\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4."
+        "# \uc2e4\ud5d8 \uacb0\uacfc, \ubaa8\ub378 \ubcd1\ub82c \ucca0\ub9ac\ud558\uc5ec \ud559\uc2b5\ud558\ub294 \uc2dc\uac04\uc774 \ub2e8\uc77c GPU\ub85c \ud559\uc2b5\ud558\ub294 \uc2dc\uac04\ubcf4\ub2e4 \uc57d 7% ``4.02/3.75-1=7%``\uc815\ub3c4\n# \uc624\ub798 \uac78\ub9ac\ub294 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ubbc0\ub85c, \uc21c\uc804\ud30c\uc640 \uc5ed\uc804\ud30c\ub97c \uc9c4\ud589\ud558\uba74\uc11c GPU \uac04 \ud150\uc11c\uac12\ub4e4\uc774\n# \ubcf5\uc81c\ub418\uc5b4 \uc774\uc6a9\ud558\ub294 \uc2dc\uac04\uc774 \uc57d 7%\uc815\ub3c4 \uc18c\uc694\ub418\ub294 \uac83\uc73c\ub85c \uacb0\ub860\uc9c0\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud559\uc2b5\ud558\ub294 \uacfc\uc815 \uc18d\uc5d0\uc11c\n# 2\uac1c\uc758 GPU \uc911 1\uac1c\uc758 GPU\uac00 \uacc4\uc0b0\ud558\uc9c0 \uc54a\uace0 \ub300\uae30\ud558\uace0 \uc788\uae30 \ub54c\ubb38\uc5d0, \uc774\ub97c \ud574\uacb0\ud558\uc5ec\n# \ud559\uc2b5 \uc2dc\uac04\uc744 \ube60\ub974\uac8c \uac1c\uc120\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \ud55c \uac00\uc9c0 \ubc29\ubc95\uc740, \ud559\uc2b5 \ub2e8\uc704\uc778 \ubbf8\ub2c8 \ubc30\uce58 1\uac1c\uc758 \ub370\uc774\ud130\ub97c\n# 2\uac1c\ub85c \ubd84\ud560\ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc0dd\uc131\ud558\uc5ec, \ubd84\ud560\ub41c \uccab \ubc88\uc9f8 \ub370\uc774\ud130\uac00 \uccab \ubc88\uc9f8 \uce35\uc744 \ud1b5\uacfc\ud558\uc5ec \ub450 \ubc88\uc9f8 \uce35\uc73c\ub85c\n# \ubcf5\uc81c\ub418\uace0, \ub450 \ubc88\uc9f8 \uce35\uc744 \ud1b5\uacfc\ud560 \ub54c, \ub450\ubc88\uc7ac\ub85c \ubd84\ud560\ub41c \ub370\uc774\ud130\uac00 \uccab \ubc88\uca30 \uce35\uc744 \ud1b5\ud574 \uacc4\uc0b0\ub418\ub294 \ubc29\uc2dd\uc73c\ub85c \uc124\uc815\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n# \uc774\ub7ec\ud55c \ubc29\ubc95\uc744 \ud1b5\ud574\uc11c 2\uac1c\uc758 GPU\uac00 2\uac1c\ub85c \ubd84\ud560\ub41c \ub370\uc774\ud130\ub97c \ub3d9\uc2dc\uc5d0 \ucc98\ub9ac\ud560 \uc218 \uc788\uc73c\uba70 \ud559\uc2b5 \uc2dc\uac04\uc744 \ub2e8\ucd95\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc785\ub825 \ud150\uc11c\uac12\uc744 \ubd84\ud560\ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc124\uacc4\ud558\uc5ec \ud559\uc2b5 \uc2dc\uac04\uc744 \ub2e8\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uc608\uc81c\n-----------------------------\n\n\uc544\ub798\uc5d0 \uc788\ub294 \uc2e4\ud5d8\uc740, 120\uac1c\uc758 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub41c 1\uac1c\uc758 \ubbf8\ub2c8 \ubc30\uce58 \ub370\uc774\ud130\ub97c 20\uac1c\uc529 \ub098\ub204\uc5b4 \uc9c4\ud589\ud558\ub294 \n\uacfc\uc815\uc785\ub2c8\ub2e4. \uc544\ub798\uc758 \uacfc\uc815\uc744 \uc2e4\ud589\ud560 \ub54c, PyTorch\uac00 CUDA \uc5f0\uc0b0\uc744 \ube44\ub3d9\uae30\uc801\uc73c\ub85c \uc774\uc6a9\ud558\uae30 \ub54c\ubb38\uc5d0, \n\ud504\ub85c\uc138\uc2a4\ub97c \uc2e4\ud589\ud558\ub294 \uc2a4\ub808\ub4dc\ub97c \uc5ec\ub7ec\uac1c \uc0dd\uc131\ud560 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n"
+        "\uc785\ub825 \ud150\uc11c\uac12\uc744 \ubd84\ud560\ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc124\uacc4\ud558\uc5ec \ud559\uc2b5 \uc2dc\uac04\uc744 \ub2e8\ucd95\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uc608\uc81c\n---------------------------------------------------------------------------------------\n\n\uc544\ub798\uc5d0 \uc788\ub294 \uc2e4\ud5d8\uc740, 120\uac1c\uc758 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub41c 1\uac1c\uc758 \ubbf8\ub2c8 \ubc30\uce58 \ub370\uc774\ud130\ub97c 20\uac1c\uc529 \ub098\ub204\uc5b4 \uc9c4\ud589\ud558\ub294\n\uacfc\uc815\uc785\ub2c8\ub2e4. \uc544\ub798\uc758 \uacfc\uc815\uc744 \uc2e4\ud589\ud560 \ub54c, PyTorch\uac00 CUDA \uc5f0\uc0b0\uc744 \ube44\ub3d9\uae30\uc801\uc73c\ub85c \uc774\uc6a9\ud558\uae30 \ub54c\ubb38\uc5d0,\n\ud504\ub85c\uc138\uc2a4\ub97c \uc2e4\ud589\ud558\ub294 \uc2a4\ub808\ub4dc\ub97c \uc5ec\ub7ec\uac1c \uc0dd\uc131\ud560 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -141,21 +141,21 @@
       },
       "outputs": [],
       "source": [
-        "class PipelineParallelResNet50(ModelParallelResNet50):\n    def __init__(self, split_size=20, *args, **kwargs):\n        super(PipelineParallelResNet50, self).__init__(*args, **kwargs)\n        self.split_size = split_size\n\n    def forward(self, x):\n        splits = iter(x.split(self.split_size, dim=0))\n        s_next = next(splits)\n        s_prev = self.seq1(s_next).to('cuda:1')\n        ret = []\n\n        for s_next in splits:\n            # A. s_prev\ub294 \ub450 \ubc88\uc9f8 GPU\uc5d0\uc11c \uc2e4\ud589\ub429\ub2c8\ub2e4.\n            s_prev = self.seq2(s_prev)\n            ret.append(self.fc(s_prev.view(s_prev.size(0), -1)))\n\n            # B. s_next\ub294 A.\uc640 \ub3d9\uc2dc\uc5d0 \uc9c4\ud589\ub418\uba74\uc11c \uccab \ubc88\uc9f8 GPU\uc5d0\uc11c \uc2e4\ud589\ub429\ub2c8\ub2e4. \n            s_prev = self.seq1(s_next).to('cuda:1')\n\n        s_prev = self.seq2(s_prev)\n        ret.append(self.fc(s_prev.view(s_prev.size(0), -1)))\n\n        return torch.cat(ret)\n\n\nsetup = \"model = PipelineParallelResNet50()\"\npp_run_times = timeit.repeat(\n    stmt, setup, number=1, repeat=num_repeat, globals=globals())\npp_mean, pp_std = np.mean(pp_run_times), np.std(pp_run_times)\n\nplot([mp_mean, rn_mean, pp_mean],\n     [mp_std, rn_std, pp_std],\n     ['Model Parallel', 'Single GPU', 'Pipelining Model Parallel'],\n     'mp_vs_rn_vs_pp.png')"
+        "class PipelineParallelResNet50(ModelParallelResNet50):\n    def __init__(self, split_size=20, *args, **kwargs):\n        super(PipelineParallelResNet50, self).__init__(*args, **kwargs)\n        self.split_size = split_size\n\n    def forward(self, x):\n        splits = iter(x.split(self.split_size, dim=0))\n        s_next = next(splits)\n        s_prev = self.seq1(s_next).to('cuda:1')\n        ret = []\n\n        for s_next in splits:\n            # A. s_prev\ub294 \ub450 \ubc88\uc9f8 GPU\uc5d0\uc11c \uc2e4\ud589\ub429\ub2c8\ub2e4.\n            s_prev = self.seq2(s_prev)\n            ret.append(self.fc(s_prev.view(s_prev.size(0), -1)))\n\n            # B. s_next\ub294 A.\uc640 \ub3d9\uc2dc\uc5d0 \uc9c4\ud589\ub418\uba74\uc11c \uccab \ubc88\uc9f8 GPU\uc5d0\uc11c \uc2e4\ud589\ub429\ub2c8\ub2e4.\n            s_prev = self.seq1(s_next).to('cuda:1')\n\n        s_prev = self.seq2(s_prev)\n        ret.append(self.fc(s_prev.view(s_prev.size(0), -1)))\n\n        return torch.cat(ret)\n\n\nsetup = \"model = PipelineParallelResNet50()\"\npp_run_times = timeit.repeat(\n    stmt, setup, number=1, repeat=num_repeat, globals=globals())\npp_mean, pp_std = np.mean(pp_run_times), np.std(pp_run_times)\n\nplot([mp_mean, rn_mean, pp_mean],\n     [mp_std, rn_std, pp_std],\n     ['Model Parallel', 'Single GPU', 'Pipelining Model Parallel'],\n     'mp_vs_rn_vs_pp.png')"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "GPU \uac04 \ud150\uc11c\uac12\uc774 \ubcf5\uc0ac\ub418\ub294 \uac83\uc740 \ud604\uc7ac \uacc4\uc0b0\ub418\uace0 \uc788\ub294 \uc18c\uc2a4\uac12\uacfc, \uc18c\uc2a4\uac12\uc758 \ubaa9\uc801\uc9c0 GPU \uac04 \uc5f0\uc0b0\ub418\uace0 \uc788\ub294\n\uc2a4\ud2b8\ub9bc\uacfc \ub3d9\uae30\ud654\ub418\ub294 \uac83\uc744 \uc8fc\uc758\ud558\uc138\uc694. \ub9cc\uc57d \uc5ec\ub7ec \uc2a4\ud2b8\ub9bc\uc744 \uc0dd\uc131\ud558\uc5ec \uc9c4\ud589\ud558\uace0 \uc788\ub2e4\uba74, GPU \uac04 \ud150\uc11c\uac12\uc774\n\uc815\uc0c1\uc801\uc73c\ub85c \ubcf5\uc0ac\ub418\uc5b4 \uacc4\uc0b0\ub418\uace0 \uc788\ub294\uc9c0 \uaf2d \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4. \ub9cc\uc57d \ubcf5\uc0ac\ub418\ub294 \uacfc\uc815 \uc911\uc5d0 \uc18c\uc2a4\uac12\uc744 \uc774\uc6a9\ud558\uac70\ub098,\nGPU\uc758 \ud150\uc11c\uac12\uc744 \uc77d\uac70\ub098 \uc4f0\ub294 \uac83\uc740 \uc62c\ubc14\ub974\uac8c \uacc4\uc0b0\ub418\uc9c0 \uc54a\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc704\uc758 \uc608\uc81c\uc5d0\uc11c\ub294 \uc18c\uc2a4\uac12 \ubc0f GPU \n\ud150\uc11c\uac12\uc744 \uae30\ubcf8 \uc2a4\ud2b8\ub9bc\ub9cc \uc774\uc6a9\ud558\uc5ec \uc9c4\ud589\ud558\ubbc0\ub85c \ucd94\uac00\uc801\uc778 \ub3d9\uae30\ud654 \uacfc\uc815\uc744 \uc9c4\ud589\ud560 \ud544\uc694\ub294 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n"
+        "GPU \uac04 \ud150\uc11c\uac12\uc774 \ubcf5\uc0ac\ub418\ub294 \uac83\uc740 \ud604\uc7ac \uacc4\uc0b0\ub418\uace0 \uc788\ub294 \uc18c\uc2a4\uac12\uacfc, \uc18c\uc2a4\uac12\uc758 \ubaa9\uc801\uc9c0 GPU \uac04 \uc5f0\uc0b0\ub418\uace0 \uc788\ub294\n\uc2a4\ud2b8\ub9bc\uacfc \ub3d9\uae30\ud654\ub418\ub294 \uac83\uc744 \uc8fc\uc758\ud558\uc138\uc694. \ub9cc\uc57d \uc5ec\ub7ec \uc2a4\ud2b8\ub9bc\uc744 \uc0dd\uc131\ud558\uc5ec \uc9c4\ud589\ud558\uace0 \uc788\ub2e4\uba74, GPU \uac04 \ud150\uc11c\uac12\uc774\n\uc815\uc0c1\uc801\uc73c\ub85c \ubcf5\uc0ac\ub418\uc5b4 \uacc4\uc0b0\ub418\uace0 \uc788\ub294\uc9c0 \uaf2d \ud655\uc778\ud574\uc57c \ud569\ub2c8\ub2e4. \ub9cc\uc57d \ubcf5\uc0ac\ub418\ub294 \uacfc\uc815 \uc911\uc5d0 \uc18c\uc2a4\uac12\uc744 \uc774\uc6a9\ud558\uac70\ub098,\nGPU\uc758 \ud150\uc11c\uac12\uc744 \uc77d\uac70\ub098 \uc4f0\ub294 \uac83\uc740 \uc62c\ubc14\ub974\uac8c \uacc4\uc0b0\ub418\uc9c0 \uc54a\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc704\uc758 \uc608\uc81c\uc5d0\uc11c\ub294 \uc18c\uc2a4\uac12 \ubc0f GPU\n\ud150\uc11c\uac12\uc744 \uae30\ubcf8 \uc2a4\ud2b8\ub9bc\ub9cc \uc774\uc6a9\ud558\uc5ec \uc9c4\ud589\ud558\ubbc0\ub85c \ucd94\uac00\uc801\uc778 \ub3d9\uae30\ud654 \uacfc\uc815\uc744 \uc9c4\ud589\ud560 \ud544\uc694\ub294 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        ".. figure:: /_static/img/model-parallel-images/mp_vs_rn_vs_pp.png\n   :alt:\n\n\ud30c\uc774\ud504\ub77c\uc778\uc744 \uc774\uc6a9\ud558\uc5ec \ubbf8\ub2c8 \ubc30\uce58 \ub0b4 \ub370\uc774\ud130\ub97c \ubd84\ud560\ud558\uc5ec \uc801\uc6a9\ud558\uc600\uc744 \ub54c, ResNet50 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc758\n\ud559\uc2b5 \uc2dc\uac04\uc774 \uc57d 49% ``3.75/2.51-1=49%`` \uc815\ub3c4 \ub2e8\ucd95\ub41c \uac83\uc744 \uc774\ubc88 \uc2e4\ud5d8\uc744 \ud1b5\ud574 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc, \uc774\uc0c1\uc801\uc73c\ub85c\n\ud559\uc2b5 \uc2dc\uac04\uc774 2\ubc30 \ub2e8\ucd95\ub418\ub294 \uac83\uc5d0 \ube44\ud574 \ub2e4\uc18c \uc801\uac8c \ud559\uc2b5 \uc2dc\uac04\uc774 \ub2e8\ucd95\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc774\uc6a9\ud560 \ub54c,\n``split_sizes`` \ub9e4\uac1c\ubcc0\uc218\ub97c \ub3c4\uc785\ud558\uc600\uae30 \ub54c\ubb38\uc5d0, \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc774\uc6a9\ud558\ub294 \uac83\uc774 \ud559\uc2b5 \uc2dc\uac04 \ub2e8\ucd95\uc5d0 \uc5bc\ub9c8\ub098\n\uc601\ud5a5\uc744 \ubbf8\ucce4\ub294\uc9c0 \ubd88\ubd84\uba85\ud569\ub2c8\ub2e4. \uc9c1\uad00\uc801\uc73c\ub85c \uc0dd\uac01\ud558\uc600\uc744 \ub54c, ``split_sizes`` \ub9e4\uac1c\ubcc0\uc218 \uac12\uc744 \uc791\uac8c \uc124\uc815\ud55c\ub2e4\uba74,\n\uc544\uc8fc \uc18c\uaddc\ubaa8\uc758 CUDA \uc5f0\uc0b0\uc774 \ub9ce\uc774 \uc9c4\ud589\ub418\uace0, ``split_sizes`` \ub9e4\uac1c\ubcc0\uc218 \uac12\uc744 \ud06c\uac8c \uc124\uc815\ud55c\ub2e4\uba74, \uccab \ubc88\uc9f8\uc640 \n\ub9c8\uc9c0\ub9c9 \ubd84\ub9ac\ub420 \ub54c \ube44\uad50\uc801 \uae34 \uc2dc\uac04 \ub3d9\uc548 CUDA \uc5f0\uc0b0\uc774 \uc774\ub8e8\uc5b4\uc9c0\uac8c \ub429\ub2c8\ub2e4. \ub458 \ub2e4 \ucd5c\uc801\uc758 \uc124\uc815\uc774 \uc544\ub2d9\ub2c8\ub2e4.\n\ub530\ub77c\uc11c, ``split_sizes`` \ub9e4\uac1c\ubcc0\uc218 \uac12\uc744 \ucd5c\uc801\uc73c\ub85c \uc124\uc815\ud558\uc600\uc744 \ub54c, \ud559\uc2b5 \uc2dc\uac04 \uacfc\uc815\uc774 \ub2e8\ucd95\ub420 \uc218 \uc788\uc744 \uac83\uc774\ub77c\n\uae30\ub300\ub429\ub2c8\ub2e4. ``split_sizes`` \ub9e4\uac1c\ubcc0\uc218 \uac12\uc744 \uc870\uc815\ud558\uc5ec \uc2e4\ud5d8\ud558\uba74\uc11c \ucd5c\uc801\uc758 \uac12\uc744 \ucc3e\uc544\ubd05\uc2dc\ub2e4.\n\n"
+        ".. figure:: /_static/img/model-parallel-images/mp_vs_rn_vs_pp.png\n   :alt:\n\n\ud30c\uc774\ud504\ub77c\uc778\uc744 \uc774\uc6a9\ud558\uc5ec \ubbf8\ub2c8 \ubc30\uce58 \ub0b4 \ub370\uc774\ud130\ub97c \ubd84\ud560\ud558\uc5ec \uc801\uc6a9\ud558\uc600\uc744 \ub54c, ResNet50 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc758\n\ud559\uc2b5 \uc2dc\uac04\uc774 \uc57d 49% ``3.75/2.51-1=49%`` \uc815\ub3c4 \ub2e8\ucd95\ub41c \uac83\uc744 \uc774\ubc88 \uc2e4\ud5d8\uc744 \ud1b5\ud574 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc, \uc774\uc0c1\uc801\uc73c\ub85c\n\ud559\uc2b5 \uc2dc\uac04\uc774 2\ubc30 \ub2e8\ucd95\ub418\ub294 \uac83\uc5d0 \ube44\ud574 \ub2e4\uc18c \uc801\uac8c \ud559\uc2b5 \uc2dc\uac04\uc774 \ub2e8\ucd95\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc774\uc6a9\ud560 \ub54c,\n``split_sizes`` \ub9e4\uac1c\ubcc0\uc218\ub97c \ub3c4\uc785\ud558\uc600\uae30 \ub54c\ubb38\uc5d0, \ud30c\uc774\ud504\ub77c\uc778\uc744 \uc774\uc6a9\ud558\ub294 \uac83\uc774 \ud559\uc2b5 \uc2dc\uac04 \ub2e8\ucd95\uc5d0 \uc5bc\ub9c8\ub098\n\uc601\ud5a5\uc744 \ubbf8\ucce4\ub294\uc9c0 \ubd88\ubd84\uba85\ud569\ub2c8\ub2e4. \uc9c1\uad00\uc801\uc73c\ub85c \uc0dd\uac01\ud558\uc600\uc744 \ub54c, ``split_sizes`` \ub9e4\uac1c\ubcc0\uc218 \uac12\uc744 \uc791\uac8c \uc124\uc815\ud55c\ub2e4\uba74,\n\uc544\uc8fc \uc18c\uaddc\ubaa8\uc758 CUDA \uc5f0\uc0b0\uc774 \ub9ce\uc774 \uc9c4\ud589\ub418\uace0, ``split_sizes`` \ub9e4\uac1c\ubcc0\uc218 \uac12\uc744 \ud06c\uac8c \uc124\uc815\ud55c\ub2e4\uba74, \uccab \ubc88\uc9f8\uc640\n\ub9c8\uc9c0\ub9c9 \ubd84\ub9ac\ub420 \ub54c \ube44\uad50\uc801 \uae34 \uc2dc\uac04 \ub3d9\uc548 CUDA \uc5f0\uc0b0\uc774 \uc774\ub8e8\uc5b4\uc9c0\uac8c \ub429\ub2c8\ub2e4. \ub458 \ub2e4 \ucd5c\uc801\uc758 \uc124\uc815\uc774 \uc544\ub2d9\ub2c8\ub2e4.\n\ub530\ub77c\uc11c, ``split_sizes`` \ub9e4\uac1c\ubcc0\uc218 \uac12\uc744 \ucd5c\uc801\uc73c\ub85c \uc124\uc815\ud558\uc600\uc744 \ub54c, \ud559\uc2b5 \uc2dc\uac04 \uacfc\uc815\uc774 \ub2e8\ucd95\ub420 \uc218 \uc788\uc744 \uac83\uc774\ub77c\n\uae30\ub300\ub429\ub2c8\ub2e4. ``split_sizes`` \ub9e4\uac1c\ubcc0\uc218 \uac12\uc744 \uc870\uc815\ud558\uc5ec \uc2e4\ud5d8\ud558\uba74\uc11c \ucd5c\uc801\uc758 \uac12\uc744 \ucc3e\uc544\ubd05\uc2dc\ub2e4.\n\n"
       ]
     },
     {
@@ -173,7 +173,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        ".. figure:: /_static/img/model-parallel-images/split_size_tradeoff.png\n   :alt:\n\n\uc2e4\ud5d8 \uacb0\uacfc, ``split_size`` \ub9e4\uac1c\ubcc0\uc218\uac12\uc744 12\ub85c \uc124\uc815\ud558\uc600\uc744 \ub54c, \ud559\uc2b5 \uc2dc\uac04\uc774 54% \uc218\uc900\uc73c\ub85c \n\uac00\uc7a5 \ub9ce\uc774 \ub2e8\ucd95\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc544\uc9c1 \ud559\uc2b5 \uc2dc\uac04\uc744 \ub354 \ub2e8\ucd95\uc2dc\ud0ac \uc218 \uc788\ub294 \ubc29\ubc95\uc740 \ub2e4\uc591\ud558\uac8c \uc874\uc7ac\ud569\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uc5b4, \uccab \ubc88\uc9f8 GPU\uc5d0\uc11c \ubaa8\ub4e0 \uc5f0\uc0b0\uacfc\uc815\uc774 \uae30\ubcf8\uc73c\ub85c \uc124\uc815\ub418\uc5b4 \uc9c4\ud589\ub429\ub2c8\ub2e4. \uc774\ub294 \ubbf8\ub2c8\ubc30\uce58 \ubd84\ud560 \uacfc\uc815 \uc911,\n\ud604\uc7ac \uc9c4\ud589\ub418\ub294 \uacfc\uc815\uc758 \ub2e4\uc74c \ub2e8\uacc4\ub294 \ud604\uc7ac \uc9c4\ud589\ub418\ub294 \uacfc\uc815\uacfc \ub3d9\uc2dc\uc5d0 \ubcf5\uc81c\uac00 \uc774\ub8e8\uc5b4\uc9c8 \uc218 \uc5c6\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098, \uc774\uc804\uacfc \ub2e4\uc74c \ub2e8\uacc4\uc758 \ubd84\ud560\uacfc\uc815\uc774 \ub2e4\ub978 \ud150\uc11c\uac12\uc744 \uc774\uc6a9\ud558\uae30 \ub54c\ubb38\uc5d0, \ub2e4\ub978 \uacc4\uc0b0\uacfc \uc911\ubcf5\ub418\uc5b4 \uc9c4\ud589\ub418\uc5b4\ub3c4\n\ubb38\uc81c\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \uc774\uc5d0 \ub300\ud574\uc11c, 2\uac1c GPU\uc5d0 \uc5ec\ub7ec\uac1c\uc758 \uc2a4\ud2b8\ub9bc\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \ud544\uc694\ud558\uba70, \uc11c\ub85c \ub2e4\ub978 \uc11c\ube0c \ub124\ud2b8\uc6cc\ud06c \n\uad6c\uc870\uac00 \uc11c\ub85c \ub2e4\ub978 \uc2a4\ud2b8\ub9bc\uc744 \uad00\ub9ac\ud558\ub294 \uc804\ub7b5\uc774 \uc694\uad6c\ub429\ub2c8\ub2e4. \ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\uc5d0 \ub300\ud574\uc11c \uc5ec\ub7ec \uc2a4\ud2b8\ub9bc\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc774 \n\uc77c\ubc18\uc801\uc744\ub85c \uc874\uc7ac\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 \uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc124\uba85\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n"
+        ".. figure:: /_static/img/model-parallel-images/split_size_tradeoff.png\n   :alt:\n\n\uc2e4\ud5d8 \uacb0\uacfc, ``split_size`` \ub9e4\uac1c\ubcc0\uc218\uac12\uc744 12\ub85c \uc124\uc815\ud558\uc600\uc744 \ub54c, \ud559\uc2b5 \uc2dc\uac04\uc774 54% \uc218\uc900\uc73c\ub85c\n\uac00\uc7a5 \ub9ce\uc774 \ub2e8\ucd95\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc544\uc9c1 \ud559\uc2b5 \uc2dc\uac04\uc744 \ub354 \ub2e8\ucd95\uc2dc\ud0ac \uc218 \uc788\ub294 \ubc29\ubc95\uc740 \ub2e4\uc591\ud558\uac8c \uc874\uc7ac\ud569\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uc5b4, \uccab \ubc88\uc9f8 GPU\uc5d0\uc11c \ubaa8\ub4e0 \uc5f0\uc0b0\uacfc\uc815\uc774 \uae30\ubcf8\uc73c\ub85c \uc124\uc815\ub418\uc5b4 \uc9c4\ud589\ub429\ub2c8\ub2e4. \uc774\ub294 \ubbf8\ub2c8\ubc30\uce58 \ubd84\ud560 \uacfc\uc815 \uc911,\n\ud604\uc7ac \uc9c4\ud589\ub418\ub294 \uacfc\uc815\uc758 \ub2e4\uc74c \ub2e8\uacc4\ub294 \ud604\uc7ac \uc9c4\ud589\ub418\ub294 \uacfc\uc815\uacfc \ub3d9\uc2dc\uc5d0 \ubcf5\uc81c\uac00 \uc774\ub8e8\uc5b4\uc9c8 \uc218 \uc5c6\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098, \uc774\uc804\uacfc \ub2e4\uc74c \ub2e8\uacc4\uc758 \ubd84\ud560\uacfc\uc815\uc774 \ub2e4\ub978 \ud150\uc11c\uac12\uc744 \uc774\uc6a9\ud558\uae30 \ub54c\ubb38\uc5d0, \ub2e4\ub978 \uacc4\uc0b0\uacfc \uc911\ubcf5\ub418\uc5b4 \uc9c4\ud589\ub418\uc5b4\ub3c4\n\ubb38\uc81c\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \uc774\uc5d0 \ub300\ud574\uc11c, 2\uac1c GPU\uc5d0 \uc5ec\ub7ec\uac1c\uc758 \uc2a4\ud2b8\ub9bc\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 \ud544\uc694\ud558\uba70, \uc11c\ub85c \ub2e4\ub978 \uc11c\ube0c \ub124\ud2b8\uc6cc\ud06c\n\uad6c\uc870\uac00 \uc11c\ub85c \ub2e4\ub978 \uc2a4\ud2b8\ub9bc\uc744 \uad00\ub9ac\ud558\ub294 \uc804\ub7b5\uc774 \uc694\uad6c\ub429\ub2c8\ub2e4. \ubaa8\ub378 \ubcd1\ub82c \ucc98\ub9ac\uc5d0 \ub300\ud574\uc11c \uc5ec\ub7ec \uc2a4\ud2b8\ub9bc\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc774\n\uc77c\ubc18\uc801\uc744\ub85c \uc874\uc7ac\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 \uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc124\uba85\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -184,7 +184,7 @@
       },
       "outputs": [],
       "source": [
-        "\"\"\"\n.. note::\n   \uc774\ubc88 \uac8c\uc2dc\ubb3c\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \uc131\ub2a5 \uce21\uc815\uac12\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc740 \uc704\uc758 \uc608\uc81c\ub97c \uc2e4\ud589\ud560 \ub54c \ub9c8\ub2e4 \ub9e4\ubc88\n   \ub2e4\ub978 \uacb0\uacfc\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc774\uc720\ub294, \uc774\uc6a9\ud558\ub294 \uc18c\ud504\ud2b8\uc6e8\uc5b4 \ubc0f \ud558\ub4dc\uc6e8\uc5b4\uc5d0 \ub530\ub77c \uacb0\uacfc\uac00 \n   \ub2e4\ub974\uac8c \ub098\ud0c0\ub098\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc774 \uc774\uc6a9\ud558\uace0 \uc788\ub294 \ud658\uacbd \ub0b4\uc5d0\uc11c \uac00\uc7a5 \uc88b\uc740 \uc131\ub2a5\uc744 \uc5bb\uae30 \uc704\ud574\uc11c\ub294, \uace1\uc120\uc744 \uadf8\ub824\uc11c\n   \ucd5c\uc801\uc758 ``split_size`` \uac12\uc744 \ub3c4\ucd9c\ud55c \ud6c4, \ud574\ub2f9 \uac12\uc744 \uc774\uc6a9\ud558\uc5ec \ubbf8\ub2c8 \ubc30\uce58 \ub0b4 \ub370\uc774\ud130\ub97c \ubd84\ub9ac\ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc744 \n   \uc0dd\uc131\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\"\"\""
+        "\"\"\"\n.. note::\n   \uc774\ubc88 \uac8c\uc2dc\ubb3c\uc5d0\uc11c\ub294 \ub2e4\uc591\ud55c \uc131\ub2a5 \uce21\uc815\uac12\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc740 \uc704\uc758 \uc608\uc81c\ub97c \uc2e4\ud589\ud560 \ub54c \ub9c8\ub2e4 \ub9e4\ubc88\n   \ub2e4\ub978 \uacb0\uacfc\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uadf8 \uc774\uc720\ub294, \uc774\uc6a9\ud558\ub294 \uc18c\ud504\ud2b8\uc6e8\uc5b4 \ubc0f \ud558\ub4dc\uc6e8\uc5b4\uc5d0 \ub530\ub77c \uacb0\uacfc\uac00\n   \ub2e4\ub974\uac8c \ub098\ud0c0\ub098\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc774 \uc774\uc6a9\ud558\uace0 \uc788\ub294 \ud658\uacbd \ub0b4\uc5d0\uc11c \uac00\uc7a5 \uc88b\uc740 \uc131\ub2a5\uc744 \uc5bb\uae30 \uc704\ud574\uc11c\ub294, \uace1\uc120\uc744 \uadf8\ub824\uc11c\n   \ucd5c\uc801\uc758 ``split_size`` \uac12\uc744 \ub3c4\ucd9c\ud55c \ud6c4, \ud574\ub2f9 \uac12\uc744 \uc774\uc6a9\ud558\uc5ec \ubbf8\ub2c8 \ubc30\uce58 \ub0b4 \ub370\uc774\ud130\ub97c \ubd84\ub9ac\ud558\ub294 \ud30c\uc774\ud504\ub77c\uc778\uc744\n   \uc0dd\uc131\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\"\"\""
       ]
     }
   ],
@@ -204,7 +204,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/0abb91e66579f9acfdfbf93bc4b69955/named_tensor_tutorial.ipynb b/docs/_downloads/0abb91e66579f9acfdfbf93bc4b69955/named_tensor_tutorial.ipynb
index 28298040e..097c6be46 100644
--- a/docs/_downloads/0abb91e66579f9acfdfbf93bc4b69955/named_tensor_tutorial.ipynb
+++ b/docs/_downloads/0abb91e66579f9acfdfbf93bc4b69955/named_tensor_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n(prototype) Introduction to Named Tensors in PyTorch\n*******************************************************\n**Author**: `Richard Zou <https://github.com/zou3519>`_\n\nNamed Tensors aim to make tensors easier to use by allowing users to associate\nexplicit names with tensor dimensions. In most cases, operations that take\ndimension parameters will accept dimension names, avoiding the need to track\ndimensions by position. In addition, named tensors use names to automatically\ncheck that APIs are being used correctly at runtime, providing extra safety.\nNames can also be used to rearrange dimensions, for example, to support\n\"broadcasting by name\" rather than \"broadcasting by position\".\n\nThis tutorial is intended as a guide to the functionality that will\nbe included with the 1.3 launch. By the end of it, you will be able to:\n\n- Create Tensors with named dimensions, as well as remove or rename those\n  dimensions\n- Understand the basics of how operations propagate dimension names\n- See how naming dimensions enables clearer code in two key areas:\n    - Broadcasting operations\n    - Flattening and unflattening dimensions\n\nFinally, we'll put this into practice by writing a multi-head attention module\nusing named tensors.\n\nNamed tensors in PyTorch are inspired by and done in collaboration with\n`Sasha Rush <https://tech.cornell.edu/people/alexander-rush/>`_.\nSasha proposed the original idea and proof of concept in his\n`January 2019 blog post <http://nlp.seas.harvard.edu/NamedTensor>`_.\n\nBasics: named dimensions\n========================\n\nPyTorch now allows Tensors to have named dimensions; factory functions\ntake a new `names` argument that associates a name with each dimension.\nThis works with most factory functions, such as\n\n- `tensor`\n- `empty`\n- `ones`\n- `zeros`\n- `randn`\n- `rand`\n\nHere we construct a tensor with names:\n\n"
+        "\n(prototype) Introduction to Named Tensors in PyTorch\n*******************************************************\n**Author**: `Richard Zou <https://github.com/zou3519>`_\n\nNamed Tensors aim to make tensors easier to use by allowing users to associate\nexplicit names with tensor dimensions. In most cases, operations that take\ndimension parameters will accept dimension names, avoiding the need to track\ndimensions by position. In addition, named tensors use names to automatically\ncheck that APIs are being used correctly at runtime, providing extra safety.\nNames can also be used to rearrange dimensions, for example, to support\n\"broadcasting by name\" rather than \"broadcasting by position\".\n\nThis tutorial is intended as a guide to the functionality that will\nbe included with the 1.3 launch. By the end of it, you will be able to:\n\n- Create Tensors with named dimensions, as well as remove or rename those\n  dimensions\n- Understand the basics of how operations propagate dimension names\n- See how naming dimensions enables clearer code in two key areas:\n    - Broadcasting operations\n    - Flattening and unflattening dimensions\n\nFinally, we'll put this into practice by writing a multi-head attention module\nusing named tensors.\n\nNamed tensors in PyTorch are inspired by and done in collaboration with\n`Sasha Rush <https://tech.cornell.edu/people/alexander-rush/>`_.\nSasha proposed the original idea and proof of concept in his\n`January 2019 blog post <http://nlp.seas.harvard.edu/NamedTensor>`_.\n\nBasics: named dimensions\n========================\n\nPyTorch now allows Tensors to have named dimensions; factory functions\ntake a new `names` argument that associates a name with each dimension.\nThis works with most factory functions, such as\n\n- `tensor`\n- `empty`\n- `ones`\n- `zeros`\n- `randn`\n- `rand`\n\nHere we construct a tensor with names:\n"
       ]
     },
     {
@@ -503,7 +503,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/13b143c2380f4768d9432d808ad50799/char_rnn_classification_tutorial.ipynb b/docs/_downloads/13b143c2380f4768d9432d808ad50799/char_rnn_classification_tutorial.ipynb
index a5a12b000..82eccb9c0 100644
--- a/docs/_downloads/13b143c2380f4768d9432d808ad50799/char_rnn_classification_tutorial.ipynb
+++ b/docs/_downloads/13b143c2380f4768d9432d808ad50799/char_rnn_classification_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP: \ubb38\uc790-\ub2e8\uc704 RNN\uc73c\ub85c \uc774\ub984 \ubd84\ub958\ud558\uae30\n********************************************************************************\n**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_\n  **\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\n\ub2e8\uc5b4\ub97c \ubd84\ub958\ud558\uae30 \uc704\ud574 \uae30\ucd08\uc801\uc778 \ubb38\uc790-\ub2e8\uc704 RNN\uc744 \uad6c\ucd95\ud558\uace0 \ud559\uc2b5 \ud560 \uc608\uc815\uc785\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 (\uc774\ud6c4 2\uac1c \ud29c\ud1a0\ub9ac\uc5bc\uacfc \ud568\uaed8) NLP \ubaa8\ub378\ub9c1\uc744 \uc704\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c\n`torchtext` \uc758 \ud3b8\ub9ac\ud55c \ub9ce\uc740 \uae30\ub2a5\ub4e4\uc744 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\uace0 \uc5b4\ub5bb\uac8c \ud558\ub294\uc9c0 \"\uae30\ucd08\ubd80\ud130(from scratch)\"\n\ubcf4\uc5ec\uc8fc\uae30 \ub584\ubb38\uc5d0  NLP \ubaa8\ub378\ub9c1\uc744 \uc704\ud55c \uc804\ucc98\ub9ac\uac00 \uc800\uc218\uc900\uc5d0\uc11c \uc5b4\ub5bb\uac8c \uc9c4\ud589\ub418\ub294\uc9c0\ub97c \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\ubb38\uc790-\ub2e8\uc704 RNN\uc740 \ub2e8\uc5b4\ub97c \ubb38\uc790\uc758 \uc5f0\uc18d\uc73c\ub85c \uc77d\uc5b4 \ub4e4\uc5ec\uc11c \uac01 \ub2e8\uacc4\uc758 \uc608\uce21\uacfc\n\"\uc740\ub2c9 \uc0c1\ud0dc(Hidden State)\" \ucd9c\ub825\ud558\uace0, \ub2e4\uc74c \ub2e8\uacc4\uc5d0 \uc774\uc804 \uc740\ub2c9 \uc0c1\ud0dc\ub97c \uc804\ub2ec\ud569\ub2c8\ub2e4.\n\ub2e8\uc5b4\uac00 \uc18d\ud55c \ud074\ub798\uc2a4\ub85c \ucd9c\ub825\uc774 \ub418\ub3c4\ub85d \ucd5c\uc885 \uc608\uce21\uc73c\ub85c \uc120\ud0dd\ud569\ub2c8\ub2e4.\n\n\uad6c\uccb4\uc801\uc73c\ub85c, 18\uac1c \uc5b8\uc5b4\ub85c \ub41c \uc218\ucc9c \uac1c\uc758 \uc131(\u59d3)\uc744 \ud6c8\ub828\uc2dc\ud0a4\uace0,\n\ucca0\uc790\uc5d0 \ub530\ub77c \uc774\ub984\uc774 \uc5b4\ub5a4 \uc5b8\uc5b4\uc778\uc9c0 \uc608\uce21\ud569\ub2c8\ub2e4:\n\n::\n\n    $ python predict.py Hinton\n    (-0.47) Scottish\n    (-1.52) English\n    (-3.57) Irish\n\n    $ python predict.py Schmidhuber\n    (-0.19) German\n    (-2.48) Czech\n    (-2.68) Dutch\n\n\n**\ucd94\ucc9c \uc790\ub8cc:**\n\nPytorch\ub97c \uc124\uce58\ud588\uace0, Python\uc744 \uc54c\uace0, Tensor\ub97c \uc774\ud574\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4:\n\n-  https://pytorch.org/ \uc124\uce58 \uc548\ub0b4\n-  :doc:`/beginner/deep_learning_60min_blitz` PyTorch \uc2dc\uc791\ud558\uae30\n-  :doc:`/beginner/pytorch_with_examples` \ub113\uace0 \uae4a\uc740 \ud1b5\ucc30\uc744 \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/former_torchies_tutorial` \uc774\uc804 Lua Torch \uc0ac\uc6a9\uc790\ub97c \uc704\ud55c \uc790\ub8cc\n\nRNN\uacfc \uc791\ub3d9 \ubc29\uc2dd\uc744 \uc544\ub294 \uac83 \ub610\ud55c \uc720\uc6a9\ud569\ub2c8\ub2e4:\n\n-  `The Unreasonable Effectiveness of Recurrent Neural\n   Networks <https://karpathy.github.io/2015/05/21/rnn-effectiveness/>`__\n   \uc2e4\uc0dd\ud65c \uc608\uc81c\ub97c \ubcf4\uc5ec \uc90d\ub2c8\ub2e4.\n-  `Understanding LSTM\n   Networks <https://colah.github.io/posts/2015-08-Understanding-LSTMs/>`__\n   LSTM\uc5d0 \uad00\ud55c \uac83\uc774\uc9c0\ub9cc RNN\uc5d0 \uad00\ud574\uc11c\ub3c4 \uc720\uc775\ud569\ub2c8\ub2e4.\n\n\ub370\uc774\ud130 \uc900\ube44\n==================\n\n.. NOTE::\n   `\uc5ec\uae30 <https://download.pytorch.org/tutorial/data.zip>`__ \uc5d0\uc11c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4 \ubc1b\uace0,\n   \ud604\uc7ac \ub514\ub809\ud1a0\ub9ac\uc5d0 \uc555\ucd95\uc744 \ud478\uc2ed\uc2dc\uc624.\n\n``data/names`` \ub514\ub809\ud1a0\ub9ac\uc5d0\ub294 \"[Language].txt\" \ub77c\ub294 18 \uac1c\uc758 \ud14d\uc2a4\ud2b8 \ud30c\uc77c\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n\uac01 \ud30c\uc77c\uc5d0\ub294 \ud55c \uc904\uc5d0 \ud558\ub098\uc758 \uc774\ub984\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc73c\uba70 \ub300\ubd80\ubd84 \ub85c\ub9c8\uc790\ub85c \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4\n(\uadf8\ub7ec\ub098, \uc720\ub2c8\ucf54\ub4dc\uc5d0\uc11c ASCII\ub85c \ubcc0\ud658\ud574\uc57c \ud568).\n\n\uac01 \uc5b8\uc5b4 \ubcc4\ub85c \uc774\ub984 \ubaa9\ub85d \uc0ac\uc804 ``{language: [names ...]}`` \uc744 \ub9cc\ub4ed\ub2c8\ub2e4.\n\uc77c\ubc18 \ubcc0\uc218 \"category\" \uc640 \"line\" (\uc6b0\ub9ac\uc758 \uacbd\uc6b0 \uc5b8\uc5b4\uc640 \uc774\ub984)\uc740 \uc774\ud6c4\uc758 \ud655\uc7a5\uc131\uc744 \uc704\ud574 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n\n.. NOTE::\n\uc5ed\uc790 \uc8fc:  \"line\" \uc5d0 \uc785\ub825\uc744 \"category\"\uc5d0 \ud074\ub798\uc2a4\ub97c \uc801\uc6a9\ud558\uc5ec \ub2e4\ub978 \ubb38\uc81c\uc5d0\ub3c4 \ud65c\uc6a9 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc5ec\uae30\uc11c\ub294 \"line\"\uc5d0 \uc774\ub984(ex. Robert )\ub97c \uc785\ub825\uc73c\ub85c \"category\"\uc5d0 \ud074\ub798\uc2a4(ex. english)\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n"
+        "\n\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP: \ubb38\uc790-\ub2e8\uc704 RNN\uc73c\ub85c \uc774\ub984 \ubd84\ub958\ud558\uae30\n********************************************************************************\n**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_\n  **\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\n\ub2e8\uc5b4\ub97c \ubd84\ub958\ud558\uae30 \uc704\ud574 \uae30\ucd08\uc801\uc778 \ubb38\uc790-\ub2e8\uc704 RNN\uc744 \uad6c\ucd95\ud558\uace0 \ud559\uc2b5 \ud560 \uc608\uc815\uc785\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 (\uc774\ud6c4 2\uac1c \ud29c\ud1a0\ub9ac\uc5bc\uacfc \ud568\uaed8) NLP \ubaa8\ub378\ub9c1\uc744 \uc704\ud55c \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ub97c\n`torchtext` \uc758 \ud3b8\ub9ac\ud55c \ub9ce\uc740 \uae30\ub2a5\ub4e4\uc744 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\uace0 \uc5b4\ub5bb\uac8c \ud558\ub294\uc9c0 \"\uae30\ucd08\ubd80\ud130(from scratch)\"\n\ubcf4\uc5ec\uc8fc\uae30 \ub584\ubb38\uc5d0  NLP \ubaa8\ub378\ub9c1\uc744 \uc704\ud55c \uc804\ucc98\ub9ac\uac00 \uc800\uc218\uc900\uc5d0\uc11c \uc5b4\ub5bb\uac8c \uc9c4\ud589\ub418\ub294\uc9c0\ub97c \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\ubb38\uc790-\ub2e8\uc704 RNN\uc740 \ub2e8\uc5b4\ub97c \ubb38\uc790\uc758 \uc5f0\uc18d\uc73c\ub85c \uc77d\uc5b4 \ub4e4\uc5ec\uc11c \uac01 \ub2e8\uacc4\uc758 \uc608\uce21\uacfc\n\"\uc740\ub2c9 \uc0c1\ud0dc(Hidden State)\" \ucd9c\ub825\ud558\uace0, \ub2e4\uc74c \ub2e8\uacc4\uc5d0 \uc774\uc804 \uc740\ub2c9 \uc0c1\ud0dc\ub97c \uc804\ub2ec\ud569\ub2c8\ub2e4.\n\ub2e8\uc5b4\uac00 \uc18d\ud55c \ud074\ub798\uc2a4\ub85c \ucd9c\ub825\uc774 \ub418\ub3c4\ub85d \ucd5c\uc885 \uc608\uce21\uc73c\ub85c \uc120\ud0dd\ud569\ub2c8\ub2e4.\n\n\uad6c\uccb4\uc801\uc73c\ub85c, 18\uac1c \uc5b8\uc5b4\ub85c \ub41c \uc218\ucc9c \uac1c\uc758 \uc131(\u59d3)\uc744 \ud6c8\ub828\uc2dc\ud0a4\uace0,\n\ucca0\uc790\uc5d0 \ub530\ub77c \uc774\ub984\uc774 \uc5b4\ub5a4 \uc5b8\uc5b4\uc778\uc9c0 \uc608\uce21\ud569\ub2c8\ub2e4:\n\n::\n\n    $ python predict.py Hinton\n    (-0.47) Scottish\n    (-1.52) English\n    (-3.57) Irish\n\n    $ python predict.py Schmidhuber\n    (-0.19) German\n    (-2.48) Czech\n    (-2.68) Dutch\n\n\n**\ucd94\ucc9c \uc790\ub8cc:**\n\nPytorch\ub97c \uc124\uce58\ud588\uace0, Python\uc744 \uc54c\uace0, Tensor\ub97c \uc774\ud574\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4:\n\n-  https://pytorch.org/ \uc124\uce58 \uc548\ub0b4\n-  :doc:`/beginner/deep_learning_60min_blitz` PyTorch \uc2dc\uc791\ud558\uae30\n-  :doc:`/beginner/pytorch_with_examples` \ub113\uace0 \uae4a\uc740 \ud1b5\ucc30\uc744 \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/former_torchies_tutorial` \uc774\uc804 Lua Torch \uc0ac\uc6a9\uc790\ub97c \uc704\ud55c \uc790\ub8cc\n\nRNN\uacfc \uc791\ub3d9 \ubc29\uc2dd\uc744 \uc544\ub294 \uac83 \ub610\ud55c \uc720\uc6a9\ud569\ub2c8\ub2e4:\n\n-  `The Unreasonable Effectiveness of Recurrent Neural\n   Networks <https://karpathy.github.io/2015/05/21/rnn-effectiveness/>`__\n   \uc2e4\uc0dd\ud65c \uc608\uc81c\ub97c \ubcf4\uc5ec \uc90d\ub2c8\ub2e4.\n-  `Understanding LSTM\n   Networks <https://colah.github.io/posts/2015-08-Understanding-LSTMs/>`__\n   LSTM\uc5d0 \uad00\ud55c \uac83\uc774\uc9c0\ub9cc RNN\uc5d0 \uad00\ud574\uc11c\ub3c4 \uc720\uc775\ud569\ub2c8\ub2e4.\n\n\ub370\uc774\ud130 \uc900\ube44\n==================\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>`\uc5ec\uae30 <https://download.pytorch.org/tutorial/data.zip>`__ \uc5d0\uc11c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4 \ubc1b\uace0,\n   \ud604\uc7ac \ub514\ub809\ud1a0\ub9ac\uc5d0 \uc555\ucd95\uc744 \ud478\uc2ed\uc2dc\uc624.</p></div>\n\n``data/names`` \ub514\ub809\ud1a0\ub9ac\uc5d0\ub294 \"[Language].txt\" \ub77c\ub294 18 \uac1c\uc758 \ud14d\uc2a4\ud2b8 \ud30c\uc77c\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n\uac01 \ud30c\uc77c\uc5d0\ub294 \ud55c \uc904\uc5d0 \ud558\ub098\uc758 \uc774\ub984\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc73c\uba70 \ub300\ubd80\ubd84 \ub85c\ub9c8\uc790\ub85c \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4\n(\uadf8\ub7ec\ub098, \uc720\ub2c8\ucf54\ub4dc\uc5d0\uc11c ASCII\ub85c \ubcc0\ud658\ud574\uc57c \ud568).\n\n\uac01 \uc5b8\uc5b4 \ubcc4\ub85c \uc774\ub984 \ubaa9\ub85d \uc0ac\uc804 ``{language: [names ...]}`` \uc744 \ub9cc\ub4ed\ub2c8\ub2e4.\n\uc77c\ubc18 \ubcc0\uc218 \"category\" \uc640 \"line\" (\uc6b0\ub9ac\uc758 \uacbd\uc6b0 \uc5b8\uc5b4\uc640 \uc774\ub984)\uc740 \uc774\ud6c4\uc758 \ud655\uc7a5\uc131\uc744 \uc704\ud574 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>\uc5ed\uc790 \uc8fc:  \"line\" \uc5d0 \uc785\ub825\uc744 \"category\"\uc5d0 \ud074\ub798\uc2a4\ub97c \uc801\uc6a9\ud558\uc5ec \ub2e4\ub978 \ubb38\uc81c\uc5d0\ub3c4 \ud65c\uc6a9 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n   \uc5ec\uae30\uc11c\ub294 \"line\"\uc5d0 \uc774\ub984(ex. Robert )\ub97c \uc785\ub825\uc73c\ub85c \"category\"\uc5d0 \ud074\ub798\uc2a4(ex. english)\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.</p></div>\n"
       ]
     },
     {
@@ -308,7 +308,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/16b28c991d1ecd15e9c3baccf501a92e/torchscript_freezing.ipynb b/docs/_downloads/16b28c991d1ecd15e9c3baccf501a92e/torchscript_freezing.ipynb
index b25fec4e4..6201beee3 100644
--- a/docs/_downloads/16b28c991d1ecd15e9c3baccf501a92e/torchscript_freezing.ipynb
+++ b/docs/_downloads/16b28c991d1ecd15e9c3baccf501a92e/torchscript_freezing.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nModel Freezing in TorchScript\n=============================\n\nIn this tutorial, we introduce the syntax for *model freezing* in TorchScript.\nFreezing is the process of inlining Pytorch module parameters and attributes\nvalues into the TorchScript internal representation. Parameter and attribute\nvalues are treated as final values and they cannot be modified in the resulting\nFrozen module.\n\nBasic Syntax\n------------\nModel freezing can be invoked using API below:\n\n ``torch.jit.freeze(mod : ScriptModule, names : str[]) -> SciptModule``\n\nNote the input module can either be the result of scripting or tracing.\nSee https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html\n\nNext, we demonstrate how freezing works using an example:\n\n"
+        "\nModel Freezing in TorchScript\n=============================\n\nIn this tutorial, we introduce the syntax for *model freezing* in TorchScript.\nFreezing is the process of inlining Pytorch module parameters and attributes\nvalues into the TorchScript internal representation. Parameter and attribute\nvalues are treated as final values and they cannot be modified in the resulting\nFrozen module.\n\nBasic Syntax\n------------\nModel freezing can be invoked using API below:\n\n ``torch.jit.freeze(mod : ScriptModule, names : str[]) -> SciptModule``\n\nNote the input module can either be the result of scripting or tracing.\nSee https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html\n\nNext, we demonstrate how freezing works using an example:\n"
       ]
     },
     {
@@ -67,7 +67,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/1b58d206e701317cf46c92dcf2a8978d/parallelism_tutorial.ipynb b/docs/_downloads/1b58d206e701317cf46c92dcf2a8978d/parallelism_tutorial.ipynb
index 1b453dcd2..ef0d775f4 100644
--- a/docs/_downloads/1b58d206e701317cf46c92dcf2a8978d/parallelism_tutorial.ipynb
+++ b/docs/_downloads/1b58d206e701317cf46c92dcf2a8978d/parallelism_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uba40\ud2f0-GPU \uc608\uc81c\n==================\n\n\ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac(Data Parallelism)\ub294 \ubbf8\ub2c8-\ubc30\uce58\ub97c \uc5ec\ub7ec \uac1c\uc758 \ub354 \uc791\uc740 \ubbf8\ub2c8-\ubc30\uce58\ub85c\n\uc790\ub974\uace0 \uac01\uac01\uc758 \uc791\uc740 \ubbf8\ub2c8\ubc30\uce58\ub97c \ubcd1\ub82c\uc801\uc73c\ub85c \uc5f0\uc0b0\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\n\ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac\ub294 ``torch.nn.DataParallel`` \uc744 \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud569\ub2c8\ub2e4.\n``DataParallel`` \ub85c \uac10\uc300 \uc218 \uc788\ub294 \ubaa8\ub4c8\uc740 \ubc30\uce58 \ucc28\uc6d0(batch dimension)\uc5d0\uc11c\n\uc5ec\ub7ec GPU\ub85c \ubcd1\ub82c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\nDataParallel\n-------------\n\n"
+        "\n\uba40\ud2f0-GPU \uc608\uc81c\n==================\n\n\ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac(Data Parallelism)\ub294 \ubbf8\ub2c8-\ubc30\uce58\ub97c \uc5ec\ub7ec \uac1c\uc758 \ub354 \uc791\uc740 \ubbf8\ub2c8-\ubc30\uce58\ub85c\n\uc790\ub974\uace0 \uac01\uac01\uc758 \uc791\uc740 \ubbf8\ub2c8\ubc30\uce58\ub97c \ubcd1\ub82c\uc801\uc73c\ub85c \uc5f0\uc0b0\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\n\ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac\ub294 ``torch.nn.DataParallel`` \uc744 \uc0ac\uc6a9\ud558\uc5ec \uad6c\ud604\ud569\ub2c8\ub2e4.\n``DataParallel`` \ub85c \uac10\uc300 \uc218 \uc788\ub294 \ubaa8\ub4c8\uc740 \ubc30\uce58 \ucc28\uc6d0(batch dimension)\uc5d0\uc11c\n\uc5ec\ub7ec GPU\ub85c \ubcd1\ub82c \ucc98\ub9ac\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\nDataParallel\n-------------\n"
       ]
     },
     {
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/256861ec2cab5f0dc50c523f520dfefd/dynamic_quantization_tutorial.py b/docs/_downloads/256861ec2cab5f0dc50c523f520dfefd/dynamic_quantization_tutorial.py
index cabdd90d2..94555b115 100644
--- a/docs/_downloads/256861ec2cab5f0dc50c523f520dfefd/dynamic_quantization_tutorial.py
+++ b/docs/_downloads/256861ec2cab5f0dc50c523f520dfefd/dynamic_quantization_tutorial.py
@@ -1,26 +1,27 @@
+# -*- coding: utf-8 -*-
 """
-(beta) Dynamic Quantization on an LSTM Word Language Model
+(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화
 ==================================================================
 
 **Author**: `James Reed <https://github.com/jamesr66a>`_
 
 **Edited by**: `Seth Weidman <https://github.com/SethHWeidman/>`_
 
-Introduction
+**번역**: `박경림 <https://github.com/kypark7/>`_ `Myungha Kwon <https://github.com/kwonmha/>`_ 
+
+시작하기
 ------------
 
-Quantization involves converting the weights and activations of your model from float
-to int, which can result in smaller model size and faster inference with only a small
-hit to accuracy.
+양자화는 모델의 크기를 줄이고 추론 속도를 높이면서도 정확도는 별로 낮아지지 않도록,
+모델의 가중치와 활성 함수를 실수형에서 정수형으로 변환합니다.
 
-In this tutorial, we'll apply the easiest form of quantization -
-`dynamic quantization <https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic>`_ -
-to an LSTM-based next word-prediction model, closely following the
-`word language model <https://github.com/pytorch/examples/tree/master/word_language_model>`_
-from the PyTorch examples.
+이 튜토리얼에서는 PyTorch의 `단어 단위 언어 모델 <https://github.com/pytorch/examples/tree/master/word_language_model>`_
+예제를 따라하면서, LSTM 기반의 단어 예측 모델에 가장 간단한 양자화 기법인
+`동적 양자화 <https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic>`_
+를 적용해 보겠습니다.
 """
 
-# imports
+# 불러오기
 import os
 from io import open
 import time
@@ -30,15 +31,14 @@
 import torch.nn.functional as F
 
 ######################################################################
-# 1. Define the model
+# 1. 모델 정의하기
 # -------------------
 #
-# Here we define the LSTM model architecture, following the
-# `model <https://github.com/pytorch/examples/blob/master/word_language_model/model.py>`_
-# from the word language model example.
+# 단어 단위 언어 모델 예제에서 사용된 `모델 <https://github.com/pytorch/examples/blob/master/word_language_model/model.py>`_ 을
+# 따라 LSTM 모델 아키텍처를 정의합니다.
 
 class LSTMModel(nn.Module):
-    """Container module with an encoder, a recurrent module, and a decoder."""
+    """인코더, 반복 모듈 및 디코더가 있는 컨테이너 모듈."""
 
     def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):
         super(LSTMModel, self).__init__()
@@ -71,14 +71,11 @@ def init_hidden(self, bsz):
                 weight.new_zeros(self.nlayers, bsz, self.nhid))
 
 ######################################################################
-# 2. Load in the text data
+# 2. 텍스트 데이터 불러오기
 # ------------------------
 #
-# Next, we load the
-# `Wikitext-2 dataset <https://www.google.com/search?q=wikitext+2+data>`_ into a `Corpus`,
-# again following the
-# `preprocessing <https://github.com/pytorch/examples/blob/master/word_language_model/data.py>`_
-# from the word language model example.
+# 다음으로, 단어 단위 언어 모델 예제의 `전처리 <https://github.com/pytorch/examples/blob/master/word_language_model/data.py>`_
+# 과정을 따라 `Wikitext-2 데이터셋 <https://www.google.com/search?q=wikitext+2+data>`_ 을 `Corpus` 인스턴스에 불러옵니다.
 
 class Dictionary(object):
     def __init__(self):
@@ -103,16 +100,17 @@ def __init__(self, path):
         self.test = self.tokenize(os.path.join(path, 'test.txt'))
 
     def tokenize(self, path):
-        """Tokenizes a text file."""
         assert os.path.exists(path)
-        # Add words to the dictionary
+        """텍스트 파일 토큰화"""
+        assert os.path.exists(path)
+        # 사전에 단어 추가
         with open(path, 'r', encoding="utf8") as f:
             for line in f:
                 words = line.split() + ['<eos>']
                 for word in words:
                     self.dictionary.add_word(word)
 
-        # Tokenize file content
+        # 파일 내용 토큰화
         with open(path, 'r', encoding="utf8") as f:
             idss = []
             for line in f:
@@ -130,14 +128,13 @@ def tokenize(self, path):
 corpus = Corpus(model_data_filepath + 'wikitext-2')
 
 ######################################################################
-# 3. Load the pre-trained model
+# 3. 사전 학습된 모델 불러오기
 # -----------------------------
 #
-# This is a tutorial on dynamic quantization, a quantization technique
-# that is applied after a model has been trained. Therefore, we'll simply load some
-# pre-trained weights into this model architecture; these weights were obtained
-# by training for five epochs using the default settings in the word language model
-# example.
+# 이 튜토리얼은 모델이 학습된 후 적용되는 양자화 기술인 동적 양자화에 대한 튜토리얼입니다.
+# 따라서 우리는 미리 학습된 가중치를 모델 아키텍처에 로드할 것 입니다. 이 가중치는 word 
+# language 모델 예제의 기본 설정을 사용하여 5개의 epoch 동안 학습하여 얻은 것입니다.
+# 
 
 ntokens = len(corpus.dictionary)
 
@@ -159,9 +156,8 @@ def tokenize(self, path):
 print(model)
 
 ######################################################################
-# Now let's generate some text to ensure that the pre-trained model is working
-# properly - similarly to before, we follow
-# `here <https://github.com/pytorch/examples/blob/master/word_language_model/generate.py>`_
+# 이제 사전 학습된 모델이 잘 동작하는지 확인해보기 위해 텍스트를 생성해 보겠습니다.
+# 지금까지 튜토리얼을 진행했던 방식처럼 `이 예제 <https://github.com/pytorch/examples/blob/master/word_language_model/generate.py>`_ 를 따라 하겠습니다.
 
 input_ = torch.randint(ntokens, (1, 1), dtype=torch.long)
 hidden = model.init_hidden(1)
@@ -169,7 +165,7 @@ def tokenize(self, path):
 num_words = 1000
 
 with open(model_data_filepath + 'out.txt', 'w') as outf:
-    with torch.no_grad():  # no tracking history
+    with torch.no_grad():  # 기록을 추적하지 않습니다.
         for i in range(num_words):
             output, hidden = model(input_, hidden)
             word_weights = output.squeeze().div(temperature).exp().cpu()
@@ -188,28 +184,27 @@ def tokenize(self, path):
     print(all_output)
 
 ######################################################################
-# It's no GPT-2, but it looks like the model has started to learn the structure of
-# language!
+# 이 모델이 GPT-2는 아니지만, 언어의 구조를 배우기 시작한 것처럼 보입니다!
+#
 #
-# We're almost ready to demonstrate dynamic quantization. We just need to define a few more
-# helper functions:
+# 동적 양자화를 시연할 준비가 거의 끝났습니다. 몇 가지 helper 함수를 정의하기만 하면 됩니다:
 
 bptt = 25
 criterion = nn.CrossEntropyLoss()
 eval_batch_size = 1
 
-# create test data set
+# 테스트 데이터셋 만들기
 def batchify(data, bsz):
-    # Work out how cleanly we can divide the dataset into bsz parts.
+    # 데이터셋을 bsz 부분으로 얼마나 깔끔하게 나눌 수 있는지 계산합니다.
     nbatch = data.size(0) // bsz
-    # Trim off any extra elements that wouldn't cleanly fit (remainders).
+    # 깔끔하게 맞지 않는 추가적인 부분(나머지들)을 잘라냅니다.
     data = data.narrow(0, 0, nbatch * bsz)
-    # Evenly divide the data across the bsz batches.
+    # 데이터에 대하여 bsz 배치들로 동등하게 나눕니다.
     return data.view(bsz, -1).t().contiguous()
 
 test_data = batchify(corpus.test, eval_batch_size)
 
-# Evaluation functions
+# 평가 함수들
 def get_batch(source, i):
     seq_len = min(bptt, len(source) - 1 - i)
     data = source[i:i+seq_len]
@@ -217,7 +212,7 @@ def get_batch(source, i):
     return data, target
 
 def repackage_hidden(h):
-  """Wraps hidden states in new Tensors, to detach them from their history."""
+  """은닉 상태를 변화도 기록에서 제거된 새로운 tensor로 만듭니다."""
 
   if isinstance(h, torch.Tensor):
       return h.detach()
@@ -225,7 +220,7 @@ def repackage_hidden(h):
       return tuple(repackage_hidden(v) for v in h)
 
 def evaluate(model_, data_source):
-    # Turn on evaluation mode which disables dropout.
+    # Dropout을 중지시키는 평가 모드로 실행합니다.
     model_.eval()
     total_loss = 0.
     hidden = model_.init_hidden(eval_batch_size)
@@ -239,15 +234,14 @@ def evaluate(model_, data_source):
     return total_loss / (len(data_source) - 1)
 
 ######################################################################
-# 4. Test dynamic quantization
+# 4. 동적 양자화 테스트하기
 # ----------------------------
 #
-# Finally, we can call ``torch.quantization.quantize_dynamic`` on the model!
-# Specifically,
+# 마지막으로 모델에서 ``torch.quantization.quantize_dynamic`` 을 호출 할 수 있습니다!
+# 구체적으로,
 #
-# - We specify that we want the ``nn.LSTM`` and ``nn.Linear`` modules in our
-#   model to be quantized
-# - We specify that we want weights to be converted to ``int8`` values
+# - 모델의 ``nn.LSTM`` 과 ``nn.Linear`` 모듈을 양자화 하도록 명시합니다.
+# - 가중치들이 ``int8`` 값으로 변환되도록 명시합니다.
 
 import torch.quantization
 
@@ -257,8 +251,8 @@ def evaluate(model_, data_source):
 print(quantized_model)
 
 ######################################################################
-# The model looks the same; how has this benefited us? First, we see a
-# significant reduction in model size:
+# 모델은 동일하게 보입니다. 이것이 어떻게 이득을 주는 것일까요? 첫째, 모델 크기가
+# 상당히 줄어 듭니다:
 
 def print_size_of_model(model):
     torch.save(model.state_dict(), "temp.p")
@@ -269,10 +263,10 @@ def print_size_of_model(model):
 print_size_of_model(quantized_model)
 
 ######################################################################
-# Second, we see faster inference time, with no difference in evaluation loss:
-#
-# Note: we number of threads to one for single threaded comparison, since quantized
-# models run single threaded.
+# 두 번째로, 평가 손실값은 같으나 추론(inference) 속도가 빨라졌습니다.
+
+# 메모: 양자화 된 모델은 단일 스레드로 실행되기 때문에 단일 스레드 비교를 위해
+# 스레드 수를 1로 설정했습니다.
 
 torch.set_num_threads(1)
 
@@ -286,14 +280,14 @@ def time_model_evaluation(model, test_data):
 time_model_evaluation(quantized_model, test_data)
 
 ######################################################################
-# Running this locally on a MacBook Pro, without quantization, inference takes about 200 seconds,
-# and with quantization it takes just about 100 seconds.
+# MacBook Pro에서 로컬로 실행하는 경우, 양자화 없이는 추론(inference)에 약 200초가 걸리고 
+# 양자화를 사용하면 약 100초가 걸립니다.
 #
-# Conclusion
+# 마치며
 # ----------
 #
-# Dynamic quantization can be an easy way to reduce model size while only
-# having a limited effect on accuracy.
+# 동적 양자화는 정확도에 제한적인 영향을 미치면서 모델 크기를 줄이는
+# 쉬운 방법이 될 수 있습니다.
 #
-# Thanks for reading! As always, we welcome any feedback, so please create an issue
-# `here <https://github.com/pytorch/pytorch/issues>`_ if you have any.
+# 읽어주셔서 감사합니다. 언제나처럼 어떠한 피드백도 환영이니, 의견이 있다면
+# `여기 <https://github.com/pytorch/pytorch/issues>`_ 에 이슈를 남겨 주세요.
diff --git a/docs/_downloads/27f4b1d04aea1872e99a8fc10c08ba13/two_layer_net_tensor.ipynb b/docs/_downloads/27f4b1d04aea1872e99a8fc10c08ba13/two_layer_net_tensor.ipynb
index 6cf3e28b5..797865072 100644
--- a/docs/_downloads/27f4b1d04aea1872e99a8fc10c08ba13/two_layer_net_tensor.ipynb
+++ b/docs/_downloads/27f4b1d04aea1872e99a8fc10c08ba13/two_layer_net_tensor.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch: Tensors\n----------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc5ec\uae30\uc11c\ub294 PyTorch tensor\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc21c\uc804\ud30c, \uc190\uc2e4(loss), \uadf8\ub9ac\uace0 \uc5ed\uc804\ud30c \uc5f0\uc0b0\uae4c\uc9c0\n\uc9c1\uc811 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch Tensor\ub294 \uae30\ubcf8\uc801\uc73c\ub85c NumPy \ubc30\uc5f4\uacfc \ub3d9\uc77c\ud569\ub2c8\ub2e4; \ub525\ub7ec\ub2dd\uc774\ub098 \uc5f0\uc0b0 \uadf8\ub798\ud504\n(computational graph), \ubcc0\ud654\ub3c4(gradient)\ub294 \uc54c\uc9c0 \ubabb\ud558\uba70 \uc784\uc758\uc758 \uc22b\uc790 \uacc4\uc0b0\uc5d0 \uc0ac\uc6a9\ud558\ub294\n\uc77c\ubc18\uc801\uc778 N\ucc28\uc6d0 \ubc30\uc5f4\uc785\ub2c8\ub2e4.\n\nNumPy \ubc30\uc5f4\uacfc PyTorch Tensor\uc758 \uac00\uc7a5 \ud070 \ucc28\uc774\uc810\uc740 PyTorch Tensor\ub294 CPU\ub098 GPU \uc5b4\ub514\uc11c\ub4e0\n\uc2e4\ud589\uc774 \uac00\ub2a5\ud558\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4. GPU\uc5d0\uc11c \uc5f0\uc0b0\uc744 \ud558\uae30 \uc704\ud574\uc11c\ub294, Tensor\ub97c CUDA \uc790\ub8cc\ud615\uc73c\ub85c\n\ubcc0\ud658(cast)\ud574\uc8fc\uae30\ub9cc \ud558\uba74 \ub429\ub2c8\ub2e4.\n\n"
+        "\nPyTorch: Tensors\n----------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc5ec\uae30\uc11c\ub294 PyTorch tensor\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc21c\uc804\ud30c, \uc190\uc2e4(loss), \uadf8\ub9ac\uace0 \uc5ed\uc804\ud30c \uc5f0\uc0b0\uae4c\uc9c0\n\uc9c1\uc811 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch Tensor\ub294 \uae30\ubcf8\uc801\uc73c\ub85c NumPy \ubc30\uc5f4\uacfc \ub3d9\uc77c\ud569\ub2c8\ub2e4; \ub525\ub7ec\ub2dd\uc774\ub098 \uc5f0\uc0b0 \uadf8\ub798\ud504\n(computational graph), \ubcc0\ud654\ub3c4(gradient)\ub294 \uc54c\uc9c0 \ubabb\ud558\uba70 \uc784\uc758\uc758 \uc22b\uc790 \uacc4\uc0b0\uc5d0 \uc0ac\uc6a9\ud558\ub294\n\uc77c\ubc18\uc801\uc778 N\ucc28\uc6d0 \ubc30\uc5f4\uc785\ub2c8\ub2e4.\n\nNumPy \ubc30\uc5f4\uacfc PyTorch Tensor\uc758 \uac00\uc7a5 \ud070 \ucc28\uc774\uc810\uc740 PyTorch Tensor\ub294 CPU\ub098 GPU \uc5b4\ub514\uc11c\ub4e0\n\uc2e4\ud589\uc774 \uac00\ub2a5\ud558\ub2e4\ub294 \uc810\uc785\ub2c8\ub2e4. GPU\uc5d0\uc11c \uc5f0\uc0b0\uc744 \ud558\uae30 \uc704\ud574\uc11c\ub294, Tensor\ub97c CUDA \uc790\ub8cc\ud615\uc73c\ub85c\n\ubcc0\ud658(cast)\ud574\uc8fc\uae30\ub9cc \ud558\uba74 \ub429\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/2a6717c6d63c217a6ca736f62c4dc768/tf_two_layer_net.ipynb b/docs/_downloads/2a6717c6d63c217a6ca736f62c4dc768/tf_two_layer_net.ipynb
index f6f932fe0..9e6073762 100644
--- a/docs/_downloads/2a6717c6d63c217a6ca736f62c4dc768/tf_two_layer_net.ipynb
+++ b/docs/_downloads/2a6717c6d63c217a6ca736f62c4dc768/tf_two_layer_net.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nTensorFlow: \uc815\uc801 \uadf8\ub798\ud504(Static Graph)\n--------------------------------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uae30\ubcf8\uc801\uc778 TensorFlow \uc5f0\uc0b0\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c \uad6c\uc131\ud55c \ub2e4\uc74c, \uadf8\ub798\ud504\ub97c \uc5ec\ub7ec \ucc28\ub840\n\uc2e4\ud589\ud558\uc5ec \uc2e4\uc81c\ub85c \uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\uc2dc\ucf1c\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nTensorflow\uc640 PyTorch\uc758 \uc8fc\uc694\ud55c \ucc28\uc774\uc810 \uc911 \ud558\ub098\ub294, PyTorch\ub294 \ub3d9\uc801 \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c\n\uc0ac\uc6a9\ud558\ub294\ub370 \ubc18\ud574 Tensorflow\ub294 \uc815\uc801 \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c \uc0ac\uc6a9\ud55c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4.\n\n\uba3c\uc800 Tensorflow\uc5d0\uc11c \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c \uad6c\uc131\ud55c \ub2e4\uc74c, \ud574\ub2f9 \uadf8\ub798\ud504\ub97c \uc5ec\ub7ec \ucc28\ub840\n\uc2e4\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n"
+        "\nTensorFlow: \uc815\uc801 \uadf8\ub798\ud504(Static Graph)\n--------------------------------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uae30\ubcf8\uc801\uc778 TensorFlow \uc5f0\uc0b0\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c \uad6c\uc131\ud55c \ub2e4\uc74c, \uadf8\ub798\ud504\ub97c \uc5ec\ub7ec \ucc28\ub840\n\uc2e4\ud589\ud558\uc5ec \uc2e4\uc81c\ub85c \uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\uc2dc\ucf1c\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nTensorflow\uc640 PyTorch\uc758 \uc8fc\uc694\ud55c \ucc28\uc774\uc810 \uc911 \ud558\ub098\ub294, PyTorch\ub294 \ub3d9\uc801 \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c\n\uc0ac\uc6a9\ud558\ub294\ub370 \ubc18\ud574 Tensorflow\ub294 \uc815\uc801 \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c \uc0ac\uc6a9\ud55c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4.\n\n\uba3c\uc800 Tensorflow\uc5d0\uc11c \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c \uad6c\uc131\ud55c \ub2e4\uc74c, \ud574\ub2f9 \uadf8\ub798\ud504\ub97c \uc5ec\ub7ec \ucc28\ub840\n\uc2e4\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/2a7469f7250c4f405b1c4f885994ea69/advanced_tutorial.py b/docs/_downloads/2a7469f7250c4f405b1c4f885994ea69/advanced_tutorial.py
index 0d03593f9..b4cda9e34 100644
--- a/docs/_downloads/2a7469f7250c4f405b1c4f885994ea69/advanced_tutorial.py
+++ b/docs/_downloads/2a7469f7250c4f405b1c4f885994ea69/advanced_tutorial.py
@@ -1,120 +1,112 @@
 # -*- coding: utf-8 -*-
 r"""
-Advanced: Making Dynamic Decisions and the Bi-LSTM CRF
+심화 과정 : Bi-LSTM CRF와 동적 결정
 ======================================================
 
-Dynamic versus Static Deep Learning Toolkits
+동적, 정적 딥 러닝 툴킷(toolkits) 비교
 --------------------------------------------
 
-Pytorch is a *dynamic* neural network kit. Another example of a dynamic
-kit is `Dynet <https://github.com/clab/dynet>`__ (I mention this because
-working with Pytorch and Dynet is similar. If you see an example in
-Dynet, it will probably help you implement it in Pytorch). The opposite
-is the *static* tool kit, which includes Theano, Keras, TensorFlow, etc.
-The core difference is the following:
-
-* In a static toolkit, you define
-  a computation graph once, compile it, and then stream instances to it.
-* In a dynamic toolkit, you define a computation graph *for each
-  instance*. It is never compiled and is executed on-the-fly
-
-Without a lot of experience, it is difficult to appreciate the
-difference. One example is to suppose we want to build a deep
-constituent parser. Suppose our model involves roughly the following
-steps:
-
-* We build the tree bottom up
-* Tag the root nodes (the words of the sentence)
-* From there, use a neural network and the embeddings
-  of the words to find combinations that form constituents. Whenever you
-  form a new constituent, use some sort of technique to get an embedding
-  of the constituent. In this case, our network architecture will depend
-  completely on the input sentence. In the sentence "The green cat
-  scratched the wall", at some point in the model, we will want to combine
-  the span :math:`(i,j,r) = (1, 3, \text{NP})` (that is, an NP constituent
-  spans word 1 to word 3, in this case "The green cat").
-
-However, another sentence might be "Somewhere, the big fat cat scratched
-the wall". In this sentence, we will want to form the constituent
-:math:`(2, 4, NP)` at some point. The constituents we will want to form
-will depend on the instance. If we just compile the computation graph
-once, as in a static toolkit, it will be exceptionally difficult or
-impossible to program this logic. In a dynamic toolkit though, there
-isn't just 1 pre-defined computation graph. There can be a new
-computation graph for each instance, so this problem goes away.
-
-Dynamic toolkits also have the advantage of being easier to debug and
-the code more closely resembling the host language (by that I mean that
-Pytorch and Dynet look more like actual Python code than Keras or
-Theano).
-
-Bi-LSTM Conditional Random Field Discussion
+Pytorch는 *동적* 신경망 툴킷입니다. 다른 동적 신경망 툴킷으로는
+`Dynet <https://github.com/clab/dynet>`_ 이 있습니다.(이 툴킷을
+예로 든 이유는 사용하는 법이 Pytorch와 비슷하기 때문입니다. Dynet의 예제를 보면
+Pytorch로 구현할 때도 도움이 될 것입니다.) 반대로 *정적* 툴킷들로
+Theano, Keras, TensorFlow 등이 있습니다. 주요 차이점은 다음과 같습니다:
+
+* 정적 툴킷을 쓸 때는 계산 그래프를 한 번만 정의하고, 컴파일 한 후,
+  데이터를 계산 그래프에 넘깁니다.
+* 동적 툴킷에서는 *각 데이터* 의 계산 그래프를 정의하며 컴파일하지
+  않고 즉각 실행됩니다.
+
+경험이 많지 않다면 두 방식의 차이를 알기 어렵습니다. 딥 러닝 기반의
+구구조 분석기(constituent parser)를 예로 들어보겠습니다. 모델은 대략
+다음과 같은 과정을 수행합니다:
+
+* 트리를 상향식(bottom-up)으로 만들어 나갑니다.
+* 최상위 노드를 태깅합니다. (문장의 각 단어)
+* 거기서부터 신경망과 단어들의 임베딩을 이용해 구구조를 이루는 조합을
+  찾아냅니다. 새로운 구구조를 생성할 때마다 구구조의 임베딩을 얻기 위한
+  어떤 기술이 필요합니다. 지금은 신경망이 오직 입력 문장만 참고할
+  것입니다. "The green cat scratched the wall"이란 문장에서, 모델의 어느 시점에
+  :math:`(i,j,r) = (1, 3, \text{NP})` 범위 (단어 1에서부터 단어 3까지가
+  NP 구구조라는 뜻이며, 이 문장에서는 "The green cat") 를 결합하길 원할
+  것입니다.
+
+그런데, 또다른 문장 "Somewhere, the big fat cat scratched the wall" 에서는
+어느 시점에 :math:`(2, 4, NP)` 구구조를 만들기를 원할 것입니다. 우리가
+만들기 원하는 구구조들은 문장에 따라 다릅니다. 만약 정적 툴킷에서처럼
+계산 그래프를 한 번만 컴파일한다면, 이 과정을 프로그래밍하기 매우 어렵거나
+불가능할 것입니다. 하지만 동적 툴킷에서는 하나의 계산 그래프만 있지
+않습니다. 각 문장들마다 새로운 계산 그래프가 있을 수 있기 때문에 이런
+문제가 없습니다.
+
+동적 틀킷은 디버깅 하기 더 쉽고, 코드가 기반 언어와 더 비슷합니다
+(Pytorch와 Dynet이 Keras 또는 Theano 보다 Python 코드와 더 비슷합니다).
+
+Bi-LSTM Conditional Rnadom Field 설명
 -------------------------------------------
 
-For this section, we will see a full, complicated example of a Bi-LSTM
-Conditional Random Field for named-entity recognition. The LSTM tagger
-above is typically sufficient for part-of-speech tagging, but a sequence
-model like the CRF is really essential for strong performance on NER.
-Familiarity with CRF's is assumed. Although this name sounds scary, all
-the model is is a CRF but where an LSTM provides the features. This is
-an advanced model though, far more complicated than any earlier model in
-this tutorial. If you want to skip it, that is fine. To see if you're
-ready, see if you can:
-
--  Write the recurrence for the viterbi variable at step i for tag k.
--  Modify the above recurrence to compute the forward variables instead.
--  Modify again the above recurrence to compute the forward variables in
-   log-space (hint: log-sum-exp)
-
-If you can do those three things, you should be able to understand the
-code below. Recall that the CRF computes a conditional probability. Let
-:math:`y` be a tag sequence and :math:`x` an input sequence of words.
-Then we compute
+이 영역에서는 개체명 인식을 수행하는 완성된 Bi-LSTM Conditional Random
+Field 예시를 살펴보겠습니다. 위에 나온 LSTM 태거(tagger)는 일반적으로
+품사 태깅을 하기에 충분합니다. 하지만 CRF 같은 연속된 데이터를 다루는
+모델은 좋은 개체명 인식 모델(NER)에 꼭 필요합니다. 여러분이 CRF를 잘 알고
+있다고 가정하겠습니다. 이름이 무섭게 들릴 수도 있지만, LSTM이 특징을
+제공하는 점을 제외하면 이 모델은 CRF 입니다. 하지만 더 발전된 모델이며,
+이 튜토리얼의 앞부분에 나왔던 모델보다 훨씬 복잡합니다. 넘어가고 싶다면
+넘어가도 괜찮습니다. 이해할 수 있다고 생각한다면, 아래를 읽어보세요:
+
+-  태그 k에 대한 i번째 단계의 비터비(viterbi) 변수를 위해 순환 흐름을 만든다.
+-  순방향 변수를 계산하기 위해 위의 순한 흐름을 조정한다.
+-  순방향 변수를 로그 공간에서 계산하기 위해 다시 한 번 조정한다.
+   (힌트 : 로그-합-지수승)
+
+위의 세가지를 할 수 있다면, 아래의 코드를 이해할 수 있을 것입니다.
+CRF는 조건부 확률을 계산한다는 점을 기억하세요. :math:`y` 를 연속된
+태그라 하고, :math:`x` 를 연속된 입력 단어라 하겠습니다. 그러면 아래의
+식을 계산할 수 있습니다.
 
 .. math::  P(y|x) = \frac{\exp{(\text{Score}(x, y)})}{\sum_{y'} \exp{(\text{Score}(x, y')})}
 
-Where the score is determined by defining some log potentials
-:math:`\log \psi_i(x,y)` such that
+점수(score) 함수는 아래와 같이 정의된 로그 포텐셜(potential) :math:`\log \psi_i(x,y)`
+함수에 의해 결정됩니다.
 
 .. math::  \text{Score}(x,y) = \sum_i \log \psi_i(x,y)
 
-To make the partition function tractable, the potentials must look only
-at local features.
+분배 함수(partition function)를 단순화하기 위해서, 포텐셜이 주변의
+특징들만 반영한다고 하겠습니다.
 
-In the Bi-LSTM CRF, we define two kinds of potentials: emission and
-transition. The emission potential for the word at index :math:`i` comes
-from the hidden state of the Bi-LSTM at timestep :math:`i`. The
-transition scores are stored in a :math:`|T|x|T|` matrix
-:math:`\textbf{P}`, where :math:`T` is the tag set. In my
-implementation, :math:`\textbf{P}_{j,k}` is the score of transitioning
-to tag :math:`j` from tag :math:`k`. So:
+Bi-LSTM CRF 안에 배출(emission), 전이(transition) 두 종류의 포텐셜을
+정의합니다. :math:`i` 번째 단어에 대한 배출 포텐셜은 Bi-LSTM의
+:math:`i` 번째 시점의 은닉 상태가 결정합니다. 전이 점수는 :math:`|T|x|T|`
+형태인 행렬 :math:`\textbf{P}` 에 저장되어 있습니다. :math:`T` 는
+태그의 집합입니다. 이 구현에서, :math:`\textbf{P}_{j,k}` 는 tag :math:`j` 에서
+tag :math:`k` 로의 전이 점수를 의미합니다. 따라서:
 
 .. math::  \text{Score}(x,y) = \sum_i \log \psi_\text{EMIT}(y_i \rightarrow x_i) + \log \psi_\text{TRANS}(y_{i-1} \rightarrow y_i)
 
 .. math::  = \sum_i h_i[y_i] + \textbf{P}_{y_i, y_{i-1}}
 
-where in this second expression, we think of the tags as being assigned
-unique non-negative indices.
+두 번째 식에서 고유하고 음수가 아닌 인덱스에 의해 태그가 부여됐다고
+간주합니다.
 
-If the above discussion was too brief, you can check out
-`this <http://www.cs.columbia.edu/%7Emcollins/crf.pdf>`__ write up from
-Michael Collins on CRFs.
+위의 설명이 너무 간단하다고 생각한다면 CRF에 대한 Michael Collins의
+글을 `여기 <http://www.cs.columbia.edu/%7Emcollins/crf.pdf>`__ 에서
+읽어보세요.
 
-Implementation Notes
+구현 문서
 --------------------
 
-The example below implements the forward algorithm in log space to
-compute the partition function, and the viterbi algorithm to decode.
-Backpropagation will compute the gradients automatically for us. We
-don't have to do anything by hand.
+아래의 예시는 로그 공간에서 분배 함수를 계산하기 위한 순방향 알고리즘과
+복호화하기 위한 비터비 알고리즘을 구현한 것입니다. 역전파
+단계에서 변화도는 자동으로 계산될 것입니다. 우리가 직접 할 일은
+없습니다.
 
-The implementation is not optimized. If you understand what is going on,
-you'll probably quickly see that iterating over the next tag in the
-forward algorithm could probably be done in one big operation. I wanted
-to code to be more readable. If you want to make the relevant change,
-you could probably use this tagger for real tasks.
+이 구현은 최적의 상태가 아닙니다. 과정을 이해했다면, 순방향 알고리즘
+상에서 다음 태그를 순차적으로 처리하는 과정을 하나의 큰 연산으로 줄일
+수 있다는 것을 아마 빠르게 알 수 있을 것입니다. 이 코드는 가능한 읽기
+쉽게 작성했습니다. 적절하게 수정하면, 이 태거를 실제 문제들에 사용할
+수도 있을 것입니다.
 """
-# Author: Robert Guthrie
+# 작성자: Robert Guthrie
 
 import torch
 import torch.autograd as autograd
@@ -124,11 +116,11 @@
 torch.manual_seed(1)
 
 #####################################################################
-# Helper functions to make the code more readable.
+# 코드 가독성을 높여주는 보조 함수들
 
 
 def argmax(vec):
-    # return the argmax as a python int
+    # argmax를 파이썬 정수형으로 반환합니다.
     _, idx = torch.max(vec, 1)
     return idx.item()
 
@@ -138,7 +130,7 @@ def prepare_sequence(seq, to_ix):
     return torch.tensor(idxs, dtype=torch.long)
 
 
-# Compute log sum exp in a numerically stable way for the forward algorithm
+# 순방향 알고리즘을 위해 수치적으로 안정적인 방법으로 로그 합 지수승을 계산합니다.
 def log_sum_exp(vec):
     max_score = vec[0, argmax(vec)]
     max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])
@@ -146,7 +138,7 @@ def log_sum_exp(vec):
         torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))
 
 #####################################################################
-# Create model
+# 모델 생성
 
 
 class BiLSTM_CRF(nn.Module):
@@ -163,16 +155,15 @@ def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):
         self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,
                             num_layers=1, bidirectional=True)
 
-        # Maps the output of the LSTM into tag space.
+        # LSTM의 출력을 태그 공간으로 대응시킵니다.
         self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)
 
-        # Matrix of transition parameters.  Entry i,j is the score of
-        # transitioning *to* i *from* j.
+        # 전이 매개변수 행렬. i, j 성분은 i에서 j로 변할 때의 점수입니다.
         self.transitions = nn.Parameter(
             torch.randn(self.tagset_size, self.tagset_size))
 
-        # These two statements enforce the constraint that we never transfer
-        # to the start tag and we never transfer from the stop tag
+        # 이 두 코드는 시작 태그로 전이하지 않고, 정지 태그에서부터
+        # 전이하지 않도록 강제합니다.
         self.transitions.data[tag_to_ix[START_TAG], :] = -10000
         self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000
 
@@ -183,30 +174,28 @@ def init_hidden(self):
                 torch.randn(2, 1, self.hidden_dim // 2))
 
     def _forward_alg(self, feats):
-        # Do the forward algorithm to compute the partition function
+        # 분배 함수를 계산하기 위해 순방향 알고리즘을 수행합니다.
         init_alphas = torch.full((1, self.tagset_size), -10000.)
-        # START_TAG has all of the score.
+        # START_TAG는 모든 점수를 갖고 있습니다.
         init_alphas[0][self.tag_to_ix[START_TAG]] = 0.
 
-        # Wrap in a variable so that we will get automatic backprop
+        # 자동으로 역전파 되도록 변수로 감쌉니다.
         forward_var = init_alphas
 
-        # Iterate through the sentence
+        # 문장의 각 성분을 반복 처리합니다.
         for feat in feats:
-            alphas_t = []  # The forward tensors at this timestep
+            alphas_t = []  # 현재 시점의 순방향 텐서
             for next_tag in range(self.tagset_size):
-                # broadcast the emission score: it is the same regardless of
-                # the previous tag
+                # 이전의 태그와 상관없이 배출 점수를 전파합니다.
                 emit_score = feat[next_tag].view(
                     1, -1).expand(1, self.tagset_size)
-                # the ith entry of trans_score is the score of transitioning to
-                # next_tag from i
+                # trans_score의 i번째 성분은 i로부터 next_tag로 전이할 점수입니다.
                 trans_score = self.transitions[next_tag].view(1, -1)
-                # The ith entry of next_tag_var is the value for the
-                # edge (i -> next_tag) before we do log-sum-exp
+                # next_tag_var의 i번째 성분은 로그-합-지수승을 계산하기 전
+                # i에서 next_tag로 가는 간선의 값입니다.
                 next_tag_var = forward_var + trans_score + emit_score
-                # The forward variable for this tag is log-sum-exp of all the
-                # scores.
+                # 이 태그의 순방향 변수는 모든 점수들의 로그-합-지수승 계산
+                # 결과입니다.
                 alphas_t.append(log_sum_exp(next_tag_var).view(1))
             forward_var = torch.cat(alphas_t).view(1, -1)
         terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
@@ -222,7 +211,7 @@ def _get_lstm_features(self, sentence):
         return lstm_feats
 
     def _score_sentence(self, feats, tags):
-        # Gives the score of a provided tag sequence
+        # 주어진 태그 순열에 점수를 매깁니다.
         score = torch.zeros(1)
         tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])
         for i, feat in enumerate(feats):
@@ -234,44 +223,43 @@ def _score_sentence(self, feats, tags):
     def _viterbi_decode(self, feats):
         backpointers = []
 
-        # Initialize the viterbi variables in log space
+        # 비터비 변수를 로그 공간 상에 초기화합니다.
         init_vvars = torch.full((1, self.tagset_size), -10000.)
         init_vvars[0][self.tag_to_ix[START_TAG]] = 0
 
-        # forward_var at step i holds the viterbi variables for step i-1
+        # i 단계의 forward_var는 i-1 단계의 비터비 변수를 갖고 있습니다.
         forward_var = init_vvars
         for feat in feats:
-            bptrs_t = []  # holds the backpointers for this step
-            viterbivars_t = []  # holds the viterbi variables for this step
+            bptrs_t = []  # 현재 단계의 backpointer를 갖고 있습니다.
+            viterbivars_t = []  # 현재 단계의 비터비 변수를 갖고 있습니다.
 
             for next_tag in range(self.tagset_size):
-                # next_tag_var[i] holds the viterbi variable for tag i at the
-                # previous step, plus the score of transitioning
-                # from tag i to next_tag.
-                # We don't include the emission scores here because the max
-                # does not depend on them (we add them in below)
+                # next_tag_var[i]는 이전 단계의 태그 i에 대한 비터비 변수와,
+                # 태그 i에서 next_tag로 전이할 점수를 더한 값을 갖고 있습니다.
+                # 배출 점수는 argmax와 상관 없기 때문에(아래 코드에서 추가할 것입니다)
+                # 여기에 포함되지 않습니다.
                 next_tag_var = forward_var + self.transitions[next_tag]
                 best_tag_id = argmax(next_tag_var)
                 bptrs_t.append(best_tag_id)
                 viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))
-            # Now add in the emission scores, and assign forward_var to the set
-            # of viterbi variables we just computed
+            # 이제 배출 점수를 더합니다. 그리고 방금 계산한 비터비 변수의
+            # 집합을 forward_var에 할당합니다.
             forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)
             backpointers.append(bptrs_t)
 
-        # Transition to STOP_TAG
+        # STAP_TAG로의 전이
         terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]
         best_tag_id = argmax(terminal_var)
         path_score = terminal_var[0][best_tag_id]
 
-        # Follow the back pointers to decode the best path.
+        # 최적의 경로를 구하기 위해 back pointer를 따라갑니다.
         best_path = [best_tag_id]
         for bptrs_t in reversed(backpointers):
             best_tag_id = bptrs_t[best_tag_id]
             best_path.append(best_tag_id)
-        # Pop off the start tag (we dont want to return that to the caller)
+        # 시작 태그를 빼냅니다 (시작 태그는 반환된 필요가 없습니다)
         start = best_path.pop()
-        assert start == self.tag_to_ix[START_TAG]  # Sanity check
+        assert start == self.tag_to_ix[START_TAG]  # 완결성 검사 (Sanity check)
         best_path.reverse()
         return path_score, best_path
 
@@ -281,16 +269,16 @@ def neg_log_likelihood(self, sentence, tags):
         gold_score = self._score_sentence(feats, tags)
         return forward_score - gold_score
 
-    def forward(self, sentence):  # dont confuse this with _forward_alg above.
-        # Get the emission scores from the BiLSTM
+    def forward(self, sentence):  # 이 함수와 위의 _forward_alg를 헷갈리지 마세요.
+        # Bi-LSTM으로부터 배출 점수를 얻습니다.
         lstm_feats = self._get_lstm_features(sentence)
 
-        # Find the best path, given the features.
+        # 주어진 특징(배출 점수)들로 최적의 경로를 찾아냅니다.
         score, tag_seq = self._viterbi_decode(lstm_feats)
         return score, tag_seq
 
 #####################################################################
-# Run training
+# 훈련 실행
 
 
 START_TAG = "<START>"
@@ -298,7 +286,7 @@ def forward(self, sentence):  # dont confuse this with _forward_alg above.
 EMBEDDING_DIM = 5
 HIDDEN_DIM = 4
 
-# Make up some training data
+# 훈련용 데이터를 만듭니다.
 training_data = [(
     "the wall street journal reported today that apple corporation made money".split(),
     "B I I I O O O B I O O".split()
@@ -318,57 +306,55 @@ def forward(self, sentence):  # dont confuse this with _forward_alg above.
 model = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)
 optimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)
 
-# Check predictions before training
+# 훈련 전 예측 결과를 확인합니다.
 with torch.no_grad():
     precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)
     precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long)
     print(model(precheck_sent))
 
 # Make sure prepare_sequence from earlier in the LSTM section is loaded
+# 위의 보조 함수 영역에 있는 prepare_sequence 함수가 불러와 졌는지 확인합니다.
 for epoch in range(
-        300):  # again, normally you would NOT do 300 epochs, it is toy data
+        300):  # 다시 말하지만, 아마 300 에폭을 실행하진 않을 것입니다. 이것은 연습용 데이터입니다.
     for sentence, tags in training_data:
-        # Step 1. Remember that Pytorch accumulates gradients.
-        # We need to clear them out before each instance
+        # 1단계. Pytorch가 변화도를 누적한다는 것을 기억하세요.
+        # 그것들을 제거합니다.
         model.zero_grad()
 
-        # Step 2. Get our inputs ready for the network, that is,
-        # turn them into Tensors of word indices.
+        # 2단계. 입력 데이터를 신경망에 사용될 수 있도록 단어
+        # 인덱스들의 텐서로 변환합니다.
         sentence_in = prepare_sequence(sentence, word_to_ix)
         targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)
 
-        # Step 3. Run our forward pass.
+        # 3단계. 순방향 계산을 수행합니다.
         loss = model.neg_log_likelihood(sentence_in, targets)
 
-        # Step 4. Compute the loss, gradients, and update the parameters by
-        # calling optimizer.step()
+        # 4단계. 손실값, 변화도를 계산하고 optimizer.step()을 호출하여
+        # 매개변수들을 갱신합니다.
         loss.backward()
         optimizer.step()
 
-# Check predictions after training
+# 훈련이 끝난 후 예측 결과를 확인합니다.
 with torch.no_grad():
     precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)
     print(model(precheck_sent))
-# We got it!
+# 다 했습니다!
 
 
 ######################################################################
-# Exercise: A new loss function for discriminative tagging
-# --------------------------------------------------------
+# 연습 : 판별적(discriminative) 태깅을 위한 새로운 손실 함수
+# ------------------------------------------------------------
 #
-# It wasn't really necessary for us to create a computation graph when
-# doing decoding, since we do not backpropagate from the viterbi path
-# score. Since we have it anyway, try training the tagger where the loss
-# function is the difference between the Viterbi path score and the score
-# of the gold-standard path. It should be clear that this function is
-# non-negative and 0 when the predicted tag sequence is the correct tag
-# sequence. This is essentially *structured perceptron*.
+# 사실 복호화 할 때는 비터비 경로 점수로 역전파를 하지 않기 때문에 계산
+# 그래프를 만들 필요가 없었습니다. 그러나 이미 만들었으니, 비터비 경로
+# 점수와 실제 정답 경로 점수의 차이를 손실 함수로 사용해서 태거를
+# 학습시켜 보세요. 손실 함수의 값은 음수가 아니어야 하며, 예측된 태그
+# 순열이 정답이라면 손실 함수의 값은 0이어야 합니다. 이것은 본질적으로
+# *구조화된 퍼셉트론* 입니다.
 #
-# This modification should be short, since Viterbi and score\_sentence are
-# already implemented. This is an example of the shape of the computation
-# graph *depending on the training instance*. Although I haven't tried
-# implementing this in a static toolkit, I imagine that it is possible but
-# much less straightforward.
-#
-# Pick up some real data and do a comparison!
+# 이미 비터비와 score_sentence 함수가 구현되어 있기 때문에 간단히 수정할
+# 수 있습니다. 이 모델은 *학습 데이터에 따라 변하는* 계산 그래프의 한
+# 예시입니다. 이 모델을 정적 툴킷에서 구현해 보지는 않았는데, 구현이
+# 가능하지만 덜 직관적일 수 있습니다.
 #
+# 실제 데이터를 사용해보고 비교해보세요!
diff --git a/docs/_downloads/303a511ec52e0933ffe70a0320cc68dc/tensor_tutorial_old.ipynb b/docs/_downloads/303a511ec52e0933ffe70a0320cc68dc/tensor_tutorial_old.ipynb
index 5233d3d5f..bcc852cfd 100644
--- a/docs/_downloads/303a511ec52e0933ffe70a0320cc68dc/tensor_tutorial_old.ipynb
+++ b/docs/_downloads/303a511ec52e0933ffe70a0320cc68dc/tensor_tutorial_old.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nTensor\n=======\n\nPyTorch\uc5d0\uc11c\uc758 Tensor\ub294 Torch\uc5d0\uc11c\uc640 \uac70\uc758 \ub3d9\uc77c\ud558\uac8c \ub3d9\uc791\ud569\ub2c8\ub2e4.\n\n\ucd08\uae30\ud654\ub418\uc9c0 \uc54a\uc740 (5 x 7) \ud06c\uae30\uc758 Tensor\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4:\n\n\n"
+        "\nTensor\n=======\n\nPyTorch\uc5d0\uc11c\uc758 Tensor\ub294 Torch\uc5d0\uc11c\uc640 \uac70\uc758 \ub3d9\uc77c\ud558\uac8c \ub3d9\uc791\ud569\ub2c8\ub2e4.\n\n\ucd08\uae30\ud654\ub418\uc9c0 \uc54a\uc740 (5 x 7) \ud06c\uae30\uc758 Tensor\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4:\n"
       ]
     },
     {
@@ -234,7 +234,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/35afa48d3c96a0db7f505da116ff8978/learning_hybrid_frontend_through_example_tutorial.ipynb b/docs/_downloads/35afa48d3c96a0db7f505da116ff8978/learning_hybrid_frontend_through_example_tutorial.ipynb
index 08d625a2c..f3985b4f4 100644
--- a/docs/_downloads/35afa48d3c96a0db7f505da116ff8978/learning_hybrid_frontend_through_example_tutorial.ipynb
+++ b/docs/_downloads/35afa48d3c96a0db7f505da116ff8978/learning_hybrid_frontend_through_example_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nLearning Hybrid Frontend Syntax Through Example\n===============================================\n**Author:** `Nathan Inkawhich <https://github.com/inkawhich>`_\n\nThis document is meant to highlight the syntax of the Hybrid Frontend\nthrough a non-code intensive example. The Hybrid Frontend is one of the\nnew shiny features of Pytorch 1.0 and provides an avenue for developers\nto transition their models from **eager** to **graph** mode. PyTorch\nusers are very familiar with eager mode as it provides the ease-of-use\nand flexibility that we all enjoy as researchers. Caffe2 users are more\naquainted with graph mode which has the benefits of speed, optimization\nopportunities, and functionality in C++ runtime environments. The hybrid\nfrontend bridges the gap between the the two modes by allowing\nresearchers to develop and refine their models in eager mode (i.e.\nPyTorch), then gradually transition the proven model to graph mode for\nproduction, when speed and resouce consumption become critical.\n\nHybrid Frontend Information\n---------------------------\n\nThe process for transitioning a model to graph mode is as follows.\nFirst, the developer constructs, trains, and tests the model in eager\nmode. Then they incrementally **trace** and **script** each\nfunction/module of the model with the Just-In-Time (JIT) compiler, at\neach step verifying that the output is correct. Finally, when each of\nthe components of the top-level model have been traced and scripted, the\nmodel itself is traced. At which point the model has been transitioned\nto graph mode, and has a complete python-free representation. With this\nrepresentation, the model runtime can take advantage of high-performance\nCaffe2 operators and graph based optimizations.\n\nBefore we continue, it is important to understand the idea of tracing\nand scripting, and why they are separate. The goal of **trace** and\n**script** is the same, and that is to create a graph representation of\nthe operations taking place in a given function. The discrepency comes\nfrom the flexibility of eager mode that allows for **data-dependent\ncontrol flows** within the model architecture. When a function does NOT\nhave a data-dependent control flow, it may be *traced* with\n``torch.jit.trace``. However, when the function *has* a data-dependent\ncontrol flow it must be *scripted* with ``torch.jit.script``. We will\nleave the details of the interworkings of the hybrid frontend for\nanother document, but the code example below will show the syntax of how\nto trace and script different pure python functions and torch Modules.\nHopefully, you will find that using the hybrid frontend is non-intrusive\nas it mostly involves adding decorators to the existing function and\nclass definitions.\n\nMotivating Example\n------------------\n\nIn this example we will implement a strange math function that may be\nlogically broken up into four parts that do, and do not contain\ndata-dependent control flows. The purpose here is to show a non-code\nintensive example where the use of the JIT is highlighted. This example\nis a stand-in representation of a useful model, whose implementation has\nbeen divided into various pure python functions and modules.\n\nThe function we seek to implement, $Y(x)$, is defined for\n$x \\epsilon \\mathbb{N}$ as\n\n\\begin{align}z(x) = \\Biggl \\lfloor \\frac{\\sqrt{\\prod_{i=1}^{|2 x|}i}}{5} \\Biggr \\rfloor\\end{align}\n\n\\begin{align}Y(x) = \\begin{cases}\n      \\frac{z(x)}{2}  &  \\text{if } z(x)\\%2 == 0, \\\\\n      z(x)             &  \\text{otherwise}\n    \\end{cases}\\end{align}\n\n\\begin{align}\\begin{array}{| r  | r |} \\hline\n    x &1 &2 &3 &4 &5 &6 &7 \\\\ \\hline\n    Y(x) &0 &0 &-5 &20 &190 &-4377 &-59051 \\\\ \\hline\n    \\end{array}\\end{align}\n\nAs mentioned, the computation is split into four parts. Part one is the\nsimple tensor calculation of $|2x|$, which can be traced. Part two\nis the iterative product calculation that represents a data dependent\ncontrol flow to be scripted (the number of loop iteration depends on the\ninput at runtime). Part three is a trace-able\n$\\lfloor \\sqrt{a/5} \\rfloor$ calculation. Finally, part 4 handles\nthe output cases depending on the value of $z(x)$ and must be\nscripted due to the data dependency. Now, let's see how this looks in\ncode.\n\nPart 1 - Tracing a pure python function\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWe can implement part one as a pure python function as below. Notice, to\ntrace this function we call ``torch.jit.trace`` and pass in the function\nto be traced. Since the trace requires a dummy input of the expected\nruntime type and shape, we also include the ``torch.rand`` to generate a\nsingle valued torch tensor.\n\n\n"
+        "\nLearning Hybrid Frontend Syntax Through Example\n===============================================\n**Author:** `Nathan Inkawhich <https://github.com/inkawhich>`_\n\nThis document is meant to highlight the syntax of the Hybrid Frontend\nthrough a non-code intensive example. The Hybrid Frontend is one of the\nnew shiny features of Pytorch 1.0 and provides an avenue for developers\nto transition their models from **eager** to **graph** mode. PyTorch\nusers are very familiar with eager mode as it provides the ease-of-use\nand flexibility that we all enjoy as researchers. Caffe2 users are more\naquainted with graph mode which has the benefits of speed, optimization\nopportunities, and functionality in C++ runtime environments. The hybrid\nfrontend bridges the gap between the the two modes by allowing\nresearchers to develop and refine their models in eager mode (i.e.\nPyTorch), then gradually transition the proven model to graph mode for\nproduction, when speed and resouce consumption become critical.\n\nHybrid Frontend Information\n---------------------------\n\nThe process for transitioning a model to graph mode is as follows.\nFirst, the developer constructs, trains, and tests the model in eager\nmode. Then they incrementally **trace** and **script** each\nfunction/module of the model with the Just-In-Time (JIT) compiler, at\neach step verifying that the output is correct. Finally, when each of\nthe components of the top-level model have been traced and scripted, the\nmodel itself is traced. At which point the model has been transitioned\nto graph mode, and has a complete python-free representation. With this\nrepresentation, the model runtime can take advantage of high-performance\nCaffe2 operators and graph based optimizations.\n\nBefore we continue, it is important to understand the idea of tracing\nand scripting, and why they are separate. The goal of **trace** and\n**script** is the same, and that is to create a graph representation of\nthe operations taking place in a given function. The discrepency comes\nfrom the flexibility of eager mode that allows for **data-dependent\ncontrol flows** within the model architecture. When a function does NOT\nhave a data-dependent control flow, it may be *traced* with\n``torch.jit.trace``. However, when the function *has* a data-dependent\ncontrol flow it must be *scripted* with ``torch.jit.script``. We will\nleave the details of the interworkings of the hybrid frontend for\nanother document, but the code example below will show the syntax of how\nto trace and script different pure python functions and torch Modules.\nHopefully, you will find that using the hybrid frontend is non-intrusive\nas it mostly involves adding decorators to the existing function and\nclass definitions.\n\nMotivating Example\n------------------\n\nIn this example we will implement a strange math function that may be\nlogically broken up into four parts that do, and do not contain\ndata-dependent control flows. The purpose here is to show a non-code\nintensive example where the use of the JIT is highlighted. This example\nis a stand-in representation of a useful model, whose implementation has\nbeen divided into various pure python functions and modules.\n\nThe function we seek to implement, $Y(x)$, is defined for\n$x \\epsilon \\mathbb{N}$ as\n\n\\begin{align}z(x) = \\Biggl \\lfloor \\frac{\\sqrt{\\prod_{i=1}^{|2 x|}i}}{5} \\Biggr \\rfloor\\end{align}\n\n\\begin{align}Y(x) = \\begin{cases}\n      \\frac{z(x)}{2}  &  \\text{if } z(x)\\%2 == 0, \\\\\n      z(x)             &  \\text{otherwise}\n    \\end{cases}\\end{align}\n\n\\begin{align}\\begin{array}{| r  | r |} \\hline\n    x &1 &2 &3 &4 &5 &6 &7 \\\\ \\hline\n    Y(x) &0 &0 &-5 &20 &190 &-4377 &-59051 \\\\ \\hline\n    \\end{array}\\end{align}\n\nAs mentioned, the computation is split into four parts. Part one is the\nsimple tensor calculation of $|2x|$, which can be traced. Part two\nis the iterative product calculation that represents a data dependent\ncontrol flow to be scripted (the number of loop iteration depends on the\ninput at runtime). Part three is a trace-able\n$\\lfloor \\sqrt{a/5} \\rfloor$ calculation. Finally, part 4 handles\nthe output cases depending on the value of $z(x)$ and must be\nscripted due to the data dependency. Now, let's see how this looks in\ncode.\n\nPart 1 - Tracing a pure python function\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nWe can implement part one as a pure python function as below. Notice, to\ntrace this function we call ``torch.jit.trace`` and pass in the function\nto be traced. Since the trace requires a dummy input of the expected\nruntime type and shape, we also include the ``torch.rand`` to generate a\nsingle valued torch tensor.\n"
       ]
     },
     {
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/363afc3b7c522e4e56981679c22f1ad6/word_embeddings_tutorial.ipynb b/docs/_downloads/363afc3b7c522e4e56981679c22f1ad6/word_embeddings_tutorial.ipynb
index 035b6c7c2..6db7f7875 100644
--- a/docs/_downloads/363afc3b7c522e4e56981679c22f1ad6/word_embeddings_tutorial.ipynb
+++ b/docs/_downloads/363afc3b7c522e4e56981679c22f1ad6/word_embeddings_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\ub2e8\uc5b4 \uc784\ubca0\ub529: \uc5b4\ud718\uc758 \uc758\ubbf8\ub97c \uc778\ucf54\ub529\ud558\uae30\n===========================================\n**\ubc88\uc5ed**: `\uc784\uc131\uc5f0 <http://github.com/sylim2357>`_\n\n\ub2e8\uc5b4 \uc784\ubca0\ub529(word embedding)\uc774\ub780 \ub9d0\ubb49\uce58(\ud639\uc740 \ucf54\ud37c\uc2a4, corpus) \ub0b4 \uac01 \ub2e8\uc5b4\uc5d0 \uc77c\ub300\uc77c\ub85c \ub300\uc751\ud558\ub294 \ubc00\uc9d1\ub41c \uc2e4\uc218 \ubca1\ud130(dense vector)\uc758 \uc9d1\ud569, \ud639\uc740 \uc774 \ubca1\ud130\ub97c\n\uad6c\ud558\ub294 \ud589\uc704\ub97c \uac00\ub9ac\ud0b5\ub2c8\ub2e4. \uc8fc\ub85c \ub2e8\uc5b4\ub97c \ud53c\ucc98(feature)\ub85c \uc0ac\uc6a9\ud558\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c\ub294 \ub2e8\uc5b4\ub97c \ucef4\ud4e8\ud130 \uce5c\ud654\uc801\uc778\n\ud615\ud0dc\ub85c \ubc14\uafb8\uc5b4 \uc8fc\ub294 \uc791\uc5c5\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ucef4\ud4e8\ud130\uac00 \ub2e8\uc5b4\ub97c \ubc14\ub85c \uc774\ud574\ud558\uae30\ub294 \uc0c1\ub2f9\ud788 \uc5b4\ub835\uae30 \ub54c\ubb38\uc774\uc8e0.\n\uadf8\ub807\ub2e4\uba74, \ub2e8\uc5b4\ub97c \uc5b4\ub5bb\uac8c \ud45c\ud604\ud558\ub294 \uac83\uc774 \uc88b\uc744\uae4c\uc694? \ubb3c\ub860 \uac01 \ubb38\uc790\uc5d0 \ud574\ub2f9\ud558\ub294 ASCII\ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uaca0\uc9c0\ub9cc,\nASCII\ucf54\ub4dc\ub294 \uc774 \ub2e8\uc5b4\uac00 *\ubb34\uc5c7* \uc778\uc9c0\ub97c \uc54c\ub824\uc904 \ubfd0, \ub2e8\uc5b4\uac00 \uc5b4\ub5a4 *\uc758\ubbf8* \ub97c \uac00\uc9c0\ub294\uc9c0\ub294 \uc54c\ub824\uc8fc\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n(\ub8f0\ubca0\uc774\uc2a4\ub85c \uc5b4\ubbf8 \ub4f1 \ubb38\ubc95\uc801 \ud2b9\uc9d5\uc744 \ud65c\uc6a9\ud558\uac70\ub098 \uc601\uc5b4\uc758 \uacbd\uc6b0 \ub300\ubb38\uc790\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uaca0\uc9c0\ub9cc \ucda9\ubd84\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.)\n\ub2e8\uc5b4\ub97c \uc5b4\ub5bb\uac8c \ud45c\ud604\ud560\uc9c0 \ubfd0 \uc544\ub2c8\ub77c, \uc774 \ud45c\ud604\ubc95\uc744 \uc5b4\ub5a0\ud55c \ubc29\uc2dd\uc73c\ub85c \uc5f0\uc0b0\ud574\uc57c \ud560 \uc9c0 \ub610\ud55c \ud070 \ubb38\uc81c\uc785\ub2c8\ub2e4.\n\ubcf4\ud1b5 \uc774\ub7ec\ud55c \ubc00\ub3c4 \ub192\uc740 \ubca1\ud130\ub97c \uc5bb\uae30 \uc704\ud574 \uc0ac\uc6a9\ud558\ub294 \ub274\ub7f4\ub137 \ubaa8\ub378\uc740 $|V|$ (\ub9d0\ubb49\uce58\uc758 \ub2e8\uc5b4 \uac1c\uc218)\uc758\n\ud070 \uc785\ub825 \ucc28\uc6d0\uacfc \uba87 \uc548\ub418\ub294 (\ud14d\uc2a4\ub97c \ubd84\ub958\ud558\ub294 \ubb38\uc81c\ub77c\uace0 \ud560 \uacbd\uc6b0) \uc791\uc740 \ucd9c\ub825 \ucc28\uc6d0\uc744 \uac00\uc9d1\ub2c8\ub2e4.\n\uc989, \ub2e8\uc5b4\ub4e4 \uac04\uc758 \uc5f0\uc0b0\uc774 \ud544\uc218\uc785\ub2c8\ub2e4. \uc5b4\ub5bb\uac8c \uc774 \ud070 \ucc28\uc6d0\uc758 \uacf5\uac04\uc744 \uc791\uc740 \uacf5\uac04\uc73c\ub85c \ubcc0\ud615\uc2dc\ud0ac \uc218 \uc788\uc744\uae4c\uc694?\n\n\uba3c\uc800, \uc0c1\uae30\ud55c ASCII\ucf54\ub4dc \ub300\uc2e0 \uc6d0\ud56b \uc778\ucf54\ub529(one-hot encoding)\uc744 \uc0ac\uc6a9\ud574\ubcf4\ub294 \uac83\uc740 \uc5b4\ub5a8\uae4c\uc694? \uc6d0\ud56b \uc778\ucf54\ub529\uc774\ub780\n\ud558\ub098\uc758 \ub2e8\uc5b4 $w$ \ub97c \uc544\ub798\uc758 \ubca1\ud130\ub85c \ud45c\ud604\ud558\ub294 \uac83\uc744 \ub9d0\ud569\ub2c8\ub2e4.\n\n\\begin{align}\\overbrace{\\left[ 0, 0, \\dots, 1, \\dots, 0, 0 \\right]}^\\text{|V| elements}\\end{align}\n\n\uc5ec\uae30\uc11c 1\uc740 \ud574\ub2f9 \ubca1\ud130\uac00 \ud45c\ud604\ud558\uace0\uc790 \ud558\ub294 \ub2e8\uc5b4\uc5d0 \ud574\ub2f9\ud558\ub294 \uc704\uce58 1\uacf3\uc5d0 \uc790\ub9ac\ud569\ub2c8\ub2e4. (\ub098\uba38\uc9c0\ub294 \uc804\ubd80\n0\uc785\ub2c8\ub2e4.) \ub2e4\ub978 \ub2e8\uc5b4\ub97c \ub098\ud0c0\ub0b4\ub294 \ubca1\ud130\uc5d0\uc120 1\uc774 \ub2e4\ub978 \uacf3\uc5d0 \uc704\uce58\ud574 \uc788\uaca0\uc8e0.\n\n\uc6d0\ud56b \uc778\ucf54\ub529\uc740 \ub9cc\ub4e4\uae30\uac00 \uc27d\ub2e4\ub294 \uc7a5\uc810\uc774 \uc788\uc9c0\ub9cc, \ub2e8\uc21c\ud55c \ub9cc\ud07c \ub2e8\uc810\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ub2e8 \ub2e8\uc5b4 \ubca1\ud130 \ud55c \uac1c\ub294\n\ubaa8\ub4e0 \ub2e8\uc5b4\ub97c \ud45c\ud604\ud560 \uc218 \uc788\uc744 \ub9cc\ud55c \ud06c\uae30\uac00 \ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4. \uc6b0\ub9ac\uac00 \uc5bc\ub9c8\ub098 \ub9ce\uc740 \uc885\ub958\uc758 \ub2e8\uc5b4\ub97c\n\uc0ac\uc6a9\ud558\ub294\uc9c0\ub97c \uc0dd\uac01 \ud55c\ub2e4\uba74 \uc5b4\ub9c8\uc5b4\ub9c8\ud558\uac8c \ud070 \ubca1\ud130\ub77c\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc8e0. \uc774 \ubfd0\ub9cc\uc774 \uc544\ub2d9\ub2c8\ub2e4.\n\uc6d0\ud56b \ubca1\ud130\ub294 \ubaa8\ub4e0 \ub2e8\uc5b4\ub97c \ub3c5\ub9bd\uc801\uc778 \uac1c\uccb4\ub85c \uac00\uc815\ud558\ub294 \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc989, \uacf5\uac04\uc0c1\uc5d0\uc11c\n\uc644\uc804\ud788 \ub2e4\ub978 \ucd95\uc5d0 \uc704\uce58\ud574 \uc788\uc5b4\uc11c \ub2e8\uc5b4\uac04\uc758 \uad00\uacc4\ub97c \ub098\ud0c0\ub0bc \uc218\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc6b0\ub9ac\ub294 \ub2e8\uc5b4\n\uc0ac\uc774\uc758 *\uc720\uc0ac\ub3c4* \ub97c \uc5b4\ub5bb\uac8c\ub4e0 \uacc4\uc0b0\ud558\uace0 \uc2f6\uc740\uac70\uc8e0. \uc65c \uc720\uc0ac\ub3c4\uac00 \uc911\uc694\ud558\ub0d0\uad6c\uc694? \ub2e4\uc74c \uc608\uc81c\ub97c \ubd05\uc2dc\ub2e4.\n\n\uc6b0\ub9ac\uc758 \ubaa9\ud45c\uac00 \uc5b8\uc5b4 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uac83\uc774\ub77c\uace0 \uac00\uc815\ud558\uace0 \ub2e4\uc74c\uc758 \ubb38\uc7a5\uc774 \ud559\uc2b5 \ub370\uc774\ud130\ub85c\uc368 \uc8fc\uc5b4\uc84c\ub2e4\uace0 \ud574\ubd05\uc2dc\ub2e4.\n\n* \uc218\ud559\uc790\uac00 \uac00\uac8c\ub85c \ub6f0\uc5b4\uac14\ub2e4.\n* \ubb3c\ub9ac\ud559\uc790\uac00 \uac00\uac8c\ub85c \ub6f0\uc5b4\uac14\ub2e4.\n* \uc218\ud559\uc790\uac00 \ub9ac\ub9cc \uac00\uc124\uc744 \uc99d\uba85\ud588\ub2e4.\n\n\ub610\ud55c \ud559\uc2b5 \ub370\uc774\ud130\uc5d0\ub294 \uc5c6\ub294 \uc544\ub798 \ubb38\uc7a5\uc774 \uc788\ub2e4\uace0 \uc0dd\uac01\ud574\ubd05\uc2dc\ub2e4.\n\n* \ubb3c\ub9ac\ud559\uc790\uac00 \ub9ac\ub9cc \uac00\uc124\uc744 \uc99d\uba85\ud588\ub2e4.\n\nASCII \ucf54\ub4dc\ub098 \uc6d0\ud56b \uc778\ucf54\ub529 \uae30\ubc18 \uc5b8\uc5b4 \ubaa8\ub378\uc740 \uc704 \ubb38\uc7a5\uc744 \uc5b4\ub290\uc815\ub3c4 \ub2e4\ub8f0 \uc218 \uc788\uaca0\uc9c0\ub9cc, \uac1c\uc120\uc758 \uc5ec\uc9c0\uac00 \uc788\uc9c0 \uc54a\uc744\uae4c\uc694?\n\uba3c\uc800 \uc544\ub798\uc758 \ub450 \uc0ac\uc2e4\uc744 \uc0dd\uac01\ud574\ubd05\uc2dc\ub2e4.\n\n* '\uc218\ud559\uc790'\uc640 '\ubb3c\ub9ac\ud559\uc790'\uac00 \ubb38\uc7a5 \ub0b4\uc5d0\uc11c \uac19\uc740 \uc5ed\ud560\uc744 \ub9e1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub450 \ub2e8\uc5b4\ub294 \uc5b4\ub5bb\uac8c\ub4e0 \uc758\ubbf8\uc801\uc778 \uc5f0\uad00\uc131\uc774 \uc788\uc744 \uac81\ub2c8\ub2e4.\n* \uc0c8\ub85c\uc6b4 \ubb38\uc7a5\uc5d0\uc11c '\ubb3c\ub9ac\ud559\uc790'\uac00 \ub9e1\uc740 \uc5ed\ud560\uc744 '\uc218\ud559\uc790'\uac00 \ub9e1\ub294 \uac83\uc744 \ud559\uc2b5 \ub370\uc774\ud130\uc5d0\uc11c \ubcf8 \uc801\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc6b0\ub9ac \ubaa8\ub378\uc774 \uc704\uc758 \uc0ac\uc2e4\uc744 \ud1b5\ud574 '\ubb3c\ub9ac\ud559\uc790'\uac00 \uc0c8 \ubb38\uc7a5\uc5d0 \uc798 \ub4e4\uc5b4 \ub9de\ub294\ub2e4\ub294 \uac83\uc744 \ucd94\ub860\ud560 \uc218\n\uc788\ub2e4\uba74 \ucc38 \uc88b\uc744 \uac83\uc785\ub2c8\ub2e4. \uc774\uac83\uc774 \uc704\uc5d0\uc11c \uc5b8\uae09\ud55c \uc720\uc0ac\ub3c4\uc758 \uc758\ubbf8\uc785\ub2c8\ub2e4. \ucca0\uc790\uc801 \uc720\uc0ac\ub3c4 \ubfd0\n\uc544\ub2c8\ub77c *\uc758\ubbf8\uc801 \uc720\uc0ac\ub3c4* \uc778 \uac83\uc785\ub2c8\ub2e4. \uc774\uac83\uc774\uc57c\ub9d0\ub85c \uc5b8\uc5b4 \ub370\uc774\ud130\uc5d0 \ub0b4\uc7ac\ud558\ub294 \ud76c\ubc15\uc131(sparsity)\uc5d0\n\ub300\ud55c \ucc98\ubc29\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\uac00 \ubcf8 \uac83\uacfc \uc544\uc9c1 \ubcf4\uc9c0 \uc54a\uc740 \uac83 \uc0ac\uc774\ub97c \uc774\uc5b4\uc8fc\ub294 \uac83\uc774\uc8e0.\n\uc55e\uc73c\ub85c\ub294 \ub2e4\uc74c\uc758 \uc5b8\uc5b4\ud559\uc801 \uae30\ubcf8 \uba85\uc81c\ub97c \uac00\uc815\ud558\ub3c4\ub85d \ud569\uc2dc\ub2e4. \ubc14\ub85c \ube44\uc2b7\ud55c \ub9e5\ub77d\uc5d0\uc11c \ub4f1\uc7a5\ud558\ub294\n\ub2e8\uc5b4\ub4e4\uc740 \uc11c\ub85c \uc758\ubbf8\uc801 \uc5f0\uad00\uc131\uc744 \uac00\uc9c4\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uc5b8\uc5b4\ud559\uc801\uc73c\ub85c\ub294 `\ubd84\uc0b0 \uc758\ubbf8 \uac00\uc124(distributional\nhypothesis) <https://en.wikipedia.org/wiki/Distributional_semantics>`__ \uc774\ub77c\uace0\ub3c4 \ud569\ub2c8\ub2e4.\n\n\n\ubc00\uc9d1\ub41c \ub2e8\uc5b4 \uc784\ubca0\ub529 \uad6c\ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\uc5b4\ub5bb\uac8c \ub2e8\uc5b4\uc758 \uc758\ubbf8\uc801 \uc720\uc0ac\ub3c4\ub97c \uc778\ucf54\ub529 \ud560 \uc218 \uc788\uc744\uae4c\uc694? \ub2e4\uc2dc \ub9d0\ud574, \uc5b4\ub5bb\uac8c \ud574\uc57c \ub2e8\uc5b4\uc758 \uc720\uc0ac\ub3c4\ub97c\n\ub2e8\uc5b4 \ubca1\ud130\uc5d0 \ubc18\uc601\ud560 \uc218 \uc788\uc744\uae4c\uc694? \ub2e8\uc5b4 \ub370\uc774\ud130\uc5d0 \uc758\ubbf8\uc801 \uc18d\uc131(attribute)\uc744 \ubd80\uc5ec\ud558\ub294 \uac74 \uc5b4\ub5a4\uac00\uc694?\n\uc608\ub97c \ub4e4\uc5b4 '\uc218\ud559\uc790'\uc640 '\ubb3c\ub9ac\ud559\uc790'\uac00 \ubaa8\ub450 \ub6f8 \uc218 \uc788\ub2e4\uba74, \ud574\ub2f9 \ub2e8\uc5b4\uc758 '\ub6f8 \uc218 \uc788\uc74c' \uc18d\uc131\uc5d0 \ub192\uc740 \uc810\uc218\ub97c \uc8fc\ub294 \uac81\ub2c8\ub2e4.\n\uacc4\uc18d \ud574\ubd05\uc2dc\ub2e4. \ub2e4\ub978 \ub2e8\uc5b4\ub4e4\uc5d0 \ub300\ud574\uc11c\ub294 \uc5b4\ub5a0\ud55c \uc18d\uc131\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc744\uc9c0 \uc0dd\uac01\ud574\ubd05\uc2dc\ub2e4.\n\n\ub9cc\uc57d \uac01 \uc18d\uc131\uc744 \ud558\ub098\uc758 \ucc28\uc6d0\uc774\ub77c\uace0 \ubcf8\ub2e4\uba74 \ud558\ub098\uc758 \ub2e8\uc5b4\uc5d0 \uc544\ub798\uc640 \uac19\uc740 \ubca1\ud130\ub97c \ubc30\uc815\ud560 \uc218 \uc788\uc744\uac81\ub2c8\ub2e4.\n\n\\begin{align}q_\\text{\uc218\ud559\uc790} = \\left[ \\overbrace{2.3}^\\text{\ub6f8 \uc218 \uc788\uc74c},\n   \\overbrace{9.4}^\\text{\ucee4\ud53c\ub97c \uc88b\uc544\ud568}, \\overbrace{-5.5}^\\text{\ubb3c\ub9ac \uc804\uacf5\uc784}, \\dots \\right]\\end{align}\n\n\\begin{align}q_\\text{\ubb3c\ub9ac\ud559\uc790} = \\left[ \\overbrace{2.5}^\\text{\ub6f8 \uc218 \uc788\uc74c},\n   \\overbrace{9.1}^\\text{\ucee4\ud53c\ub97c \uc88b\uc544\ud568}, \\overbrace{6.4}^\\text{\ubb3c\ub9ac \uc804\uacf5\uc784}, \\dots \\right]\\end{align}\n\n\uadf8\ub7ec\uba74 \uc544\ub798\uc640 \uac19\uc774 \ub450 \ub2e8\uc5b4 \uc0ac\uc774\uc758 \uc720\uc0ac\ub3c4\ub97c \uad6c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. ('\uc720\uc0ac\ub3c4'\ub77c\ub294 \ud568\uc218\ub97c \uc815\uc758\ud558\ub294 \uac81\ub2c8\ub2e4)\n\n\\begin{align}\\text{\uc720\uc0ac\ub3c4}(\\text{\ubb3c\ub9ac\ud559\uc790}, \\text{\uc218\ud559\uc790}) = q_\\text{\ubb3c\ub9ac\ud559\uc790} \\cdot q_\\text{\uc218\ud559\uc790}\\end{align}\n\n\ubb3c\ub860 \ubcf4\ud1b5\uc740 \uc774\ub807\uac8c \ubca1\ud130\uc758 \uae38\uc774\ub85c \ub098\ub220\uc8fc\uc9c0\ub9cc\uc694.\n\n\\begin{align}\\text{\uc720\uc0ac\ub3c4}(\\text{\ubb3c\ub9ac\ud559\uc790}, \\text{\uc218\ud559\uc790}) = \\frac{q_\\text{\ubb3c\ub9ac\ud559\uc790} \\cdot q_\\text{\uc218\ud559\uc790}}\n   {\\| q_\\text{\ubb3c\ub9ac\ud559\uc790} \\| \\| q_\\text{\uc218\ud559\uc790} \\|} = \\cos (\\phi)\\end{align}\n\n$\\phi$ \ub294 \ub450 \ubca1\ud130 \uc0ac\uc774\uc758 \uac01\uc785\ub2c8\ub2e4. \uc774\ub7f0 \uc2dd\uc774\uba74 \uc815\ub9d0 \ube44\uc2b7\ud55c \ub2e8\uc5b4\ub294 \uc720\uc0ac\ub3c4 1\uc744 \uac16\uace0,\n\uc815\ub9d0 \ub2e4\ub978 \ub2e8\uc5b4\ub294 \uc720\uc0ac\ub3c4 -1\uc744 \uac16\uaca0\uc8e0. \ube44\uc2b7\ud55c \uc758\ubbf8\ub97c \uac00\uc9c8\uc218\ub85d \uac19\uc740 \ubc29\ud5a5\uc744 \uac00\ub9ac\ud0a4\uace0 \uc788\uc744 \ud14c\ub2c8\uae4c\uc694.\n\n\uc774 \uae00 \ucd08\ubc18\uc5d0 \ub098\uc628 \ud76c\ubc15\ud55c \uc6d0\ud56b \ubca1\ud130\uac00 \uc0ac\uc2e4\uc740 \uc6b0\ub9ac\uac00 \ubc29\uae08 \uc815\uc758\ud55c \uc758\ubbf8 \ubca1\ud130\uc758\n\ud2b9\uc774 \ucf00\uc774\uc2a4\ub77c\ub294 \uac83\uc744 \uae08\ubc29 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e8\uc5b4 \ubca1\ud130\uc758 \uac01 \uc6d0\uc18c\ub294 \uadf8 \ub2e8\uc5b4\uc758 \uc758\ubbf8\uc801 \uc18d\uc131\uc744\n\ud45c\ud604\ud558\uace0, \ubaa8\ub4e0 \ub2e8\uc5b4 \uc30d\uc758 \uc720\uc0ac\ub3c4\ub294 0\uc774\uae30 \ub54c\ubb38\uc774\uc8e0. \uc704\uc5d0\uc11c \uc815\uc758\ud55c \uc758\ubbf8 \ubca1\ud130\ub294 *\ubc00\uc9d1* \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\n\uc989, \uc6d0\ud56b \ubca1\ud130\uc5d0 \ube44\ud574 0 \uc6d0\uc18c\uc758 \uc218\uac00 \uc801\ub2e4\uace0 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ud558\uc9c0\ub9cc \uc774 \ubca1\ud130\ub4e4\uc740 \uad6c\ud558\uae30\uac00 \uc9c4\uc9dc \uc5b4\ub835\uc2b5\ub2c8\ub2e4. \ub2e8\uc5b4\uac04\uc758 \uc720\uc0ac\ub3c4\ub97c \uacb0\uc815 \uc9c0\uc744 \ub9cc\ud55c\n\uc758\ubbf8\uc801 \uc18d\uc131\uc740 \uc5b4\ub5bb\uac8c \uacb0\uc815\ud560 \uac83\uc774\uba70, \uc18d\uc131\uc744 \uacb0\uc815\ud588\ub2e4\uace0 \ud558\ub354\ub77c\ub3c4 \uac01 \uc18d\uc131\uc5d0\n\ud574\ub2f9\ud558\ub294 \uac12\uc740 \ub3c4\ub300\uccb4 \uc5b4\ub5a0\ud55c \uae30\uc900\uc73c\ub85c \uc815\ud574\uc57c \ud560\uae4c\uc694? \uc18d\uc131\uacfc \uac12\uc744 \ub370\uc774\ud130\uc5d0 \uae30\ubc18\ud574\n\ub9cc\ub4e4\uace0 \uc790\ub3d9\uc73c\ub85c \ub2e8\uc5b4 \ubca1\ud130\ub97c \ub9cc\ub4e4 \uc218\ub294 \uc5c6\uc744\uae4c\uc694? \uc788\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc744 \uc0ac\uc6a9\ud558\uba74 \ub9d0\uc774\uc8e0.\n\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud558\uc5ec \uc0ac\ub78c\uc758 \uac1c\uc785 \uc5c6\uc774 \uc18d\uc131\uc758 \ud45c\ud604 \ubc29\ubc95\uc744 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4.\n\uc774\ub97c \uc774\uc6a9\ud574 \ub2e8\uc5b4 \ubca1\ud130\ub97c \ubaa8\ub378 \ubaa8\uc218\ub85c \uc124\uc815\ud558\uace0 \ubaa8\ub378 \ud559\uc2b5\uc2dc\uc5d0 \ub2e8\uc5b4 \ubca1\ud130\ub3c4 \ud568\uaed8 \uc5c5\ub370\uc774\ud2b8 \ud558\uba74\n\ub420 \uac83\uc785\ub2c8\ub2e4. \uc774\ub807\uac8c \uc6b0\ub9ac \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc740 \uc801\uc5b4\ub3c4 \uc774\ub860\uc0c1\uc73c\ub85c\ub294 \ucda9\ubd84\ud788 \ud559\uc2b5\ud560 \uc218 \uc788\ub294\n*\uc7a0\uc7ac \uc758\ubbf8 \uc18d\uc131* \uc744 \ucc3e\uc744 \uac83\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c \ub9d0\ud558\ub294 \uc7a0\uc7ac \uc758\ubbf8 \uc18d\uc131\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9c4 \ubca1\ud130\ub294 \uc0ac\ub78c\uc774\n\ud574\uc11d\ud558\uae30 \uc0c1\ub2f9\ud788 \uc5b4\ub835\ub2e4\ub294 \uc810\uc744 \uae30\uc5b5\ud574 \ub450\uc138\uc694. \uc704\uc5d0\uc11c \uc218\ud559\uc790\uc640 \ubb3c\ub9ac\ud559\uc790\uc5d0\uac8c \ucee4\ud53c\ub97c \uc88b\uc544\ud55c\ub2e4\ub294\n\ub4f1 \uc0ac\ub78c\uc774 \uc784\uc758\uc801\uc73c\ub85c \ub2e8\uc5b4\uc5d0 \ubd80\uc5ec\ud55c \uc18d\uc131\uacfc\ub294 \ub2ec\ub9ac, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc774 \uc790\ub3d9\uc73c\ub85c \ub2e8\uc5b4\uc758 \uc18d\uc131\uc744 \ucc3e\ub294\ub2e4\uba74\n\uadf8 \uc18d\uc131\uacfc \uac12\uc774 \uc758\ubbf8\ud558\ub294 \ubc14\ub97c \uc54c\uae30\uac00 \uc5b4\ub824\uc6b8 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4\uc11c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc774 \ucc3e\uc740 '\uc218\ud559\uc790'\uc640\n'\ubb3c\ub9ac\ud559\uc790'\uc758 \ud45c\ud604 \ubca1\ud130 \ub458 \ub2e4 \ub450\ubc88\uc9f8 \uc6d0\uc18c\uac00 \ud06c\ub2e4\uace0 \uac00\uc815\ud574 \ubd05\uc2dc\ub2e4. \ub458\uc774 \ube44\uc2b7\ud558\ub2e4\ub294 \uac74 \uc54c\uaca0\uc9c0\ub9cc,\n\ub3c4\ub300\uccb4 \ub450\ubc88\uc9f8 \uc6d0\uc18c\uac00 \ubb34\uc5c7\uc744 \uc758\ubbf8\ud558\ub294\uc9c0\ub294 \uc54c\uae30\uac00 \ub9e4\uc6b0 \ud798\ub4e0 \uac83\uc785\ub2c8\ub2e4. \ud45c\ud604 \ubca1\ud130 \uacf5\uac04\uc0c1\uc5d0\uc11c\n\ube44\uc2b7\ud558\ub2e4\ub294 \uc815\ubcf4 \uc678\uc5d0\ub294 \uc544\ub9c8 \ub9ce\uc740 \uc815\ubcf4\ub97c \uc8fc\uae34 \uc5b4\ub824\uc6b8 \uac83\uc785\ub2c8\ub2e4.\n\n\uc694\uc57d\ud558\uc790\uba74, **\ub2e8\uc5b4 \uc784\ubca0\ub529\uc740 \ub2e8\uc5b4\uc758 *\uc758\ubbf8* \ub97c \ud45c\ud604\ud558\ub294 \ubc29\ubc95\uc774\uba70, \ucc28\ud6c4\uc5d0 \uc784\ubca0\ub529\uc744 \uc0ac\uc6a9\ud574\uc11c\n\ud480\uace0\uc790 \ud558\ub294 \ubb38\uc81c\uc5d0 \uc720\uc6a9\ud560 \uc758\ubbf8 \uc815\ubcf4\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uc778\ucf54\ub529\ud55c \uac83\uc785\ub2c8\ub2e4.** \ud488\uc0ac \ud0dc\uadf8, \ud30c\uc2a4 \ud2b8\ub9ac(parse tree) \ub4f1\n\ub2e8\uc5b4\uc758 \uc758\ubbf8 \uc678\uc5d0 \ub2e4\ub978 \uac83\ub3c4 \uc778\ucf54\ub529 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4! \ud53c\ucc98 \uc784\ubca0\ub529\uc758 \uac1c\ub150\uc744 \uc7a1\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.\n\n\n\ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \ub2e8\uc5b4 \uc784\ubca0\ub529 \ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\uc2e4\uc81c\ub85c \ucf54\ub4dc\uc640 \uc608\uc2dc\ub97c \ubcf4\uae30 \uc804\uc5d0, \ud30c\uc774\ud1a0\uce58\ub97c \ube44\ub86f\ud574 \ub525\ub7ec\ub2dd \uad00\ub828 \ud504\ub85c\uadf8\ub798\ubc0d\uc744 \ud560 \ub54c\n\ub2e8\uc5b4 \uc784\ubca0\ub529\uc744 \uc5b4\ub5bb\uac8c \uc0ac\uc6a9\ud558\ub294\uc9c0\uc5d0 \ub300\ud574 \uc870\uae08 \uc54c\uc544\ubd05\uc2dc\ub2e4. \ub9e8 \uc704\uc5d0\uc11c \uc6d0\ud56b \ubca1\ud130\ub97c\n\uc815\uc758\ud588\ub358 \uac83 \ucc98\ub7fc, \ub2e8\uc5b4 \uc784\ubca0\ub529\uc744 \uc0ac\uc6a9\ud560 \ub54c\uc5d0\ub3c4 \uac01 \ub2e8\uc5b4\uc5d0 \uc778\ub371\uc2a4\ub97c \ubd80\uc5ec\ud574\uc57c \ud569\ub2c8\ub2e4.\n\uc774 \uc778\ub371\uc2a4\ub97c \ucc38\uc870 \ud14c\uc774\ube14(look-up table)\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \uc989, $|V| \\times D$ \ud06c\uae30\uc758 \ud589\ub82c\uc5d0\n\ub2e8\uc5b4 \uc784\ubca0\ub529\uc744 \uc800\uc7a5\ud558\ub294\ub370, $D$ \ucc28\uc6d0\uc758 \uc784\ubca0\ub529 \ubca1\ud130\uac00 \ud589\ub82c\uc758 $i$ \ubc88\uc9f8 \ud589\uc5d0\n\uc800\uc7a5\ub418\uc5b4\uc788\uc5b4 $i$ \ub97c \uc778\ub371\uc2a4\ub85c \ud65c\uc6a9\ud574 \uc784\ubca0\ub529 \ubca1\ud130\ub97c \ucc38\uc870\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\uc544\ub798\uc758 \ubaa8\ub4e0 \ucf54\ub4dc\uc5d0\uc11c\ub294 \ub2e8\uc5b4\uc640 \uc778\ub371\uc2a4\ub97c \ub9e4\ud551\ud574\uc8fc\ub294 \ub515\uc154\ub108\ub9ac\ub97c word\\_to\\_ix\ub77c \uce6d\ud569\ub2c8\ub2e4.\n\n\ud30c\uc774\ud1a0\uce58\ub294 \uc784\ubca0\ub529\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c torch.nn.Embedding\uc5d0 \uc704\uc5d0\uc11c \uc124\uba85\ud55c \ucc38\uc870 \ud14c\uc774\ube14\n\uae30\ub2a5\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub4c8\uc740 \ub2e8\uc5b4\uc758 \uac1c\uc218\uc640 \uc784\ubca0\ub529\uc758 \ucc28\uc6d0, \ucd1d 2\uac1c\uc758 \ubcc0\uc218\ub97c \uc785\ub825 \ubcc0\uc218\ub85c \ubc1b\uc2b5\ub2c8\ub2e4.\n\ntorch.nn.Embedding \ud14c\uc774\ube14\uc758 \uc784\ubca0\ub529\uc744 \ucc38\uc870\ud558\uae30 \uc704\ud574\uc120 torch.LongTensor \ud0c0\uc785\uc758 \uc778\ub371\uc2a4 \ubcc0\uc218\ub97c\n\uaf2d \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4. (\uc778\ub371\uc2a4\ub294 \uc2e4\uc218\uac00 \uc544\ub2cc \uc815\uc218\uc774\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.)\n\n\n"
+        "\n\ub2e8\uc5b4 \uc784\ubca0\ub529: \uc5b4\ud718\uc758 \uc758\ubbf8\ub97c \uc778\ucf54\ub529\ud558\uae30\n===========================================\n**\ubc88\uc5ed**: `\uc784\uc131\uc5f0 <http://github.com/sylim2357>`_\n\n\ub2e8\uc5b4 \uc784\ubca0\ub529(word embedding)\uc774\ub780 \ub9d0\ubb49\uce58(\ud639\uc740 \ucf54\ud37c\uc2a4, corpus) \ub0b4 \uac01 \ub2e8\uc5b4\uc5d0 \uc77c\ub300\uc77c\ub85c \ub300\uc751\ud558\ub294 \ubc00\uc9d1\ub41c \uc2e4\uc218 \ubca1\ud130(dense vector)\uc758 \uc9d1\ud569, \ud639\uc740 \uc774 \ubca1\ud130\ub97c\n\uad6c\ud558\ub294 \ud589\uc704\ub97c \uac00\ub9ac\ud0b5\ub2c8\ub2e4. \uc8fc\ub85c \ub2e8\uc5b4\ub97c \ud53c\ucc98(feature)\ub85c \uc0ac\uc6a9\ud558\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\uc5d0\uc11c\ub294 \ub2e8\uc5b4\ub97c \ucef4\ud4e8\ud130 \uce5c\ud654\uc801\uc778\n\ud615\ud0dc\ub85c \ubc14\uafb8\uc5b4 \uc8fc\ub294 \uc791\uc5c5\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \ucef4\ud4e8\ud130\uac00 \ub2e8\uc5b4\ub97c \ubc14\ub85c \uc774\ud574\ud558\uae30\ub294 \uc0c1\ub2f9\ud788 \uc5b4\ub835\uae30 \ub54c\ubb38\uc774\uc8e0.\n\uadf8\ub807\ub2e4\uba74, \ub2e8\uc5b4\ub97c \uc5b4\ub5bb\uac8c \ud45c\ud604\ud558\ub294 \uac83\uc774 \uc88b\uc744\uae4c\uc694? \ubb3c\ub860 \uac01 \ubb38\uc790\uc5d0 \ud574\ub2f9\ud558\ub294 ASCII\ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uaca0\uc9c0\ub9cc,\nASCII\ucf54\ub4dc\ub294 \uc774 \ub2e8\uc5b4\uac00 *\ubb34\uc5c7* \uc778\uc9c0\ub97c \uc54c\ub824\uc904 \ubfd0, \ub2e8\uc5b4\uac00 \uc5b4\ub5a4 *\uc758\ubbf8* \ub97c \uac00\uc9c0\ub294\uc9c0\ub294 \uc54c\ub824\uc8fc\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n(\ub8f0\ubca0\uc774\uc2a4\ub85c \uc5b4\ubbf8 \ub4f1 \ubb38\ubc95\uc801 \ud2b9\uc9d5\uc744 \ud65c\uc6a9\ud558\uac70\ub098 \uc601\uc5b4\uc758 \uacbd\uc6b0 \ub300\ubb38\uc790\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uaca0\uc9c0\ub9cc \ucda9\ubd84\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.)\n\ub2e8\uc5b4\ub97c \uc5b4\ub5bb\uac8c \ud45c\ud604\ud560\uc9c0 \ubfd0 \uc544\ub2c8\ub77c, \uc774 \ud45c\ud604\ubc95\uc744 \uc5b4\ub5a0\ud55c \ubc29\uc2dd\uc73c\ub85c \uc5f0\uc0b0\ud574\uc57c \ud560 \uc9c0 \ub610\ud55c \ud070 \ubb38\uc81c\uc785\ub2c8\ub2e4.\n\ubcf4\ud1b5 \uc774\ub7ec\ud55c \ubc00\ub3c4 \ub192\uc740 \ubca1\ud130\ub97c \uc5bb\uae30 \uc704\ud574 \uc0ac\uc6a9\ud558\ub294 \ub274\ub7f4\ub137 \ubaa8\ub378\uc740 $|V|$ (\ub9d0\ubb49\uce58\uc758 \ub2e8\uc5b4 \uac1c\uc218)\uc758\n\ud070 \uc785\ub825 \ucc28\uc6d0\uacfc \uba87 \uc548\ub418\ub294 (\ud14d\uc2a4\ub97c \ubd84\ub958\ud558\ub294 \ubb38\uc81c\ub77c\uace0 \ud560 \uacbd\uc6b0) \uc791\uc740 \ucd9c\ub825 \ucc28\uc6d0\uc744 \uac00\uc9d1\ub2c8\ub2e4.\n\uc989, \ub2e8\uc5b4\ub4e4 \uac04\uc758 \uc5f0\uc0b0\uc774 \ud544\uc218\uc785\ub2c8\ub2e4. \uc5b4\ub5bb\uac8c \uc774 \ud070 \ucc28\uc6d0\uc758 \uacf5\uac04\uc744 \uc791\uc740 \uacf5\uac04\uc73c\ub85c \ubcc0\ud615\uc2dc\ud0ac \uc218 \uc788\uc744\uae4c\uc694?\n\n\uba3c\uc800, \uc0c1\uae30\ud55c ASCII\ucf54\ub4dc \ub300\uc2e0 \uc6d0\ud56b \uc778\ucf54\ub529(one-hot encoding)\uc744 \uc0ac\uc6a9\ud574\ubcf4\ub294 \uac83\uc740 \uc5b4\ub5a8\uae4c\uc694? \uc6d0\ud56b \uc778\ucf54\ub529\uc774\ub780\n\ud558\ub098\uc758 \ub2e8\uc5b4 $w$ \ub97c \uc544\ub798\uc758 \ubca1\ud130\ub85c \ud45c\ud604\ud558\ub294 \uac83\uc744 \ub9d0\ud569\ub2c8\ub2e4.\n\n\\begin{align}\\overbrace{\\left[ 0, 0, \\dots, 1, \\dots, 0, 0 \\right]}^\\text{|V| elements}\\end{align}\n\n\uc5ec\uae30\uc11c 1\uc740 \ud574\ub2f9 \ubca1\ud130\uac00 \ud45c\ud604\ud558\uace0\uc790 \ud558\ub294 \ub2e8\uc5b4\uc5d0 \ud574\ub2f9\ud558\ub294 \uc704\uce58 1\uacf3\uc5d0 \uc790\ub9ac\ud569\ub2c8\ub2e4. (\ub098\uba38\uc9c0\ub294 \uc804\ubd80\n0\uc785\ub2c8\ub2e4.) \ub2e4\ub978 \ub2e8\uc5b4\ub97c \ub098\ud0c0\ub0b4\ub294 \ubca1\ud130\uc5d0\uc120 1\uc774 \ub2e4\ub978 \uacf3\uc5d0 \uc704\uce58\ud574 \uc788\uaca0\uc8e0.\n\n\uc6d0\ud56b \uc778\ucf54\ub529\uc740 \ub9cc\ub4e4\uae30\uac00 \uc27d\ub2e4\ub294 \uc7a5\uc810\uc774 \uc788\uc9c0\ub9cc, \ub2e8\uc21c\ud55c \ub9cc\ud07c \ub2e8\uc810\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ub2e8 \ub2e8\uc5b4 \ubca1\ud130 \ud55c \uac1c\ub294\n\ubaa8\ub4e0 \ub2e8\uc5b4\ub97c \ud45c\ud604\ud560 \uc218 \uc788\uc744 \ub9cc\ud55c \ud06c\uae30\uac00 \ub418\uc5b4\uc57c \ud569\ub2c8\ub2e4. \uc6b0\ub9ac\uac00 \uc5bc\ub9c8\ub098 \ub9ce\uc740 \uc885\ub958\uc758 \ub2e8\uc5b4\ub97c\n\uc0ac\uc6a9\ud558\ub294\uc9c0\ub97c \uc0dd\uac01 \ud55c\ub2e4\uba74 \uc5b4\ub9c8\uc5b4\ub9c8\ud558\uac8c \ud070 \ubca1\ud130\ub77c\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc8e0. \uc774 \ubfd0\ub9cc\uc774 \uc544\ub2d9\ub2c8\ub2e4.\n\uc6d0\ud56b \ubca1\ud130\ub294 \ubaa8\ub4e0 \ub2e8\uc5b4\ub97c \ub3c5\ub9bd\uc801\uc778 \uac1c\uccb4\ub85c \uac00\uc815\ud558\ub294 \uac83\uc744 \ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc989, \uacf5\uac04\uc0c1\uc5d0\uc11c\n\uc644\uc804\ud788 \ub2e4\ub978 \ucd95\uc5d0 \uc704\uce58\ud574 \uc788\uc5b4\uc11c \ub2e8\uc5b4\uac04\uc758 \uad00\uacc4\ub97c \ub098\ud0c0\ub0bc \uc218\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc6b0\ub9ac\ub294 \ub2e8\uc5b4\n\uc0ac\uc774\uc758 *\uc720\uc0ac\ub3c4* \ub97c \uc5b4\ub5bb\uac8c\ub4e0 \uacc4\uc0b0\ud558\uace0 \uc2f6\uc740\uac70\uc8e0. \uc65c \uc720\uc0ac\ub3c4\uac00 \uc911\uc694\ud558\ub0d0\uad6c\uc694? \ub2e4\uc74c \uc608\uc81c\ub97c \ubd05\uc2dc\ub2e4.\n\n\uc6b0\ub9ac\uc758 \ubaa9\ud45c\uac00 \uc5b8\uc5b4 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \uac83\uc774\ub77c\uace0 \uac00\uc815\ud558\uace0 \ub2e4\uc74c\uc758 \ubb38\uc7a5\uc774 \ud559\uc2b5 \ub370\uc774\ud130\ub85c\uc368 \uc8fc\uc5b4\uc84c\ub2e4\uace0 \ud574\ubd05\uc2dc\ub2e4.\n\n* \uc218\ud559\uc790\uac00 \uac00\uac8c\ub85c \ub6f0\uc5b4\uac14\ub2e4.\n* \ubb3c\ub9ac\ud559\uc790\uac00 \uac00\uac8c\ub85c \ub6f0\uc5b4\uac14\ub2e4.\n* \uc218\ud559\uc790\uac00 \ub9ac\ub9cc \uac00\uc124\uc744 \uc99d\uba85\ud588\ub2e4.\n\n\ub610\ud55c \ud559\uc2b5 \ub370\uc774\ud130\uc5d0\ub294 \uc5c6\ub294 \uc544\ub798 \ubb38\uc7a5\uc774 \uc788\ub2e4\uace0 \uc0dd\uac01\ud574\ubd05\uc2dc\ub2e4.\n\n* \ubb3c\ub9ac\ud559\uc790\uac00 \ub9ac\ub9cc \uac00\uc124\uc744 \uc99d\uba85\ud588\ub2e4.\n\nASCII \ucf54\ub4dc\ub098 \uc6d0\ud56b \uc778\ucf54\ub529 \uae30\ubc18 \uc5b8\uc5b4 \ubaa8\ub378\uc740 \uc704 \ubb38\uc7a5\uc744 \uc5b4\ub290\uc815\ub3c4 \ub2e4\ub8f0 \uc218 \uc788\uaca0\uc9c0\ub9cc, \uac1c\uc120\uc758 \uc5ec\uc9c0\uac00 \uc788\uc9c0 \uc54a\uc744\uae4c\uc694?\n\uba3c\uc800 \uc544\ub798\uc758 \ub450 \uc0ac\uc2e4\uc744 \uc0dd\uac01\ud574\ubd05\uc2dc\ub2e4.\n\n* '\uc218\ud559\uc790'\uc640 '\ubb3c\ub9ac\ud559\uc790'\uac00 \ubb38\uc7a5 \ub0b4\uc5d0\uc11c \uac19\uc740 \uc5ed\ud560\uc744 \ub9e1\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub450 \ub2e8\uc5b4\ub294 \uc5b4\ub5bb\uac8c\ub4e0 \uc758\ubbf8\uc801\uc778 \uc5f0\uad00\uc131\uc774 \uc788\uc744 \uac81\ub2c8\ub2e4.\n* \uc0c8\ub85c\uc6b4 \ubb38\uc7a5\uc5d0\uc11c '\ubb3c\ub9ac\ud559\uc790'\uac00 \ub9e1\uc740 \uc5ed\ud560\uc744 '\uc218\ud559\uc790'\uac00 \ub9e1\ub294 \uac83\uc744 \ud559\uc2b5 \ub370\uc774\ud130\uc5d0\uc11c \ubcf8 \uc801\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc6b0\ub9ac \ubaa8\ub378\uc774 \uc704\uc758 \uc0ac\uc2e4\uc744 \ud1b5\ud574 '\ubb3c\ub9ac\ud559\uc790'\uac00 \uc0c8 \ubb38\uc7a5\uc5d0 \uc798 \ub4e4\uc5b4 \ub9de\ub294\ub2e4\ub294 \uac83\uc744 \ucd94\ub860\ud560 \uc218\n\uc788\ub2e4\uba74 \ucc38 \uc88b\uc744 \uac83\uc785\ub2c8\ub2e4. \uc774\uac83\uc774 \uc704\uc5d0\uc11c \uc5b8\uae09\ud55c \uc720\uc0ac\ub3c4\uc758 \uc758\ubbf8\uc785\ub2c8\ub2e4. \ucca0\uc790\uc801 \uc720\uc0ac\ub3c4 \ubfd0\n\uc544\ub2c8\ub77c *\uc758\ubbf8\uc801 \uc720\uc0ac\ub3c4* \uc778 \uac83\uc785\ub2c8\ub2e4. \uc774\uac83\uc774\uc57c\ub9d0\ub85c \uc5b8\uc5b4 \ub370\uc774\ud130\uc5d0 \ub0b4\uc7ac\ud558\ub294 \ud76c\ubc15\uc131(sparsity)\uc5d0\n\ub300\ud55c \ucc98\ubc29\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\uac00 \ubcf8 \uac83\uacfc \uc544\uc9c1 \ubcf4\uc9c0 \uc54a\uc740 \uac83 \uc0ac\uc774\ub97c \uc774\uc5b4\uc8fc\ub294 \uac83\uc774\uc8e0.\n\uc55e\uc73c\ub85c\ub294 \ub2e4\uc74c\uc758 \uc5b8\uc5b4\ud559\uc801 \uae30\ubcf8 \uba85\uc81c\ub97c \uac00\uc815\ud558\ub3c4\ub85d \ud569\uc2dc\ub2e4. \ubc14\ub85c \ube44\uc2b7\ud55c \ub9e5\ub77d\uc5d0\uc11c \ub4f1\uc7a5\ud558\ub294\n\ub2e8\uc5b4\ub4e4\uc740 \uc11c\ub85c \uc758\ubbf8\uc801 \uc5f0\uad00\uc131\uc744 \uac00\uc9c4\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uc5b8\uc5b4\ud559\uc801\uc73c\ub85c\ub294 `\ubd84\uc0b0 \uc758\ubbf8 \uac00\uc124(distributional\nhypothesis) <https://en.wikipedia.org/wiki/Distributional_semantics>`__ \uc774\ub77c\uace0\ub3c4 \ud569\ub2c8\ub2e4.\n\n\n\ubc00\uc9d1\ub41c \ub2e8\uc5b4 \uc784\ubca0\ub529 \uad6c\ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\uc5b4\ub5bb\uac8c \ub2e8\uc5b4\uc758 \uc758\ubbf8\uc801 \uc720\uc0ac\ub3c4\ub97c \uc778\ucf54\ub529 \ud560 \uc218 \uc788\uc744\uae4c\uc694? \ub2e4\uc2dc \ub9d0\ud574, \uc5b4\ub5bb\uac8c \ud574\uc57c \ub2e8\uc5b4\uc758 \uc720\uc0ac\ub3c4\ub97c\n\ub2e8\uc5b4 \ubca1\ud130\uc5d0 \ubc18\uc601\ud560 \uc218 \uc788\uc744\uae4c\uc694? \ub2e8\uc5b4 \ub370\uc774\ud130\uc5d0 \uc758\ubbf8\uc801 \uc18d\uc131(attribute)\uc744 \ubd80\uc5ec\ud558\ub294 \uac74 \uc5b4\ub5a4\uac00\uc694?\n\uc608\ub97c \ub4e4\uc5b4 '\uc218\ud559\uc790'\uc640 '\ubb3c\ub9ac\ud559\uc790'\uac00 \ubaa8\ub450 \ub6f8 \uc218 \uc788\ub2e4\uba74, \ud574\ub2f9 \ub2e8\uc5b4\uc758 '\ub6f8 \uc218 \uc788\uc74c' \uc18d\uc131\uc5d0 \ub192\uc740 \uc810\uc218\ub97c \uc8fc\ub294 \uac81\ub2c8\ub2e4.\n\uacc4\uc18d \ud574\ubd05\uc2dc\ub2e4. \ub2e4\ub978 \ub2e8\uc5b4\ub4e4\uc5d0 \ub300\ud574\uc11c\ub294 \uc5b4\ub5a0\ud55c \uc18d\uc131\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc744\uc9c0 \uc0dd\uac01\ud574\ubd05\uc2dc\ub2e4.\n\n\ub9cc\uc57d \uac01 \uc18d\uc131\uc744 \ud558\ub098\uc758 \ucc28\uc6d0\uc774\ub77c\uace0 \ubcf8\ub2e4\uba74 \ud558\ub098\uc758 \ub2e8\uc5b4\uc5d0 \uc544\ub798\uc640 \uac19\uc740 \ubca1\ud130\ub97c \ubc30\uc815\ud560 \uc218 \uc788\uc744\uac81\ub2c8\ub2e4.\n\n\\begin{align}q_\\text{\uc218\ud559\uc790} = \\left[ \\overbrace{2.3}^\\text{\ub6f8 \uc218 \uc788\uc74c},\n   \\overbrace{9.4}^\\text{\ucee4\ud53c\ub97c \uc88b\uc544\ud568}, \\overbrace{-5.5}^\\text{\ubb3c\ub9ac \uc804\uacf5\uc784}, \\dots \\right]\\end{align}\n\n\\begin{align}q_\\text{\ubb3c\ub9ac\ud559\uc790} = \\left[ \\overbrace{2.5}^\\text{\ub6f8 \uc218 \uc788\uc74c},\n   \\overbrace{9.1}^\\text{\ucee4\ud53c\ub97c \uc88b\uc544\ud568}, \\overbrace{6.4}^\\text{\ubb3c\ub9ac \uc804\uacf5\uc784}, \\dots \\right]\\end{align}\n\n\uadf8\ub7ec\uba74 \uc544\ub798\uc640 \uac19\uc774 \ub450 \ub2e8\uc5b4 \uc0ac\uc774\uc758 \uc720\uc0ac\ub3c4\ub97c \uad6c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. ('\uc720\uc0ac\ub3c4'\ub77c\ub294 \ud568\uc218\ub97c \uc815\uc758\ud558\ub294 \uac81\ub2c8\ub2e4)\n\n\\begin{align}\\text{\uc720\uc0ac\ub3c4}(\\text{\ubb3c\ub9ac\ud559\uc790}, \\text{\uc218\ud559\uc790}) = q_\\text{\ubb3c\ub9ac\ud559\uc790} \\cdot q_\\text{\uc218\ud559\uc790}\\end{align}\n\n\ubb3c\ub860 \ubcf4\ud1b5\uc740 \uc774\ub807\uac8c \ubca1\ud130\uc758 \uae38\uc774\ub85c \ub098\ub220\uc8fc\uc9c0\ub9cc\uc694.\n\n\\begin{align}\\text{\uc720\uc0ac\ub3c4}(\\text{\ubb3c\ub9ac\ud559\uc790}, \\text{\uc218\ud559\uc790}) = \\frac{q_\\text{\ubb3c\ub9ac\ud559\uc790} \\cdot q_\\text{\uc218\ud559\uc790}}\n   {\\| q_\\text{\ubb3c\ub9ac\ud559\uc790} \\| \\| q_\\text{\uc218\ud559\uc790} \\|} = \\cos (\\phi)\\end{align}\n\n$\\phi$ \ub294 \ub450 \ubca1\ud130 \uc0ac\uc774\uc758 \uac01\uc785\ub2c8\ub2e4. \uc774\ub7f0 \uc2dd\uc774\uba74 \uc815\ub9d0 \ube44\uc2b7\ud55c \ub2e8\uc5b4\ub294 \uc720\uc0ac\ub3c4 1\uc744 \uac16\uace0,\n\uc815\ub9d0 \ub2e4\ub978 \ub2e8\uc5b4\ub294 \uc720\uc0ac\ub3c4 -1\uc744 \uac16\uaca0\uc8e0. \ube44\uc2b7\ud55c \uc758\ubbf8\ub97c \uac00\uc9c8\uc218\ub85d \uac19\uc740 \ubc29\ud5a5\uc744 \uac00\ub9ac\ud0a4\uace0 \uc788\uc744 \ud14c\ub2c8\uae4c\uc694.\n\n\uc774 \uae00 \ucd08\ubc18\uc5d0 \ub098\uc628 \ud76c\ubc15\ud55c \uc6d0\ud56b \ubca1\ud130\uac00 \uc0ac\uc2e4\uc740 \uc6b0\ub9ac\uac00 \ubc29\uae08 \uc815\uc758\ud55c \uc758\ubbf8 \ubca1\ud130\uc758\n\ud2b9\uc774 \ucf00\uc774\uc2a4\ub77c\ub294 \uac83\uc744 \uae08\ubc29 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e8\uc5b4 \ubca1\ud130\uc758 \uac01 \uc6d0\uc18c\ub294 \uadf8 \ub2e8\uc5b4\uc758 \uc758\ubbf8\uc801 \uc18d\uc131\uc744\n\ud45c\ud604\ud558\uace0, \ubaa8\ub4e0 \ub2e8\uc5b4 \uc30d\uc758 \uc720\uc0ac\ub3c4\ub294 0\uc774\uae30 \ub54c\ubb38\uc774\uc8e0. \uc704\uc5d0\uc11c \uc815\uc758\ud55c \uc758\ubbf8 \ubca1\ud130\ub294 *\ubc00\uc9d1* \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\n\uc989, \uc6d0\ud56b \ubca1\ud130\uc5d0 \ube44\ud574 0 \uc6d0\uc18c\uc758 \uc218\uac00 \uc801\ub2e4\uace0 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ud558\uc9c0\ub9cc \uc774 \ubca1\ud130\ub4e4\uc740 \uad6c\ud558\uae30\uac00 \uc9c4\uc9dc \uc5b4\ub835\uc2b5\ub2c8\ub2e4. \ub2e8\uc5b4\uac04\uc758 \uc720\uc0ac\ub3c4\ub97c \uacb0\uc815 \uc9c0\uc744 \ub9cc\ud55c\n\uc758\ubbf8\uc801 \uc18d\uc131\uc740 \uc5b4\ub5bb\uac8c \uacb0\uc815\ud560 \uac83\uc774\uba70, \uc18d\uc131\uc744 \uacb0\uc815\ud588\ub2e4\uace0 \ud558\ub354\ub77c\ub3c4 \uac01 \uc18d\uc131\uc5d0\n\ud574\ub2f9\ud558\ub294 \uac12\uc740 \ub3c4\ub300\uccb4 \uc5b4\ub5a0\ud55c \uae30\uc900\uc73c\ub85c \uc815\ud574\uc57c \ud560\uae4c\uc694? \uc18d\uc131\uacfc \uac12\uc744 \ub370\uc774\ud130\uc5d0 \uae30\ubc18\ud574\n\ub9cc\ub4e4\uace0 \uc790\ub3d9\uc73c\ub85c \ub2e8\uc5b4 \ubca1\ud130\ub97c \ub9cc\ub4e4 \uc218\ub294 \uc5c6\uc744\uae4c\uc694? \uc788\uc2b5\ub2c8\ub2e4. \ub525\ub7ec\ub2dd\uc744 \uc0ac\uc6a9\ud558\uba74 \ub9d0\uc774\uc8e0.\n\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uc774\uc6a9\ud558\uc5ec \uc0ac\ub78c\uc758 \uac1c\uc785 \uc5c6\uc774 \uc18d\uc131\uc758 \ud45c\ud604 \ubc29\ubc95\uc744 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4.\n\uc774\ub97c \uc774\uc6a9\ud574 \ub2e8\uc5b4 \ubca1\ud130\ub97c \ubaa8\ub378 \ubaa8\uc218\ub85c \uc124\uc815\ud558\uace0 \ubaa8\ub378 \ud559\uc2b5\uc2dc\uc5d0 \ub2e8\uc5b4 \ubca1\ud130\ub3c4 \ud568\uaed8 \uc5c5\ub370\uc774\ud2b8 \ud558\uba74\n\ub420 \uac83\uc785\ub2c8\ub2e4. \uc774\ub807\uac8c \uc6b0\ub9ac \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc740 \uc801\uc5b4\ub3c4 \uc774\ub860\uc0c1\uc73c\ub85c\ub294 \ucda9\ubd84\ud788 \ud559\uc2b5\ud560 \uc218 \uc788\ub294\n*\uc7a0\uc7ac \uc758\ubbf8 \uc18d\uc131* \uc744 \ucc3e\uc744 \uac83\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c \ub9d0\ud558\ub294 \uc7a0\uc7ac \uc758\ubbf8 \uc18d\uc131\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9c4 \ubca1\ud130\ub294 \uc0ac\ub78c\uc774\n\ud574\uc11d\ud558\uae30 \uc0c1\ub2f9\ud788 \uc5b4\ub835\ub2e4\ub294 \uc810\uc744 \uae30\uc5b5\ud574 \ub450\uc138\uc694. \uc704\uc5d0\uc11c \uc218\ud559\uc790\uc640 \ubb3c\ub9ac\ud559\uc790\uc5d0\uac8c \ucee4\ud53c\ub97c \uc88b\uc544\ud55c\ub2e4\ub294\n\ub4f1 \uc0ac\ub78c\uc774 \uc784\uc758\uc801\uc73c\ub85c \ub2e8\uc5b4\uc5d0 \ubd80\uc5ec\ud55c \uc18d\uc131\uacfc\ub294 \ub2ec\ub9ac, \uc778\uacf5\uc2e0\uacbd\ub9dd\uc774 \uc790\ub3d9\uc73c\ub85c \ub2e8\uc5b4\uc758 \uc18d\uc131\uc744 \ucc3e\ub294\ub2e4\uba74\n\uadf8 \uc18d\uc131\uacfc \uac12\uc774 \uc758\ubbf8\ud558\ub294 \ubc14\ub97c \uc54c\uae30\uac00 \uc5b4\ub824\uc6b8 \uac83\uc785\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4\uc11c \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc774 \ucc3e\uc740 '\uc218\ud559\uc790'\uc640\n'\ubb3c\ub9ac\ud559\uc790'\uc758 \ud45c\ud604 \ubca1\ud130 \ub458 \ub2e4 \ub450\ubc88\uc9f8 \uc6d0\uc18c\uac00 \ud06c\ub2e4\uace0 \uac00\uc815\ud574 \ubd05\uc2dc\ub2e4. \ub458\uc774 \ube44\uc2b7\ud558\ub2e4\ub294 \uac74 \uc54c\uaca0\uc9c0\ub9cc,\n\ub3c4\ub300\uccb4 \ub450\ubc88\uc9f8 \uc6d0\uc18c\uac00 \ubb34\uc5c7\uc744 \uc758\ubbf8\ud558\ub294\uc9c0\ub294 \uc54c\uae30\uac00 \ub9e4\uc6b0 \ud798\ub4e0 \uac83\uc785\ub2c8\ub2e4. \ud45c\ud604 \ubca1\ud130 \uacf5\uac04\uc0c1\uc5d0\uc11c\n\ube44\uc2b7\ud558\ub2e4\ub294 \uc815\ubcf4 \uc678\uc5d0\ub294 \uc544\ub9c8 \ub9ce\uc740 \uc815\ubcf4\ub97c \uc8fc\uae34 \uc5b4\ub824\uc6b8 \uac83\uc785\ub2c8\ub2e4.\n\n\uc694\uc57d\ud558\uc790\uba74, **\ub2e8\uc5b4 \uc784\ubca0\ub529\uc740 \ub2e8\uc5b4\uc758 *\uc758\ubbf8* \ub97c \ud45c\ud604\ud558\ub294 \ubc29\ubc95\uc774\uba70, \ucc28\ud6c4\uc5d0 \uc784\ubca0\ub529\uc744 \uc0ac\uc6a9\ud574\uc11c\n\ud480\uace0\uc790 \ud558\ub294 \ubb38\uc81c\uc5d0 \uc720\uc6a9\ud560 \uc758\ubbf8 \uc815\ubcf4\ub97c \ud6a8\uc728\uc801\uc73c\ub85c \uc778\ucf54\ub529\ud55c \uac83\uc785\ub2c8\ub2e4.** \ud488\uc0ac \ud0dc\uadf8, \ud30c\uc2a4 \ud2b8\ub9ac(parse tree) \ub4f1\n\ub2e8\uc5b4\uc758 \uc758\ubbf8 \uc678\uc5d0 \ub2e4\ub978 \uac83\ub3c4 \uc778\ucf54\ub529 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4! \ud53c\ucc98 \uc784\ubca0\ub529\uc758 \uac1c\ub150\uc744 \uc7a1\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.\n\n\n\ud30c\uc774\ud1a0\uce58\uc5d0\uc11c \ub2e8\uc5b4 \uc784\ubca0\ub529 \ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\uc2e4\uc81c\ub85c \ucf54\ub4dc\uc640 \uc608\uc2dc\ub97c \ubcf4\uae30 \uc804\uc5d0, \ud30c\uc774\ud1a0\uce58\ub97c \ube44\ub86f\ud574 \ub525\ub7ec\ub2dd \uad00\ub828 \ud504\ub85c\uadf8\ub798\ubc0d\uc744 \ud560 \ub54c\n\ub2e8\uc5b4 \uc784\ubca0\ub529\uc744 \uc5b4\ub5bb\uac8c \uc0ac\uc6a9\ud558\ub294\uc9c0\uc5d0 \ub300\ud574 \uc870\uae08 \uc54c\uc544\ubd05\uc2dc\ub2e4. \ub9e8 \uc704\uc5d0\uc11c \uc6d0\ud56b \ubca1\ud130\ub97c\n\uc815\uc758\ud588\ub358 \uac83 \ucc98\ub7fc, \ub2e8\uc5b4 \uc784\ubca0\ub529\uc744 \uc0ac\uc6a9\ud560 \ub54c\uc5d0\ub3c4 \uac01 \ub2e8\uc5b4\uc5d0 \uc778\ub371\uc2a4\ub97c \ubd80\uc5ec\ud574\uc57c \ud569\ub2c8\ub2e4.\n\uc774 \uc778\ub371\uc2a4\ub97c \ucc38\uc870 \ud14c\uc774\ube14(look-up table)\uc5d0\uc11c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. \uc989, $|V| \\times D$ \ud06c\uae30\uc758 \ud589\ub82c\uc5d0\n\ub2e8\uc5b4 \uc784\ubca0\ub529\uc744 \uc800\uc7a5\ud558\ub294\ub370, $D$ \ucc28\uc6d0\uc758 \uc784\ubca0\ub529 \ubca1\ud130\uac00 \ud589\ub82c\uc758 $i$ \ubc88\uc9f8 \ud589\uc5d0\n\uc800\uc7a5\ub418\uc5b4\uc788\uc5b4 $i$ \ub97c \uc778\ub371\uc2a4\ub85c \ud65c\uc6a9\ud574 \uc784\ubca0\ub529 \ubca1\ud130\ub97c \ucc38\uc870\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\uc544\ub798\uc758 \ubaa8\ub4e0 \ucf54\ub4dc\uc5d0\uc11c\ub294 \ub2e8\uc5b4\uc640 \uc778\ub371\uc2a4\ub97c \ub9e4\ud551\ud574\uc8fc\ub294 \ub515\uc154\ub108\ub9ac\ub97c word\\_to\\_ix\ub77c \uce6d\ud569\ub2c8\ub2e4.\n\n\ud30c\uc774\ud1a0\uce58\ub294 \uc784\ubca0\ub529\uc744 \uc190\uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\uac8c torch.nn.Embedding\uc5d0 \uc704\uc5d0\uc11c \uc124\uba85\ud55c \ucc38\uc870 \ud14c\uc774\ube14\n\uae30\ub2a5\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. \uc774 \ubaa8\ub4c8\uc740 \ub2e8\uc5b4\uc758 \uac1c\uc218\uc640 \uc784\ubca0\ub529\uc758 \ucc28\uc6d0, \ucd1d 2\uac1c\uc758 \ubcc0\uc218\ub97c \uc785\ub825 \ubcc0\uc218\ub85c \ubc1b\uc2b5\ub2c8\ub2e4.\n\ntorch.nn.Embedding \ud14c\uc774\ube14\uc758 \uc784\ubca0\ub529\uc744 \ucc38\uc870\ud558\uae30 \uc704\ud574\uc120 torch.LongTensor \ud0c0\uc785\uc758 \uc778\ub371\uc2a4 \ubcc0\uc218\ub97c\n\uaf2d \uc0ac\uc6a9\ud574\uc57c \ud569\ub2c8\ub2e4. (\uc778\ub371\uc2a4\ub294 \uc2e4\uc218\uac00 \uc544\ub2cc \uc815\uc218\uc774\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.)\n"
       ]
     },
     {
@@ -93,7 +93,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/377bf4a7b1761e5f081e057385870d8e/fgsm_tutorial.py b/docs/_downloads/377bf4a7b1761e5f081e057385870d8e/fgsm_tutorial.py
index 374272351..ea0b4c651 100644
--- a/docs/_downloads/377bf4a7b1761e5f081e057385870d8e/fgsm_tutorial.py
+++ b/docs/_downloads/377bf4a7b1761e5f081e057385870d8e/fgsm_tutorial.py
@@ -20,37 +20,37 @@
 ######################################################################
 # 위협 모델
 # ------------
-# 
+#
 # 상황에 따라 다양한 범주의 적대적 공격이 있는데 각각 목표가 다르고 공격자가 알고 있는 정보
-# 대한 가정도 다릅니다. 그러나 보통 가장 중요한 목표는 입력 데이터에 최소한의 작은 변화를 
-# 추가하여 이것이 의도적으로 잘못 분류되게 하는 것입니다. 공격자가 가지고 있는 정보에 대한 
-# 가정에는 여러 종류가 있는데, 보통 **화이트박스** 와 **블랙박스** 두 가지가 있습니다. 
+# 대한 가정도 다릅니다. 그러나 보통 가장 중요한 목표는 입력 데이터에 최소한의 작은 변화를
+# 추가하여 이것이 의도적으로 잘못 분류되게 하는 것입니다. 공격자가 가지고 있는 정보에 대한
+# 가정에는 여러 종류가 있는데, 보통 **화이트박스** 와 **블랙박스** 두 가지가 있습니다.
 # *화이트박스* 공격은 공격자가 모델에 대해 아키텍처, 입력, 출력, 가중치를 포함한 모든 것을
-# 알고 있고 접근할 수 있다고 가정합니다. *블랙박스* 공격은 공격자가 모델의 입력과 출력에 
-# 대해서만 접근 가능하고 모델의 가중치와 아키텍처에 관한 내용은 모른다고 가정합니다. 
-# 공격자의 목표는 오분류 및 **소스/타겟 오분류** 를 포함하는 여러 유형이 있습니다. 
+# 알고 있고 접근할 수 있다고 가정합니다. *블랙박스* 공격은 공격자가 모델의 입력과 출력에
+# 대해서만 접근 가능하고 모델의 가중치와 아키텍처에 관한 내용은 모른다고 가정합니다.
+# 공격자의 목표는 오분류 및 **소스/타겟 오분류** 를 포함하는 여러 유형이 있습니다.
 # *오분류* 의 목표는 공격자가 출력으로 나온 분류 결과가 잘못 되도록 하나 새로운 분류 결과가
-# 어떤 것이 나오는지 신경 쓰지 않는 것을 의미합니다. *소스/타겟 오분류* 는 공격자가 
+# 어떤 것이 나오는지 신경 쓰지 않는 것을 의미합니다. *소스/타겟 오분류* 는 공격자가
 # 원래 특정 소스 클래스의 이미지를 다른 특정 대상 클래스로 분류하도록 변경하려고 함을 의미합니다.
 #
-# 
-# 이 경우 FGSM 공격은 *오분류* 를 목표로 하는 화이트 박스 공격입니다. 
+#
+# 이 경우 FGSM 공격은 *오분류* 를 목표로 하는 화이트 박스 공격입니다.
 # 이런 배경 정보를 갖고 공격에 대해 자세히 알아 보겠습니다.
-# 
+#
 # 빠른 변화도 부호 공격
 # -------------------------
-# 
-# 공격 방법에 있어 초기 방식이면서 가장 유명한 방식은 *빠른 변화도 부호 공격 (FGSM)* 이라고 하며 
-# `적대적 예제에 대한 설명과 활용 <https://arxiv.org/abs/1412.6572>`__ 에서 
+#
+# 공격 방법에 있어 초기 방식이면서 가장 유명한 방식은 *빠른 변화도 부호 공격 (FGSM)* 이라고 하며
+# `적대적 예제에 대한 설명과 활용 <https://arxiv.org/abs/1412.6572>`__ 에서
 # 이안 갓펠로우가 기고하였습니다.
-# 이 공격법은 놀랍도록 강력하지만 직관적입니다. 학습 방식, *변화도(gradients)* 를 활용하여 신경망을 공격하도록 
+# 이 공격법은 놀랍도록 강력하지만 직관적입니다. 학습 방식, *변화도(gradients)* 를 활용하여 신경망을 공격하도록
 # 설계 되었습니다. 아이디어는 간단합니다. 역전파 변화도를 기반으로 가중치를 조정하여 손실을 최소화하기보다는
-# 공격이 동일한 역전파 변화도를 기반으로 *손실을 최대화하하는 방향으로 입력 데이터를 조정* 합니다. 
+# 공격이 동일한 역전파 변화도를 기반으로 *손실을 최대화하하는 방향으로 입력 데이터를 조정* 합니다.
 # 다시 말해 공격은 입력 데이터에서 계산된 손실 변화도를 사용하고 입력 데이터를 조정하여 손실이 최대가 되게 합니다.
 
 
-# 
-# 코드로 넘어가기 전에 유명한 `FGSM <https://arxiv.org/abs/1412.6572>`__ 판다 예제를 
+#
+# 코드로 넘어가기 전에 유명한 `FGSM <https://arxiv.org/abs/1412.6572>`__ 판다 예제를
 # 보고 몇 가지 표기법을 정리하겠습니다.
 #
 # .. 그림:: /_static/img/fgsm_panda_image.png
@@ -60,13 +60,13 @@
 # :math:`y` 는 :math:`\mathbf{x}` 를 위한 정답 라벨이며, :math:`\mathbf{\theta}` 는 모델의
 # 파라미터를, :math:`J(\mathbf{\theta}, \mathbf{x}, y)` 는 네트워크의 학습을 위해서 사용되는 손실을 나타냅니다.
 # 공격은 :math:`\nabla_{x} J(\mathbf{\theta}, \mathbf{x}, y)` 계산을 위해 입력 데이터에 변화도를 역전파합니다.
-# 그러고 나서, 변화도는 손실 값이 최대화되는 방향으로 (예를 들면, :math:`sign(\nabla_{x} J(\mathbf{\theta}, \mathbf{x}, y))` ) 
+# 그러고 나서, 변화도는 손실 값이 최대화되는 방향으로 (예를 들면, :math:`sign(\nabla_{x} J(\mathbf{\theta}, \mathbf{x}, y))` )
 # 작은 스텝(step) 만큼 (그림에서는 :math:`\epsilon` 혹은 :math:`0.007`) 입력 데이터에 적용됩니다.
 # 결과로 나오는 작은 변화된 이미지( :math:`x'` )는 타겟 네트워크에 의해 "긴팔원숭이"로 *오분류* 되나 여전히 육안으로는
 # 분명히 "판다" 입니다.
-# 
+#
 # 이제 본 튜토리얼의 동기가 명확해지길 바라며, 구현으로 넘어가 보겠습니다.
-# 
+#
 
 from __future__ import print_function
 import torch
@@ -81,27 +81,27 @@
 ######################################################################
 # 구현
 # --------------
-# 
+#
 # 이 섹션에서는 튜토리얼의 입력 매개 변수에 대해 설명하고 공격중인 모델을
 # 정의한 다음 공격을 코딩하고 일부 테스트를 실행합니다.
-# 
+#
 # 입력
 # ~~~~~~
-# 
+#
 # 이 학습서에는 입력이 3 개이며 다음과 같이 정의됩니다:
-# 
+#
 # - **epsilons** - 실행에 사용할 엡실론의 리스트입니다. 엡실론 0의 값은 원래 테스트 셋의 모델 성능을
 #   나타내므로 목록에 유지하는 것이 중요합니다. 또한 직관적으로 엡실론이 클수록 작은 변화가 더 눈에 띄지만
-#   모델 정확도를 저하 시키는 측면에서 더 효과가 있습니다. 여기서 데이터의 범위는 0-1 이기 때문에 
+#   모델 정확도를 저하 시키는 측면에서 더 효과가 있습니다. 여기서 데이터의 범위는 0-1 이기 때문에
 #   엡실론의 값은 1을 초과할 수 없습니다.
-# 
-# - **pretrained_model** - `pytorch/examples/mnist <https://github.com/pytorch/examples/tree/master/mnist>`__ 
-#    를 통해 미리 학습된 MNIST 모델의 경로. 
+#
+# - **pretrained_model** - `pytorch/examples/mnist <https://github.com/pytorch/examples/tree/master/mnist>`__
+#    를 통해 미리 학습된 MNIST 모델의 경로.
 #    튜토리얼을 간편하게 하려면 `여기 <https://drive.google.com/drive/folders/1fn83DF14tWmit0RTKWRhPq5uVXt73e0h?usp=sharing>`__ 에서 미리 학습된 모델을 다운로드하세요.
-# 
-# -  **use_cuda** - CUDA 를 사용할지 말지 정하는 이진 플래그. 
+#
+# -  **use_cuda** - CUDA 를 사용할지 말지 정하는 이진 플래그.
 #    본 튜토리얼에서는 CPU 시간이 오래 걸리지 않으므로 CUDA를 지원하는 GPU 의 여부는 중요하지 않습니다.
-# 
+#
 
 epsilons = [0, .05, .1, .15, .2, .25, .3]
 pretrained_model = "data/lenet_mnist_model.pth"
@@ -111,12 +111,12 @@
 ######################################################################
 # 공격을 받는 모델
 # ~~~~~~~~~~~~~~~~~~
-# 
+#
 # 앞서 말한대로, 공격을 받는 모델은 `pytorch/examples/mnist <https://github.com/pytorch/examples/tree/master/mnist>`__
 # 와 동일한 MNIST 모델입니다. 본인의 MNIST 모델을 학습 및 저장하는 방식으로 하거나 제공된 모델을 다운로드 해 사용하는 식으로 진행할 수 있습니다.
-# 여기서 *Net* 정의 및 테스트 데이터 로더는 MNIST 예제에서 복사 하였습니다. 
+# 여기서 *Net* 정의 및 테스트 데이터 로더는 MNIST 예제에서 복사 하였습니다.
 # 이 섹션의 목적은 모델과 데이터 로더를 정의한 다음, 모델을 초기화하고 미리 학습된 가중치를 읽어오는 것입니다.
-# 
+#
 
 # LeNet 모델 정의
 class Net(nn.Module):
@@ -141,7 +141,7 @@ def forward(self, x):
 test_loader = torch.utils.data.DataLoader(
     datasets.MNIST('../data', train=False, download=True, transform=transforms.Compose([
             transforms.ToTensor(),
-            ])), 
+            ])),
         batch_size=1, shuffle=True)
 
 # 어떤 디바이스를 사용할지 정의
@@ -161,17 +161,17 @@ def forward(self, x):
 ######################################################################
 # FGSM 공격
 # ~~~~~~~~~~~
-# 
-# 이제 원래 입력을 교란시켜 적대적인 예를 만드는 함수를 정의 할 수 있습니다. 
-# ``fgsm_attack`` 함수는 입력 파라미터로 3가지를 가집니다. 첫번째는 원본 *이미지* ( :math:`x` ), 
-# 두번째는 *엡실론* 으로 픽셀 단위의 작은 변화를 주는 값입니다 ( :math:`\epsilon` ). 
-# 마지막은 *data_grad* 로 입력 영상 ( :math:`\nabla_{x} J(\mathbf{\theta}, \mathbf{x}, y)` ) 에 대한 변화도 손실 값입니다. 
+#
+# 이제 원래 입력을 교란시켜 적대적인 예를 만드는 함수를 정의 할 수 있습니다.
+# ``fgsm_attack`` 함수는 입력 파라미터로 3가지를 가집니다. 첫번째는 원본 *이미지* ( :math:`x` ),
+# 두번째는 *엡실론* 으로 픽셀 단위의 작은 변화를 주는 값입니다 ( :math:`\epsilon` ).
+# 마지막은 *data_grad* 로 입력 영상 ( :math:`\nabla_{x} J(\mathbf{\theta}, \mathbf{x}, y)` ) 에 대한 변화도 손실 값입니다.
 # 아래 식에 따른 작은 변화가 적용된 이미지를 생성합니다.
-# 
+#
 # .. math:: perturbed\_image = image + epsilon*sign(data\_grad) = x + \epsilon * sign(\nabla_{x} J(\mathbf{\theta}, \mathbf{x}, y))
-# 
+#
 # 마지막으로 데이터의 원래 범위를 유지하기 위해, 작은 변화가 적용된 이미지가 :math:`[0,1]` 범위로 잘립니다.
-# 
+#
 
 
 # FGSM 공격 코드
@@ -189,14 +189,14 @@ def fgsm_attack(image, epsilon, data_grad):
 ######################################################################
 # 테스팅 함수
 # ~~~~~~~~~~~~~~~~
-# 
-# 마지막으로 본 튜토리얼의 핵심 결과는 ``테스트`` 함수에서 오게 됩니다. 
+#
+# 마지막으로 본 튜토리얼의 핵심 결과는 ``테스트`` 함수에서 오게 됩니다.
 # 이 테스트 기능을 호출 할 때마다 MNIST 테스트 셋에서 전체 테스트 단계를 수행하고 최종 정확도를 보고합니다.
-# 그러나 이 함수에는 *엡실론* 입력도 필요합니다. 이는 ``테스트``` 함수가 :math:`\epsilon` 크기에 따라 공격자의 공격을 받는 모델의 
-# 정확도을 보고하기 때문입니다. 더 구체적으로 보면 테스트 셋의 각각의 샘플에서 테스트 함수는 입력 데이터에 대한 손실 변화도( :math:`data\_grad` )를 계산하고, 
-#  ``FGSM 공격``( :math:`perturbed\_data` )을 받은 작은 변화가 적용된 이미지를 만들고 나서 작은 변화가 적용된 이미지가 적대적인지 확인을 합니다. 
+# 그러나 이 함수에는 *엡실론* 입력도 필요합니다. 이는 ``테스트``` 함수가 :math:`\epsilon` 크기에 따라 공격자의 공격을 받는 모델의
+# 정확도을 보고하기 때문입니다. 더 구체적으로 보면 테스트 셋의 각각의 샘플에서 테스트 함수는 입력 데이터에 대한 손실 변화도( :math:`data\_grad` )를 계산하고,
+# ``FGSM 공격`` (:math:`perturbed\_data`) 을 받은 작은 변화가 적용된 이미지를 만들고 나서 작은 변화가 적용된 이미지가 적대적인지 확인을 합니다.
 # 추가로 모델의 정확도를 테스트하기 위해서 테스트 함수는 나중에 시각화하여 볼 수 있도록 성공적으로 얻은 적대적 이미지를 저장하고 반환합니다.
-# 
+#
 
 def test( model, device, test_loader, epsilon ):
 
@@ -264,12 +264,12 @@ def test( model, device, test_loader, epsilon ):
 ######################################################################
 # 공격 실행
 # ~~~~~~~~~~
-# 
-# 구현의 마지막 부분은 공격을 실행하는 것입니다. 여기서 전체 테스트 스텝을 각 *엡실론* 값에 실행합니다. 
-# 각 엡실론마다 최종 정확도와 성공적인 일부 적대 사례를 저장하여 다음 섹션에 표시합니다. 
-# 엡실론 값이 증가함에 따라 출력된 정확도가 어떻게 감소하는지 보십시오. 
+#
+# 구현의 마지막 부분은 공격을 실행하는 것입니다. 여기서 전체 테스트 스텝을 각 *엡실론* 값에 실행합니다.
+# 각 엡실론마다 최종 정확도와 성공적인 일부 적대 사례를 저장하여 다음 섹션에 표시합니다.
+# 엡실론 값이 증가함에 따라 출력된 정확도가 어떻게 감소하는지 보십시오.
 # 또한, :math:`\epsilon=0` 인 경우에는 공격이 없는 원본 테스트 정확도임을 보입니다.
-# 
+#
 
 accuracies = []
 examples = []
@@ -284,11 +284,11 @@ def test( model, device, test_loader, epsilon ):
 ######################################################################
 # 결과
 # -------
-# 
+#
 # 정확도 vs 엡실론
 # ~~~~~~~~~~~~~~~~~~~
-# 
-# 첫 번째 결과는 정확도 vs 엡실론 을 도식화 한 것 입니다. 
+#
+# 첫 번째 결과는 정확도 vs 엡실론 을 도식화 한 것 입니다.
 # 앞에서 언급했듯이, 엡실론이 증가함에 따라 우리는 테스트 정확도가 감소할 것으로 예상합니다.
 # 이는 학습을 더 진행해 갈수록 엡실론이 클수록 손실을 극대화 할 방향으로 진행되기 때문입니다.
 # 엡실론 값이 선형적으로 분포하더라도 곡선의 추세는 선형의 형태가 아닙니다.
@@ -310,8 +310,8 @@ def test( model, device, test_loader, epsilon ):
 ######################################################################
 # 샘플 적대적 예제들
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~
-# 
-# 공짜 점심은 없다는 것을 기억하시나요? 이 경우에는 엡실론이 증가할수록 테스트 정확도는 떨어집니다. 
+#
+# 공짜 점심은 없다는 것을 기억하시나요? 이 경우에는 엡실론이 증가할수록 테스트 정확도는 떨어집니다.
 # **그러나** 작은 변화는 더 쉽게 인식할 수 있게 됩니다.
 # 실제로 정확도 저하와 공격자가 고려해야 하는 이해도 사이에는 상충 관계(tradeoff)가 있습니다.
 # 여기서 우리는 각 엡실론 값에서 성공적인 대적 사례를 보이는 몇 가지 예를 보겠습니다.
@@ -341,19 +341,19 @@ def test( model, device, test_loader, epsilon ):
 ######################################################################
 # 다음 단계는?
 # -----------------
-# 
+#
 # 이번 튜토리얼에서 적대적 머신 러닝에 대한 통찰을 얻을 수 있었기를 바랍니다.
 # 튜토리얼의 내용으로부터 앞으로 더 많은 것들을 알아나갈 수 있습니다.
-# 튜토리얼의 적대적 공격 예제는 본 분야의 초급 단계이며 
+# 튜토리얼의 적대적 공격 예제는 본 분야의 초급 단계이며
 # 적대적 상황으로부터 ML 모델을 공격하고 방어하는 방법에 대한 많은 후속 아이디어가 있습니다.
 # 사실 NIPS 2017 에서 적대적 공격과 방어에 대한 경쟁(competition)이 있었고 여기서 사용된
 # 다양한 방법들은 다음 논문에 정리 되어 있습니다: `적대적 공격과 방어 경쟁 <https://arxiv.org/pdf/1804.00097.pdf>`__.
 # 방어에 대한 연구는 자연스럽게 교란 및 해킹 목적으로 제작된 입력에 대해 머신 러닝 모델을
 # 보다 *견고하게(robust)* 만드는 아이디어로 이어집니다.
-#  
-# 또 다른 방향은 다른 도메인에서의 적의 공격과 방어입니다. 적대적 연구는 이미지 도메인에 제한되어 있지 않습니다. 
+#
+# 또 다른 방향은 다른 도메인에서의 적의 공격과 방어입니다. 적대적 연구는 이미지 도메인에 제한되어 있지 않습니다.
 # `여기 <https://arxiv.org/pdf/1801.01944.pdf>`__ 에서 음성-텍스트 변환 모델에서의 공격도 확인할 수 있습니다.
 # 그러나 적대적 머신 러닝 분야에 대해서 많은 것을 알기 위한 최고의 방법은 많이 시도해보는 것입니다.
 # NIPS 2017 경쟁에서 소개된 다양한 공격 방법을 직접 구현해 보고, FGSM 과 어떤 점이 다른지 연구해 보세요.
 # 그리고 나서 직접 만든 공격으로부터 모델을 방어해 보세요.
-# 
+#
diff --git a/docs/_downloads/37c8905519d3fd3f437b783a48d06eac/char_rnn_classification_tutorial.py b/docs/_downloads/37c8905519d3fd3f437b783a48d06eac/char_rnn_classification_tutorial.py
index 4d766380e..da8d22535 100644
--- a/docs/_downloads/37c8905519d3fd3f437b783a48d06eac/char_rnn_classification_tutorial.py
+++ b/docs/_downloads/37c8905519d3fd3f437b783a48d06eac/char_rnn_classification_tutorial.py
@@ -51,7 +51,7 @@
 데이터 준비
 ==================
 
-.. NOTE::
+.. note::
    `여기 <https://download.pytorch.org/tutorial/data.zip>`__ 에서 데이터를 다운 받고,
    현재 디렉토리에 압축을 푸십시오.
 
@@ -62,9 +62,10 @@
 각 언어 별로 이름 목록 사전 ``{language: [names ...]}`` 을 만듭니다.
 일반 변수 "category" 와 "line" (우리의 경우 언어와 이름)은 이후의 확장성을 위해 사용됩니다.
 
-.. NOTE::
-역자 주:  "line" 에 입력을 "category"에 클래스를 적용하여 다른 문제에도 활용 할 수 있습니다.
-여기서는 "line"에 이름(ex. Robert )를 입력으로 "category"에 클래스(ex. english)로 사용합니다.
+.. note::
+   역자 주:  "line" 에 입력을 "category"에 클래스를 적용하여 다른 문제에도 활용 할 수 있습니다.
+   여기서는 "line"에 이름(ex. Robert )를 입력으로 "category"에 클래스(ex. english)로 사용합니다.
+
 """
 from __future__ import unicode_literals, print_function, division
 from io import open
diff --git a/docs/_downloads/3dbbd6931d76adb0dc37d4e88b328852/tensor_tutorial.ipynb b/docs/_downloads/3dbbd6931d76adb0dc37d4e88b328852/tensor_tutorial.ipynb
index 6f6a85cd5..83981770a 100644
--- a/docs/_downloads/3dbbd6931d76adb0dc37d4e88b328852/tensor_tutorial.ipynb
+++ b/docs/_downloads/3dbbd6931d76adb0dc37d4e88b328852/tensor_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\uac00 \ubb34\uc5c7\uc778\uac00\uc694?\n=======================\n\nPython \uae30\ubc18\uc758 \uacfc\ud559 \uc5f0\uc0b0 \ud328\ud0a4\uc9c0\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \ub450 \uc9d1\ub2e8\uc744 \ub300\uc0c1\uc73c\ub85c \ud569\ub2c8\ub2e4:\n\n- NumPy\ub97c \ub300\uccb4\ud558\uba74\uc11c GPU\ub97c \uc774\uc6a9\ud55c \uc5f0\uc0b0\uc774 \ud544\uc694\ud55c \uacbd\uc6b0\n- \ucd5c\ub300\ud55c\uc758 \uc720\uc5f0\uc131\uacfc \uc18d\ub3c4\ub97c \uc81c\uacf5\ud558\ub294 \ub525\ub7ec\ub2dd \uc5f0\uad6c \ud50c\ub7ab\ud3fc\uc774 \ud544\uc694\ud55c \uacbd\uc6b0\n\n\uc2dc\uc791\ud558\uae30\n-----------\n\nTensors\n^^^^^^^\n\nTensor\ub294 NumPy\uc758 ndarray\uc640 \uc720\uc0ac\ud558\uba70, GPU\ub97c \uc0ac\uc6a9\ud55c \uc5f0\uc0b0 \uac00\uc18d\ub3c4 \uac00\ub2a5\ud569\ub2c8\ub2e4.\n\n"
+        "\nPyTorch\uac00 \ubb34\uc5c7\uc778\uac00\uc694?\n=======================\n\nPython \uae30\ubc18\uc758 \uacfc\ud559 \uc5f0\uc0b0 \ud328\ud0a4\uc9c0\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \ub450 \uc9d1\ub2e8\uc744 \ub300\uc0c1\uc73c\ub85c \ud569\ub2c8\ub2e4:\n\n- NumPy\ub97c \ub300\uccb4\ud558\uba74\uc11c GPU\ub97c \uc774\uc6a9\ud55c \uc5f0\uc0b0\uc774 \ud544\uc694\ud55c \uacbd\uc6b0\n- \ucd5c\ub300\ud55c\uc758 \uc720\uc5f0\uc131\uacfc \uc18d\ub3c4\ub97c \uc81c\uacf5\ud558\ub294 \ub525\ub7ec\ub2dd \uc5f0\uad6c \ud50c\ub7ab\ud3fc\uc774 \ud544\uc694\ud55c \uacbd\uc6b0\n\n\uc2dc\uc791\ud558\uae30\n-----------\n\nTensors\n^^^^^^^\n\nTensor\ub294 NumPy\uc758 ndarray\uc640 \uc720\uc0ac\ud558\uba70, GPU\ub97c \uc0ac\uc6a9\ud55c \uc5f0\uc0b0 \uac00\uc18d\ub3c4 \uac00\ub2a5\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -370,7 +370,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/40e628499d80c649ce6bf96fef98fcd2/two_layer_net_numpy.ipynb b/docs/_downloads/40e628499d80c649ce6bf96fef98fcd2/two_layer_net_numpy.ipynb
index 75eb1e3f0..f6b0706bd 100644
--- a/docs/_downloads/40e628499d80c649ce6bf96fef98fcd2/two_layer_net_numpy.ipynb
+++ b/docs/_downloads/40e628499d80c649ce6bf96fef98fcd2/two_layer_net_numpy.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uc900\ube44 \uc6b4\ub3d9: NumPy\n-----------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uc624\ucc28(Euclidean error)\ub97c \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\nNumPy\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc218\ub3d9\uc73c\ub85c \uc21c\uc804\ud30c, \uc190\uc2e4(loss), \uadf8\ub9ac\uace0 \uc5ed\uc804\ud30c \uc5f0\uc0b0\uc744 \ud558\ub294 \uac83\uc744\n\uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nNumPy \ubc30\uc5f4\uc740 \uc77c\ubc18\uc801\uc740 N\ucc28\uc6d0 \ubc30\uc5f4\uc785\ub2c8\ub2e4; \ub525\ub7ec\ub2dd\uc774\ub098 \ubcc0\ud654\ub3c4(gradient), \uc5f0\uc0b0\n\uadf8\ub798\ud504(computational graph)\ub294 \uc54c\uc9c0 \ubabb\ud558\uba70, \uc77c\ubc18\uc801\uc778 \uc218\uce58 \uc5f0\uc0b0\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n\n"
+        "\n\uc900\ube44 \uc6b4\ub3d9: NumPy\n-----------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uc624\ucc28(Euclidean error)\ub97c \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\nNumPy\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc218\ub3d9\uc73c\ub85c \uc21c\uc804\ud30c, \uc190\uc2e4(loss), \uadf8\ub9ac\uace0 \uc5ed\uc804\ud30c \uc5f0\uc0b0\uc744 \ud558\ub294 \uac83\uc744\n\uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nNumPy \ubc30\uc5f4\uc740 \uc77c\ubc18\uc801\uc740 N\ucc28\uc6d0 \ubc30\uc5f4\uc785\ub2c8\ub2e4; \ub525\ub7ec\ub2dd\uc774\ub098 \ubcc0\ud654\ub3c4(gradient), \uc5f0\uc0b0\n\uadf8\ub798\ud504(computational graph)\ub294 \uc54c\uc9c0 \ubabb\ud558\uba70, \uc77c\ubc18\uc801\uc778 \uc218\uce58 \uc5f0\uc0b0\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/443402f98df8c37ed150fc9266eb8dee/super_resolution_with_onnxruntime.py b/docs/_downloads/443402f98df8c37ed150fc9266eb8dee/super_resolution_with_onnxruntime.py
index 029987847..635bca82b 100644
--- a/docs/_downloads/443402f98df8c37ed150fc9266eb8dee/super_resolution_with_onnxruntime.py
+++ b/docs/_downloads/443402f98df8c37ed150fc9266eb8dee/super_resolution_with_onnxruntime.py
@@ -107,10 +107,10 @@ def _initialize_weights(self):
 # 이 함수는 모델을 실행하여 어떤 연산자들이 출력값을 계산하는데 사용되었는지를 기록합니다.
 # ``export`` 함수가 모델을 실행하기 때문에, 우리가 직접 텐서를 입력값으로 넘겨주어야 합니다.
 # 이 텐서의 값은 알맞은 자료형과 모양이라면 랜덤하게 결정되어도 무방합니다.
-# 특정 차원을 동적인 차원으로 지정하지 않는 이상, ONNX로 변환된 그래프의 경우 입력값의 사이즈는 모든 차원에 대해 고정됩니다.  
+# 특정 차원을 동적인 차원으로 지정하지 않는 이상, ONNX로 변환된 그래프의 경우 입력값의 사이즈는 모든 차원에 대해 고정됩니다.
 # 예시에서는 모델이 항상 배치 사이즈 1을 사용하도록 변환하였지만, 첫번째 차원을 ``torch.onnx.export()`` 의
 # ``dynamic_axes`` 인자에 동적인 차원으로 지정해주면 됩니다.
-# 
+#
 # PyTorch의 변환 인터페이스에 대해 더 자세히 알고 싶다면
 # `torch.onnx 문서 <https://pytorch.org/docs/master/onnx.html>`__ 를 참고해주세요.
 #
@@ -156,7 +156,7 @@ def _initialize_weights(self):
 # 이 부분은 보통 별도의 프로세스 또는 별도의 머신에서 실행되지만, 이 튜토리얼에서는 모델이
 # ONNX 런타임과 PyTorch에서 동일한 결과를 출력하는지를 확인하기 위해 동일한 프로세스에서 계속 실행하도록
 # 하겠습니다.
-# 
+#
 # 모델을 ONNX 런타임에서 실행하기 위해서는 미리 설정된 인자들(본 예제에서는 기본값을 사용합니다)로
 # 모델을 위한 추론 세션을 생성해야 합니다.
 # 세션이 생성되면, 모델의 run() API를 사용하여 모델을 실행합니다.
@@ -189,7 +189,7 @@ def to_numpy(tensor):
 
 ######################################################################
 # ONNX 런타임에서 이미지를 입력값으로 모델을 실행하기
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
 
@@ -275,7 +275,7 @@ def to_numpy(tensor):
 # 더 자세한 내용은 `링크 <https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-onnx>`__ 를
 # 참조해주십시오.
 #
-# ONNX 런타임의 성능에 관한 것은 `여기 <https://github.com/microsoft/onnxruntime#high-performance>`__ 에서 
+# ONNX 런타임의 성능에 관한 것은 `여기 <https://github.com/microsoft/onnxruntime#high-performance>`__ 에서
 # 확인하실 수 있습니다.
 #
 # ONNX 런타임에 관한 더 자세한 내용은 `이 곳 <https://github.com/microsoft/onnxruntime>`__ 을 참조해 주세요.
diff --git a/docs/_downloads/44a84f8c1764dbf61662d306ff9ed43a/chatbot_tutorial.ipynb b/docs/_downloads/44a84f8c1764dbf61662d306ff9ed43a/chatbot_tutorial.ipynb
index d9bd42a2f..94d46168e 100644
--- a/docs/_downloads/44a84f8c1764dbf61662d306ff9ed43a/chatbot_tutorial.ipynb
+++ b/docs/_downloads/44a84f8c1764dbf61662d306ff9ed43a/chatbot_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\ucc57\ubd07 \ud29c\ud1a0\ub9ac\uc5bc\n================\n**Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_\n  **\ubc88\uc5ed**: `\uae40\uc9c4\ud604 <https://github.com/lewha0>`_\n\n"
+        "\n\ucc57\ubd07 \ud29c\ud1a0\ub9ac\uc5bc\n================\n**Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_\n  **\ubc88\uc5ed**: `\uae40\uc9c4\ud604 <https://github.com/lewha0>`_\n"
       ]
     },
     {
@@ -353,7 +353,7 @@
       },
       "outputs": [],
       "source": [
-        "# \ud559\uc2b5 \ubc0f \ucd5c\uc801\ud654 \uc124\uc815\nclip = 50.0\nteacher_forcing_ratio = 1.0\nlearning_rate = 0.0001\ndecoder_learning_ratio = 5.0\nn_iteration = 4000\nprint_every = 1\nsave_every = 500\n\n# Dropout \ub808\uc774\uc5b4\ub97c \ud559\uc2b5 \ubaa8\ub4dc\ub85c \ub461\ub2c8\ub2e4\nencoder.train()\ndecoder.train()\n\n# Optimizer\ub97c \ucd08\uae30\ud654\ud569\ub2c8\ub2e4\nprint('Building optimizers ...')\nencoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)\ndecoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)\nif loadFilename:\n    encoder_optimizer.load_state_dict(encoder_optimizer_sd)\n    decoder_optimizer.load_state_dict(decoder_optimizer_sd)\n\n# cuda\uac00 \uc788\ub2e4\uba74 cuda\ub97c \uc124\uc815\ud569\ub2c8\ub2e4\nfor state in encoder_optimizer.state.values():\n    for k, v in state.items():\n        if isinstance(v, torch.Tensor):\n            state[k] = v.cuda()\n\nfor state in decoder_optimizer.state.values():\n    for k, v in state.items():\n        if isinstance(v, torch.Tensor):\n            state[k] = v.cuda()\n    \n# \ud559\uc2b5 \ub2e8\uacc4\ub97c \uc218\ud589\ud569\ub2c8\ub2e4\nprint(\"Starting Training!\")\ntrainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer,\n           embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size,\n           print_every, save_every, clip, corpus_name, loadFilename)"
+        "# \ud559\uc2b5 \ubc0f \ucd5c\uc801\ud654 \uc124\uc815\nclip = 50.0\nteacher_forcing_ratio = 1.0\nlearning_rate = 0.0001\ndecoder_learning_ratio = 5.0\nn_iteration = 4000\nprint_every = 1\nsave_every = 500\n\n# Dropout \ub808\uc774\uc5b4\ub97c \ud559\uc2b5 \ubaa8\ub4dc\ub85c \ub461\ub2c8\ub2e4\nencoder.train()\ndecoder.train()\n\n# Optimizer\ub97c \ucd08\uae30\ud654\ud569\ub2c8\ub2e4\nprint('Building optimizers ...')\nencoder_optimizer = optim.Adam(encoder.parameters(), lr=learning_rate)\ndecoder_optimizer = optim.Adam(decoder.parameters(), lr=learning_rate * decoder_learning_ratio)\nif loadFilename:\n    encoder_optimizer.load_state_dict(encoder_optimizer_sd)\n    decoder_optimizer.load_state_dict(decoder_optimizer_sd)\n\n# cuda\uac00 \uc788\ub2e4\uba74 cuda\ub97c \uc124\uc815\ud569\ub2c8\ub2e4\nfor state in encoder_optimizer.state.values():\n    for k, v in state.items():\n        if isinstance(v, torch.Tensor):\n            state[k] = v.cuda()\n\nfor state in decoder_optimizer.state.values():\n    for k, v in state.items():\n        if isinstance(v, torch.Tensor):\n            state[k] = v.cuda()\n\n# \ud559\uc2b5 \ub2e8\uacc4\ub97c \uc218\ud589\ud569\ub2c8\ub2e4\nprint(\"Starting Training!\")\ntrainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer,\n           embedding, encoder_n_layers, decoder_n_layers, save_dir, n_iteration, batch_size,\n           print_every, save_every, clip, corpus_name, loadFilename)"
       ]
     },
     {
@@ -398,7 +398,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/4e865243430a47a00d551ca0579a6f6c/cifar10_tutorial.ipynb b/docs/_downloads/4e865243430a47a00d551ca0579a6f6c/cifar10_tutorial.ipynb
index 4c98387b7..16e7d46f5 100644
--- a/docs/_downloads/4e865243430a47a00d551ca0579a6f6c/cifar10_tutorial.ipynb
+++ b/docs/_downloads/4e865243430a47a00d551ca0579a6f6c/cifar10_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\ubd84\ub958\uae30(Classifier) \ud559\uc2b5\ud558\uae30\n============================\n\n\uc9c0\uae08\uae4c\uc9c0 \uc5b4\ub5bb\uac8c \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\uace0, \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uba70 \ub610 \uac00\uc911\uce58\ub97c \uac31\uc2e0\ud558\ub294\uc9c0\uc5d0\n\ub300\ud574\uc11c \ubc30\uc6e0\uc2b5\ub2c8\ub2e4.\n\n\uc774\uc81c \uc544\ub9c8\ub3c4 \uc774\ub7f0 \uc0dd\uac01\uc744 \ud558\uace0 \uacc4\uc2e4\ud150\ub370\uc694,\n\n\ub370\uc774\ud130\ub294 \uc5b4\ub5bb\uac8c \ud558\ub098\uc694?\n------------------------\n\n\uc77c\ubc18\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0\ub098 \ud14d\uc2a4\ud2b8, \uc624\ub514\uc624\ub098 \ube44\ub514\uc624 \ub370\uc774\ud130\ub97c \ub2e4\ub8f0 \ub54c\ub294 \ud45c\uc900 Python \ud328\ud0a4\uc9c0\ub97c\n\uc774\uc6a9\ud558\uc5ec NumPy \ubc30\uc5f4\ub85c \ubd88\ub7ec\uc624\uba74 \ub429\ub2c8\ub2e4. \uadf8 \ud6c4 \uadf8 \ubc30\uc5f4\uc744 ``torch.*Tensor`` \ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.\n\n-  \uc774\ubbf8\uc9c0\ub294 Pillow\ub098 OpenCV \uac19\uc740 \ud328\ud0a4\uc9c0\uac00 \uc720\uc6a9\ud569\ub2c8\ub2e4.\n-  \uc624\ub514\uc624\ub97c \ucc98\ub9ac\ud560 \ub54c\ub294 SciPy\uc640 LibROSA\uac00 \uc720\uc6a9\ud558\uace0\uc694.\n-  \ud14d\uc2a4\ud2b8\uc758 \uacbd\uc6b0\uc5d0\ub294 \uadf8\ub0e5 Python\uc774\ub098 Cython\uc744 \uc0ac\uc6a9\ud574\ub3c4 \ub418\uace0, NLTK\ub098 SpaCy\ub3c4\n   \uc720\uc6a9\ud569\ub2c8\ub2e4.\n\n\ud2b9\ubcc4\ud788 \uc601\uc0c1 \ubd84\uc57c\ub97c \uc704\ud55c ``torchvision`` \uc774\ub77c\ub294 \ud328\ud0a4\uc9c0\uac00 \ub9cc\ub4e4\uc5b4\uc838 \uc788\ub294\ub370,\n\uc5ec\uae30\uc5d0\ub294 Imagenet\uc774\ub098 CIFAR10, MNIST \ub4f1\uacfc \uac19\uc774 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ud558\ub294 \ub370\uc774\ud130\uc14b\uc744 \uc704\ud55c\n\ub370\uc774\ud130 \ub85c\ub354(data loader), \uc989 ``torchvision.datasets`` \uacfc \uc774\ubbf8\uc9c0\uc6a9 \ub370\uc774\ud130 \ubcc0\ud658\uae30\n(data transformer), \uc989 ``torch.utils.data.DataLoader`` \uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774\ub7ec\ud55c \uae30\ub2a5\uc740 \uc5c4\uccad\ub098\uac8c \ud3b8\ub9ac\ud558\uba70, \ub9e4\ubc88 \uc720\uc0ac\ud55c \ucf54\ub4dc(boilerplate code)\ub97c \ubc18\ubcf5\ud574\uc11c\n\uc791\uc131\ud558\ub294 \uac83\uc744 \ud53c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 CIFAR10 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ubd84\ub958\ub4e4\uc774\n\uc788\uc2b5\ub2c8\ub2e4: '\ube44\ud589\uae30(airplane)', '\uc790\ub3d9\ucc28(automobile)', '\uc0c8(bird)', '\uace0\uc591\uc774(cat)',\n'\uc0ac\uc2b4(deer)', '\uac1c(dog)', '\uac1c\uad6c\ub9ac(frog)', '\ub9d0(horse)', '\ubc30(ship)', '\ud2b8\ub7ed(truck)'.\n\uadf8\ub9ac\uace0 CIFAR10\uc5d0 \ud3ec\ud568\ub41c \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\ub294 3x32x32\ub85c, \uc774\ub294 32x32 \ud53d\uc140 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\uac00\n3\uac1c \ucc44\ub110(channel)\uc758 \uc0c9\uc0c1\ub85c \uc774\ub904\uc838 \uc788\ub2e4\ub294 \uac83\uc744 \ub73b\ud569\ub2c8\ub2e4.\n\n.. figure:: /_static/img/cifar10.png\n   :alt: cifar10\n\n   cifar10\n\n\n\uc774\ubbf8\uc9c0 \ubd84\ub958\uae30 \ud559\uc2b5\ud558\uae30\n----------------------------\n\n\ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc9c4\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\n1. ``torchvision`` \uc744 \uc0ac\uc6a9\ud558\uc5ec CIFAR10\uc758 \ud559\uc2b5\uc6a9 / \uc2dc\ud5d8\uc6a9 \ub370\uc774\ud130\uc14b\uc744\n   \ubd88\ub7ec\uc624\uace0, \uc815\uaddc\ud654(nomarlizing)\ud569\ub2c8\ub2e4.\n2. \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolution Neural Network)\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\n3. \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.\n4. \ud559\uc2b5\uc6a9 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.\n5. \uc2dc\ud5d8\uc6a9 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uac80\uc0ac\ud569\ub2c8\ub2e4.\n\n1. CIFAR10\ub97c \ubd88\ub7ec\uc624\uace0 \uc815\uaddc\ud654\ud558\uae30\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``torchvision`` \uc744 \uc0ac\uc6a9\ud558\uc5ec \ub9e4\uc6b0 \uc27d\uac8c CIFAR10\uc744 \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\n\ubd84\ub958\uae30(Classifier) \ud559\uc2b5\ud558\uae30\n============================\n\n\uc9c0\uae08\uae4c\uc9c0 \uc5b4\ub5bb\uac8c \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\uace0, \uc190\uc2e4\uc744 \uacc4\uc0b0\ud558\uba70 \ub610 \uac00\uc911\uce58\ub97c \uac31\uc2e0\ud558\ub294\uc9c0\uc5d0\n\ub300\ud574\uc11c \ubc30\uc6e0\uc2b5\ub2c8\ub2e4.\n\n\uc774\uc81c \uc544\ub9c8\ub3c4 \uc774\ub7f0 \uc0dd\uac01\uc744 \ud558\uace0 \uacc4\uc2e4\ud150\ub370\uc694,\n\n\ub370\uc774\ud130\ub294 \uc5b4\ub5bb\uac8c \ud558\ub098\uc694?\n------------------------\n\n\uc77c\ubc18\uc801\uc73c\ub85c \uc774\ubbf8\uc9c0\ub098 \ud14d\uc2a4\ud2b8, \uc624\ub514\uc624\ub098 \ube44\ub514\uc624 \ub370\uc774\ud130\ub97c \ub2e4\ub8f0 \ub54c\ub294 \ud45c\uc900 Python \ud328\ud0a4\uc9c0\ub97c\n\uc774\uc6a9\ud558\uc5ec NumPy \ubc30\uc5f4\ub85c \ubd88\ub7ec\uc624\uba74 \ub429\ub2c8\ub2e4. \uadf8 \ud6c4 \uadf8 \ubc30\uc5f4\uc744 ``torch.*Tensor`` \ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.\n\n-  \uc774\ubbf8\uc9c0\ub294 Pillow\ub098 OpenCV \uac19\uc740 \ud328\ud0a4\uc9c0\uac00 \uc720\uc6a9\ud569\ub2c8\ub2e4.\n-  \uc624\ub514\uc624\ub97c \ucc98\ub9ac\ud560 \ub54c\ub294 SciPy\uc640 LibROSA\uac00 \uc720\uc6a9\ud558\uace0\uc694.\n-  \ud14d\uc2a4\ud2b8\uc758 \uacbd\uc6b0\uc5d0\ub294 \uadf8\ub0e5 Python\uc774\ub098 Cython\uc744 \uc0ac\uc6a9\ud574\ub3c4 \ub418\uace0, NLTK\ub098 SpaCy\ub3c4\n   \uc720\uc6a9\ud569\ub2c8\ub2e4.\n\n\ud2b9\ubcc4\ud788 \uc601\uc0c1 \ubd84\uc57c\ub97c \uc704\ud55c ``torchvision`` \uc774\ub77c\ub294 \ud328\ud0a4\uc9c0\uac00 \ub9cc\ub4e4\uc5b4\uc838 \uc788\ub294\ub370,\n\uc5ec\uae30\uc5d0\ub294 Imagenet\uc774\ub098 CIFAR10, MNIST \ub4f1\uacfc \uac19\uc774 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ud558\ub294 \ub370\uc774\ud130\uc14b\uc744 \uc704\ud55c\n\ub370\uc774\ud130 \ub85c\ub354(data loader), \uc989 ``torchvision.datasets`` \uacfc \uc774\ubbf8\uc9c0\uc6a9 \ub370\uc774\ud130 \ubcc0\ud658\uae30\n(data transformer), \uc989 ``torch.utils.data.DataLoader`` \uac00 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774\ub7ec\ud55c \uae30\ub2a5\uc740 \uc5c4\uccad\ub098\uac8c \ud3b8\ub9ac\ud558\uba70, \ub9e4\ubc88 \uc720\uc0ac\ud55c \ucf54\ub4dc(boilerplate code)\ub97c \ubc18\ubcf5\ud574\uc11c\n\uc791\uc131\ud558\ub294 \uac83\uc744 \ud53c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 CIFAR10 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ubd84\ub958\ub4e4\uc774\n\uc788\uc2b5\ub2c8\ub2e4: '\ube44\ud589\uae30(airplane)', '\uc790\ub3d9\ucc28(automobile)', '\uc0c8(bird)', '\uace0\uc591\uc774(cat)',\n'\uc0ac\uc2b4(deer)', '\uac1c(dog)', '\uac1c\uad6c\ub9ac(frog)', '\ub9d0(horse)', '\ubc30(ship)', '\ud2b8\ub7ed(truck)'.\n\uadf8\ub9ac\uace0 CIFAR10\uc5d0 \ud3ec\ud568\ub41c \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\ub294 3x32x32\ub85c, \uc774\ub294 32x32 \ud53d\uc140 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\uac00\n3\uac1c \ucc44\ub110(channel)\uc758 \uc0c9\uc0c1\ub85c \uc774\ub904\uc838 \uc788\ub2e4\ub294 \uac83\uc744 \ub73b\ud569\ub2c8\ub2e4.\n\n.. figure:: /_static/img/cifar10.png\n   :alt: cifar10\n\n   cifar10\n\n\n\uc774\ubbf8\uc9c0 \ubd84\ub958\uae30 \ud559\uc2b5\ud558\uae30\n----------------------------\n\n\ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc9c4\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\n1. ``torchvision`` \uc744 \uc0ac\uc6a9\ud558\uc5ec CIFAR10\uc758 \ud559\uc2b5\uc6a9 / \uc2dc\ud5d8\uc6a9 \ub370\uc774\ud130\uc14b\uc744\n   \ubd88\ub7ec\uc624\uace0, \uc815\uaddc\ud654(nomarlizing)\ud569\ub2c8\ub2e4.\n2. \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolution Neural Network)\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\n3. \uc190\uc2e4 \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.\n4. \ud559\uc2b5\uc6a9 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.\n5. \uc2dc\ud5d8\uc6a9 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uac80\uc0ac\ud569\ub2c8\ub2e4.\n\n1. CIFAR10\ub97c \ubd88\ub7ec\uc624\uace0 \uc815\uaddc\ud654\ud558\uae30\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``torchvision`` \uc744 \uc0ac\uc6a9\ud558\uc5ec \ub9e4\uc6b0 \uc27d\uac8c CIFAR10\uc744 \ubd88\ub7ec\uc62c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -298,7 +298,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb b/docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb
index 3a87a6f52..155f96c8f 100644
--- a/docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb
+++ b/docs/_downloads/52d4aaa33601a2b3990ace6aa45546ce/numpy_extensions_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nCreating Extensions Using numpy and scipy\n=========================================\n**Author**: `Adam Paszke <https://github.com/apaszke>`_\n\n**Updated by**: `Adam Dziedzic <https://github.com/adam-dziedzic>`_\n\nIn this tutorial, we shall go through two tasks:\n\n1. Create a neural network layer with no parameters.\n\n    -  This calls into **numpy** as part of its implementation\n\n2. Create a neural network layer that has learnable weights\n\n    -  This calls into **SciPy** as part of its implementation\n\n"
+        "\nnumpy \uc640 scipy \ub97c \uc774\uc6a9\ud55c \ud655\uc7a5(Extensions) \ub9cc\ub4e4\uae30\n=====================================================\n**Author**: `Adam Paszke <https://github.com/apaszke>`_\n\n**Updated by**: `Adam Dziedzic <https://github.com/adam-dziedzic>`_\n\n**\ubc88\uc5ed**: `Ajin Jeong <https://github.com/ajin-jng>`_\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub450 \uac00\uc9c0 \uc791\uc5c5\uc744 \uc218\ud589\ud560 \uac83\uc785\ub2c8\ub2e4:\n\n1. \ub9e4\uac1c \ubcc0\uc218\uac00 \uc5c6\ub294 \uc2e0\uacbd\ub9dd \uacc4\uce35(layer) \ub9cc\ub4e4\uae30\n    - \uc774\ub294 \uad6c\ud604\uc758 \uc77c\ubd80\ub85c **numpy** \ub97c \ud638\ucd9c\ud569\ub2c8\ub2e4.\n\n2. \ud559\uc2b5 \uac00\ub2a5\ud55c \uac00\uc911\uce58\uac00 \uc788\ub294 \uc2e0\uacbd\ub9dd \uacc4\uce35(layer) \ub9cc\ub4e4\uae30\n    - \uc774\ub294 \uad6c\ud604\uc758 \uc77c\ubd80\ub85c **Scipy** \ub97c \ud638\ucd9c\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -33,7 +33,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Parameter-less example\n----------------------\n\nThis layer doesn\u2019t particularly do anything useful or mathematically\ncorrect.\n\nIt is aptly named BadFFTFunction\n\n**Layer Implementation**\n\n"
+        "\ub9e4\uac1c \ubcc0\uc218\uac00 \uc5c6\ub294(Parameter-less) \uc608\uc2dc\n----------------------------------------\n\n\uc774 \uacc4\uce35(layer)\uc740 \ud2b9\ubcc4\ud788 \uc720\uc6a9\ud558\uac70\ub098 \uc218\ud559\uc801\uc73c\ub85c \uc62c\ubc14\ub978 \uc791\uc5c5\uc744 \uc218\ud589\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n\uc774\ub984\uc740 \ub300\ucda9 BadFFTFunction\uc73c\ub85c \uc9c0\uc5c8\uc2b5\ub2c8\ub2e4.\n\n**\uacc4\uce35(layer) \uad6c\ud604**\n\n"
       ]
     },
     {
@@ -44,14 +44,14 @@
       },
       "outputs": [],
       "source": [
-        "from numpy.fft import rfft2, irfft2\n\n\nclass BadFFTFunction(Function):\n    @staticmethod\n    def forward(ctx, input):\n        numpy_input = input.detach().numpy()\n        result = abs(rfft2(numpy_input))\n        return input.new(result)\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        numpy_go = grad_output.numpy()\n        result = irfft2(numpy_go)\n        return grad_output.new(result)\n\n# since this layer does not have any parameters, we can\n# simply declare this as a function, rather than as an nn.Module class\n\n\ndef incorrect_fft(input):\n    return BadFFTFunction.apply(input)"
+        "from numpy.fft import rfft2, irfft2\n\n\nclass BadFFTFunction(Function):\n    @staticmethod\n    def forward(ctx, input):\n        numpy_input = input.detach().numpy()\n        result = abs(rfft2(numpy_input))\n        return input.new(result)\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        numpy_go = grad_output.numpy()\n        result = irfft2(numpy_go)\n        return grad_output.new(result)\n\n# \uc774 \uacc4\uce35\uc5d0\ub294 \ub9e4\uac1c \ubcc0\uc218\uac00 \uc5c6\uc73c\ubbc0\ub85c nn.Module \ud074\ub798\uc2a4\uac00 \uc544\ub2cc \ud568\uc218\ub85c \uac04\ub2e8\ud788 \uc120\uc5b8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ndef incorrect_fft(input):\n    return BadFFTFunction.apply(input)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "**Example usage of the created layer:**\n\n"
+        "**\uc0dd\uc131\ub41c \uacc4\uce35(layer)\uc758 \uc0ac\uc6a9 \uc608\uc2dc:**\n\n"
       ]
     },
     {
@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Parametrized example\n--------------------\n\nIn deep learning literature, this layer is confusingly referred\nto as convolution while the actual operation is cross-correlation\n(the only difference is that filter is flipped for convolution,\nwhich is not the case for cross-correlation).\n\nImplementation of a layer with learnable weights, where cross-correlation\nhas a filter (kernel) that represents weights.\n\nThe backward pass computes the gradient wrt the input and the gradient wrt the filter.\n\n"
+        "\ub9e4\uac1c \ubcc0\uc218\uac00 \uc788\ub294(Parameterized) \uc608\uc2dc\n----------------------------------------\n\n\ub525\ub7ec\ub2dd \ubb38\ud5cc\uc5d0\uc11c \uc774 \uacc4\uce35(layer)\uc758 \uc2e4\uc81c \uc5f0\uc0b0\uc740 \uc0c1\ud638 \uc0c1\uad00(cross-correlation)\uc774\uc9c0\ub9cc\n\ud569\uc131\uacf1(convolution)\uc774\ub77c\uace0 \ud5f7\uac08\ub9ac\uac8c \ubd80\ub974\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n(\ud569\uc131\uacf1\uc740 \ud544\ud130\ub97c \ub4a4\uc9d1\uc5b4\uc11c \uc5f0\uc0b0\uc744 \ud558\ub294 \ubc18\uba74, \uc0c1\ud638 \uc0c1\uad00\uc740 \uadf8\ub807\uc9c0 \uc54a\uc740 \ucc28\uc774\uac00 \uc788\uc2b5\ub2c8\ub2e4)\n\n\ud559\uc2b5 \uac00\ub2a5\ud55c \uac00\uc911\uce58\ub97c \uac00\ub294 \ud544\ud130(\ucee4\ub110)\ub97c \uac16\ub294 \uc0c1\ud638 \uc0c1\uad00 \uacc4\uce35\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc5ed\uc804\ud30c \ub2e8\uacc4(backward pass)\uc5d0\uc11c\ub294 \uc785\ub825\uc5d0 \ub300\ud55c \uae30\uc6b8\uae30(gradient)\uc640 \ud544\ud130\uc5d0 \ub300\ud55c \uae30\uc6b8\uae30\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -80,14 +80,14 @@
       },
       "outputs": [],
       "source": [
-        "from numpy import flip\nimport numpy as np\nfrom scipy.signal import convolve2d, correlate2d\nfrom torch.nn.modules.module import Module\nfrom torch.nn.parameter import Parameter\n\n\nclass ScipyConv2dFunction(Function):\n    @staticmethod\n    def forward(ctx, input, filter, bias):\n        # detach so we can cast to NumPy\n        input, filter, bias = input.detach(), filter.detach(), bias.detach()\n        result = correlate2d(input.numpy(), filter.numpy(), mode='valid')\n        result += bias.numpy()\n        ctx.save_for_backward(input, filter, bias)\n        return torch.as_tensor(result, dtype=input.dtype)\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        grad_output = grad_output.detach()\n        input, filter, bias = ctx.saved_tensors\n        grad_output = grad_output.numpy()\n        grad_bias = np.sum(grad_output, keepdims=True)\n        grad_input = convolve2d(grad_output, filter.numpy(), mode='full')\n        # the previous line can be expressed equivalently as:\n        # grad_input = correlate2d(grad_output, flip(flip(filter.numpy(), axis=0), axis=1), mode='full')\n        grad_filter = correlate2d(input.numpy(), grad_output, mode='valid')\n        return torch.from_numpy(grad_input), torch.from_numpy(grad_filter).to(torch.float), torch.from_numpy(grad_bias).to(torch.float)\n\n\nclass ScipyConv2d(Module):\n    def __init__(self, filter_width, filter_height):\n        super(ScipyConv2d, self).__init__()\n        self.filter = Parameter(torch.randn(filter_width, filter_height))\n        self.bias = Parameter(torch.randn(1, 1))\n\n    def forward(self, input):\n        return ScipyConv2dFunction.apply(input, self.filter, self.bias)"
+        "from numpy import flip\nimport numpy as np\nfrom scipy.signal import convolve2d, correlate2d\nfrom torch.nn.modules.module import Module\nfrom torch.nn.parameter import Parameter\n\n\nclass ScipyConv2dFunction(Function):\n    @staticmethod\n    def forward(ctx, input, filter, bias):\n        # \ubd84\ub9ac(detach)\ud558\uc5ec NumPy\ub85c \ubcc0\ud658(cast)\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n        input, filter, bias = input.detach(), filter.detach(), bias.detach()\n        result = correlate2d(input.numpy(), filter.numpy(), mode='valid')\n        result += bias.numpy()\n        ctx.save_for_backward(input, filter, bias)\n        return torch.as_tensor(result, dtype=input.dtype)\n\n    @staticmethod\n    def backward(ctx, grad_output):\n        grad_output = grad_output.detach()\n        input, filter, bias = ctx.saved_tensors\n        grad_output = grad_output.numpy()\n        grad_bias = np.sum(grad_output, keepdims=True)\n        grad_input = convolve2d(grad_output, filter.numpy(), mode='full')\n        # \uc717\uc904\uc740 \ub2e4\uc74c\uacfc \uac19\uc774 \ud45c\ud604\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4:\n        # grad_input = correlate2d(grad_output, flip(flip(filter.numpy(), axis=0), axis=1), mode='full')\n        grad_filter = correlate2d(input.numpy(), grad_output, mode='valid')\n        return torch.from_numpy(grad_input), torch.from_numpy(grad_filter).to(torch.float), torch.from_numpy(grad_bias).to(torch.float)\n\n\nclass ScipyConv2d(Module):\n    def __init__(self, filter_width, filter_height):\n        super(ScipyConv2d, self).__init__()\n        self.filter = Parameter(torch.randn(filter_width, filter_height))\n        self.bias = Parameter(torch.randn(1, 1))\n\n    def forward(self, input):\n        return ScipyConv2dFunction.apply(input, self.filter, self.bias)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "**Example usage:**\n\n"
+        "**\uc0ac\uc6a9 \uc608\uc2dc:**\n\n"
       ]
     },
     {
@@ -105,7 +105,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "**Check the gradients:**\n\n"
+        "**\uae30\uc6b8\uae30(gradient) \ud655\uc778:**\n\n"
       ]
     },
     {
@@ -136,7 +136,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/56c122e1c18e5e07666673e900acaed5/fgsm_tutorial.ipynb b/docs/_downloads/56c122e1c18e5e07666673e900acaed5/fgsm_tutorial.ipynb
index 138b61a85..df7cebe47 100644
--- a/docs/_downloads/56c122e1c18e5e07666673e900acaed5/fgsm_tutorial.ipynb
+++ b/docs/_downloads/56c122e1c18e5e07666673e900acaed5/fgsm_tutorial.ipynb
@@ -15,14 +15,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uc801\ub300\uc801 \uc608\uc81c \uc0dd\uc131(Adversarial Example Generation)\n====================================================\n\n**\uc800\uc790:** `Nathan Inkawhich <https://github.com/inkawhich>`__\n**\ubc88\uc5ed:** `BONGMO KIM <https://github.com/bongmo>`__\n\n\uc774 \uae00\uc744 \uc77d\uace0 \uc788\ub2e4\uba74, \uc5ec\ub7ec\ubd84\uc740 \uc774\ubbf8 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc774 \uc5bc\ub9c8\ub098 \ud6a8\uacfc\uc801\uc778\uc9c0 \uadf8 \uc9c4\uac00\ub97c \uc54c\uace0 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n\uba38\uc2e0 \ub7ec\ub2dd \uc5f0\uad6c\ub294 ML(Machine Learning) \ubaa8\ub378\uc744 \ub354\uc6b1 \ube60\ub974\uace0 \uc815\ud655\ud558\uba70 \ud6a8\uc728\uc801\uc774\uac8c \ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \uc9c4\ud589 \ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \ubaa8\ub378\uc744 \uc18d\uc774\ub824\ud558\ub294 \uc801\uc5d0 \ub300\ud55c \ubcf4\uc548\uacfc \uacac\uace0\ud568\uc740 \ubaa8\ub378\uc744 \uc124\uacc4\ud558\uace0 \ud6c8\ub828\ud560 \ub54c \uc885\uc885 \uac04\uacfc\ub418\ub294 \ubd80\ubd84\uc785\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 ML \ubaa8\ub378\ub4e4\uc758 \ubcf4\uc548 \ucde8\uc57d\uc810\uc5d0 \ub300\ud55c \uc778\uc2dd\uc744 \ub192\uc774\uace0, \uc694\uc998 \ud654\ub450\uac00 \ub418\uace0\uc788\ub294 \uc801\ub300\uc801 \uba38\uc2e0 \ub7ec\ub2dd\uc5d0 \ub300\ud55c \ud1b5\ucc30\ub825\uc744 \uc81c\uacf5\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774\ubbf8\uc9c0\uc5d0 \ub208\uce58\ucc4c \uc218 \uc5c6\ub294 \uc791\uc740 \ubcc0\ud654(perturbation)\ub97c \ucd94\uac00\ud558\uba74 \ubaa8\ub378 \uc131\ub2a5\uc774 \ud06c\uac8c \ub2ec\ub77c\uc9c8 \uc218 \uc788\ub2e4\ub294 \uc0ac\uc2e4\uc5d0 \ub180\ub784 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774\ubbf8\uc9c0 \ubd84\ub958\uae30\uc758 \uc608\uc81c\ub97c \ud1b5\ud574 \uc704 \ub0b4\uc6a9\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcfc \uac83\uc785\ub2c8\ub2e4.\n\ud2b9\ud788 \uc6b0\ub9ac\ub294 \uac00\uc7a5 \ub9ce\uc774 \uc0ac\uc6a9\ub418\ub294 \uacf5\uaca9 \ubc29\ubc95 \uc911 \ud558\ub098\uc778 FGSM (Fast Gradient Sign Attack)\uc744 \uc774\uc6a9\ud574 MNIST \ubd84\ub958\uae30\ub97c \uc18d\uc5ec \ubcfc \uac83\uc785\ub2c8\ub2e4.\n\n"
+        "\n\uc801\ub300\uc801 \uc608\uc81c \uc0dd\uc131(Adversarial Example Generation)\n====================================================\n\n**\uc800\uc790:** `Nathan Inkawhich <https://github.com/inkawhich>`__\n**\ubc88\uc5ed:** `BONGMO KIM <https://github.com/bongmo>`__\n\n\uc774 \uae00\uc744 \uc77d\uace0 \uc788\ub2e4\uba74, \uc5ec\ub7ec\ubd84\uc740 \uc774\ubbf8 \uba38\uc2e0\ub7ec\ub2dd \ubaa8\ub378\uc774 \uc5bc\ub9c8\ub098 \ud6a8\uacfc\uc801\uc778\uc9c0 \uadf8 \uc9c4\uac00\ub97c \uc54c\uace0 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n\uba38\uc2e0 \ub7ec\ub2dd \uc5f0\uad6c\ub294 ML(Machine Learning) \ubaa8\ub378\uc744 \ub354\uc6b1 \ube60\ub974\uace0 \uc815\ud655\ud558\uba70 \ud6a8\uc728\uc801\uc774\uac8c \ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \uc9c4\ud589 \ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \ubaa8\ub378\uc744 \uc18d\uc774\ub824\ud558\ub294 \uc801\uc5d0 \ub300\ud55c \ubcf4\uc548\uacfc \uacac\uace0\ud568\uc740 \ubaa8\ub378\uc744 \uc124\uacc4\ud558\uace0 \ud6c8\ub828\ud560 \ub54c \uc885\uc885 \uac04\uacfc\ub418\ub294 \ubd80\ubd84\uc785\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 ML \ubaa8\ub378\ub4e4\uc758 \ubcf4\uc548 \ucde8\uc57d\uc810\uc5d0 \ub300\ud55c \uc778\uc2dd\uc744 \ub192\uc774\uace0, \uc694\uc998 \ud654\ub450\uac00 \ub418\uace0\uc788\ub294 \uc801\ub300\uc801 \uba38\uc2e0 \ub7ec\ub2dd\uc5d0 \ub300\ud55c \ud1b5\ucc30\ub825\uc744 \uc81c\uacf5\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774\ubbf8\uc9c0\uc5d0 \ub208\uce58\ucc4c \uc218 \uc5c6\ub294 \uc791\uc740 \ubcc0\ud654(perturbation)\ub97c \ucd94\uac00\ud558\uba74 \ubaa8\ub378 \uc131\ub2a5\uc774 \ud06c\uac8c \ub2ec\ub77c\uc9c8 \uc218 \uc788\ub2e4\ub294 \uc0ac\uc2e4\uc5d0 \ub180\ub784 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc774\ubbf8\uc9c0 \ubd84\ub958\uae30\uc758 \uc608\uc81c\ub97c \ud1b5\ud574 \uc704 \ub0b4\uc6a9\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcfc \uac83\uc785\ub2c8\ub2e4.\n\ud2b9\ud788 \uc6b0\ub9ac\ub294 \uac00\uc7a5 \ub9ce\uc774 \uc0ac\uc6a9\ub418\ub294 \uacf5\uaca9 \ubc29\ubc95 \uc911 \ud558\ub098\uc778 FGSM (Fast Gradient Sign Attack)\uc744 \uc774\uc6a9\ud574 MNIST \ubd84\ub958\uae30\ub97c \uc18d\uc5ec \ubcfc \uac83\uc785\ub2c8\ub2e4.\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc704\ud611 \ubaa8\ub378\n------------\n\n\uc0c1\ud669\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ubc94\uc8fc\uc758 \uc801\ub300\uc801 \uacf5\uaca9\uc774 \uc788\ub294\ub370 \uac01\uac01 \ubaa9\ud45c\uac00 \ub2e4\ub974\uace0 \uacf5\uaca9\uc790\uac00 \uc54c\uace0 \uc788\ub294 \uc815\ubcf4\n\ub300\ud55c \uac00\uc815\ub3c4 \ub2e4\ub985\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ubcf4\ud1b5 \uac00\uc7a5 \uc911\uc694\ud55c \ubaa9\ud45c\ub294 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ucd5c\uc18c\ud55c\uc758 \uc791\uc740 \ubcc0\ud654\ub97c \n\ucd94\uac00\ud558\uc5ec \uc774\uac83\uc774 \uc758\ub3c4\uc801\uc73c\ub85c \uc798\ubabb \ubd84\ub958\ub418\uac8c \ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uacf5\uaca9\uc790\uac00 \uac00\uc9c0\uace0 \uc788\ub294 \uc815\ubcf4\uc5d0 \ub300\ud55c \n\uac00\uc815\uc5d0\ub294 \uc5ec\ub7ec \uc885\ub958\uac00 \uc788\ub294\ub370, \ubcf4\ud1b5 **\ud654\uc774\ud2b8\ubc15\uc2a4** \uc640 **\ube14\ub799\ubc15\uc2a4** \ub450 \uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4. \n*\ud654\uc774\ud2b8\ubc15\uc2a4* \uacf5\uaca9\uc740 \uacf5\uaca9\uc790\uac00 \ubaa8\ub378\uc5d0 \ub300\ud574 \uc544\ud0a4\ud14d\ucc98, \uc785\ub825, \ucd9c\ub825, \uac00\uc911\uce58\ub97c \ud3ec\ud568\ud55c \ubaa8\ub4e0 \uac83\uc744\n\uc54c\uace0 \uc788\uace0 \uc811\uadfc\ud560 \uc218 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. *\ube14\ub799\ubc15\uc2a4* \uacf5\uaca9\uc740 \uacf5\uaca9\uc790\uac00 \ubaa8\ub378\uc758 \uc785\ub825\uacfc \ucd9c\ub825\uc5d0 \n\ub300\ud574\uc11c\ub9cc \uc811\uadfc \uac00\ub2a5\ud558\uace0 \ubaa8\ub378\uc758 \uac00\uc911\uce58\uc640 \uc544\ud0a4\ud14d\ucc98\uc5d0 \uad00\ud55c \ub0b4\uc6a9\uc740 \ubaa8\ub978\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. \n\uacf5\uaca9\uc790\uc758 \ubaa9\ud45c\ub294 \uc624\ubd84\ub958 \ubc0f **\uc18c\uc2a4/\ud0c0\uac9f \uc624\ubd84\ub958** \ub97c \ud3ec\ud568\ud558\ub294 \uc5ec\ub7ec \uc720\ud615\uc774 \uc788\uc2b5\ub2c8\ub2e4. \n*\uc624\ubd84\ub958* \uc758 \ubaa9\ud45c\ub294 \uacf5\uaca9\uc790\uac00 \ucd9c\ub825\uc73c\ub85c \ub098\uc628 \ubd84\ub958 \uacb0\uacfc\uac00 \uc798\ubabb \ub418\ub3c4\ub85d \ud558\ub098 \uc0c8\ub85c\uc6b4 \ubd84\ub958 \uacb0\uacfc\uac00\n\uc5b4\ub5a4 \uac83\uc774 \ub098\uc624\ub294\uc9c0 \uc2e0\uacbd \uc4f0\uc9c0 \uc54a\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. *\uc18c\uc2a4/\ud0c0\uac9f \uc624\ubd84\ub958* \ub294 \uacf5\uaca9\uc790\uac00 \n\uc6d0\ub798 \ud2b9\uc815 \uc18c\uc2a4 \ud074\ub798\uc2a4\uc758 \uc774\ubbf8\uc9c0\ub97c \ub2e4\ub978 \ud2b9\uc815 \ub300\uc0c1 \ud074\ub798\uc2a4\ub85c \ubd84\ub958\ud558\ub3c4\ub85d \ubcc0\uacbd\ud558\ub824\uace0 \ud568\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.\n\n\n\uc774 \uacbd\uc6b0 FGSM \uacf5\uaca9\uc740 *\uc624\ubd84\ub958* \ub97c \ubaa9\ud45c\ub85c \ud558\ub294 \ud654\uc774\ud2b8 \ubc15\uc2a4 \uacf5\uaca9\uc785\ub2c8\ub2e4. \n\uc774\ub7f0 \ubc30\uacbd \uc815\ubcf4\ub97c \uac16\uace0 \uacf5\uaca9\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\ube60\ub978 \ubcc0\ud654\ub3c4 \ubd80\ud638 \uacf5\uaca9\n-------------------------\n\n\uacf5\uaca9 \ubc29\ubc95\uc5d0 \uc788\uc5b4 \ucd08\uae30 \ubc29\uc2dd\uc774\uba74\uc11c \uac00\uc7a5 \uc720\uba85\ud55c \ubc29\uc2dd\uc740 *\ube60\ub978 \ubcc0\ud654\ub3c4 \ubd80\ud638 \uacf5\uaca9 (FGSM)* \uc774\ub77c\uace0 \ud558\uba70 \n`\uc801\ub300\uc801 \uc608\uc81c\uc5d0 \ub300\ud55c \uc124\uba85\uacfc \ud65c\uc6a9 <https://arxiv.org/abs/1412.6572>`__ \uc5d0\uc11c \n\uc774\uc548 \uac13\ud3a0\ub85c\uc6b0\uac00 \uae30\uace0\ud558\uc600\uc2b5\ub2c8\ub2e4.\n\uc774 \uacf5\uaca9\ubc95\uc740 \ub180\ub78d\ub3c4\ub85d \uac15\ub825\ud558\uc9c0\ub9cc \uc9c1\uad00\uc801\uc785\ub2c8\ub2e4. \ud559\uc2b5 \ubc29\uc2dd, *\ubcc0\ud654\ub3c4(gradients)* \ub97c \ud65c\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uacf5\uaca9\ud558\ub3c4\ub85d \n\uc124\uacc4 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc544\uc774\ub514\uc5b4\ub294 \uac04\ub2e8\ud569\ub2c8\ub2e4. \uc5ed\uc804\ud30c \ubcc0\ud654\ub3c4\ub97c \uae30\ubc18\uc73c\ub85c \uac00\uc911\uce58\ub97c \uc870\uc815\ud558\uc5ec \uc190\uc2e4\uc744 \ucd5c\uc18c\ud654\ud558\uae30\ubcf4\ub2e4\ub294\n\uacf5\uaca9\uc774 \ub3d9\uc77c\ud55c \uc5ed\uc804\ud30c \ubcc0\ud654\ub3c4\ub97c \uae30\ubc18\uc73c\ub85c *\uc190\uc2e4\uc744 \ucd5c\ub300\ud654\ud558\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \uc785\ub825 \ub370\uc774\ud130\ub97c \uc870\uc815* \ud569\ub2c8\ub2e4. \n\ub2e4\uc2dc \ub9d0\ud574 \uacf5\uaca9\uc740 \uc785\ub825 \ub370\uc774\ud130\uc5d0\uc11c \uacc4\uc0b0\ub41c \uc190\uc2e4 \ubcc0\ud654\ub3c4\ub97c \uc0ac\uc6a9\ud558\uace0 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc870\uc815\ud558\uc5ec \uc190\uc2e4\uc774 \ucd5c\ub300\uac00 \ub418\uac8c \ud569\ub2c8\ub2e4.\n\n"
+        "\uc704\ud611 \ubaa8\ub378\n------------\n\n\uc0c1\ud669\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ubc94\uc8fc\uc758 \uc801\ub300\uc801 \uacf5\uaca9\uc774 \uc788\ub294\ub370 \uac01\uac01 \ubaa9\ud45c\uac00 \ub2e4\ub974\uace0 \uacf5\uaca9\uc790\uac00 \uc54c\uace0 \uc788\ub294 \uc815\ubcf4\n\ub300\ud55c \uac00\uc815\ub3c4 \ub2e4\ub985\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \ubcf4\ud1b5 \uac00\uc7a5 \uc911\uc694\ud55c \ubaa9\ud45c\ub294 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ucd5c\uc18c\ud55c\uc758 \uc791\uc740 \ubcc0\ud654\ub97c\n\ucd94\uac00\ud558\uc5ec \uc774\uac83\uc774 \uc758\ub3c4\uc801\uc73c\ub85c \uc798\ubabb \ubd84\ub958\ub418\uac8c \ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uacf5\uaca9\uc790\uac00 \uac00\uc9c0\uace0 \uc788\ub294 \uc815\ubcf4\uc5d0 \ub300\ud55c\n\uac00\uc815\uc5d0\ub294 \uc5ec\ub7ec \uc885\ub958\uac00 \uc788\ub294\ub370, \ubcf4\ud1b5 **\ud654\uc774\ud2b8\ubc15\uc2a4** \uc640 **\ube14\ub799\ubc15\uc2a4** \ub450 \uac00\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n*\ud654\uc774\ud2b8\ubc15\uc2a4* \uacf5\uaca9\uc740 \uacf5\uaca9\uc790\uac00 \ubaa8\ub378\uc5d0 \ub300\ud574 \uc544\ud0a4\ud14d\ucc98, \uc785\ub825, \ucd9c\ub825, \uac00\uc911\uce58\ub97c \ud3ec\ud568\ud55c \ubaa8\ub4e0 \uac83\uc744\n\uc54c\uace0 \uc788\uace0 \uc811\uadfc\ud560 \uc218 \uc788\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4. *\ube14\ub799\ubc15\uc2a4* \uacf5\uaca9\uc740 \uacf5\uaca9\uc790\uac00 \ubaa8\ub378\uc758 \uc785\ub825\uacfc \ucd9c\ub825\uc5d0\n\ub300\ud574\uc11c\ub9cc \uc811\uadfc \uac00\ub2a5\ud558\uace0 \ubaa8\ub378\uc758 \uac00\uc911\uce58\uc640 \uc544\ud0a4\ud14d\ucc98\uc5d0 \uad00\ud55c \ub0b4\uc6a9\uc740 \ubaa8\ub978\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.\n\uacf5\uaca9\uc790\uc758 \ubaa9\ud45c\ub294 \uc624\ubd84\ub958 \ubc0f **\uc18c\uc2a4/\ud0c0\uac9f \uc624\ubd84\ub958** \ub97c \ud3ec\ud568\ud558\ub294 \uc5ec\ub7ec \uc720\ud615\uc774 \uc788\uc2b5\ub2c8\ub2e4.\n*\uc624\ubd84\ub958* \uc758 \ubaa9\ud45c\ub294 \uacf5\uaca9\uc790\uac00 \ucd9c\ub825\uc73c\ub85c \ub098\uc628 \ubd84\ub958 \uacb0\uacfc\uac00 \uc798\ubabb \ub418\ub3c4\ub85d \ud558\ub098 \uc0c8\ub85c\uc6b4 \ubd84\ub958 \uacb0\uacfc\uac00\n\uc5b4\ub5a4 \uac83\uc774 \ub098\uc624\ub294\uc9c0 \uc2e0\uacbd \uc4f0\uc9c0 \uc54a\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4. *\uc18c\uc2a4/\ud0c0\uac9f \uc624\ubd84\ub958* \ub294 \uacf5\uaca9\uc790\uac00\n\uc6d0\ub798 \ud2b9\uc815 \uc18c\uc2a4 \ud074\ub798\uc2a4\uc758 \uc774\ubbf8\uc9c0\ub97c \ub2e4\ub978 \ud2b9\uc815 \ub300\uc0c1 \ud074\ub798\uc2a4\ub85c \ubd84\ub958\ud558\ub3c4\ub85d \ubcc0\uacbd\ud558\ub824\uace0 \ud568\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.\n\n\n\uc774 \uacbd\uc6b0 FGSM \uacf5\uaca9\uc740 *\uc624\ubd84\ub958* \ub97c \ubaa9\ud45c\ub85c \ud558\ub294 \ud654\uc774\ud2b8 \ubc15\uc2a4 \uacf5\uaca9\uc785\ub2c8\ub2e4.\n\uc774\ub7f0 \ubc30\uacbd \uc815\ubcf4\ub97c \uac16\uace0 \uacf5\uaca9\uc5d0 \ub300\ud574 \uc790\uc138\ud788 \uc54c\uc544 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\ube60\ub978 \ubcc0\ud654\ub3c4 \ubd80\ud638 \uacf5\uaca9\n-------------------------\n\n\uacf5\uaca9 \ubc29\ubc95\uc5d0 \uc788\uc5b4 \ucd08\uae30 \ubc29\uc2dd\uc774\uba74\uc11c \uac00\uc7a5 \uc720\uba85\ud55c \ubc29\uc2dd\uc740 *\ube60\ub978 \ubcc0\ud654\ub3c4 \ubd80\ud638 \uacf5\uaca9 (FGSM)* \uc774\ub77c\uace0 \ud558\uba70\n`\uc801\ub300\uc801 \uc608\uc81c\uc5d0 \ub300\ud55c \uc124\uba85\uacfc \ud65c\uc6a9 <https://arxiv.org/abs/1412.6572>`__ \uc5d0\uc11c\n\uc774\uc548 \uac13\ud3a0\ub85c\uc6b0\uac00 \uae30\uace0\ud558\uc600\uc2b5\ub2c8\ub2e4.\n\uc774 \uacf5\uaca9\ubc95\uc740 \ub180\ub78d\ub3c4\ub85d \uac15\ub825\ud558\uc9c0\ub9cc \uc9c1\uad00\uc801\uc785\ub2c8\ub2e4. \ud559\uc2b5 \ubc29\uc2dd, *\ubcc0\ud654\ub3c4(gradients)* \ub97c \ud65c\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uacf5\uaca9\ud558\ub3c4\ub85d\n\uc124\uacc4 \ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc544\uc774\ub514\uc5b4\ub294 \uac04\ub2e8\ud569\ub2c8\ub2e4. \uc5ed\uc804\ud30c \ubcc0\ud654\ub3c4\ub97c \uae30\ubc18\uc73c\ub85c \uac00\uc911\uce58\ub97c \uc870\uc815\ud558\uc5ec \uc190\uc2e4\uc744 \ucd5c\uc18c\ud654\ud558\uae30\ubcf4\ub2e4\ub294\n\uacf5\uaca9\uc774 \ub3d9\uc77c\ud55c \uc5ed\uc804\ud30c \ubcc0\ud654\ub3c4\ub97c \uae30\ubc18\uc73c\ub85c *\uc190\uc2e4\uc744 \ucd5c\ub300\ud654\ud558\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \uc785\ub825 \ub370\uc774\ud130\ub97c \uc870\uc815* \ud569\ub2c8\ub2e4.\n\ub2e4\uc2dc \ub9d0\ud574 \uacf5\uaca9\uc740 \uc785\ub825 \ub370\uc774\ud130\uc5d0\uc11c \uacc4\uc0b0\ub41c \uc190\uc2e4 \ubcc0\ud654\ub3c4\ub97c \uc0ac\uc6a9\ud558\uace0 \uc785\ub825 \ub370\uc774\ud130\ub97c \uc870\uc815\ud558\uc5ec \uc190\uc2e4\uc774 \ucd5c\ub300\uac00 \ub418\uac8c \ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -33,14 +33,14 @@
       },
       "outputs": [],
       "source": [
-        "# \n# \ucf54\ub4dc\ub85c \ub118\uc5b4\uac00\uae30 \uc804\uc5d0 \uc720\uba85\ud55c `FGSM <https://arxiv.org/abs/1412.6572>`__ \ud310\ub2e4 \uc608\uc81c\ub97c \n# \ubcf4\uace0 \uba87 \uac00\uc9c0 \ud45c\uae30\ubc95\uc744 \uc815\ub9ac\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n#\n# .. \uadf8\ub9bc:: /_static/img/fgsm_panda_image.png\n#    :alt: fgsm_panda_image\n#\n# \uadf8\ub9bc\uc73c\ub85c\ubd80\ud130, :math:`\\mathbf{x}` \ub294 \uc6d0\ubcf8 \uc785\ub825 \uc774\ubbf8\uc9c0\uac00 \"\ud310\ub2e4\" \ub85c \uc62c\ubc14\ub974\uac8c \ubd84\ub958\ub41c \uac83\uc744 \uc758\ubbf8\ud558\uace0,\n# :math:`y` \ub294 :math:`\\mathbf{x}` \ub97c \uc704\ud55c \uc815\ub2f5 \ub77c\ubca8\uc774\uba70, :math:`\\mathbf{\\theta}` \ub294 \ubaa8\ub378\uc758\n# \ud30c\ub77c\ubbf8\ud130\ub97c, :math:`J(\\mathbf{\\theta}, \\mathbf{x}, y)` \ub294 \ub124\ud2b8\uc6cc\ud06c\uc758 \ud559\uc2b5\uc744 \uc704\ud574\uc11c \uc0ac\uc6a9\ub418\ub294 \uc190\uc2e4\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n# \uacf5\uaca9\uc740 :math:`\\nabla_{x} J(\\mathbf{\\theta}, \\mathbf{x}, y)` \uacc4\uc0b0\uc744 \uc704\ud574 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ubcc0\ud654\ub3c4\ub97c \uc5ed\uc804\ud30c\ud569\ub2c8\ub2e4.\n# \uadf8\ub7ec\uace0 \ub098\uc11c, \ubcc0\ud654\ub3c4\ub294 \uc190\uc2e4 \uac12\uc774 \ucd5c\ub300\ud654\ub418\ub294 \ubc29\ud5a5\uc73c\ub85c (\uc608\ub97c \ub4e4\uba74, :math:`sign(\\nabla_{x} J(\\mathbf{\\theta}, \\mathbf{x}, y))` ) \n# \uc791\uc740 \uc2a4\ud15d(step) \ub9cc\ud07c (\uadf8\ub9bc\uc5d0\uc11c\ub294 :math:`\\epsilon` \ud639\uc740 :math:`0.007`) \uc785\ub825 \ub370\uc774\ud130\uc5d0 \uc801\uc6a9\ub429\ub2c8\ub2e4.\n# \uacb0\uacfc\ub85c \ub098\uc624\ub294 \uc791\uc740 \ubcc0\ud654\ub41c \uc774\ubbf8\uc9c0( :math:`x'` )\ub294 \ud0c0\uac9f \ub124\ud2b8\uc6cc\ud06c\uc5d0 \uc758\ud574 \"\uae34\ud314\uc6d0\uc22d\uc774\"\ub85c *\uc624\ubd84\ub958* \ub418\ub098 \uc5ec\uc804\ud788 \uc721\uc548\uc73c\ub85c\ub294\n# \ubd84\uba85\ud788 \"\ud310\ub2e4\" \uc785\ub2c8\ub2e4.\n# \n# \uc774\uc81c \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ub3d9\uae30\uac00 \uba85\ud655\ud574\uc9c0\uae38 \ubc14\ub77c\uba70, \uad6c\ud604\uc73c\ub85c \ub118\uc5b4\uac00 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n# \n\nfrom __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nimport numpy as np\nimport matplotlib.pyplot as plt"
+        "#\n# \ucf54\ub4dc\ub85c \ub118\uc5b4\uac00\uae30 \uc804\uc5d0 \uc720\uba85\ud55c `FGSM <https://arxiv.org/abs/1412.6572>`__ \ud310\ub2e4 \uc608\uc81c\ub97c\n# \ubcf4\uace0 \uba87 \uac00\uc9c0 \ud45c\uae30\ubc95\uc744 \uc815\ub9ac\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n#\n# .. \uadf8\ub9bc:: /_static/img/fgsm_panda_image.png\n#    :alt: fgsm_panda_image\n#\n# \uadf8\ub9bc\uc73c\ub85c\ubd80\ud130, :math:`\\mathbf{x}` \ub294 \uc6d0\ubcf8 \uc785\ub825 \uc774\ubbf8\uc9c0\uac00 \"\ud310\ub2e4\" \ub85c \uc62c\ubc14\ub974\uac8c \ubd84\ub958\ub41c \uac83\uc744 \uc758\ubbf8\ud558\uace0,\n# :math:`y` \ub294 :math:`\\mathbf{x}` \ub97c \uc704\ud55c \uc815\ub2f5 \ub77c\ubca8\uc774\uba70, :math:`\\mathbf{\\theta}` \ub294 \ubaa8\ub378\uc758\n# \ud30c\ub77c\ubbf8\ud130\ub97c, :math:`J(\\mathbf{\\theta}, \\mathbf{x}, y)` \ub294 \ub124\ud2b8\uc6cc\ud06c\uc758 \ud559\uc2b5\uc744 \uc704\ud574\uc11c \uc0ac\uc6a9\ub418\ub294 \uc190\uc2e4\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n# \uacf5\uaca9\uc740 :math:`\\nabla_{x} J(\\mathbf{\\theta}, \\mathbf{x}, y)` \uacc4\uc0b0\uc744 \uc704\ud574 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ubcc0\ud654\ub3c4\ub97c \uc5ed\uc804\ud30c\ud569\ub2c8\ub2e4.\n# \uadf8\ub7ec\uace0 \ub098\uc11c, \ubcc0\ud654\ub3c4\ub294 \uc190\uc2e4 \uac12\uc774 \ucd5c\ub300\ud654\ub418\ub294 \ubc29\ud5a5\uc73c\ub85c (\uc608\ub97c \ub4e4\uba74, :math:`sign(\\nabla_{x} J(\\mathbf{\\theta}, \\mathbf{x}, y))` )\n# \uc791\uc740 \uc2a4\ud15d(step) \ub9cc\ud07c (\uadf8\ub9bc\uc5d0\uc11c\ub294 :math:`\\epsilon` \ud639\uc740 :math:`0.007`) \uc785\ub825 \ub370\uc774\ud130\uc5d0 \uc801\uc6a9\ub429\ub2c8\ub2e4.\n# \uacb0\uacfc\ub85c \ub098\uc624\ub294 \uc791\uc740 \ubcc0\ud654\ub41c \uc774\ubbf8\uc9c0( :math:`x'` )\ub294 \ud0c0\uac9f \ub124\ud2b8\uc6cc\ud06c\uc5d0 \uc758\ud574 \"\uae34\ud314\uc6d0\uc22d\uc774\"\ub85c *\uc624\ubd84\ub958* \ub418\ub098 \uc5ec\uc804\ud788 \uc721\uc548\uc73c\ub85c\ub294\n# \ubd84\uba85\ud788 \"\ud310\ub2e4\" \uc785\ub2c8\ub2e4.\n#\n# \uc774\uc81c \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ub3d9\uae30\uac00 \uba85\ud655\ud574\uc9c0\uae38 \ubc14\ub77c\uba70, \uad6c\ud604\uc73c\ub85c \ub118\uc5b4\uac00 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n#\n\nfrom __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nimport numpy as np\nimport matplotlib.pyplot as plt"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uad6c\ud604\n--------------\n\n\uc774 \uc139\uc158\uc5d0\uc11c\ub294 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \uc785\ub825 \ub9e4\uac1c \ubcc0\uc218\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uace0 \uacf5\uaca9\uc911\uc778 \ubaa8\ub378\uc744\n\uc815\uc758\ud55c \ub2e4\uc74c \uacf5\uaca9\uc744 \ucf54\ub529\ud558\uace0 \uc77c\ubd80 \ud14c\uc2a4\ud2b8\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4.\n\n\uc785\ub825\n~~~~~~\n\n\uc774 \ud559\uc2b5\uc11c\uc5d0\ub294 \uc785\ub825\uc774 3 \uac1c\uc774\uba70 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\uc758\ub429\ub2c8\ub2e4:\n\n- **epsilons** - \uc2e4\ud589\uc5d0 \uc0ac\uc6a9\ud560 \uc5e1\uc2e4\ub860\uc758 \ub9ac\uc2a4\ud2b8\uc785\ub2c8\ub2e4. \uc5e1\uc2e4\ub860 0\uc758 \uac12\uc740 \uc6d0\ub798 \ud14c\uc2a4\ud2b8 \uc14b\uc758 \ubaa8\ub378 \uc131\ub2a5\uc744\n  \ub098\ud0c0\ub0b4\ubbc0\ub85c \ubaa9\ub85d\uc5d0 \uc720\uc9c0\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ub610\ud55c \uc9c1\uad00\uc801\uc73c\ub85c \uc5e1\uc2e4\ub860\uc774 \ud074\uc218\ub85d \uc791\uc740 \ubcc0\ud654\uac00 \ub354 \ub208\uc5d0 \ub744\uc9c0\ub9cc\n  \ubaa8\ub378 \uc815\ud655\ub3c4\ub97c \uc800\ud558 \uc2dc\ud0a4\ub294 \uce21\uba74\uc5d0\uc11c \ub354 \ud6a8\uacfc\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c \ub370\uc774\ud130\uc758 \ubc94\uc704\ub294 0-1 \uc774\uae30 \ub54c\ubb38\uc5d0 \n  \uc5e1\uc2e4\ub860\uc758 \uac12\uc740 1\uc744 \ucd08\uacfc\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n- **pretrained_model** - `pytorch/examples/mnist <https://github.com/pytorch/examples/tree/master/mnist>`__ \n   \ub97c \ud1b5\ud574 \ubbf8\ub9ac \ud559\uc2b5\ub41c MNIST \ubaa8\ub378\uc758 \uacbd\ub85c. \n   \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uac04\ud3b8\ud558\uac8c \ud558\ub824\uba74 `\uc5ec\uae30 <https://drive.google.com/drive/folders/1fn83DF14tWmit0RTKWRhPq5uVXt73e0h?usp=sharing>`__ \uc5d0\uc11c \ubbf8\ub9ac \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uc138\uc694.\n\n-  **use_cuda** - CUDA \ub97c \uc0ac\uc6a9\ud560\uc9c0 \ub9d0\uc9c0 \uc815\ud558\ub294 \uc774\uc9c4 \ud50c\ub798\uadf8. \n   \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 CPU \uc2dc\uac04\uc774 \uc624\ub798 \uac78\ub9ac\uc9c0 \uc54a\uc73c\ubbc0\ub85c CUDA\ub97c \uc9c0\uc6d0\ud558\ub294 GPU \uc758 \uc5ec\ubd80\ub294 \uc911\uc694\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n\n"
+        "\uad6c\ud604\n--------------\n\n\uc774 \uc139\uc158\uc5d0\uc11c\ub294 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \uc785\ub825 \ub9e4\uac1c \ubcc0\uc218\uc5d0 \ub300\ud574 \uc124\uba85\ud558\uace0 \uacf5\uaca9\uc911\uc778 \ubaa8\ub378\uc744\n\uc815\uc758\ud55c \ub2e4\uc74c \uacf5\uaca9\uc744 \ucf54\ub529\ud558\uace0 \uc77c\ubd80 \ud14c\uc2a4\ud2b8\ub97c \uc2e4\ud589\ud569\ub2c8\ub2e4.\n\n\uc785\ub825\n~~~~~~\n\n\uc774 \ud559\uc2b5\uc11c\uc5d0\ub294 \uc785\ub825\uc774 3 \uac1c\uc774\uba70 \ub2e4\uc74c\uacfc \uac19\uc774 \uc815\uc758\ub429\ub2c8\ub2e4:\n\n- **epsilons** - \uc2e4\ud589\uc5d0 \uc0ac\uc6a9\ud560 \uc5e1\uc2e4\ub860\uc758 \ub9ac\uc2a4\ud2b8\uc785\ub2c8\ub2e4. \uc5e1\uc2e4\ub860 0\uc758 \uac12\uc740 \uc6d0\ub798 \ud14c\uc2a4\ud2b8 \uc14b\uc758 \ubaa8\ub378 \uc131\ub2a5\uc744\n  \ub098\ud0c0\ub0b4\ubbc0\ub85c \ubaa9\ub85d\uc5d0 \uc720\uc9c0\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \ub610\ud55c \uc9c1\uad00\uc801\uc73c\ub85c \uc5e1\uc2e4\ub860\uc774 \ud074\uc218\ub85d \uc791\uc740 \ubcc0\ud654\uac00 \ub354 \ub208\uc5d0 \ub744\uc9c0\ub9cc\n  \ubaa8\ub378 \uc815\ud655\ub3c4\ub97c \uc800\ud558 \uc2dc\ud0a4\ub294 \uce21\uba74\uc5d0\uc11c \ub354 \ud6a8\uacfc\uac00 \uc788\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c \ub370\uc774\ud130\uc758 \ubc94\uc704\ub294 0-1 \uc774\uae30 \ub54c\ubb38\uc5d0\n  \uc5e1\uc2e4\ub860\uc758 \uac12\uc740 1\uc744 \ucd08\uacfc\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n- **pretrained_model** - `pytorch/examples/mnist <https://github.com/pytorch/examples/tree/master/mnist>`__\n   \ub97c \ud1b5\ud574 \ubbf8\ub9ac \ud559\uc2b5\ub41c MNIST \ubaa8\ub378\uc758 \uacbd\ub85c.\n   \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uac04\ud3b8\ud558\uac8c \ud558\ub824\uba74 `\uc5ec\uae30 <https://drive.google.com/drive/folders/1fn83DF14tWmit0RTKWRhPq5uVXt73e0h?usp=sharing>`__ \uc5d0\uc11c \ubbf8\ub9ac \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ub2e4\uc6b4\ub85c\ub4dc\ud558\uc138\uc694.\n\n-  **use_cuda** - CUDA \ub97c \uc0ac\uc6a9\ud560\uc9c0 \ub9d0\uc9c0 \uc815\ud558\ub294 \uc774\uc9c4 \ud50c\ub798\uadf8.\n   \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 CPU \uc2dc\uac04\uc774 \uc624\ub798 \uac78\ub9ac\uc9c0 \uc54a\uc73c\ubbc0\ub85c CUDA\ub97c \uc9c0\uc6d0\ud558\ub294 GPU \uc758 \uc5ec\ubd80\ub294 \uc911\uc694\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -58,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uacf5\uaca9\uc744 \ubc1b\ub294 \ubaa8\ub378\n~~~~~~~~~~~~~~~~~~\n\n\uc55e\uc11c \ub9d0\ud55c\ub300\ub85c, \uacf5\uaca9\uc744 \ubc1b\ub294 \ubaa8\ub378\uc740 `pytorch/examples/mnist <https://github.com/pytorch/examples/tree/master/mnist>`__\n\uc640 \ub3d9\uc77c\ud55c MNIST \ubaa8\ub378\uc785\ub2c8\ub2e4. \ubcf8\uc778\uc758 MNIST \ubaa8\ub378\uc744 \ud559\uc2b5 \ubc0f \uc800\uc7a5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ud558\uac70\ub098 \uc81c\uacf5\ub41c \ubaa8\ub378\uc744 \ub2e4\uc6b4\ub85c\ub4dc \ud574 \uc0ac\uc6a9\ud558\ub294 \uc2dd\uc73c\ub85c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc5ec\uae30\uc11c *Net* \uc815\uc758 \ubc0f \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ub85c\ub354\ub294 MNIST \uc608\uc81c\uc5d0\uc11c \ubcf5\uc0ac \ud558\uc600\uc2b5\ub2c8\ub2e4. \n\uc774 \uc139\uc158\uc758 \ubaa9\uc801\uc740 \ubaa8\ub378\uacfc \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc815\uc758\ud55c \ub2e4\uc74c, \ubaa8\ub378\uc744 \ucd08\uae30\ud654\ud558\uace0 \ubbf8\ub9ac \ud559\uc2b5\ub41c \uac00\uc911\uce58\ub97c \uc77d\uc5b4\uc624\ub294 \uac83\uc785\ub2c8\ub2e4.\n\n\n"
+        "\uacf5\uaca9\uc744 \ubc1b\ub294 \ubaa8\ub378\n~~~~~~~~~~~~~~~~~~\n\n\uc55e\uc11c \ub9d0\ud55c\ub300\ub85c, \uacf5\uaca9\uc744 \ubc1b\ub294 \ubaa8\ub378\uc740 `pytorch/examples/mnist <https://github.com/pytorch/examples/tree/master/mnist>`__\n\uc640 \ub3d9\uc77c\ud55c MNIST \ubaa8\ub378\uc785\ub2c8\ub2e4. \ubcf8\uc778\uc758 MNIST \ubaa8\ub378\uc744 \ud559\uc2b5 \ubc0f \uc800\uc7a5\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \ud558\uac70\ub098 \uc81c\uacf5\ub41c \ubaa8\ub378\uc744 \ub2e4\uc6b4\ub85c\ub4dc \ud574 \uc0ac\uc6a9\ud558\ub294 \uc2dd\uc73c\ub85c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc5ec\uae30\uc11c *Net* \uc815\uc758 \ubc0f \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ub85c\ub354\ub294 MNIST \uc608\uc81c\uc5d0\uc11c \ubcf5\uc0ac \ud558\uc600\uc2b5\ub2c8\ub2e4.\n\uc774 \uc139\uc158\uc758 \ubaa9\uc801\uc740 \ubaa8\ub378\uacfc \ub370\uc774\ud130 \ub85c\ub354\ub97c \uc815\uc758\ud55c \ub2e4\uc74c, \ubaa8\ub378\uc744 \ucd08\uae30\ud654\ud558\uace0 \ubbf8\ub9ac \ud559\uc2b5\ub41c \uac00\uc911\uce58\ub97c \uc77d\uc5b4\uc624\ub294 \uac83\uc785\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -69,14 +69,14 @@
       },
       "outputs": [],
       "source": [
-        "# LeNet \ubaa8\ub378 \uc815\uc758\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return F.log_softmax(x, dim=1)\n\n# MNIST \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uacfc \ub370\uc774\ud130\ub85c\ub354 \uc120\uc5b8\ntest_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('../data', train=False, download=True, transform=transforms.Compose([\n            transforms.ToTensor(),\n            ])), \n        batch_size=1, shuffle=True)\n\n# \uc5b4\ub5a4 \ub514\ubc14\uc774\uc2a4\ub97c \uc0ac\uc6a9\ud560\uc9c0 \uc815\uc758\nprint(\"CUDA Available: \",torch.cuda.is_available())\ndevice = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\ud558\uae30\nmodel = Net().to(device)\n\n# \ubbf8\ub9ac \ud559\uc2b5\ub41c \ubaa8\ub378 \uc77d\uc5b4\uc624\uae30\nmodel.load_state_dict(torch.load(pretrained_model, map_location='cpu'))\n\n# \ubaa8\ub378\uc744 \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815\ud558\uae30. \ub4dc\ub86d\uc544\uc6c3 \ub808\uc774\uc5b4\ub4e4\uc744 \uc704\ud574 \uc0ac\uc6a9\ub428\nmodel.eval()"
+        "# LeNet \ubaa8\ub378 \uc815\uc758\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return F.log_softmax(x, dim=1)\n\n# MNIST \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b\uacfc \ub370\uc774\ud130\ub85c\ub354 \uc120\uc5b8\ntest_loader = torch.utils.data.DataLoader(\n    datasets.MNIST('../data', train=False, download=True, transform=transforms.Compose([\n            transforms.ToTensor(),\n            ])),\n        batch_size=1, shuffle=True)\n\n# \uc5b4\ub5a4 \ub514\ubc14\uc774\uc2a4\ub97c \uc0ac\uc6a9\ud560\uc9c0 \uc815\uc758\nprint(\"CUDA Available: \",torch.cuda.is_available())\ndevice = torch.device(\"cuda\" if (use_cuda and torch.cuda.is_available()) else \"cpu\")\n\n# \ubaa8\ub378 \ucd08\uae30\ud654\ud558\uae30\nmodel = Net().to(device)\n\n# \ubbf8\ub9ac \ud559\uc2b5\ub41c \ubaa8\ub378 \uc77d\uc5b4\uc624\uae30\nmodel.load_state_dict(torch.load(pretrained_model, map_location='cpu'))\n\n# \ubaa8\ub378\uc744 \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc124\uc815\ud558\uae30. \ub4dc\ub86d\uc544\uc6c3 \ub808\uc774\uc5b4\ub4e4\uc744 \uc704\ud574 \uc0ac\uc6a9\ub428\nmodel.eval()"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "FGSM \uacf5\uaca9\n~~~~~~~~~~~\n\n\uc774\uc81c \uc6d0\ub798 \uc785\ub825\uc744 \uad50\ub780\uc2dc\ucf1c \uc801\ub300\uc801\uc778 \uc608\ub97c \ub9cc\ub4dc\ub294 \ud568\uc218\ub97c \uc815\uc758 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \n``fgsm_attack`` \ud568\uc218\ub294 \uc785\ub825 \ud30c\ub77c\ubbf8\ud130\ub85c 3\uac00\uc9c0\ub97c \uac00\uc9d1\ub2c8\ub2e4. \uccab\ubc88\uc9f8\ub294 \uc6d0\ubcf8 *\uc774\ubbf8\uc9c0* ( $x$ ), \n\ub450\ubc88\uc9f8\ub294 *\uc5e1\uc2e4\ub860* \uc73c\ub85c \ud53d\uc140 \ub2e8\uc704\uc758 \uc791\uc740 \ubcc0\ud654\ub97c \uc8fc\ub294 \uac12\uc785\ub2c8\ub2e4 ( $\\epsilon$ ). \n\ub9c8\uc9c0\ub9c9\uc740 *data_grad* \ub85c \uc785\ub825 \uc601\uc0c1 ( $\\nabla_{x} J(\\mathbf{\\theta}, \\mathbf{x}, y)$ ) \uc5d0 \ub300\ud55c \ubcc0\ud654\ub3c4 \uc190\uc2e4 \uac12\uc785\ub2c8\ub2e4. \n\uc544\ub798 \uc2dd\uc5d0 \ub530\ub978 \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.\n\n\\begin{align}perturbed\\_image = image + epsilon*sign(data\\_grad) = x + \\epsilon * sign(\\nabla_{x} J(\\mathbf{\\theta}, \\mathbf{x}, y))\\end{align}\n\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \ub370\uc774\ud130\uc758 \uc6d0\ub798 \ubc94\uc704\ub97c \uc720\uc9c0\ud558\uae30 \uc704\ud574, \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\uac00 $[0,1]$ \ubc94\uc704\ub85c \uc798\ub9bd\ub2c8\ub2e4.\n\n\n"
+        "FGSM \uacf5\uaca9\n~~~~~~~~~~~\n\n\uc774\uc81c \uc6d0\ub798 \uc785\ub825\uc744 \uad50\ub780\uc2dc\ucf1c \uc801\ub300\uc801\uc778 \uc608\ub97c \ub9cc\ub4dc\ub294 \ud568\uc218\ub97c \uc815\uc758 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n``fgsm_attack`` \ud568\uc218\ub294 \uc785\ub825 \ud30c\ub77c\ubbf8\ud130\ub85c 3\uac00\uc9c0\ub97c \uac00\uc9d1\ub2c8\ub2e4. \uccab\ubc88\uc9f8\ub294 \uc6d0\ubcf8 *\uc774\ubbf8\uc9c0* ( $x$ ),\n\ub450\ubc88\uc9f8\ub294 *\uc5e1\uc2e4\ub860* \uc73c\ub85c \ud53d\uc140 \ub2e8\uc704\uc758 \uc791\uc740 \ubcc0\ud654\ub97c \uc8fc\ub294 \uac12\uc785\ub2c8\ub2e4 ( $\\epsilon$ ).\n\ub9c8\uc9c0\ub9c9\uc740 *data_grad* \ub85c \uc785\ub825 \uc601\uc0c1 ( $\\nabla_{x} J(\\mathbf{\\theta}, \\mathbf{x}, y)$ ) \uc5d0 \ub300\ud55c \ubcc0\ud654\ub3c4 \uc190\uc2e4 \uac12\uc785\ub2c8\ub2e4.\n\uc544\ub798 \uc2dd\uc5d0 \ub530\ub978 \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.\n\n\\begin{align}perturbed\\_image = image + epsilon*sign(data\\_grad) = x + \\epsilon * sign(\\nabla_{x} J(\\mathbf{\\theta}, \\mathbf{x}, y))\\end{align}\n\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \ub370\uc774\ud130\uc758 \uc6d0\ub798 \ubc94\uc704\ub97c \uc720\uc9c0\ud558\uae30 \uc704\ud574, \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\uac00 $[0,1]$ \ubc94\uc704\ub85c \uc798\ub9bd\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -94,7 +94,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ud14c\uc2a4\ud305 \ud568\uc218\n~~~~~~~~~~~~~~~~\n\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ud575\uc2ec \uacb0\uacfc\ub294 ``\ud14c\uc2a4\ud2b8`` \ud568\uc218\uc5d0\uc11c \uc624\uac8c \ub429\ub2c8\ub2e4. \n\uc774 \ud14c\uc2a4\ud2b8 \uae30\ub2a5\uc744 \ud638\ucd9c \ud560 \ub54c\ub9c8\ub2e4 MNIST \ud14c\uc2a4\ud2b8 \uc14b\uc5d0\uc11c \uc804\uccb4 \ud14c\uc2a4\ud2b8 \ub2e8\uacc4\ub97c \uc218\ud589\ud558\uace0 \ucd5c\uc885 \uc815\ud655\ub3c4\ub97c \ubcf4\uace0\ud569\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \uc774 \ud568\uc218\uc5d0\ub294 *\uc5e1\uc2e4\ub860* \uc785\ub825\ub3c4 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 ``\ud14c\uc2a4\ud2b8``` \ud568\uc218\uac00 $\\epsilon$ \ud06c\uae30\uc5d0 \ub530\ub77c \uacf5\uaca9\uc790\uc758 \uacf5\uaca9\uc744 \ubc1b\ub294 \ubaa8\ub378\uc758 \n\uc815\ud655\ub3c4\uc744 \ubcf4\uace0\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \ub354 \uad6c\uccb4\uc801\uc73c\ub85c \ubcf4\uba74 \ud14c\uc2a4\ud2b8 \uc14b\uc758 \uac01\uac01\uc758 \uc0d8\ud50c\uc5d0\uc11c \ud14c\uc2a4\ud2b8 \ud568\uc218\ub294 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc190\uc2e4 \ubcc0\ud654\ub3c4( $data\\_grad$ )\ub97c \uacc4\uc0b0\ud558\uace0, \n ``FGSM \uacf5\uaca9``( $perturbed\\_data$ )\uc744 \ubc1b\uc740 \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4\uace0 \ub098\uc11c \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\uac00 \uc801\ub300\uc801\uc778\uc9c0 \ud655\uc778\uc744 \ud569\ub2c8\ub2e4. \n\ucd94\uac00\ub85c \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \ud14c\uc2a4\ud2b8\ud558\uae30 \uc704\ud574\uc11c \ud14c\uc2a4\ud2b8 \ud568\uc218\ub294 \ub098\uc911\uc5d0 \uc2dc\uac01\ud654\ud558\uc5ec \ubcfc \uc218 \uc788\ub3c4\ub85d \uc131\uacf5\uc801\uc73c\ub85c \uc5bb\uc740 \uc801\ub300\uc801 \uc774\ubbf8\uc9c0\ub97c \uc800\uc7a5\ud558\uace0 \ubc18\ud658\ud569\ub2c8\ub2e4.\n\n\n"
+        "\ud14c\uc2a4\ud305 \ud568\uc218\n~~~~~~~~~~~~~~~~\n\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ud575\uc2ec \uacb0\uacfc\ub294 ``\ud14c\uc2a4\ud2b8`` \ud568\uc218\uc5d0\uc11c \uc624\uac8c \ub429\ub2c8\ub2e4.\n\uc774 \ud14c\uc2a4\ud2b8 \uae30\ub2a5\uc744 \ud638\ucd9c \ud560 \ub54c\ub9c8\ub2e4 MNIST \ud14c\uc2a4\ud2b8 \uc14b\uc5d0\uc11c \uc804\uccb4 \ud14c\uc2a4\ud2b8 \ub2e8\uacc4\ub97c \uc218\ud589\ud558\uace0 \ucd5c\uc885 \uc815\ud655\ub3c4\ub97c \ubcf4\uace0\ud569\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \uc774 \ud568\uc218\uc5d0\ub294 *\uc5e1\uc2e4\ub860* \uc785\ub825\ub3c4 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub294 ``\ud14c\uc2a4\ud2b8``` \ud568\uc218\uac00 $\\epsilon$ \ud06c\uae30\uc5d0 \ub530\ub77c \uacf5\uaca9\uc790\uc758 \uacf5\uaca9\uc744 \ubc1b\ub294 \ubaa8\ub378\uc758\n\uc815\ud655\ub3c4\uc744 \ubcf4\uace0\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \ub354 \uad6c\uccb4\uc801\uc73c\ub85c \ubcf4\uba74 \ud14c\uc2a4\ud2b8 \uc14b\uc758 \uac01\uac01\uc758 \uc0d8\ud50c\uc5d0\uc11c \ud14c\uc2a4\ud2b8 \ud568\uc218\ub294 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc190\uc2e4 \ubcc0\ud654\ub3c4( $data\\_grad$ )\ub97c \uacc4\uc0b0\ud558\uace0,\n``FGSM \uacf5\uaca9`` ($perturbed\\_data$) \uc744 \ubc1b\uc740 \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\ub97c \ub9cc\ub4e4\uace0 \ub098\uc11c \uc791\uc740 \ubcc0\ud654\uac00 \uc801\uc6a9\ub41c \uc774\ubbf8\uc9c0\uac00 \uc801\ub300\uc801\uc778\uc9c0 \ud655\uc778\uc744 \ud569\ub2c8\ub2e4.\n\ucd94\uac00\ub85c \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \ud14c\uc2a4\ud2b8\ud558\uae30 \uc704\ud574\uc11c \ud14c\uc2a4\ud2b8 \ud568\uc218\ub294 \ub098\uc911\uc5d0 \uc2dc\uac01\ud654\ud558\uc5ec \ubcfc \uc218 \uc788\ub3c4\ub85d \uc131\uacf5\uc801\uc73c\ub85c \uc5bb\uc740 \uc801\ub300\uc801 \uc774\ubbf8\uc9c0\ub97c \uc800\uc7a5\ud558\uace0 \ubc18\ud658\ud569\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -112,7 +112,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uacf5\uaca9 \uc2e4\ud589\n~~~~~~~~~~\n\n\uad6c\ud604\uc758 \ub9c8\uc9c0\ub9c9 \ubd80\ubd84\uc740 \uacf5\uaca9\uc744 \uc2e4\ud589\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c \uc804\uccb4 \ud14c\uc2a4\ud2b8 \uc2a4\ud15d\uc744 \uac01 *\uc5e1\uc2e4\ub860* \uac12\uc5d0 \uc2e4\ud589\ud569\ub2c8\ub2e4. \n\uac01 \uc5e1\uc2e4\ub860\ub9c8\ub2e4 \ucd5c\uc885 \uc815\ud655\ub3c4\uc640 \uc131\uacf5\uc801\uc778 \uc77c\ubd80 \uc801\ub300 \uc0ac\ub840\ub97c \uc800\uc7a5\ud558\uc5ec \ub2e4\uc74c \uc139\uc158\uc5d0 \ud45c\uc2dc\ud569\ub2c8\ub2e4. \n\uc5e1\uc2e4\ub860 \uac12\uc774 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \ucd9c\ub825\ub41c \uc815\ud655\ub3c4\uac00 \uc5b4\ub5bb\uac8c \uac10\uc18c\ud558\ub294\uc9c0 \ubcf4\uc2ed\uc2dc\uc624. \n\ub610\ud55c, $\\epsilon=0$ \uc778 \uacbd\uc6b0\uc5d0\ub294 \uacf5\uaca9\uc774 \uc5c6\ub294 \uc6d0\ubcf8 \ud14c\uc2a4\ud2b8 \uc815\ud655\ub3c4\uc784\uc744 \ubcf4\uc785\ub2c8\ub2e4.\n\n\n"
+        "\uacf5\uaca9 \uc2e4\ud589\n~~~~~~~~~~\n\n\uad6c\ud604\uc758 \ub9c8\uc9c0\ub9c9 \ubd80\ubd84\uc740 \uacf5\uaca9\uc744 \uc2e4\ud589\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc11c \uc804\uccb4 \ud14c\uc2a4\ud2b8 \uc2a4\ud15d\uc744 \uac01 *\uc5e1\uc2e4\ub860* \uac12\uc5d0 \uc2e4\ud589\ud569\ub2c8\ub2e4.\n\uac01 \uc5e1\uc2e4\ub860\ub9c8\ub2e4 \ucd5c\uc885 \uc815\ud655\ub3c4\uc640 \uc131\uacf5\uc801\uc778 \uc77c\ubd80 \uc801\ub300 \uc0ac\ub840\ub97c \uc800\uc7a5\ud558\uc5ec \ub2e4\uc74c \uc139\uc158\uc5d0 \ud45c\uc2dc\ud569\ub2c8\ub2e4.\n\uc5e1\uc2e4\ub860 \uac12\uc774 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \ucd9c\ub825\ub41c \uc815\ud655\ub3c4\uac00 \uc5b4\ub5bb\uac8c \uac10\uc18c\ud558\ub294\uc9c0 \ubcf4\uc2ed\uc2dc\uc624.\n\ub610\ud55c, $\\epsilon=0$ \uc778 \uacbd\uc6b0\uc5d0\ub294 \uacf5\uaca9\uc774 \uc5c6\ub294 \uc6d0\ubcf8 \ud14c\uc2a4\ud2b8 \uc815\ud655\ub3c4\uc784\uc744 \ubcf4\uc785\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -130,7 +130,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uacb0\uacfc\n-------\n\n\uc815\ud655\ub3c4 vs \uc5e1\uc2e4\ub860\n~~~~~~~~~~~~~~~~~~~\n\n\uccab \ubc88\uc9f8 \uacb0\uacfc\ub294 \uc815\ud655\ub3c4 vs \uc5e1\uc2e4\ub860 \uc744 \ub3c4\uc2dd\ud654 \ud55c \uac83 \uc785\ub2c8\ub2e4. \n\uc55e\uc5d0\uc11c \uc5b8\uae09\ud588\ub4ef\uc774, \uc5e1\uc2e4\ub860\uc774 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \uc6b0\ub9ac\ub294 \ud14c\uc2a4\ud2b8 \uc815\ud655\ub3c4\uac00 \uac10\uc18c\ud560 \uac83\uc73c\ub85c \uc608\uc0c1\ud569\ub2c8\ub2e4.\n\uc774\ub294 \ud559\uc2b5\uc744 \ub354 \uc9c4\ud589\ud574 \uac08\uc218\ub85d \uc5e1\uc2e4\ub860\uc774 \ud074\uc218\ub85d \uc190\uc2e4\uc744 \uadf9\ub300\ud654 \ud560 \ubc29\ud5a5\uc73c\ub85c \uc9c4\ud589\ub418\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.\n\uc5e1\uc2e4\ub860 \uac12\uc774 \uc120\ud615\uc801\uc73c\ub85c \ubd84\ud3ec\ud558\ub354\ub77c\ub3c4 \uace1\uc120\uc758 \ucd94\uc138\ub294 \uc120\ud615\uc758 \ud615\ud0dc\uac00 \uc544\ub2d9\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uba74, math:`\\epsilon=0.05` \uc5d0\uc11c\uc758 \uc815\ud655\ub3c4\uac00 $\\epsilon=0$ \ubcf4\ub2e4 \uc57d 4% \ub0ae\uc9c0\ub9cc\n$\\epsilon=0.2$ \uc5d0\uc11c\uc758 \uc815\ud655\ub3c4\ub294 $\\epsilon=0.15$ \ubcf4\ub2e4 \uc57d 25% \uc815\ub3c4 \ub0ae\uc2b5\ub2c8\ub2e4.\n\ub610\ud55c, $\\epsilon=0.25$ \uc640 $\\epsilon=0.3$ \uc0ac\uc774\uc758 \ubaa8\ub378 \uc815\ud655\ub3c4\ub294 \ub79c\ub364\uc73c\ub85c\n10\uac1c\uc911 1\uac1c\ub97c \uc120\ud0dd\ud588\uc744 \ub54c\uc758 \uc815\ud655\ub3c4\uc640 \uc720\uc0ac\ud55c \uc218\uc900\uc785\ub2c8\ub2e4.\n\n"
+        "\uacb0\uacfc\n-------\n\n\uc815\ud655\ub3c4 vs \uc5e1\uc2e4\ub860\n~~~~~~~~~~~~~~~~~~~\n\n\uccab \ubc88\uc9f8 \uacb0\uacfc\ub294 \uc815\ud655\ub3c4 vs \uc5e1\uc2e4\ub860 \uc744 \ub3c4\uc2dd\ud654 \ud55c \uac83 \uc785\ub2c8\ub2e4.\n\uc55e\uc5d0\uc11c \uc5b8\uae09\ud588\ub4ef\uc774, \uc5e1\uc2e4\ub860\uc774 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \uc6b0\ub9ac\ub294 \ud14c\uc2a4\ud2b8 \uc815\ud655\ub3c4\uac00 \uac10\uc18c\ud560 \uac83\uc73c\ub85c \uc608\uc0c1\ud569\ub2c8\ub2e4.\n\uc774\ub294 \ud559\uc2b5\uc744 \ub354 \uc9c4\ud589\ud574 \uac08\uc218\ub85d \uc5e1\uc2e4\ub860\uc774 \ud074\uc218\ub85d \uc190\uc2e4\uc744 \uadf9\ub300\ud654 \ud560 \ubc29\ud5a5\uc73c\ub85c \uc9c4\ud589\ub418\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4.\n\uc5e1\uc2e4\ub860 \uac12\uc774 \uc120\ud615\uc801\uc73c\ub85c \ubd84\ud3ec\ud558\ub354\ub77c\ub3c4 \uace1\uc120\uc758 \ucd94\uc138\ub294 \uc120\ud615\uc758 \ud615\ud0dc\uac00 \uc544\ub2d9\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uba74, math:`\\epsilon=0.05` \uc5d0\uc11c\uc758 \uc815\ud655\ub3c4\uac00 $\\epsilon=0$ \ubcf4\ub2e4 \uc57d 4% \ub0ae\uc9c0\ub9cc\n$\\epsilon=0.2$ \uc5d0\uc11c\uc758 \uc815\ud655\ub3c4\ub294 $\\epsilon=0.15$ \ubcf4\ub2e4 \uc57d 25% \uc815\ub3c4 \ub0ae\uc2b5\ub2c8\ub2e4.\n\ub610\ud55c, $\\epsilon=0.25$ \uc640 $\\epsilon=0.3$ \uc0ac\uc774\uc758 \ubaa8\ub378 \uc815\ud655\ub3c4\ub294 \ub79c\ub364\uc73c\ub85c\n10\uac1c\uc911 1\uac1c\ub97c \uc120\ud0dd\ud588\uc744 \ub54c\uc758 \uc815\ud655\ub3c4\uc640 \uc720\uc0ac\ud55c \uc218\uc900\uc785\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -148,7 +148,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc0d8\ud50c \uc801\ub300\uc801 \uc608\uc81c\ub4e4\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\uacf5\uc9dc \uc810\uc2ec\uc740 \uc5c6\ub2e4\ub294 \uac83\uc744 \uae30\uc5b5\ud558\uc2dc\ub098\uc694? \uc774 \uacbd\uc6b0\uc5d0\ub294 \uc5e1\uc2e4\ub860\uc774 \uc99d\uac00\ud560\uc218\ub85d \ud14c\uc2a4\ud2b8 \uc815\ud655\ub3c4\ub294 \ub5a8\uc5b4\uc9d1\ub2c8\ub2e4. \n**\uadf8\ub7ec\ub098** \uc791\uc740 \ubcc0\ud654\ub294 \ub354 \uc27d\uac8c \uc778\uc2dd\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.\n\uc2e4\uc81c\ub85c \uc815\ud655\ub3c4 \uc800\ud558\uc640 \uacf5\uaca9\uc790\uac00 \uace0\ub824\ud574\uc57c \ud558\ub294 \uc774\ud574\ub3c4 \uc0ac\uc774\uc5d0\ub294 \uc0c1\ucda9 \uad00\uacc4(tradeoff)\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uc5ec\uae30\uc11c \uc6b0\ub9ac\ub294 \uac01 \uc5e1\uc2e4\ub860 \uac12\uc5d0\uc11c \uc131\uacf5\uc801\uc778 \ub300\uc801 \uc0ac\ub840\ub97c \ubcf4\uc774\ub294 \uba87 \uac00\uc9c0 \uc608\ub97c \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\uc544\ub798 \uc774\ubbf8\uc9c0\uc758 \uccab\ubc88\uc9f8\ub85c \uc5f4\uc740 $\\epsilon=0$ \uc778 \uc608\uc81c\ub4e4\ub85c \uc791\uc740 \ubcc0\ud654\uac00 \uc5c6\ub294 \uc6d0\ubcf8\uc758 \"\uae68\ub057\ud55c\" \uc774\ubbf8\uc9c0\ub4e4\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n\uac01 \uc774\ubbf8\uc9c0\uc758 \uc704\uc758 \uae00\uc790\ub294 \"\uc6d0\ub798 \ubd84\ub958 \uacb0\uacfc -> \uc801\ub300\uc801 \ubd84\ub958 \uacb0\uacfc\"\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n$\\epsilon=0.15$ \uc5d0\uc11c \uc791\uc740 \ubcc0\ud654\uac00 \ub208\uc5d0 \ub744\uae30 \uc2dc\uc791\ud558\uace0 $\\epsilon=0.3$ \uc5d0\uc11c\ub294 \ud655\uc2e4\ud574 \ubcf4\uc785\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \ubaa8\ub4e0 \uacbd\uc6b0\uc5d0 \ub300\ud574\uc11c \ub178\uc774\uc988\uac00 \ucd94\uac00\ub418\uc5c8\ub354\ub77c\ub3c4 \uc0ac\ub78c\uc740 \uc62c\ubc14\ub974\uac8c \ubd84\ub958\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\uc0d8\ud50c \uc801\ub300\uc801 \uc608\uc81c\ub4e4\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\uacf5\uc9dc \uc810\uc2ec\uc740 \uc5c6\ub2e4\ub294 \uac83\uc744 \uae30\uc5b5\ud558\uc2dc\ub098\uc694? \uc774 \uacbd\uc6b0\uc5d0\ub294 \uc5e1\uc2e4\ub860\uc774 \uc99d\uac00\ud560\uc218\ub85d \ud14c\uc2a4\ud2b8 \uc815\ud655\ub3c4\ub294 \ub5a8\uc5b4\uc9d1\ub2c8\ub2e4.\n**\uadf8\ub7ec\ub098** \uc791\uc740 \ubcc0\ud654\ub294 \ub354 \uc27d\uac8c \uc778\uc2dd\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4.\n\uc2e4\uc81c\ub85c \uc815\ud655\ub3c4 \uc800\ud558\uc640 \uacf5\uaca9\uc790\uac00 \uace0\ub824\ud574\uc57c \ud558\ub294 \uc774\ud574\ub3c4 \uc0ac\uc774\uc5d0\ub294 \uc0c1\ucda9 \uad00\uacc4(tradeoff)\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uc5ec\uae30\uc11c \uc6b0\ub9ac\ub294 \uac01 \uc5e1\uc2e4\ub860 \uac12\uc5d0\uc11c \uc131\uacf5\uc801\uc778 \ub300\uc801 \uc0ac\ub840\ub97c \ubcf4\uc774\ub294 \uba87 \uac00\uc9c0 \uc608\ub97c \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\uc544\ub798 \uc774\ubbf8\uc9c0\uc758 \uccab\ubc88\uc9f8\ub85c \uc5f4\uc740 $\\epsilon=0$ \uc778 \uc608\uc81c\ub4e4\ub85c \uc791\uc740 \ubcc0\ud654\uac00 \uc5c6\ub294 \uc6d0\ubcf8\uc758 \"\uae68\ub057\ud55c\" \uc774\ubbf8\uc9c0\ub4e4\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n\uac01 \uc774\ubbf8\uc9c0\uc758 \uc704\uc758 \uae00\uc790\ub294 \"\uc6d0\ub798 \ubd84\ub958 \uacb0\uacfc -> \uc801\ub300\uc801 \ubd84\ub958 \uacb0\uacfc\"\ub97c \ub098\ud0c0\ub0c5\ub2c8\ub2e4.\n$\\epsilon=0.15$ \uc5d0\uc11c \uc791\uc740 \ubcc0\ud654\uac00 \ub208\uc5d0 \ub744\uae30 \uc2dc\uc791\ud558\uace0 $\\epsilon=0.3$ \uc5d0\uc11c\ub294 \ud655\uc2e4\ud574 \ubcf4\uc785\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \ubaa8\ub4e0 \uacbd\uc6b0\uc5d0 \ub300\ud574\uc11c \ub178\uc774\uc988\uac00 \ucd94\uac00\ub418\uc5c8\ub354\ub77c\ub3c4 \uc0ac\ub78c\uc740 \uc62c\ubc14\ub974\uac8c \ubd84\ub958\ub97c \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -166,7 +166,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ub2e4\uc74c \ub2e8\uacc4\ub294?\n-----------------\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc801\ub300\uc801 \uba38\uc2e0 \ub7ec\ub2dd\uc5d0 \ub300\ud55c \ud1b5\ucc30\uc744 \uc5bb\uc744 \uc218 \uc788\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.\n\ud29c\ud1a0\ub9ac\uc5bc\uc758 \ub0b4\uc6a9\uc73c\ub85c\ubd80\ud130 \uc55e\uc73c\ub85c \ub354 \ub9ce\uc740 \uac83\ub4e4\uc744 \uc54c\uc544\ub098\uac08 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\ud29c\ud1a0\ub9ac\uc5bc\uc758 \uc801\ub300\uc801 \uacf5\uaca9 \uc608\uc81c\ub294 \ubcf8 \ubd84\uc57c\uc758 \ucd08\uae09 \ub2e8\uacc4\uc774\uba70 \n\uc801\ub300\uc801 \uc0c1\ud669\uc73c\ub85c\ubd80\ud130 ML \ubaa8\ub378\uc744 \uacf5\uaca9\ud558\uace0 \ubc29\uc5b4\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub9ce\uc740 \ud6c4\uc18d \uc544\uc774\ub514\uc5b4\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uc0ac\uc2e4 NIPS 2017 \uc5d0\uc11c \uc801\ub300\uc801 \uacf5\uaca9\uacfc \ubc29\uc5b4\uc5d0 \ub300\ud55c \uacbd\uc7c1(competition)\uc774 \uc788\uc5c8\uace0 \uc5ec\uae30\uc11c \uc0ac\uc6a9\ub41c\n\ub2e4\uc591\ud55c \ubc29\ubc95\ub4e4\uc740 \ub2e4\uc74c \ub17c\ubb38\uc5d0 \uc815\ub9ac \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4: `\uc801\ub300\uc801 \uacf5\uaca9\uacfc \ubc29\uc5b4 \uacbd\uc7c1 <https://arxiv.org/pdf/1804.00097.pdf>`__.\n\ubc29\uc5b4\uc5d0 \ub300\ud55c \uc5f0\uad6c\ub294 \uc790\uc5f0\uc2a4\ub7fd\uac8c \uad50\ub780 \ubc0f \ud574\ud0b9 \ubaa9\uc801\uc73c\ub85c \uc81c\uc791\ub41c \uc785\ub825\uc5d0 \ub300\ud574 \uba38\uc2e0 \ub7ec\ub2dd \ubaa8\ub378\uc744\n\ubcf4\ub2e4 *\uacac\uace0\ud558\uac8c(robust)* \ub9cc\ub4dc\ub294 \uc544\uc774\ub514\uc5b4\ub85c \uc774\uc5b4\uc9d1\ub2c8\ub2e4.\n\n\ub610 \ub2e4\ub978 \ubc29\ud5a5\uc740 \ub2e4\ub978 \ub3c4\uba54\uc778\uc5d0\uc11c\uc758 \uc801\uc758 \uacf5\uaca9\uacfc \ubc29\uc5b4\uc785\ub2c8\ub2e4. \uc801\ub300\uc801 \uc5f0\uad6c\ub294 \uc774\ubbf8\uc9c0 \ub3c4\uba54\uc778\uc5d0 \uc81c\ud55c\ub418\uc5b4 \uc788\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \n`\uc5ec\uae30 <https://arxiv.org/pdf/1801.01944.pdf>`__ \uc5d0\uc11c \uc74c\uc131-\ud14d\uc2a4\ud2b8 \ubcc0\ud658 \ubaa8\ub378\uc5d0\uc11c\uc758 \uacf5\uaca9\ub3c4 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \uc801\ub300\uc801 \uba38\uc2e0 \ub7ec\ub2dd \ubd84\uc57c\uc5d0 \ub300\ud574\uc11c \ub9ce\uc740 \uac83\uc744 \uc54c\uae30 \uc704\ud55c \ucd5c\uace0\uc758 \ubc29\ubc95\uc740 \ub9ce\uc774 \uc2dc\ub3c4\ud574\ubcf4\ub294 \uac83\uc785\ub2c8\ub2e4.\nNIPS 2017 \uacbd\uc7c1\uc5d0\uc11c \uc18c\uac1c\ub41c \ub2e4\uc591\ud55c \uacf5\uaca9 \ubc29\ubc95\uc744 \uc9c1\uc811 \uad6c\ud604\ud574 \ubcf4\uace0, FGSM \uacfc \uc5b4\ub5a4 \uc810\uc774 \ub2e4\ub978\uc9c0 \uc5f0\uad6c\ud574 \ubcf4\uc138\uc694.\n\uadf8\ub9ac\uace0 \ub098\uc11c \uc9c1\uc811 \ub9cc\ub4e0 \uacf5\uaca9\uc73c\ub85c\ubd80\ud130 \ubaa8\ub378\uc744 \ubc29\uc5b4\ud574 \ubcf4\uc138\uc694.\n\n\n"
+        "\ub2e4\uc74c \ub2e8\uacc4\ub294?\n-----------------\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc801\ub300\uc801 \uba38\uc2e0 \ub7ec\ub2dd\uc5d0 \ub300\ud55c \ud1b5\ucc30\uc744 \uc5bb\uc744 \uc218 \uc788\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.\n\ud29c\ud1a0\ub9ac\uc5bc\uc758 \ub0b4\uc6a9\uc73c\ub85c\ubd80\ud130 \uc55e\uc73c\ub85c \ub354 \ub9ce\uc740 \uac83\ub4e4\uc744 \uc54c\uc544\ub098\uac08 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\ud29c\ud1a0\ub9ac\uc5bc\uc758 \uc801\ub300\uc801 \uacf5\uaca9 \uc608\uc81c\ub294 \ubcf8 \ubd84\uc57c\uc758 \ucd08\uae09 \ub2e8\uacc4\uc774\uba70\n\uc801\ub300\uc801 \uc0c1\ud669\uc73c\ub85c\ubd80\ud130 ML \ubaa8\ub378\uc744 \uacf5\uaca9\ud558\uace0 \ubc29\uc5b4\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \ub9ce\uc740 \ud6c4\uc18d \uc544\uc774\ub514\uc5b4\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uc0ac\uc2e4 NIPS 2017 \uc5d0\uc11c \uc801\ub300\uc801 \uacf5\uaca9\uacfc \ubc29\uc5b4\uc5d0 \ub300\ud55c \uacbd\uc7c1(competition)\uc774 \uc788\uc5c8\uace0 \uc5ec\uae30\uc11c \uc0ac\uc6a9\ub41c\n\ub2e4\uc591\ud55c \ubc29\ubc95\ub4e4\uc740 \ub2e4\uc74c \ub17c\ubb38\uc5d0 \uc815\ub9ac \ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4: `\uc801\ub300\uc801 \uacf5\uaca9\uacfc \ubc29\uc5b4 \uacbd\uc7c1 <https://arxiv.org/pdf/1804.00097.pdf>`__.\n\ubc29\uc5b4\uc5d0 \ub300\ud55c \uc5f0\uad6c\ub294 \uc790\uc5f0\uc2a4\ub7fd\uac8c \uad50\ub780 \ubc0f \ud574\ud0b9 \ubaa9\uc801\uc73c\ub85c \uc81c\uc791\ub41c \uc785\ub825\uc5d0 \ub300\ud574 \uba38\uc2e0 \ub7ec\ub2dd \ubaa8\ub378\uc744\n\ubcf4\ub2e4 *\uacac\uace0\ud558\uac8c(robust)* \ub9cc\ub4dc\ub294 \uc544\uc774\ub514\uc5b4\ub85c \uc774\uc5b4\uc9d1\ub2c8\ub2e4.\n\n\ub610 \ub2e4\ub978 \ubc29\ud5a5\uc740 \ub2e4\ub978 \ub3c4\uba54\uc778\uc5d0\uc11c\uc758 \uc801\uc758 \uacf5\uaca9\uacfc \ubc29\uc5b4\uc785\ub2c8\ub2e4. \uc801\ub300\uc801 \uc5f0\uad6c\ub294 \uc774\ubbf8\uc9c0 \ub3c4\uba54\uc778\uc5d0 \uc81c\ud55c\ub418\uc5b4 \uc788\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n`\uc5ec\uae30 <https://arxiv.org/pdf/1801.01944.pdf>`__ \uc5d0\uc11c \uc74c\uc131-\ud14d\uc2a4\ud2b8 \ubcc0\ud658 \ubaa8\ub378\uc5d0\uc11c\uc758 \uacf5\uaca9\ub3c4 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 \uc801\ub300\uc801 \uba38\uc2e0 \ub7ec\ub2dd \ubd84\uc57c\uc5d0 \ub300\ud574\uc11c \ub9ce\uc740 \uac83\uc744 \uc54c\uae30 \uc704\ud55c \ucd5c\uace0\uc758 \ubc29\ubc95\uc740 \ub9ce\uc774 \uc2dc\ub3c4\ud574\ubcf4\ub294 \uac83\uc785\ub2c8\ub2e4.\nNIPS 2017 \uacbd\uc7c1\uc5d0\uc11c \uc18c\uac1c\ub41c \ub2e4\uc591\ud55c \uacf5\uaca9 \ubc29\ubc95\uc744 \uc9c1\uc811 \uad6c\ud604\ud574 \ubcf4\uace0, FGSM \uacfc \uc5b4\ub5a4 \uc810\uc774 \ub2e4\ub978\uc9c0 \uc5f0\uad6c\ud574 \ubcf4\uc138\uc694.\n\uadf8\ub9ac\uace0 \ub098\uc11c \uc9c1\uc811 \ub9cc\ub4e0 \uacf5\uaca9\uc73c\ub85c\ubd80\ud130 \ubaa8\ub378\uc744 \ubc29\uc5b4\ud574 \ubcf4\uc138\uc694.\n\n\n"
       ]
     }
   ],
@@ -186,7 +186,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/58bdf45884d385ba7031225104b471d3/word_embeddings_tutorial.py b/docs/_downloads/58bdf45884d385ba7031225104b471d3/word_embeddings_tutorial.py
index ca7d68ca6..7cc00ea6b 100644
--- a/docs/_downloads/58bdf45884d385ba7031225104b471d3/word_embeddings_tutorial.py
+++ b/docs/_downloads/58bdf45884d385ba7031225104b471d3/word_embeddings_tutorial.py
@@ -115,7 +115,7 @@
 
 
 파이토치에서 단어 임베딩 하기
-~~~~~~~~~~~~~~~~~~~~~~~~~~
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 실제로 코드와 예시를 보기 전에, 파이토치를 비롯해 딥러닝 관련 프로그래밍을 할 때
 단어 임베딩을 어떻게 사용하는지에 대해 조금 알아봅시다. 맨 위에서 원핫 벡터를
diff --git a/docs/_downloads/591028d309d0401740cd71eb6b14bf93/memory_format_tutorial.py b/docs/_downloads/591028d309d0401740cd71eb6b14bf93/memory_format_tutorial.py
index 244e23ac2..7bec8ef8e 100644
--- a/docs/_downloads/591028d309d0401740cd71eb6b14bf93/memory_format_tutorial.py
+++ b/docs/_downloads/591028d309d0401740cd71eb6b14bf93/memory_format_tutorial.py
@@ -1,112 +1,115 @@
 # -*- coding: utf-8 -*-
 """
-(beta) Channels Last Memory Format in PyTorch
+(실험용) PyTorch를 사용한 Channels Last Memory Format
 *******************************************************
+
 **Author**: `Vitaly Fedyunin <https://github.com/VitalyFedyunin>`_
 
-What is Channels Last
----------------------
+**번역**: `Choi Yoonjeong <https://github.com/potatochips178>`_
 
-Channels Last memory format is an alternative way of ordering NCHW tensors in memory preserving dimensions ordering. Channels Last tensors ordered in such a way that channels become the densest dimension (aka storing images pixel-per-pixel).
+Channels Last가 무엇인가요
+----------------------------
+Channels Last 메모리 형식(memory format)은 차원 순서를 유지하면서 메모리 상의 NCHW 텐서(tensor)를 정렬하는 또 다른 방식입니다.
+Channels Last 텐서는 채널(Channel)이 가장 밀도가 높은(densest) 차원으로 정렬(예. 이미지를 픽셀x픽셀로 저장)됩니다.
 
-For example, classic (contiguous) storage of NCHW tensor (in our case it is two 2x2 images with 3 color channels) look like this:
+예를 들어, (2개의 2 x 2 이미지에 3개의 채널이 존재하는 경우) 전형적인(연속적인) NCHW 텐서의 저장 방식은 다음과 같습니다:
 
 .. figure:: /_static/img/classic_memory_format.png
    :alt: classic_memory_format
 
-Channels Last memory format orders data differently:
+Channels Last 메모리 형식은 데이터를 다르게 정렬합니다:
 
 .. figure:: /_static/img/channels_last_memory_format.png
    :alt: channels_last_memory_format
 
-Pytorch supports memory formats (and provides back compatibility with existing models including eager, JIT, and TorchScript) by utilizing  existing strides structure.
-For example, 10x3x16x16 batch in Channels Last format will have strides equal to (768, 1, 48, 3).
+PyTorch는 기존의 스트라이드(strides) 구조를 사용함으로써 메모리 형식을 지원(하며, eager, JIT 및 TorchScript를 포함한
+기존의 모델들과 하위 호환성을 제공)합니다. 예를 들어, Channels Last 형식에서 10x3x16x16 배치(batch)는 (768, 1, 48, 3)와
+같은 폭(strides)을 가지고 있게 됩니다.
+
 """
 
 ######################################################################
-# Channels Last memory format is implemented for 4D NCWH Tensors only.
+# Channels Last 메모리 형식은 오직 4D NCWH Tensors에서만 실행할 수 있습니다.
 #
 
 import torch
 N, C, H, W = 10, 3, 32, 32
 
 ######################################################################
-# Memory Format API
-# -----------------------
+# 메모리 형식(Memory Format) API
+# ---------------------------------
 #
-# Here is how to convert tensors between contiguous and channels
-# last memory formats.
+# 연속 메모리 형식과 channels last 메모리 형식 간에 텐서를 변환하는 방법은 다음과 같습니다.
 
 ######################################################################
-# Classic PyTorch contiguous tensor
+# 전형적인 PyTorch의 연속적인 텐서(tensor)
 x = torch.empty(N, C, H, W)
-print(x.stride()) # Ouputs: (3072, 1024, 32, 1)
+print(x.stride()) # 결과: (3072, 1024, 32, 1)
 
 ######################################################################
-# Conversion operator
+# 변환 연산자
 x = x.contiguous(memory_format=torch.channels_last)
-print(x.shape) # Outputs: (10, 3, 32, 32) as dimensions order preserved
-print(x.stride()) # Outputs: (3072, 1, 96, 3)
+print(x.shape) # 결과: (10, 3, 32, 32) 차원 순서는 보존함
+print(x.stride()) # 결과: (3072, 1, 96, 3)
 
 ######################################################################
-# Back to contiguous
+# 연속적인 형식으로 되돌리기
 x = x.contiguous(memory_format=torch.contiguous_format)
-print(x.stride()) # Outputs: (3072, 1024, 32, 1)
+print(x.stride()) # 결과: (3072, 1024, 32, 1)
 
 ######################################################################
-# Alternative option
+# 다른 방식
 x = x.to(memory_format=torch.channels_last)
-print(x.stride()) # Ouputs: (3072, 1, 96, 3)
+print(x.stride()) # 결과: (3072, 1, 96, 3)
 
 ######################################################################
-# Format checks
-print(x.is_contiguous(memory_format=torch.channels_last)) # Ouputs: True
+# 형식(format) 확인
+print(x.is_contiguous(memory_format=torch.channels_last)) # 결과: True
 
 ######################################################################
-# Create as Channels Last
+# Channels Last 방식으로 생성하기
 x = torch.empty(N, C, H, W, memory_format=torch.channels_last)
-print(x.stride()) # Ouputs: (3072, 1, 96, 3)
+print(x.stride()) # 결과: (3072, 1, 96, 3)
 
 ######################################################################
-# ``clone`` preserves memory format
+# ``clone`` 은 메모리 형식을 보존합니다.
 y = x.clone()
-print(y.stride()) # Ouputs: (3072, 1, 96, 3)
+print(y.stride()) # 결과: (3072, 1, 96, 3)
 
 ######################################################################
-# ``to``, ``cuda``, ``float`` ... preserves memory format
+# ``to``, ``cuda``, ``float`` ... 등도 메모리 형식을 보존합니다.
 if torch.cuda.is_available():
     y = x.cuda()
-    print(y.stride()) # Ouputs: (3072, 1, 96, 3)
+    print(y.stride()) # 결과: (3072, 1, 96, 3)
 
 ######################################################################
-# ``empty_like``, ``*_like`` operators preserves memory format
+# ``empty_like``, ``*_like`` 연산자도 메모리 형식을 보존합니다.
 y = torch.empty_like(x)
-print(y.stride()) # Ouputs: (3072, 1, 96, 3)
+print(y.stride()) # 결과: (3072, 1, 96, 3)
 
 ######################################################################
-# Pointwise operators preserves memory format
+# Pointwise 연산자도 메모리 형식을 보존합니다.
 z = x + y
-print(z.stride()) # Ouputs: (3072, 1, 96, 3)
+print(z.stride()) # 결과: (3072, 1, 96, 3)
 
 ######################################################################
-#  Conv, Batchnorm modules support Channels Last
-#  (only works for CudNN >= 7.6)
+# Conv, Batchnorm 모듈은 Channels Last를 지원합니다. (단, CudNN >=7.6 에서만 동작)
 if torch.backends.cudnn.version() >= 7603:
     input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float32, device="cuda", requires_grad=True)
     model = torch.nn.Conv2d(8, 4, 3).cuda().float()
 
     input = input.contiguous(memory_format=torch.channels_last)
-    model = model.to(memory_format=torch.channels_last) # Module parameters need to be Channels Last
+    model = model.to(memory_format=torch.channels_last) # 모듈 인자들은 Channels Last로 변환이 필요합니다
 
     out = model(input)
-    print(out.is_contiguous(memory_format=torch.channels_last)) # Ouputs: True
+    print(out.is_contiguous(memory_format=torch.channels_last)) # 결과: True
 
 ######################################################################
-# Performance Gains
+# 성능 향상
 # -------------------------------------------------------------------------------------------
-# The most significant performance gains are observed on Nvidia's hardware with
-# Tensor Cores support. We were able to archive over 22%  perf gains while running '
-# AMP (Automated Mixed Precision) training scripts supplied by Nvidia https://github.com/NVIDIA/apex.
+# Tensor Cores를 지원하는 Nvidia의 하드웨어에서 가장 의미심장한 성능 향상을 보였습니다.
+# Nvidia가 제공하는 AMP(Automated Mixed Precision) 학습 스크립트로 22% 이상의 성능 향상을 확인할 수 있었습니다.
+# https://github.com/NVIDIA/apex
 #
 # ``python main_amp.py -a resnet50 --b 200 --workers 16 --opt-level O2  ./data``
 
@@ -143,7 +146,7 @@
 # Epoch: [0][80/125] Time 0.260 (0.335) Speed 770.324 (597.659) Loss 2.2505953312 (1.0879) Prec@1 50.500 (52.938) Prec@5 100.000 (100.000)
 
 ######################################################################
-# Passing ``--channels-last true`` allows running a model in Channels Last format with observed 22% perf gain.
+# ``--channels-last true`` 인자를 전달하여 Channels Last 형식으로 모델을 실행하면 22%의 성능 향상을 보입니다.
 #
 # ``python main_amp.py -a resnet50 --b 200 --workers 16 --opt-level O2 --channels-last true ./data``
 
@@ -184,32 +187,31 @@
 # Epoch: [0][80/125] Time 0.198 (0.269) Speed 1011.827 (743.883) Loss 2.8196096420 (2.4011) Prec@1 47.500 (50.938) Prec@5 100.000 (100.000)
 
 ######################################################################
-# The following list of models has the full support of Channels Last and showing 8%-35% perf gains on Volta devices:
+# 아래 목록의 모델들은 Channels Last 형식을 전적으로 지원(full support)하며 Volta 장비에서 8%-35%의 성능 향상을 보입니다:
 # ``alexnet``, ``mnasnet0_5``, ``mnasnet0_75``, ``mnasnet1_0``, ``mnasnet1_3``, ``mobilenet_v2``, ``resnet101``, ``resnet152``, ``resnet18``, ``resnet34``, ``resnet50``, ``resnext50_32x4d``, ``shufflenet_v2_x0_5``, ``shufflenet_v2_x1_0``, ``shufflenet_v2_x1_5``, ``shufflenet_v2_x2_0``, ``squeezenet1_0``, ``squeezenet1_1``, ``vgg11``, ``vgg11_bn``, ``vgg13``, ``vgg13_bn``, ``vgg16``, ``vgg16_bn``, ``vgg19``, ``vgg19_bn``, ``wide_resnet101_2``, ``wide_resnet50_2``
 #
 
 ######################################################################
-# Converting existing models
+# 기존 모델들 변환하기
 # --------------------------
 #
-# Channels Last support not limited by existing models, as any model can be converted to Channels Last and propagate format through the graph as soon as input formatted correctly.
+# Channels Last 지원은 기존 모델이 무엇이냐에 따라 제한되지 않으며, 어떠한 모델도 Channels Last로 변환할 수 있으며
+# 입력(input)의 형식만 맞춰주면 (신경망) 그래프를 통해 바로 전파(propagate)할 수 있습니다.
 #
 
-# Need to be done once, after model initialization (or load)
-model = model.to(memory_format=torch.channels_last) # Replace with your model
+# 모델을 초기화한(또는 불러온) 이후, 한 번 실행이 필요합니다.
+model = model.to(memory_format=torch.channels_last) # 원하는 모델로 교체하기
 
-# Need to be done for every input
-input = input.to(memory_format=torch.channels_last) # Replace with your input
+# 모든 입력에 대해서 실행이 필요합니다.
+input = input.to(memory_format=torch.channels_last) # 원하는 입력으로 교체하기
 output = model(input)
 
 #######################################################################
-# However, not all operators fully converted to support Channels Last (usually returning
-# contiguous output instead). That means you need to verify the list of used operators
-# against supported operators list https://github.com/pytorch/pytorch/wiki/Operators-with-Channels-Last-support,
-# or introduce memory format checks into eager execution mode and run your model.
+# 그러나, 모든 연산자들이 Channels Last를 지원하도록 완전히 바뀐 것은 아닙니다(일반적으로는 연속적인 출력을 대신 반환합니다).
+# 즉, Channel Last 지원 연산자 목록 https://github.com/pytorch/pytorch/wiki/Operators-with-Channels-Last-support 에서 사용한 연산자들이 존재하는지 확인하거나,
+# eager 실행 모드에서 메모리 형식 검사를 도입하고 모델을 실행해야 합니다.
 #
-# After running the code below, operators will raise an exception if the output of the
-# operator doesn't match the memory format of the input.
+# 아래 코드에서, 연산자들의 출력이 입력의 메모리 형식과 일치하지 않으면 예외(exception)를 발생시킵니다.
 #
 #
 def contains_cl(args):
@@ -281,19 +283,19 @@ def attribute(m):
 
 
 ######################################################################
-# If you found an operator that doesn't support Channels Last tensors
-# and you want to contribute, feel free to use following developers
-# guide https://github.com/pytorch/pytorch/wiki/Writing-memory-format-aware-operators.
+# 만약 Channels Last 텐서를 지원하지 않는 연산자를 발견하였고, 기여하기를 원한다면
+# 다음 개발 문서를 참고해주세요.
+# https://github.com/pytorch/pytorch/wiki/Writing-memory-format-aware-operators
 #
 
 ######################################################################
-# Work to do
+# 해야할 일
 # ----------
-# There are still many things to do, such as:
+# 다음과 같이 여전히 해야 할 일이 많이 남아있습니다:
+#
+# - N1HW와 NC11 Tensors의 모호성 해결하기;
+# - 분산 학습을 지원하는지 확인하기;
+# - 연산자 범위(operators coverage) 개선(improve)하기
 #
-# - Resolving ambiguity of N1HW and NC11 Tensors;
-# - Testing of Distributed Training support;
-# - Improving operators coverage.
+# 개선할 부분에 대한 피드백 또는 제안이 있다면 `이슈를 만들어 <https://github.com/pytorch/pytorch/issues>`_ 알려주세요.
 #
-# If you have feedback and/or suggestions for improvement, please let us
-# know by creating `an issue <https://github.com/pytorch/pytorch/issues>`_.
diff --git a/docs/_downloads/597dbaac5c207608e108534fea081ff9/what_is_state_dict.ipynb b/docs/_downloads/597dbaac5c207608e108534fea081ff9/what_is_state_dict.ipynb
index 1b96fd96b..785478c7c 100644
--- a/docs/_downloads/597dbaac5c207608e108534fea081ff9/what_is_state_dict.ipynb
+++ b/docs/_downloads/597dbaac5c207608e108534fea081ff9/what_is_state_dict.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\uc5d0\uc11c state_dict\ub780 \ubb34\uc5c7\uc778\uac00\uc694?\n======================================\nPyTorch\uc5d0\uc11c ``torch.nn.Module`` \ubaa8\ub378\uc758 \ud559\uc2b5 \uac00\ub2a5\ud55c\n\ub9e4\uac1c\ubcc0\uc218(\uc608. \uac00\uc911\uce58\uc640 \ud3b8\ud5a5)\ub4e4\uc740 \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\uc5d0 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\n(model.parameters()\ub85c \uc811\uadfc\ud569\ub2c8\ub2e4)\n``state_dict`` \ub294 \uac04\ub2e8\ud788 \ub9d0\ud574 \uac01 \uacc4\uce35\uc744 \ub9e4\uac1c\ubcc0\uc218 \ud150\uc11c\ub85c \ub9e4\ud551\ub418\ub294\nPython \uc0ac\uc804(dict) \uac1d\uccb4\uc785\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\n``state_dict`` \ub294 PyTorch\uc5d0\uc11c \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc624\ub294 \ub370 \uad00\uc2ec\uc774\n\uc788\ub2e4\uba74 \ud544\uc218\uc801\uc778 \ud56d\ubaa9\uc785\ub2c8\ub2e4.\n``state_dict`` \uac1d\uccb4\ub294 Python \uc0ac\uc804\uc774\uae30 \ub54c\ubb38\uc5d0 \uc27d\uac8c \uc800\uc7a5, \uc5c5\ub370\uc774\ud2b8,\n\ubcc0\uacbd \ubc0f \ubcf5\uc6d0\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub294 PyTorch \ubaa8\ub378\uacfc \uc635\ud2f0\ub9c8\uc774\uc800\uc5d0 \uc5c4\uccad\ub09c\n\ubaa8\ub4c8\uc131(modularity)\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774 \ub54c, \ud559\uc2b5 \uac00\ub2a5\ud55c \ub9e4\uac1c\ubcc0\uc218\ub97c \uac16\ub294 \uacc4\uce35(\ud569\uc131\uacf1 \uacc4\uce35, \uc120\ud615 \uacc4\uce35 \ub4f1)\n\ubc0f \ub4f1\ub85d\ub41c \ubc84\ud37c\ub4e4(batchnorm\uc758 running_mean)\ub9cc \ubaa8\ub378\uc758 ``state_dict``\n \ud56d\ubaa9\uc744 \uac00\uc9c4\ub2e4\ub294 \uc810\uc5d0 \uc720\uc758\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \uc635\ud2f0\ub9c8\uc774\uc800 \uac1d\uccb4\n( ``torch.optim`` ) \ub610\ud55c \uc635\ud2f0\ub9c8\uc774\uc800\uc758 \uc0c1\ud0dc \ubfd0\ub9cc \uc544\ub2c8\ub77c \uc0ac\uc6a9\ub41c\n\ud558\uc774\ud37c \ub9e4\uac1c\ubcc0\uc218 (Hyperparameter) \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c ``state_dict`` \uc744\n\uac16\uc2b5\ub2c8\ub2e4.\n\ub808\uc2dc\ud53c\uc5d0\uc11c ``state_dict`` \uc774 \uac04\ub2e8\ud55c \ubaa8\ub378\uc5d0\uc11c \uc5b4\ub5bb\uac8c \uc0ac\uc6a9\ub418\ub294\uc9c0\n\uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815\n----------\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0 ``torch`` \uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n::\n\n   pip install torchaudio\n\n\n"
+        "\nPyTorch\uc5d0\uc11c state_dict\ub780 \ubb34\uc5c7\uc778\uac00\uc694?\n======================================\nPyTorch\uc5d0\uc11c ``torch.nn.Module`` \ubaa8\ub378\uc758 \ud559\uc2b5 \uac00\ub2a5\ud55c\n\ub9e4\uac1c\ubcc0\uc218(\uc608. \uac00\uc911\uce58\uc640 \ud3b8\ud5a5)\ub4e4\uc740 \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\uc5d0 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.\n(model.parameters()\ub85c \uc811\uadfc\ud569\ub2c8\ub2e4)\n``state_dict`` \ub294 \uac04\ub2e8\ud788 \ub9d0\ud574 \uac01 \uacc4\uce35\uc744 \ub9e4\uac1c\ubcc0\uc218 \ud150\uc11c\ub85c \ub9e4\ud551\ub418\ub294\nPython \uc0ac\uc804(dict) \uac1d\uccb4\uc785\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\n``state_dict`` \ub294 PyTorch\uc5d0\uc11c \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc624\ub294 \ub370 \uad00\uc2ec\uc774\n\uc788\ub2e4\uba74 \ud544\uc218\uc801\uc778 \ud56d\ubaa9\uc785\ub2c8\ub2e4.\n``state_dict`` \uac1d\uccb4\ub294 Python \uc0ac\uc804\uc774\uae30 \ub54c\ubb38\uc5d0 \uc27d\uac8c \uc800\uc7a5, \uc5c5\ub370\uc774\ud2b8,\n\ubcc0\uacbd \ubc0f \ubcf5\uc6d0\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub294 PyTorch \ubaa8\ub378\uacfc \uc635\ud2f0\ub9c8\uc774\uc800\uc5d0 \uc5c4\uccad\ub09c\n\ubaa8\ub4c8\uc131(modularity)\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774 \ub54c, \ud559\uc2b5 \uac00\ub2a5\ud55c \ub9e4\uac1c\ubcc0\uc218\ub97c \uac16\ub294 \uacc4\uce35(\ud569\uc131\uacf1 \uacc4\uce35, \uc120\ud615 \uacc4\uce35 \ub4f1)\n\ubc0f \ub4f1\ub85d\ub41c \ubc84\ud37c\ub4e4(batchnorm\uc758 running_mean)\ub9cc \ubaa8\ub378\uc758 ``state_dict``\n \ud56d\ubaa9\uc744 \uac00\uc9c4\ub2e4\ub294 \uc810\uc5d0 \uc720\uc758\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4. \uc635\ud2f0\ub9c8\uc774\uc800 \uac1d\uccb4\n( ``torch.optim`` ) \ub610\ud55c \uc635\ud2f0\ub9c8\uc774\uc800\uc758 \uc0c1\ud0dc \ubfd0\ub9cc \uc544\ub2c8\ub77c \uc0ac\uc6a9\ub41c\n\ud558\uc774\ud37c \ub9e4\uac1c\ubcc0\uc218 (Hyperparameter) \uc815\ubcf4\uac00 \ud3ec\ud568\ub41c ``state_dict`` \uc744\n\uac16\uc2b5\ub2c8\ub2e4.\n\ub808\uc2dc\ud53c\uc5d0\uc11c ``state_dict`` \uc774 \uac04\ub2e8\ud55c \ubaa8\ub378\uc5d0\uc11c \uc5b4\ub5bb\uac8c \uc0ac\uc6a9\ub418\ub294\uc9c0\n\uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815\n----------\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0 ``torch`` \uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n::\n\n   pip install torchaudio\n"
       ]
     },
     {
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/5edaebfc06ec3968b8c1da100da2253d/sequence_models_tutorial.ipynb b/docs/_downloads/5edaebfc06ec3968b8c1da100da2253d/sequence_models_tutorial.ipynb
index 5bd07626a..2928c05cc 100644
--- a/docs/_downloads/5edaebfc06ec3968b8c1da100da2253d/sequence_models_tutorial.ipynb
+++ b/docs/_downloads/5edaebfc06ec3968b8c1da100da2253d/sequence_models_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nSequence Models and Long-Short Term Memory Networks\n===================================================\n\nAt this point, we have seen various feed-forward networks. That is,\nthere is no state maintained by the network at all. This might not be\nthe behavior we want. Sequence models are central to NLP: they are\nmodels where there is some sort of dependence through time between your\ninputs. The classical example of a sequence model is the Hidden Markov\nModel for part-of-speech tagging. Another example is the conditional\nrandom field.\n\nA recurrent neural network is a network that maintains some kind of\nstate. For example, its output could be used as part of the next input,\nso that information can propogate along as the network passes over the\nsequence. In the case of an LSTM, for each element in the sequence,\nthere is a corresponding *hidden state* $h_t$, which in principle\ncan contain information from arbitrary points earlier in the sequence.\nWe can use the hidden state to predict words in a language model,\npart-of-speech tags, and a myriad of other things.\n\n\nLSTM's in Pytorch\n~~~~~~~~~~~~~~~~~\n\nBefore getting to the example, note a few things. Pytorch's LSTM expects\nall of its inputs to be 3D tensors. The semantics of the axes of these\ntensors is important. The first axis is the sequence itself, the second\nindexes instances in the mini-batch, and the third indexes elements of\nthe input. We haven't discussed mini-batching, so lets just ignore that\nand assume we will always have just 1 dimension on the second axis. If\nwe want to run the sequence model over the sentence \"The cow jumped\",\nour input should look like\n\n\\begin{align}\\begin{bmatrix}\n   \\overbrace{q_\\text{The}}^\\text{row vector} \\\\\n   q_\\text{cow} \\\\\n   q_\\text{jumped}\n   \\end{bmatrix}\\end{align}\n\nExcept remember there is an additional 2nd dimension with size 1.\n\nIn addition, you could go through the sequence one at a time, in which\ncase the 1st axis will have size 1 also.\n\nLet's see a quick example.\n\n"
+        "\nSequence Models and Long-Short Term Memory Networks\n===================================================\n\nAt this point, we have seen various feed-forward networks. That is,\nthere is no state maintained by the network at all. This might not be\nthe behavior we want. Sequence models are central to NLP: they are\nmodels where there is some sort of dependence through time between your\ninputs. The classical example of a sequence model is the Hidden Markov\nModel for part-of-speech tagging. Another example is the conditional\nrandom field.\n\nA recurrent neural network is a network that maintains some kind of\nstate. For example, its output could be used as part of the next input,\nso that information can propogate along as the network passes over the\nsequence. In the case of an LSTM, for each element in the sequence,\nthere is a corresponding *hidden state* $h_t$, which in principle\ncan contain information from arbitrary points earlier in the sequence.\nWe can use the hidden state to predict words in a language model,\npart-of-speech tags, and a myriad of other things.\n\n\nLSTM's in Pytorch\n~~~~~~~~~~~~~~~~~\n\nBefore getting to the example, note a few things. Pytorch's LSTM expects\nall of its inputs to be 3D tensors. The semantics of the axes of these\ntensors is important. The first axis is the sequence itself, the second\nindexes instances in the mini-batch, and the third indexes elements of\nthe input. We haven't discussed mini-batching, so lets just ignore that\nand assume we will always have just 1 dimension on the second axis. If\nwe want to run the sequence model over the sentence \"The cow jumped\",\nour input should look like\n\n\\begin{align}\\begin{bmatrix}\n   \\overbrace{q_\\text{The}}^\\text{row vector} \\\\\n   q_\\text{cow} \\\\\n   q_\\text{jumped}\n   \\end{bmatrix}\\end{align}\n\nExcept remember there is an additional 2nd dimension with size 1.\n\nIn addition, you could go through the sequence one at a time, in which\ncase the 1st axis will have size 1 also.\n\nLet's see a quick example.\n"
       ]
     },
     {
@@ -118,7 +118,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/5f81194dd43910d586578638f83205a3/dcgan_faces_tutorial.ipynb b/docs/_downloads/5f81194dd43910d586578638f83205a3/dcgan_faces_tutorial.ipynb
index a45b8b6b6..540cfe3e9 100644
--- a/docs/_downloads/5f81194dd43910d586578638f83205a3/dcgan_faces_tutorial.ipynb
+++ b/docs/_downloads/5f81194dd43910d586578638f83205a3/dcgan_faces_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nDCGAN Tutorial\n==============\n\n**Author**: `Nathan Inkawhich <https://github.com/inkawhich>`__\n\n\n"
+        "\nDCGAN Tutorial\n==============\n\n**Author**: `Nathan Inkawhich <https://github.com/inkawhich>`__\n"
       ]
     },
     {
@@ -283,7 +283,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/6042bacf7948939030769777afe22e55/data_loading_tutorial.py b/docs/_downloads/6042bacf7948939030769777afe22e55/data_loading_tutorial.py
index a607d3104..8799d24ce 100644
--- a/docs/_downloads/6042bacf7948939030769777afe22e55/data_loading_tutorial.py
+++ b/docs/_downloads/6042bacf7948939030769777afe22e55/data_loading_tutorial.py
@@ -187,11 +187,12 @@ def __getitem__(self, idx):
 # 그러므로 우리는 신경망에 주기 전에 처리할 과정을 작성해야 합니다.
 #
 # 3가지의 transforms 을 만들어 봅시다:
-# -  ``Rescale``: 이미지의 크기를 조절합니다.
-# -  ``RandomCrop``: 이미지를 무작위로 자릅니다.
-#                    이것을 data augmentation이라 합니다.
-# -  ``ToTensor``: numpy 이미지에서 torch 이미지로 변경합니다.
-#                  (축변환이 필요합니다)
+#
+# - ``Rescale``: 이미지의 크기를 조절합니다.
+# - ``RandomCrop``: 이미지를 무작위로 자릅니다.
+#   이것을 data augmentation이라 합니다.
+# - ``ToTensor``: numpy 이미지에서 torch 이미지로 변경합니다.
+#   (축변환이 필요합니다)
 #
 # 간단한 함수대신에 호출 할 수 있는 클래스로 작성 합니다.
 # 이렇게 한다면, 클래스가 호출 될 때마다 전이(Transform)의 매개변수가 전달 되지 않아도 됩니다.
diff --git a/docs/_downloads/61a76849444a0a65d843361c26d1de16/Intro_to_TorchScript_tutorial.ipynb b/docs/_downloads/61a76849444a0a65d843361c26d1de16/Intro_to_TorchScript_tutorial.ipynb
index 5a7628307..d8a6cc207 100644
--- a/docs/_downloads/61a76849444a0a65d843361c26d1de16/Intro_to_TorchScript_tutorial.ipynb
+++ b/docs/_downloads/61a76849444a0a65d843361c26d1de16/Intro_to_TorchScript_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nTorchScript \uc18c\uac1c\n===========================\n\n**Author**: James Reed (jamesreed@fb.com), Michael Suo (suo@fb.com), rev2\n\n**\ubc88\uc5ed**: `\uac15\uc900\ud601 <https://github.com/k1101jh>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 C++\uc640 \uac19\uc740 \uace0\uc131\ub2a5 \ud658\uacbd\uc5d0\uc11c \uc2e4\ud589\ub420 \uc218 \uc788\ub294\nPyTorch \ubaa8\ub378(``nn.Module`` \uc758 \ud558\uc704\ud074\ub798\uc2a4)\uc758 \uc911\uac04 \ud45c\ud604\uc778\nTorchScript\uc5d0 \ub300\ud55c \uc18c\uac1c\uc785\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e4\uc74c\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4:\n\n1. \ub2e4\uc74c\uc744 \ud3ec\ud568\ud55c PyTorch\uc758 \ubaa8\ub378 \uc81c\uc791\uc758 \uae30\ubcf8:\n\n-  \ubaa8\ub4c8(Modules)\n-  ``forward`` \ud568\uc218 \uc815\uc758\ud558\uae30\n-  \ubaa8\ub4c8\uc744 \uacc4\uce35 \uad6c\uc870\ub85c \uad6c\uc131\ud558\uae30\n\n2. PyTorch \ubaa8\ub4c8\uc744 \uace0\uc131\ub2a5 \ubc30\ud3ec \ub7f0\ud0c0\uc784\uc778 TorchScript\ub85c \ubcc0\ud658\ud558\ub294 \ud2b9\uc815 \ubc29\ubc95\n\n-  \uae30\uc874 \ubaa8\ub4c8 \ucd94\uc801\ud558\uae30\n-  \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub4c8\uc744 \uc9c1\uc811 \ucef4\ud30c\uc77c\ud558\uae30\n-  \ub450 \uac00\uc9c0 \uc811\uadfc \ubc29\ubc95\uc744 \uad6c\uc131\ud558\ub294 \ubc29\ubc95\n-  TorchScript \ubaa8\ub4c8 \uc800\uc7a5 \ubc0f \ubd88\ub7ec\uc624\uae30\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc644\ub8cc\ud55c \ud6c4\uc5d0\ub294\n`\ub2e4\uc74c \ud559\uc2b5\uc11c <https://pytorch.org/tutorials/advanced/cpp_export.html>`_\n\ub97c \ud1b5\ud574 C++\uc5d0\uc11c TorchScript \ubaa8\ub378\uc744 \uc2e4\uc81c\ub85c \ud638\ucd9c\ud558\ub294 \uc608\uc81c\ub97c \uc548\ub0b4\ud569\ub2c8\ub2e4.\n\n\n"
+        "\nTorchScript \uc18c\uac1c\n===========================\n\n**Author**: James Reed (jamesreed@fb.com), Michael Suo (suo@fb.com), rev2\n\n**\ubc88\uc5ed**: `\uac15\uc900\ud601 <https://github.com/k1101jh>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 C++\uc640 \uac19\uc740 \uace0\uc131\ub2a5 \ud658\uacbd\uc5d0\uc11c \uc2e4\ud589\ub420 \uc218 \uc788\ub294\nPyTorch \ubaa8\ub378(``nn.Module`` \uc758 \ud558\uc704\ud074\ub798\uc2a4)\uc758 \uc911\uac04 \ud45c\ud604\uc778\nTorchScript\uc5d0 \ub300\ud55c \uc18c\uac1c\uc785\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \ub2e4\uc74c\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4:\n\n1. \ub2e4\uc74c\uc744 \ud3ec\ud568\ud55c PyTorch\uc758 \ubaa8\ub378 \uc81c\uc791\uc758 \uae30\ubcf8:\n\n-  \ubaa8\ub4c8(Modules)\n-  ``forward`` \ud568\uc218 \uc815\uc758\ud558\uae30\n-  \ubaa8\ub4c8\uc744 \uacc4\uce35 \uad6c\uc870\ub85c \uad6c\uc131\ud558\uae30\n\n2. PyTorch \ubaa8\ub4c8\uc744 \uace0\uc131\ub2a5 \ubc30\ud3ec \ub7f0\ud0c0\uc784\uc778 TorchScript\ub85c \ubcc0\ud658\ud558\ub294 \ud2b9\uc815 \ubc29\ubc95\n\n-  \uae30\uc874 \ubaa8\ub4c8 \ucd94\uc801\ud558\uae30\n-  \uc2a4\ud06c\ub9bd\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub4c8\uc744 \uc9c1\uc811 \ucef4\ud30c\uc77c\ud558\uae30\n-  \ub450 \uac00\uc9c0 \uc811\uadfc \ubc29\ubc95\uc744 \uad6c\uc131\ud558\ub294 \ubc29\ubc95\n-  TorchScript \ubaa8\ub4c8 \uc800\uc7a5 \ubc0f \ubd88\ub7ec\uc624\uae30\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc644\ub8cc\ud55c \ud6c4\uc5d0\ub294\n`\ub2e4\uc74c \ud559\uc2b5\uc11c <https://pytorch.org/tutorials/advanced/cpp_export.html>`_\n\ub97c \ud1b5\ud574 C++\uc5d0\uc11c TorchScript \ubaa8\ub378\uc744 \uc2e4\uc81c\ub85c \ud638\ucd9c\ud558\ub294 \uc608\uc81c\ub97c \uc548\ub0b4\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -294,7 +294,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/63192d95d276e1eaaa08642db0e4ef7f/profiler.py b/docs/_downloads/63192d95d276e1eaaa08642db0e4ef7f/profiler.py
index b32333021..14ae58b50 100644
--- a/docs/_downloads/63192d95d276e1eaaa08642db0e4ef7f/profiler.py
+++ b/docs/_downloads/63192d95d276e1eaaa08642db0e4ef7f/profiler.py
@@ -1,20 +1,18 @@
 """
-PyTorch Profiler
+PyTorch 프로파일러(Profiler)
 ====================================
-This recipe explains how to use PyTorch profiler and measure the time and
-memory consumption of the model's operators.
+이 레시피에서는 어떻게 PyTorch 프로파일러를 사용하는지, 그리고 모델의 연산자들이 소비하는 메모리와 시간을 측정하는 방법을 살펴보겠습니다.
 
-Introduction
+개요
 ------------
-PyTorch includes a simple profiler API that is useful when user needs
-to determine the most expensive operators in the model.
+PyTorch는 사용자가 모델 내의 연산 비용이 큰(expensive) 연산자들이 무엇인지 알고싶을 때 유용하게 사용할 수 있는 간단한 프로파일러 API를 포함하고 있습니다.
 
-In this recipe, we will use a simple Resnet model to demonstrate how to
-use profiler to analyze model performance.
+이 레시피에서는 모델의 성능(performance)을 분석하려고 할 때 어떻게 프로파일러를 사용해야 하는지를 보여주기 위해 간단한 ResNet 모델을 사용하겠습니다.
+
+설정(Setup)
+-------------
+``torch`` 와 ``torchvision`` 을 설치하기 위해서 아래의 커맨드를 입력합니다:
 
-Setup
------
-To install ``torch`` and ``torchvision`` use the following command:
 
 ::
 
@@ -25,20 +23,20 @@
 
 
 ######################################################################
-# Steps
-# -----
+# 단계(Steps)
+# -------------
 #
-# 1. Import all necessary libraries
-# 2. Instantiate a simple Resnet model
-# 3. Use profiler to analyze execution time
-# 4. Use profiler to analyze memory consumption
-# 5. Using tracing functionality
+# 1. 필요한 라이브러리들 불러오기
+# 2. 간단한 ResNet 모델 인스턴스화 하기
+# 3. 프로파일러를 사용하여 실행시간 분석하기
+# 4. 프로파일러를 사용하여 메모리 소비 분석하기
+# 5. 추적기능 사용하기
 #
-# 1. Import all necessary libraries
+# 1. 필요한 라이브러리들 불러오기
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# In this recipe we will use ``torch``, ``torchvision.models``
-# and ``profiler`` modules:
+# 이 레시피에서는 ``torch`` 와 ``torchvision.models``,
+# 그리고 ``profiler`` 모듈을 사용합니다:
 #
 
 import torch
@@ -47,53 +45,53 @@
 
 
 ######################################################################
-# 2. Instantiate a simple Resnet model
+# 2. 간단한 ResNet 모델 인스턴스화 하기
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# Let's create an instance of a Resnet model and prepare an input
-# for it:
+# ResNet 모델 인스턴스를 만들고 입력값을
+# 준비합니다 :
 #
 
 model = models.resnet18()
 inputs = torch.randn(5, 3, 224, 224)
 
 ######################################################################
-# 3. Use profiler to analyze execution time
+# 3. 프로파일러를 사용하여 실행시간 분석하기
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# PyTorch profiler is enabled through the context manager and accepts
-# a number of parameters, some of the most useful are:
+# PyTorch 프로파일러는 컨텍스트 메니저(context manager)를 통해 활성화되고,
+# 여러 매개변수를 받을 수 있습니다. 유용한 몇 가지 매개변수는 다음과 같습니다:
 #
-# - ``record_shapes`` - whether to record shapes of the operator inputs;
-# - ``profile_memory`` - whether to report amount of memory consumed by
-#   model's Tensors;
-# - ``use_cuda`` - whether to measure execution time of CUDA kernels.
+# - ``record_shapes`` - 연사자 입력(input)의 shape을 기록할지 여부;
+# - ``profile_memory`` - 모델의 텐서(Tensor)들이 소비하는 메모리 양을 보고(report)할지 여부;
+# - ``use_cuda`` - CUDA 커널의 실행시간을 측정할지 여부;
 #
-# Let's see how we can use profiler to analyze the execution time:
+# 프로파일러를 사용하여 어떻게 실행시간을 분석하는지 보겠습니다:
 
 with profiler.profile(record_shapes=True) as prof:
     with profiler.record_function("model_inference"):
         model(inputs)
 
 ######################################################################
-# Note that we can use ``record_function`` context manager to label
-# arbitrary code ranges with user provided names
-# (``model_inference`` is used as a label in the example above).
-# Profiler allows one to check which operators were called during the
-# execution of a code range wrapped with a profiler context manager.
-# If multiple profiler ranges are active at the same time (e.g. in
-# parallel PyTorch threads), each profiling context manager tracks only
-# the operators of its corresponding range.
-# Profiler also automatically profiles the async tasks launched
-# with ``torch.jit._fork`` and (in case of a backward pass)
-# the backward pass operators launched with ``backward()`` call.
-#
-# Let's print out the stats for the execution above:
+# ``record_function`` 컨텍스트 관리자를 사용하여 임의의 코드 범위에
+# 사용자가 지정한 이름으로 레이블(label)을 표시할 수 있습니다.
+# (위 예제에서는 ``model_inference`` 를 레이블로 사용했습니다.)
+# 프로파일러를 사용하면 프로파일러 컨텍스트 관리자로 감싸진(wrap) 코드 범위를
+# 실행하는 동안 어떤 연산자들이 호출되었는지 확인할 수 있습니다.
+#
+# 만약 여러 프로파일러의 범위가 동시에 활성화된 경우(예. PyTorch 쓰레드가 병렬로
+# 실행 중인 경우), 각 프로파일링 컨텍스트 관리자는 각각의 범위 내의 연산자들만
+# 추적(track)합니다.
+# 프로파일러는 또한 ``torch.jit._fork`` 로 실행된 비동기 작업과
+# (역전파 단계의 경우) ``backward()`` 의 호출로 실행된 역전파 연산자들도
+# 자동으로 프로파일링합니다.
+#
+# 위 코드를 실행한 통계를 출력해보겠습니다:
 
 print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=10))
 
 ######################################################################
-# The output will look like (omitting some columns):
+# (몇몇 열을 제외하고) 출력값이 이렇게 보일 것입니다:
 
 # -------------------------  --------------  ----------  ------------  ---------
 # Name                       Self CPU total   CPU total  CPU time avg  # Calls
@@ -111,16 +109,18 @@
 # -------------------------  --------------  ----------  ------------  ---------
 
 ######################################################################
-# Here we see that, as expected, most of the time is spent in convolution (and specifically in ``mkldnn_convolution``
-# for PyTorch compiled with MKL-DNN support).
-# Note the difference between self cpu time and cpu time - operators can call other operators, self cpu time exludes time
-# spent in children operator calls, while total cpu time includes it.
+# 예상했던 대로, 대부분의 시간이 합성곱(convolution) 연산(특히 MKL-DNN을 지원하도록
+# 컴파일된 PyTorch의 경우에는 ``mkldnn_convolution`` )에서 소요되는 것을 확인할 수 있습니다.
+# (결과 열들 중) Self CPU time과 CPU time의 차이에 유의해야 합니다 -
+# 연산자는 다른 연산자들을 호출할 수 있으며, Self CPU time에는 하위(child) 연산자 호출에서 발생한
+# 시간을 제외해서, Totacl CPU time에는 포함해서 표시합니다.
 #
-# To get a finer granularity of results and include operator input shapes, pass ``group_by_input_shape=True``:
+# 보다 세부적인 결과 정보 및 연산자의 입력 shape을 함께 보려면 ``group_by_input_shape=True`` 를
+# 인자로 전달하면 됩니다:
 
 print(prof.key_averages(group_by_input_shape=True).table(sort_by="cpu_time_total", row_limit=10))
 
-# (omitting some columns)
+# (몇몇 열은 제외하였습니다)
 # -------------------------  -----------  --------  -------------------------------------
 # Name                       CPU total    # Calls         Input Shapes
 # -------------------------  -----------  --------  -------------------------------------
@@ -138,21 +138,21 @@
 
 
 ######################################################################
-# 4. Use profiler to analyze memory consumption
+# 4. 프로파일러를 사용하여 메모리 소비 분석하기
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# PyTorch profiler can also show the amount of memory (used by the model's tensors)
-# that was allocated (or released) during the execution of the model's operators.
-# In the output below, 'self' memory corresponds to the memory allocated (released)
-# by the operator, excluding the children calls to the other operators.
-# To enable memory profiling functionality pass ``profile_memory=True``.
+# PyTorch 프로파일러는 모델의 연산자들을 실행하며 (모델의 텐서들이 사용하며) 할당(또는 해제)한
+# 메모리의 양도 표시할 수 있습니다.
+# 아래 출력 결과에서 'Self' memory는 해당 연산자에 의해 호출된 하위(child) 연산자들을 제외한,
+# 연산자 자체에 할당(해제)된 메모리에 해당합니다.
+# 메모리 프로파일링 기능을 활성화하려면 ``profile_memory=True`` 를 인자로 전달하면 됩니다.
 
 with profiler.profile(profile_memory=True, record_shapes=True) as prof:
     model(inputs)
 
 print(prof.key_averages().table(sort_by="self_cpu_memory_usage", row_limit=10))
 
-# (omitting some columns)
+# (몇몇 열은 제외하였습니다)
 # ---------------------------  ---------------  ---------------  ---------------
 # Name                         CPU Mem          Self CPU Mem     Number of Calls
 # ---------------------------  ---------------  ---------------  ---------------
@@ -165,7 +165,7 @@
 
 print(prof.key_averages().table(sort_by="cpu_memory_usage", row_limit=10))
 
-# (omitting some columns)
+# (몇몇 열은 제외하였습니다)
 # ---------------------------  ---------------  ---------------  ---------------
 # Name                         CPU Mem          Self CPU Mem     Number of Calls
 # ---------------------------  ---------------  ---------------  ---------------
@@ -187,10 +187,10 @@
 # ---------------------------  ---------------  ---------------  ---------------
 
 ######################################################################
-# 5. Using tracing functionality
+# 5. 추적기능 사용하기
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# Profiling results can be outputted as a .json trace file:
+# 프로파일링 결과는 .json 형태의 추적 파일(trace file)로 출력할 수 있습니다:
 
 with profiler.profile() as prof:
     with profiler.record_function("model_inference"):
@@ -199,17 +199,17 @@
 prof.export_chrome_trace("trace.json")
 
 ######################################################################
-# User can examine the sequence of profiled operators after loading the trace file
-# in Chrome (``chrome://tracing``):
+# 사용자는 Chrome 브라우저( ``chrome://tracing`` )에서 추적 파일을 불러와
+# 프로파일된 일련의 연산자들을 검토해볼 수 있습니다:
 #
 # .. image:: ../../_static/img/trace_img.png
 #    :scale: 25 %
 
 ######################################################################
-# Learn More
-# ----------
+# 더 알아보기
+# -------------
 #
-# Take a look at the following tutorial to learn how to visualize your model with TensorBoard:
+# 다음의 튜토리얼을 통해 Tensorboard를 사용하여 모델 시각화하는 방법을 살펴보세요:
 #
-# -  `Visualizing models, data, and training with TensorBoard <https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html>`_ tutorial
+# - :doc:`/intermediate/tensorboard_tutorial`
 #
diff --git a/docs/_downloads/642248c95070825e7ac912504a919140/Captum_Recipe.ipynb b/docs/_downloads/642248c95070825e7ac912504a919140/Captum_Recipe.ipynb
index 850659bce..b1e3f783e 100644
--- a/docs/_downloads/642248c95070825e7ac912504a919140/Captum_Recipe.ipynb
+++ b/docs/_downloads/642248c95070825e7ac912504a919140/Captum_Recipe.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nCaptum\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \ud574\uc11d\ud558\uae30\n===================================\n\n**\ubc88\uc5ed**: `\uc815\uc7ac\ubbfc <https://github.com/jjeamin>`_\n\n"
+        "\nCaptum\uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \ud574\uc11d\ud558\uae30\n===================================\n\n**\ubc88\uc5ed**: `\uc815\uc7ac\ubbfc <https://github.com/jjeamin>`_\n"
       ]
     },
     {
@@ -152,7 +152,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/67710f3d40ca3863323ce02dddbf8618/save_load_across_devices.ipynb b/docs/_downloads/67710f3d40ca3863323ce02dddbf8618/save_load_across_devices.ipynb
index b76d25ebc..380a07590 100644
--- a/docs/_downloads/67710f3d40ca3863323ce02dddbf8618/save_load_across_devices.ipynb
+++ b/docs/_downloads/67710f3d40ca3863323ce02dddbf8618/save_load_across_devices.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\uc5d0\uc11c \ub2e4\uc591\ud55c \uc7a5\uce58 \uac04 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\uae30\n===================================================\n\n\ub2e4\uc591\ud55c \uc7a5\uce58(device)\uc5d0\uc11c \ub2f9\uc2e0\uc758 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc624\uace0 \uc2f6\uc740 \n\uacbd\uc6b0\uac00 \uc0dd\uae38 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\n\nPyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc7a5\uce58 \uac04\uc758 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc624\ub294 \uac83\uc740 \ube44\uad50\uc801 \uac04\ub2e8\ud569\ub2c8\ub2e4.\n\uc774\ubc88 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294, CPU\uc640 GPU\uc5d0\uc11c \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ubc29\ubc95\uc744 \uc2e4\ud5d8\ud560 \uac83\uc785\ub2c8\ub2e4.\n\n\uc124\uc815\n-----\n\n\uc774\ubc88 \ub808\uc2dc\ud53c\uc5d0\uc11c \ubaa8\ub4e0 \ucf54\ub4dc \ube14\ub85d\uc774 \uc81c\ub300\ub85c \uc2e4\ud589\ub418\uac8c \ud558\ub824\uba74, \n\uc6b0\uc120 \ub7f0\ud0c0\uc784(runtime) \uc124\uc815\uc744 \"GPU\"\ub098 \ub354 \ub192\uac8c \uc9c0\uc815\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4. \n\uc774\ud6c4, \uc544\ub798\uc640 \uac19\uc774 ``torch``\ub97c \uc124\uce58\ud574\uc57c PyTorch\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n::\n\n   pip install torch\n\n\n"
+        "\nPyTorch\uc5d0\uc11c \ub2e4\uc591\ud55c \uc7a5\uce58 \uac04 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\uae30\n===================================================\n\n\ub2e4\uc591\ud55c \uc7a5\uce58(device)\uc5d0\uc11c \ub2f9\uc2e0\uc758 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc624\uace0 \uc2f6\uc740 \n\uacbd\uc6b0\uac00 \uc0dd\uae38 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\n\nPyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc7a5\uce58 \uac04\uc758 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc624\ub294 \uac83\uc740 \ube44\uad50\uc801 \uac04\ub2e8\ud569\ub2c8\ub2e4.\n\uc774\ubc88 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294, CPU\uc640 GPU\uc5d0\uc11c \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ubc29\ubc95\uc744 \uc2e4\ud5d8\ud560 \uac83\uc785\ub2c8\ub2e4.\n\n\uc124\uc815\n-----\n\n\uc774\ubc88 \ub808\uc2dc\ud53c\uc5d0\uc11c \ubaa8\ub4e0 \ucf54\ub4dc \ube14\ub85d\uc774 \uc81c\ub300\ub85c \uc2e4\ud589\ub418\uac8c \ud558\ub824\uba74, \n\uc6b0\uc120 \ub7f0\ud0c0\uc784(runtime) \uc124\uc815\uc744 \"GPU\"\ub098 \ub354 \ub192\uac8c \uc9c0\uc815\ud574\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4. \n\uc774\ud6c4, \uc544\ub798\uc640 \uac19\uc774 ``torch``\ub97c \uc124\uce58\ud574\uc57c PyTorch\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n::\n\n   pip install torch\n"
       ]
     },
     {
@@ -150,7 +150,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/695ce7a76ce07582023a85d63a717ce2/saving_and_loading_models_for_inference.ipynb b/docs/_downloads/695ce7a76ce07582023a85d63a717ce2/saving_and_loading_models_for_inference.ipynb
index 5432d037e..f08d0146b 100644
--- a/docs/_downloads/695ce7a76ce07582023a85d63a717ce2/saving_and_loading_models_for_inference.ipynb
+++ b/docs/_downloads/695ce7a76ce07582023a85d63a717ce2/saving_and_loading_models_for_inference.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\uc5d0\uc11c \ucd94\ub860(inference)\uc744 \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n================================================================\n\nPyTorch\uc5d0\uc11c\ub294 \ucd94\ub860(inference)\uc744 \uc704\ud574 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294\ub370 2\uac00\uc9c0 \uc811\uadfc\ubc95\uc774\n\uc788\uc2b5\ub2c8\ub2e4. \uccab\ubc88\uc9f8\ub294 ``state_dict`` \ub97c \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \uac83\uc774\uace0, \ub450\ubc88\uc9f8\ub294 \uc804\uccb4\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\ntorch.save() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 ``state_dict`` \ub97c \uc800\uc7a5\ud558\uba74 \uc774\ud6c4\uc5d0 \ubaa8\ub378\uc744\n\ubd88\ub7ec\uc62c \ub54c \uc720\uc5f0\ud568\uc744 \ud06c\uac8c \uc0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud559\uc2b5\ub41c \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218(parameter)\ub9cc\uc744\n\uc800\uc7a5\ud558\uba74\ub418\ubbc0\ub85c \ubaa8\ub378 \uc800\uc7a5 \uc2dc\uc5d0 \uad8c\uc7a5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ubaa8\ub378 \uc804\uccb4\ub97c \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc62c\n\ub54c\uc5d0\ub294 Python\uc758 `pickle <https://docs.python.org/3/library/pickle.html>`__ \ubaa8\ub4c8\uc744\n\uc0ac\uc6a9\ud558\uc5ec \uc804\uccb4 \ubaa8\ub4c8\uc744 \uc800\uc7a5\ud569\ub2c8\ub2e4. \uc774 \ubc29\uc2dd\uc740 \uc9c1\uad00\uc801\uc778 \ubb38\ubc95\uc744 \uc0ac\uc6a9\ud558\uba70 \ucf54\ub4dc\uc758 \uc591\ub3c4\n\uc801\uc2b5\ub2c8\ub2e4. \uc774 \ubc29\uc2dd\uc758 \ub2e8\uc810\uc740 \uc9c1\ub82c\ud654(serialized)\ub41c \ub370\uc774\ud130\uac00 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c \uc0ac\uc6a9\ud55c\n\ud2b9\uc815 \ud074\ub798\uc2a4 \ubc0f \ub514\ub809\ud1a0\ub9ac \uad6c\uc870\uc5d0 \uc885\uc18d(bind)\ub41c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uadf8 \uc774\uc720\ub294 pickle\uc774\n\ubaa8\ub378 \ud074\ub798\uc2a4 \uc790\uccb4\ub97c \uc800\uc7a5\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \uc624\ud788\ub824 \ubd88\ub7ec\uc62c \ub54c \uc0ac\uc6a9\ub418\ub294 \ud074\ub798\uc2a4\uac00\n\ud3ec\ud568\ub41c \ud30c\uc77c\uc758 \uacbd\ub85c\ub97c \uc800\uc7a5\ud569\ub2c8\ub2e4. \uc774 \ub54c\ubb38\uc5d0 \uc791\uc131\ud55c \ucf54\ub4dc\uac00 \ub2e4\ub978 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\n\uc0ac\uc6a9\ub418\uac70\ub098 \ub9ac\ud329\ud1a0\ub9c1\uc744 \uac70\uce58\ub294 \ub4f1\uc758 \uacfc\uc815\uc5d0\uc11c \ub3d9\uc791\ud558\uc9c0 \uc54a\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294\n\ucd94\ub860\uc744 \uc704\ud574 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ub450 \uac00\uc9c0 \ubc29\ubc95 \ubaa8\ub450\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815\n----------\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0 ``torch`` \uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n\n::\n\n   pip install torch\n\n\n\n"
+        "\nPyTorch\uc5d0\uc11c \ucd94\ub860(inference)\uc744 \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n================================================================\n\nPyTorch\uc5d0\uc11c\ub294 \ucd94\ub860(inference)\uc744 \uc704\ud574 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294\ub370 2\uac00\uc9c0 \uc811\uadfc\ubc95\uc774\n\uc788\uc2b5\ub2c8\ub2e4. \uccab\ubc88\uc9f8\ub294 ``state_dict`` \ub97c \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \uac83\uc774\uace0, \ub450\ubc88\uc9f8\ub294 \uc804\uccb4\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\ntorch.save() \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 ``state_dict`` \ub97c \uc800\uc7a5\ud558\uba74 \uc774\ud6c4\uc5d0 \ubaa8\ub378\uc744\n\ubd88\ub7ec\uc62c \ub54c \uc720\uc5f0\ud568\uc744 \ud06c\uac8c \uc0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud559\uc2b5\ub41c \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218(parameter)\ub9cc\uc744\n\uc800\uc7a5\ud558\uba74\ub418\ubbc0\ub85c \ubaa8\ub378 \uc800\uc7a5 \uc2dc\uc5d0 \uad8c\uc7a5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ubaa8\ub378 \uc804\uccb4\ub97c \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc62c\n\ub54c\uc5d0\ub294 Python\uc758 `pickle <https://docs.python.org/3/library/pickle.html>`__ \ubaa8\ub4c8\uc744\n\uc0ac\uc6a9\ud558\uc5ec \uc804\uccb4 \ubaa8\ub4c8\uc744 \uc800\uc7a5\ud569\ub2c8\ub2e4. \uc774 \ubc29\uc2dd\uc740 \uc9c1\uad00\uc801\uc778 \ubb38\ubc95\uc744 \uc0ac\uc6a9\ud558\uba70 \ucf54\ub4dc\uc758 \uc591\ub3c4\n\uc801\uc2b5\ub2c8\ub2e4. \uc774 \ubc29\uc2dd\uc758 \ub2e8\uc810\uc740 \uc9c1\ub82c\ud654(serialized)\ub41c \ub370\uc774\ud130\uac00 \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c \uc0ac\uc6a9\ud55c\n\ud2b9\uc815 \ud074\ub798\uc2a4 \ubc0f \ub514\ub809\ud1a0\ub9ac \uad6c\uc870\uc5d0 \uc885\uc18d(bind)\ub41c\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4. \uadf8 \uc774\uc720\ub294 pickle\uc774\n\ubaa8\ub378 \ud074\ub798\uc2a4 \uc790\uccb4\ub97c \uc800\uc7a5\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. \uc624\ud788\ub824 \ubd88\ub7ec\uc62c \ub54c \uc0ac\uc6a9\ub418\ub294 \ud074\ub798\uc2a4\uac00\n\ud3ec\ud568\ub41c \ud30c\uc77c\uc758 \uacbd\ub85c\ub97c \uc800\uc7a5\ud569\ub2c8\ub2e4. \uc774 \ub54c\ubb38\uc5d0 \uc791\uc131\ud55c \ucf54\ub4dc\uac00 \ub2e4\ub978 \ud504\ub85c\uc81d\ud2b8\uc5d0\uc11c\n\uc0ac\uc6a9\ub418\uac70\ub098 \ub9ac\ud329\ud1a0\ub9c1\uc744 \uac70\uce58\ub294 \ub4f1\uc758 \uacfc\uc815\uc5d0\uc11c \ub3d9\uc791\ud558\uc9c0 \uc54a\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294\n\ucd94\ub860\uc744 \uc704\ud574 \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ub450 \uac00\uc9c0 \ubc29\ubc95 \ubaa8\ub450\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815\n----------\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0 ``torch`` \uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n\n::\n\n   pip install torch\n"
       ]
     },
     {
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/6b6889455ef3d6c74e64c3fc1c12815b/dynamic_net.ipynb b/docs/_downloads/6b6889455ef3d6c74e64c3fc1c12815b/dynamic_net.ipynb
index 6c9e1d4d1..531061c2f 100644
--- a/docs/_downloads/6b6889455ef3d6c74e64c3fc1c12815b/dynamic_net.ipynb
+++ b/docs/_downloads/6b6889455ef3d6c74e64c3fc1c12815b/dynamic_net.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch: \uc81c\uc5b4 \ud750\ub984(Control Flow) + \uac00\uc911\uce58 \uacf5\uc720(Weight Sharing)\n---------------------------------------------------------------\n\nPyTorch \ub3d9\uc801 \uadf8\ub798\ud504\uc758 \uac15\ub825\ud568\uc744 \ubcf4\uc5ec\uc8fc\uae30 \uc704\ud574, \ub9e4\uc6b0 \uc774\uc0c1\ud55c \ubaa8\ub378\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\uac01 \uc21c\uc804\ud30c \ub2e8\uacc4\uc5d0\uc11c \ub9ce\uc740 \uc740\ub2c9 \uacc4\uce35\uc744 \uac16\ub294 \uc644\uc804\ud788 \uc5f0\uacb0(fully-connected)\ub41c ReLU\n\uc2e0\uacbd\ub9dd\uc774 \ubb34\uc791\uc704\ub85c 0 ~ 3 \uc0ac\uc774\uc758 \uc22b\uc790\ub97c \uc120\ud0dd\ud558\uace0, \uac00\uc7a5 \uc548\ucabd(innermost)\uc758 \uc740\ub2c9\uce35\ub4e4\uc744\n\uacc4\uc0b0\ud558\uae30 \uc704\ud574 \ub3d9\uc77c\ud55c \uac00\uc911\uce58\ub97c \uc5ec\ub7ec \ubc88 \uc7ac\uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n"
+        "\nPyTorch: \uc81c\uc5b4 \ud750\ub984(Control Flow) + \uac00\uc911\uce58 \uacf5\uc720(Weight Sharing)\n---------------------------------------------------------------\n\nPyTorch \ub3d9\uc801 \uadf8\ub798\ud504\uc758 \uac15\ub825\ud568\uc744 \ubcf4\uc5ec\uc8fc\uae30 \uc704\ud574, \ub9e4\uc6b0 \uc774\uc0c1\ud55c \ubaa8\ub378\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\uac01 \uc21c\uc804\ud30c \ub2e8\uacc4\uc5d0\uc11c \ub9ce\uc740 \uc740\ub2c9 \uacc4\uce35\uc744 \uac16\ub294 \uc644\uc804\ud788 \uc5f0\uacb0(fully-connected)\ub41c ReLU\n\uc2e0\uacbd\ub9dd\uc774 \ubb34\uc791\uc704\ub85c 0 ~ 3 \uc0ac\uc774\uc758 \uc22b\uc790\ub97c \uc120\ud0dd\ud558\uace0, \uac00\uc7a5 \uc548\ucabd(innermost)\uc758 \uc740\ub2c9\uce35\ub4e4\uc744\n\uacc4\uc0b0\ud558\uae30 \uc704\ud574 \ub3d9\uc77c\ud55c \uac00\uc911\uce58\ub97c \uc5ec\ub7ec \ubc88 \uc7ac\uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/6d8d81d44ebb8c150be6c39ce263fc89/torchtext_translation_tutorial.ipynb b/docs/_downloads/6d8d81d44ebb8c150be6c39ce263fc89/torchtext_translation_tutorial.ipynb
index d8c90b6f7..495229feb 100644
--- a/docs/_downloads/6d8d81d44ebb8c150be6c39ce263fc89/torchtext_translation_tutorial.ipynb
+++ b/docs/_downloads/6d8d81d44ebb8c150be6c39ce263fc89/torchtext_translation_tutorial.ipynb
@@ -15,14 +15,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nTorchText\ub85c \uc5b8\uc5b4 \ubc88\uc5ed\ud558\uae30\n===================================\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ``torchtext`` \uc758 \uc720\uc6a9\ud55c \uc5ec\ub7ec \ud074\ub798\uc2a4\ub4e4\uacfc \uc2dc\ud000\uc2a4 \ud22c \uc2dc\ud000\uc2a4(sequence-to-sequence, seq2seq)\ubaa8\ub378\uc744 \ud1b5\ud574\n\uc601\uc5b4\uc640 \ub3c5\uc77c\uc5b4 \ubb38\uc7a5\ub4e4\uc774 \ud3ec\ud568\ub41c \uc720\uba85\ud55c \ub370\uc774\ud130 \uc14b\uc744 \uc774\uc6a9\ud574\uc11c \ub3c5\uc77c\uc5b4 \ubb38\uc7a5\uc744 \uc601\uc5b4\ub85c \ubc88\uc5ed\ud574 \ubcfc \uac83\uc785\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 \nPyTorch \ucee4\ubba4\ub2c8\ud2f0 \uba64\ubc84\uc778 `Ben Trevett <https://github.com/bentrevett>`__ \uc774 \uc791\uc131\ud55c\n`\ud29c\ud1a0\ub9ac\uc5bc <https://github.com/bentrevett/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb>`__ \uc5d0 \uae30\ucd08\ud558\uace0 \uc788\uc73c\uba70\n`Seth Weidman <https://github.com/SethHWeidman/>`__ \uc774 Ben\uc758 \ud5c8\ub77d\uc744 \ubc1b\uace0 \ub9cc\ub4e4\uc5c8\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ud1b5\ud574 \uc5ec\ub7ec\ubd84\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uac83\uc744 \ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4:\n\n- ``torchtext`` \uc758 \uc544\ub798\uc640 \uac19\uc740 \uc720\uc6a9\ud55c \ud074\ub798\uc2a4\ub4e4\uc744 \ud1b5\ud574 \ubb38\uc7a5\ub4e4\uc744 NLP\ubaa8\ub378\ub9c1\uc5d0 \uc790\uc8fc \uc0ac\uc6a9\ub418\ub294 \ud615\ud0dc\ub85c \uc804\ucc98\ub9ac\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4:\n    - `TranslationDataset <https://torchtext.readthedocs.io/en/latest/datasets.html#torchtext.datasets.TranslationDataset>`__\n    - `Field <https://torchtext.readthedocs.io/en/latest/data.html#torchtext.data.Field>`__\n    - `BucketIterator <https://torchtext.readthedocs.io/en/latest/data.html#torchtext.data.BucketIterator>`__\n\n"
+        "\nTorchText\ub85c \uc5b8\uc5b4 \ubc88\uc5ed\ud558\uae30\n===================================\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ``torchtext`` \uc758 \uc720\uc6a9\ud55c \uc5ec\ub7ec \ud074\ub798\uc2a4\ub4e4\uacfc \uc2dc\ud000\uc2a4 \ud22c \uc2dc\ud000\uc2a4(sequence-to-sequence, seq2seq)\ubaa8\ub378\uc744 \ud1b5\ud574\n\uc601\uc5b4\uc640 \ub3c5\uc77c\uc5b4 \ubb38\uc7a5\ub4e4\uc774 \ud3ec\ud568\ub41c \uc720\uba85\ud55c \ub370\uc774\ud130 \uc14b\uc744 \uc774\uc6a9\ud574\uc11c \ub3c5\uc77c\uc5b4 \ubb38\uc7a5\uc744 \uc601\uc5b4\ub85c \ubc88\uc5ed\ud574 \ubcfc \uac83\uc785\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740\nPyTorch \ucee4\ubba4\ub2c8\ud2f0 \uba64\ubc84\uc778 `Ben Trevett <https://github.com/bentrevett>`__ \uc774 \uc791\uc131\ud55c\n`\ud29c\ud1a0\ub9ac\uc5bc <https://github.com/bentrevett/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb>`__ \uc5d0 \uae30\ucd08\ud558\uace0 \uc788\uc73c\uba70\n`Seth Weidman <https://github.com/SethHWeidman/>`__ \uc774 Ben\uc758 \ud5c8\ub77d\uc744 \ubc1b\uace0 \ub9cc\ub4e4\uc5c8\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ud1b5\ud574 \uc5ec\ub7ec\ubd84\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uac83\uc744 \ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4:\n\n- ``torchtext`` \uc758 \uc544\ub798\uc640 \uac19\uc740 \uc720\uc6a9\ud55c \ud074\ub798\uc2a4\ub4e4\uc744 \ud1b5\ud574 \ubb38\uc7a5\ub4e4\uc744 NLP\ubaa8\ub378\ub9c1\uc5d0 \uc790\uc8fc \uc0ac\uc6a9\ub418\ub294 \ud615\ud0dc\ub85c \uc804\ucc98\ub9ac\ud560 \uc218 \uc788\uac8c \ub429\ub2c8\ub2e4:\n    - `TranslationDataset <https://torchtext.readthedocs.io/en/latest/datasets.html#torchtext.datasets.TranslationDataset>`__\n    - `Field <https://torchtext.readthedocs.io/en/latest/data.html#torchtext.data.Field>`__\n    - `BucketIterator <https://torchtext.readthedocs.io/en/latest/data.html#torchtext.data.BucketIterator>`__\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "`Field` \uc640 `TranslationDataset`\n----------------\n``torchtext`` \uc5d0\ub294 \uc5b8\uc5b4 \ubcc0\ud658 \ubaa8\ub378\uc744 \ub9cc\ub4e4\ub54c \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4\uae30 \uc801\ud569\ud55c \ub2e4\uc591\ud55c \ub3c4\uad6c\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uadf8 \uc911\uc5d0\uc11c\ub3c4 \uc911\uc694\ud55c \ud074\ub798\uc2a4 \uc911 \ud558\ub098\uc778 `Field <https://github.com/pytorch/text/blob/master/torchtext/data/field.py#L64>`__ \ub294\n\uac01 \ubb38\uc7a5\uc774 \uc5b4\ub5bb\uac8c \uc804\ucc98\ub9ac\ub418\uc5b4\uc57c \ud558\ub294\uc9c0 \uc9c0\uc815\ud558\uba70, \ub610 \ub2e4\ub978 \uc911\uc694\ud55c \ud074\ub798\uc2a4\ub85c\ub294 `TranslationDataset` \uc774 \uc788\uc2b5\ub2c8\ub2e4. \n``torchtext`` \uc5d0\ub294 \uc774 \uc678\uc5d0\ub3c4 \ube44\uc2b7\ud55c \ub370\uc774\ud130\uc14b\ub4e4\uc774 \uc788\ub294\ub370, \uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 `Multi30k dataset <https://github.com/multi30k/dataset>`__ \uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774 \ub370\uc774\ud130 \uc14b\uc740 \ud3c9\uade0 \uc57d 13\uac1c\uc758 \ub2e8\uc5b4\ub85c \uad6c\uc131\ub41c \uc57d \uc0bc\ub9cc \uac1c\uc758 \ubb38\uc7a5\uc744 \uc601\uc5b4\uc640 \ub3c5\uc77c\uc5b4 \ub450 \uc5b8\uc5b4\ub85c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ucc38\uace0 : \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\uc758 \ud1a0\ud070\ud654(tokenization)\uc5d0\ub294 `Spacy <https://spacy.io>`__ \uac00 \ud544\uc694\ud569\ub2c8\ub2e4.\nSpacy\ub294 \uc601\uc5b4 \uc774 \uc678\uc758 \ub2e4\ub978 \uc5b8\uc5b4\uc5d0 \ub300\ud55c \uac15\ub825\ud55c \ud1a0\ud070\ud654 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uae30 \ub54c\ubb38\uc5d0 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. ``torchtext`` \ub294\n`basic_english`` \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc81c\uacf5\ud560 \ubfd0 \uc544\ub2c8\ub77c \uc601\uc5b4\uc5d0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub2e4\ub978 \ud1a0\ud06c\ub098\uc774\uc800\ub4e4(\uc608\ucee8\ub370\n`Moses <https://bitbucket.org/luismsgomes/mosestokenizer/src/default/>`__ )\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4\ub9cc, \uc5b8\uc5b4 \ubc88\uc5ed\uc744 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \uc5b8\uc5b4\ub97c\n\ub2e4\ub8e8\uc5b4\uc57c \ud558\uae30 \ub54c\ubb38\uc5d0 Spacy\uac00 \uac00\uc7a5 \uc801\ud569\ud569\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc2e4\ud589\ud558\ub824\uba74, \uc6b0\uc120 ``pip`` \ub098 ``conda`` \ub85c ``spacy`` \ub97c \uc124\uce58\ud558\uc138\uc694. \uadf8 \ub2e4\uc74c,\nSpacy \ud1a0\ud06c\ub098\uc774\uc800\uac00 \uc4f8 \uc601\uc5b4\uc640 \ub3c5\uc77c\uc5b4\uc5d0 \ub300\ud55c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uc2b5\ub2c8\ub2e4.\n\n::\n\n   python -m spacy download en\n   python -m spacy download de\n\nSpacy\uac00 \uc124\uce58\ub418\uc5b4 \uc788\ub2e4\uba74, \ub2e4\uc74c \ucf54\ub4dc\ub294 ``TranslationDataset`` \uc5d0 \uc788\ub294 \uac01 \ubb38\uc7a5\uc744 ``Field`` \uc5d0 \uc815\uc758\ub41c\n\ub0b4\uc6a9\uc744 \uae30\ubc18\uc73c\ub85c \ud1a0\ud070\ud654\ud560 \uac83\uc785\ub2c8\ub2e4.\n\n"
+        "`Field` \uc640 `TranslationDataset`\n--------------------------------\n\n``torchtext`` \uc5d0\ub294 \uc5b8\uc5b4 \ubcc0\ud658 \ubaa8\ub378\uc744 \ub9cc\ub4e4\ub54c \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4\uae30 \uc801\ud569\ud55c \ub2e4\uc591\ud55c \ub3c4\uad6c\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uadf8 \uc911\uc5d0\uc11c\ub3c4 \uc911\uc694\ud55c \ud074\ub798\uc2a4 \uc911 \ud558\ub098\uc778 `Field <https://github.com/pytorch/text/blob/master/torchtext/data/field.py#L64>`__ \ub294\n\uac01 \ubb38\uc7a5\uc774 \uc5b4\ub5bb\uac8c \uc804\ucc98\ub9ac\ub418\uc5b4\uc57c \ud558\ub294\uc9c0 \uc9c0\uc815\ud558\uba70, \ub610 \ub2e4\ub978 \uc911\uc694\ud55c \ud074\ub798\uc2a4\ub85c\ub294 `TranslationDataset` \uc774 \uc788\uc2b5\ub2c8\ub2e4.\n``torchtext`` \uc5d0\ub294 \uc774 \uc678\uc5d0\ub3c4 \ube44\uc2b7\ud55c \ub370\uc774\ud130\uc14b\ub4e4\uc774 \uc788\ub294\ub370, \uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 `Multi30k dataset <https://github.com/multi30k/dataset>`__ \uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774 \ub370\uc774\ud130 \uc14b\uc740 \ud3c9\uade0 \uc57d 13\uac1c\uc758 \ub2e8\uc5b4\ub85c \uad6c\uc131\ub41c \uc57d \uc0bc\ub9cc \uac1c\uc758 \ubb38\uc7a5\uc744 \uc601\uc5b4\uc640 \ub3c5\uc77c\uc5b4 \ub450 \uc5b8\uc5b4\ub85c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ucc38\uace0 : \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\uc758 \ud1a0\ud070\ud654(tokenization)\uc5d0\ub294 `Spacy <https://spacy.io>`__ \uac00 \ud544\uc694\ud569\ub2c8\ub2e4.\nSpacy\ub294 \uc601\uc5b4 \uc774 \uc678\uc758 \ub2e4\ub978 \uc5b8\uc5b4\uc5d0 \ub300\ud55c \uac15\ub825\ud55c \ud1a0\ud070\ud654 \uae30\ub2a5\uc744 \uc81c\uacf5\ud558\uae30 \ub54c\ubb38\uc5d0 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. ``torchtext`` \ub294\n`basic_english`` \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc81c\uacf5\ud560 \ubfd0 \uc544\ub2c8\ub77c \uc601\uc5b4\uc5d0 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \ub2e4\ub978 \ud1a0\ud06c\ub098\uc774\uc800\ub4e4(\uc608\ucee8\ub370\n`Moses <https://bitbucket.org/luismsgomes/mosestokenizer/src/default/>`__ )\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4\ub9cc, \uc5b8\uc5b4 \ubc88\uc5ed\uc744 \uc704\ud574\uc11c\ub294 \ub2e4\uc591\ud55c \uc5b8\uc5b4\ub97c\n\ub2e4\ub8e8\uc5b4\uc57c \ud558\uae30 \ub54c\ubb38\uc5d0 Spacy\uac00 \uac00\uc7a5 \uc801\ud569\ud569\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc2e4\ud589\ud558\ub824\uba74, \uc6b0\uc120 ``pip`` \ub098 ``conda`` \ub85c ``spacy`` \ub97c \uc124\uce58\ud558\uc138\uc694. \uadf8 \ub2e4\uc74c,\nSpacy \ud1a0\ud06c\ub098\uc774\uc800\uac00 \uc4f8 \uc601\uc5b4\uc640 \ub3c5\uc77c\uc5b4\uc5d0 \ub300\ud55c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4\ub85c\ub4dc \ubc1b\uc2b5\ub2c8\ub2e4.\n\n::\n\n   python -m spacy download en\n   python -m spacy download de\n\nSpacy\uac00 \uc124\uce58\ub418\uc5b4 \uc788\ub2e4\uba74, \ub2e4\uc74c \ucf54\ub4dc\ub294 ``TranslationDataset`` \uc5d0 \uc788\ub294 \uac01 \ubb38\uc7a5\uc744 ``Field`` \uc5d0 \uc815\uc758\ub41c\n\ub0b4\uc6a9\uc744 \uae30\ubc18\uc73c\ub85c \ud1a0\ud070\ud654\ud560 \uac83\uc785\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -65,7 +65,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "``BucketIterator``\n----------------\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc0ac\uc6a9\ud574 \ubcfc ``torchtext`` \uc5d0 \ud2b9\ud654\ub41c \uae30\ub2a5\uc740 \ubc14\ub85c ``BucketIterator`` \uc785\ub2c8\ub2e4.\n\uccab \ubc88\uc9f8 \uc778\uc790\ub85c ``TranslationDataset`` \uc744 \uc804\ub2ec\ubc1b\uae30 \ub54c\ubb38\uc5d0 \uc0ac\uc6a9\ud558\uae30\uac00 \uc27d\uc2b5\ub2c8\ub2e4. \ubb38\uc11c\uc5d0\uc11c\ub3c4 \ubcfc \uc218 \uc788\ub4ef\n\uc774 \uae30\ub2a5\uc740 \ube44\uc2b7\ud55c \uae38\uc774\uc758 \uc608\uc81c\ub4e4\uc744 \ubb36\uc5b4\uc8fc\ub294 \ubc18\ubcf5\uc790(iterator)\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uac01\uac01\uc758 \uc0c8\ub85c\uc6b4 \uc5d0\ud3ec\ud06c(epoch)\ub9c8\ub2e4\n\uc0c8\ub85c \uc11e\uc778 \uacb0\uacfc\ub97c \ub9cc\ub4dc\ub294\ub370 \ud544\uc694\ud55c \ud328\ub529\uc758 \uc218\ub97c \ucd5c\uc18c\ud654 \ud569\ub2c8\ub2e4. \ubc84\ucf00\ud305 \uacfc\uc815\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \uc800\uc7a5 \uacf5\uac04\uc744 \ud55c\ubc88 \uc0b4\ud3b4\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n\n"
+        "``BucketIterator``\n--------------------\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc0ac\uc6a9\ud574 \ubcfc ``torchtext`` \uc5d0 \ud2b9\ud654\ub41c \uae30\ub2a5\uc740 \ubc14\ub85c ``BucketIterator`` \uc785\ub2c8\ub2e4.\n\uccab \ubc88\uc9f8 \uc778\uc790\ub85c ``TranslationDataset`` \uc744 \uc804\ub2ec\ubc1b\uae30 \ub54c\ubb38\uc5d0 \uc0ac\uc6a9\ud558\uae30\uac00 \uc27d\uc2b5\ub2c8\ub2e4. \ubb38\uc11c\uc5d0\uc11c\ub3c4 \ubcfc \uc218 \uc788\ub4ef\n\uc774 \uae30\ub2a5\uc740 \ube44\uc2b7\ud55c \uae38\uc774\uc758 \uc608\uc81c\ub4e4\uc744 \ubb36\uc5b4\uc8fc\ub294 \ubc18\ubcf5\uc790(iterator)\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uac01\uac01\uc758 \uc0c8\ub85c\uc6b4 \uc5d0\ud3ec\ud06c(epoch)\ub9c8\ub2e4\n\uc0c8\ub85c \uc11e\uc778 \uacb0\uacfc\ub97c \ub9cc\ub4dc\ub294\ub370 \ud544\uc694\ud55c \ud328\ub529\uc758 \uc218\ub97c \ucd5c\uc18c\ud654 \ud569\ub2c8\ub2e4. \ubc84\ucf00\ud305 \uacfc\uc815\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 \uc800\uc7a5 \uacf5\uac04\uc744 \ud55c\ubc88 \uc0b4\ud3b4\ubcf4\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -83,14 +83,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc774 \ubc18\ubcf5\uc790\ub4e4\uc740 ``DataLoader`` \uc640 \ub9c8\ucc2c\uac00\uc9c0\ub85c \ud638\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 ``train`` \uacfc \n``evaluation`` \ud568\uc218\uc5d0\uc11c \ubcf4\uba74, \ub2e4\uc74c\uacfc \uac19\uc774 \uac04\ub2e8\ud788 \ud638\ucd9c\ud560 \uc218 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4 :\n::\n\n   for i, batch in enumerate(iterator):\n\n\uac01 ``batch`` \ub294 ``src`` \uc640 ``trg`` \uc18d\uc131\uc744 \uac00\uc9c0\uac8c \ub429\ub2c8\ub2e4.\n\n::\n\n   src = batch.src\n   trg = batch.trg\n\n"
+        "\uc774 \ubc18\ubcf5\uc790\ub4e4\uc740 ``DataLoader`` \uc640 \ub9c8\ucc2c\uac00\uc9c0\ub85c \ud638\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798 ``train`` \uacfc\n``evaluation`` \ud568\uc218\uc5d0\uc11c \ubcf4\uba74, \ub2e4\uc74c\uacfc \uac19\uc774 \uac04\ub2e8\ud788 \ud638\ucd9c\ud560 \uc218 \uc788\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4 :\n::\n\n   for i, batch in enumerate(iterator):\n\n\uac01 ``batch`` \ub294 ``src`` \uc640 ``trg`` \uc18d\uc131\uc744 \uac00\uc9c0\uac8c \ub429\ub2c8\ub2e4.\n\n::\n\n   src = batch.src\n   trg = batch.trg\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "``nn.Module`` \uacfc ``Optimizer`` \uc815\uc758\ud558\uae30\n----------------\n\ub300\ubd80\ubd84\uc740 ``torchtext`` \uac00 \uc54c\uc544\uc11c \ud574\uc90d\ub2c8\ub2e4 : \ub370\uc774\ud130\uc14b\uc774 \ub9cc\ub4e4\uc5b4\uc9c0\uace0 \ubc18\ubcf5\uc790\uac00 \uc815\uc758\ub418\uba74, \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\n\uc6b0\ub9ac\uac00 \ud574\uc57c \ud560 \uc77c\uc774\ub77c\uace0\ub294 \uadf8\uc800 ``nn.Module`` \uc640 ``Optimizer`` \ub97c \ubaa8\ub378\ub85c\uc11c \uc815\uc758\ud558\uace0 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uac83\uc774 \uc804\ubd80\uc785\ub2c8\ub2e4.\n\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc0ac\uc6a9\ud560 \ubaa8\ub378\uc740 `\uc774\uacf3 <https://arxiv.org/abs/1409.0473>`__ \uc5d0\uc11c \uc124\uba85\ud558\uace0 \uc788\ub294 \uad6c\uc870\ub97c \ub530\ub974\uace0 \uc788\uc73c\uba70,\n\ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 `\uc5ec\uae30 <https://github.com/SethHWeidman/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb>`__ \n\ub97c \ucc38\uace0\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n\n\ucc38\uace0 : \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \ubaa8\ub378\uc740 \uc5b8\uc5b4 \ubc88\uc5ed\uc744 \uc704\ud574 \uc0ac\uc6a9\ud560 \uc608\uc2dc \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc740\n\uc774 \uc791\uc5c5\uc5d0 \uc801\ub2f9\ud55c \ud45c\uc900 \ubaa8\ub378\uc774\uae30 \ub54c\ubb38\uc774\uc9c0, \ubc88\uc5ed\uc5d0 \uc801\ud569\ud55c \ubaa8\ub378\uc774\uae30 \ub54c\ubb38\uc740 \uc544\ub2d9\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc774 \ucd5c\uc2e0 \uae30\uc220 \ud2b8\ub80c\ub4dc\ub97c\n\uc798 \ub530\ub77c\uac00\uace0 \uc788\ub2e4\uba74 \uc798 \uc544\uc2dc\uaca0\uc9c0\ub9cc, \ud604\uc7ac \ubc88\uc5ed\uc5d0\uc11c \uac00\uc7a5 \ub6f0\uc5b4\ub09c \ubaa8\ub378\uc740 Transformers\uc785\ub2c8\ub2e4. PyTorch\uac00\nTransformer \ub808\uc774\uc5b4\ub97c \uad6c\ud604\ud55c \ub0b4\uc6a9\uc740 `\uc5ec\uae30 <https://pytorch.org/docs/stable/nn.html#transformer-layers>`__\n\uc5d0\uc11c \ud655\uc778\ud560 \uc218 \uc788\uc73c\uba70 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ubaa8\ub378\uc774 \uc0ac\uc6a9\ud558\ub294 \"attention\" \uc740 Transformer \ubaa8\ub378\uc5d0\uc11c \uc81c\uc548\ud558\ub294\n\uba40\ud2f0 \ud5e4\ub4dc \uc140\ud504 \uc5b4\ud150\uc158(multi-headed self-attention) \uacfc\ub294 \ub2e4\ub974\ub2e4\ub294 \uc810\uc744 \uc54c\ub824\ub4dc\ub9bd\ub2c8\ub2e4.\n\n"
+        "``nn.Module`` \uacfc ``Optimizer`` \uc815\uc758\ud558\uae30\n------------------------------------------\n\ub300\ubd80\ubd84\uc740 ``torchtext`` \uac00 \uc54c\uc544\uc11c \ud574\uc90d\ub2c8\ub2e4 : \ub370\uc774\ud130\uc14b\uc774 \ub9cc\ub4e4\uc5b4\uc9c0\uace0 \ubc18\ubcf5\uc790\uac00 \uc815\uc758\ub418\uba74, \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\n\uc6b0\ub9ac\uac00 \ud574\uc57c \ud560 \uc77c\uc774\ub77c\uace0\ub294 \uadf8\uc800 ``nn.Module`` \uc640 ``Optimizer`` \ub97c \ubaa8\ub378\ub85c\uc11c \uc815\uc758\ud558\uace0 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uac83\uc774 \uc804\ubd80\uc785\ub2c8\ub2e4.\n\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc0ac\uc6a9\ud560 \ubaa8\ub378\uc740 `\uc774\uacf3 <https://arxiv.org/abs/1409.0473>`__ \uc5d0\uc11c \uc124\uba85\ud558\uace0 \uc788\ub294 \uad6c\uc870\ub97c \ub530\ub974\uace0 \uc788\uc73c\uba70,\n\ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 `\uc5ec\uae30 <https://github.com/SethHWeidman/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb>`__\n\ub97c \ucc38\uace0\ud558\uc2dc\uae30 \ubc14\ub78d\ub2c8\ub2e4.\n\n\ucc38\uace0 : \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc0ac\uc6a9\ud558\ub294 \ubaa8\ub378\uc740 \uc5b8\uc5b4 \ubc88\uc5ed\uc744 \uc704\ud574 \uc0ac\uc6a9\ud560 \uc608\uc2dc \ubaa8\ub378\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub294 \uac83\uc740\n\uc774 \uc791\uc5c5\uc5d0 \uc801\ub2f9\ud55c \ud45c\uc900 \ubaa8\ub378\uc774\uae30 \ub54c\ubb38\uc774\uc9c0, \ubc88\uc5ed\uc5d0 \uc801\ud569\ud55c \ubaa8\ub378\uc774\uae30 \ub54c\ubb38\uc740 \uc544\ub2d9\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc774 \ucd5c\uc2e0 \uae30\uc220 \ud2b8\ub80c\ub4dc\ub97c\n\uc798 \ub530\ub77c\uac00\uace0 \uc788\ub2e4\uba74 \uc798 \uc544\uc2dc\uaca0\uc9c0\ub9cc, \ud604\uc7ac \ubc88\uc5ed\uc5d0\uc11c \uac00\uc7a5 \ub6f0\uc5b4\ub09c \ubaa8\ub378\uc740 Transformers\uc785\ub2c8\ub2e4. PyTorch\uac00\nTransformer \ub808\uc774\uc5b4\ub97c \uad6c\ud604\ud55c \ub0b4\uc6a9\uc740 `\uc5ec\uae30 <https://pytorch.org/docs/stable/nn.html#transformer-layers>`__\n\uc5d0\uc11c \ud655\uc778\ud560 \uc218 \uc788\uc73c\uba70 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ubaa8\ub378\uc774 \uc0ac\uc6a9\ud558\ub294 \"attention\" \uc740 Transformer \ubaa8\ub378\uc5d0\uc11c \uc81c\uc548\ud558\ub294\n\uba40\ud2f0 \ud5e4\ub4dc \uc140\ud504 \uc5b4\ud150\uc158(multi-headed self-attention) \uacfc\ub294 \ub2e4\ub974\ub2e4\ub294 \uc810\uc744 \uc54c\ub824\ub4dc\ub9bd\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/70d2076b0da469ff43a0ec4f6100b39c/numeric_suite_tutorial.ipynb b/docs/_downloads/70d2076b0da469ff43a0ec4f6100b39c/numeric_suite_tutorial.ipynb
index 4e6a0f75e..efd2ee5d1 100644
--- a/docs/_downloads/70d2076b0da469ff43a0ec4f6100b39c/numeric_suite_tutorial.ipynb
+++ b/docs/_downloads/70d2076b0da469ff43a0ec4f6100b39c/numeric_suite_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch Numeric Suite Tutorial\n==============================\n\nIntroduction\n------------\n\nQuantization is good when it works, but it\u2019s difficult to know what's wrong when it doesn't satisfy the accuracy we expect. Debugging the accuracy issue of quantization is not easy and time consuming.\n\nOne important step of debugging is to measure the statistics of the float model and its corresponding quantized model to know where are they differ most. We built a suite of numeric tools called PyTorch Numeric Suite in PyTorch quantization to enable the measurement of the statistics between quantized module and float module to support quantization debugging efforts. Even for the quantized model with good accuracy, PyTorch Numeric Suite can still be used as the profiling tool to better understand the quantization error within the model and provide the guidance for further optimization.\n\nPyTorch Numeric Suite currently supports models quantized through both static quantization and dynamic quantization with unified APIs.\n\nIn this tutorial we will first use ResNet18 as an example to show how to use PyTorch Numeric Suite to measure the statistics between static quantized model and float model in eager mode. Then we will use LSTM based sequence model as an example to show the usage of PyTorch Numeric Suite for dynamic quantized model.\n\nNumeric Suite for Static Quantization\n-------------------------------------\n\nSetup\n^^^^^^\nWe\u2019ll start by doing the necessary imports:\n\n"
+        "\nPyTorch Numeric Suite Tutorial\n==============================\n\nIntroduction\n------------\n\nQuantization is good when it works, but it\u2019s difficult to know what's wrong when it doesn't satisfy the accuracy we expect. Debugging the accuracy issue of quantization is not easy and time consuming.\n\nOne important step of debugging is to measure the statistics of the float model and its corresponding quantized model to know where are they differ most. We built a suite of numeric tools called PyTorch Numeric Suite in PyTorch quantization to enable the measurement of the statistics between quantized module and float module to support quantization debugging efforts. Even for the quantized model with good accuracy, PyTorch Numeric Suite can still be used as the profiling tool to better understand the quantization error within the model and provide the guidance for further optimization.\n\nPyTorch Numeric Suite currently supports models quantized through both static quantization and dynamic quantization with unified APIs.\n\nIn this tutorial we will first use ResNet18 as an example to show how to use PyTorch Numeric Suite to measure the statistics between static quantized model and float model in eager mode. Then we will use LSTM based sequence model as an example to show the usage of PyTorch Numeric Suite for dynamic quantized model.\n\nNumeric Suite for Static Quantization\n-------------------------------------\n\nSetup\n^^^^^^\nWe\u2019ll start by doing the necessary imports:\n"
       ]
     },
     {
@@ -528,7 +528,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/7126bf7beed4c4c3a05bcc2dac8baa3c/pruning_tutorial.ipynb b/docs/_downloads/7126bf7beed4c4c3a05bcc2dac8baa3c/pruning_tutorial.ipynb
index 06f7e21f9..c23b5a461 100644
--- a/docs/_downloads/7126bf7beed4c4c3a05bcc2dac8baa3c/pruning_tutorial.ipynb
+++ b/docs/_downloads/7126bf7beed4c4c3a05bcc2dac8baa3c/pruning_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95(Pruning) \ud29c\ud1a0\ub9ac\uc5bc\n=====================================\n**\uc800\uc790**: `Michela Paganini <https://github.com/mickypaganini>`_\n**\ubc88\uc5ed** : `\uc548\uc0c1\uc900 <https://github.com/Justin-A>`_\n\n\ucd5c\ucca8\ub2e8 \ub525\ub7ec\ub2dd \ubaa8\ub378\ub4e4\uc740 \uad49\uc7a5\ud788 \ub9ce\uc740 \uc218\uc758 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\ub85c \uad6c\uc131\ub418\uae30 \ub54c\ubb38\uc5d0, \uc27d\uac8c \ubc30\ud3ec\ub418\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.\n\uc774\uc640 \ubc18\ub300\ub85c, \uc0dd\ubb3c\ud559\uc801 \uc2e0\uacbd\ub9dd\ub4e4\uc740 \ud6a8\uc728\uc801\uc73c\ub85c \ud76c\uc18c\ud558\uac8c \uc5f0\uacb0\ub41c \uac83\uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4.\n\ubaa8\ub378\uc758 \uc815\ud655\ub3c4\uac00 \uc190\uc0c1\ub418\uc9c0 \uc54a\ub294 \ubc94\uc704\uc5d0\uc11c \uba54\ubaa8\ub9ac, \ubc30\ud130\ub9ac, \ud558\ub4dc\uc6e8\uc5b4 \uc18c\ube44\ub7c9\uc744 \uc904\uc774\uace0, \n\uae30\uae30\uc5d0 \uacbd\ub7c9\ud654\ub41c \ubaa8\ub378\uc744 \ubc30\uce58\ud558\uba70, \uac1c\uc778\uc774 \uc774\uc6a9\ud558\uace0 \uc788\ub294 \uae30\uae30\uc5d0\uc11c \ud504\ub77c\uc774\ubc84\uc2dc\uac00 \ubcf4\uc7a5\ub418\uae30 \uc704\ud574\uc11c\ub294 \n\ubaa8\ub378\uc5d0 \ud3ec\ud568\ub41c \ud30c\ub77c\ubbf8\ud130 \uc218\ub97c \uc904\uc5ec \uc555\ucd95\ud558\ub294 \ucd5c\uc801\uc758 \uae30\ubc95\uc744 \ud30c\uc545\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.\n\uc5f0\uad6c \uce21\uba74\uc5d0\uc11c\ub294, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740 \uad49\uc7a5\ud788 \ub9ce\uc740 \uc218\uc758 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\ub85c \uad6c\uc131\ub41c \ubaa8\ub378\uacfc \n\uad49\uc7a5\ud788 \uc801\uc740 \uc218\uc758 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\ub85c \uad6c\uc131\ub41c \ubaa8\ub378 \uac04 \ud559\uc2b5 \uc5ed\ud559 \ucc28\uc774\ub97c \uc870\uc0ac\ud558\ub294\ub370 \uc8fc\ub85c \uc774\uc6a9\ub418\uae30\ub3c4 \ud558\uba70,\n\ud558\uc704 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uacfc \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc758 \ucd08\uae30\ud654\uac00 \uc6b4\uc774 \uc88b\uac8c \uc798 \ub41c \ucf00\uc774\uc2a4\ub97c \ubc14\ud0d5\uc73c\ub85c \n(\"`lottery tickets <https://arxiv.org/abs/1803.03635>`_\") \uc2e0\uacbd\ub9dd \uad6c\uc870\ub97c \ucc3e\ub294 \uae30\uc220\ub4e4\uc5d0 \ub300\ud574 \ubc18\ub300 \uc758\uacac\uc744 \uc81c\uc2dc\ud558\uae30\ub3c4 \ud569\ub2c8\ub2e4.\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294, ``torch.nn.utils.prune`` \uc744 \uc774\uc6a9\ud558\uc5ec \uc5ec\ub7ec\ubd84\uc774 \uc124\uacc4\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \ub300\ud574 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud574\ubcf4\ub294 \uac83\uc744 \ubc30\uc6cc\ubcf4\uace0, \n\uc2ec\ud654\uc801\uc73c\ub85c \uc5ec\ub7ec\ubd84\uc758 \ub9de\ucda4\ud615 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ubc30\uc6cc\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc694\uad6c\uc0ac\ud56d\n------------\n``\"torch>=1.4\"``\n\n\n"
+        "\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95(Pruning) \ud29c\ud1a0\ub9ac\uc5bc\n=====================================\n**\uc800\uc790**: `Michela Paganini <https://github.com/mickypaganini>`_\n**\ubc88\uc5ed** : `\uc548\uc0c1\uc900 <https://github.com/Justin-A>`_\n\n\ucd5c\ucca8\ub2e8 \ub525\ub7ec\ub2dd \ubaa8\ub378\ub4e4\uc740 \uad49\uc7a5\ud788 \ub9ce\uc740 \uc218\uc758 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\ub85c \uad6c\uc131\ub418\uae30 \ub54c\ubb38\uc5d0, \uc27d\uac8c \ubc30\ud3ec\ub418\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.\n\uc774\uc640 \ubc18\ub300\ub85c, \uc0dd\ubb3c\ud559\uc801 \uc2e0\uacbd\ub9dd\ub4e4\uc740 \ud6a8\uc728\uc801\uc73c\ub85c \ud76c\uc18c\ud558\uac8c \uc5f0\uacb0\ub41c \uac83\uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4.\n\ubaa8\ub378\uc758 \uc815\ud655\ub3c4\uac00 \uc190\uc0c1\ub418\uc9c0 \uc54a\ub294 \ubc94\uc704\uc5d0\uc11c \uba54\ubaa8\ub9ac, \ubc30\ud130\ub9ac, \ud558\ub4dc\uc6e8\uc5b4 \uc18c\ube44\ub7c9\uc744 \uc904\uc774\uace0,\n\uae30\uae30\uc5d0 \uacbd\ub7c9\ud654\ub41c \ubaa8\ub378\uc744 \ubc30\uce58\ud558\uba70, \uac1c\uc778\uc774 \uc774\uc6a9\ud558\uace0 \uc788\ub294 \uae30\uae30\uc5d0\uc11c \ud504\ub77c\uc774\ubc84\uc2dc\uac00 \ubcf4\uc7a5\ub418\uae30 \uc704\ud574\uc11c\ub294\n\ubaa8\ub378\uc5d0 \ud3ec\ud568\ub41c \ud30c\ub77c\ubbf8\ud130 \uc218\ub97c \uc904\uc5ec \uc555\ucd95\ud558\ub294 \ucd5c\uc801\uc758 \uae30\ubc95\uc744 \ud30c\uc545\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4.\n\uc5f0\uad6c \uce21\uba74\uc5d0\uc11c\ub294, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740 \uad49\uc7a5\ud788 \ub9ce\uc740 \uc218\uc758 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\ub85c \uad6c\uc131\ub41c \ubaa8\ub378\uacfc\n\uad49\uc7a5\ud788 \uc801\uc740 \uc218\uc758 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\ub85c \uad6c\uc131\ub41c \ubaa8\ub378 \uac04 \ud559\uc2b5 \uc5ed\ud559 \ucc28\uc774\ub97c \uc870\uc0ac\ud558\ub294\ub370 \uc8fc\ub85c \uc774\uc6a9\ub418\uae30\ub3c4 \ud558\uba70,\n\ud558\uc704 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uacfc \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc758 \ucd08\uae30\ud654\uac00 \uc6b4\uc774 \uc88b\uac8c \uc798 \ub41c \ucf00\uc774\uc2a4\ub97c \ubc14\ud0d5\uc73c\ub85c\n(\"`lottery tickets <https://arxiv.org/abs/1803.03635>`_\") \uc2e0\uacbd\ub9dd \uad6c\uc870\ub97c \ucc3e\ub294 \uae30\uc220\ub4e4\uc5d0 \ub300\ud574 \ubc18\ub300 \uc758\uacac\uc744 \uc81c\uc2dc\ud558\uae30\ub3c4 \ud569\ub2c8\ub2e4.\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294, ``torch.nn.utils.prune`` \uc744 \uc774\uc6a9\ud558\uc5ec \uc5ec\ub7ec\ubd84\uc774 \uc124\uacc4\ud55c \ub525\ub7ec\ub2dd \ubaa8\ub378\uc5d0 \ub300\ud574 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud574\ubcf4\ub294 \uac83\uc744 \ubc30\uc6cc\ubcf4\uace0,\n\uc2ec\ud654\uc801\uc73c\ub85c \uc5ec\ub7ec\ubd84\uc758 \ub9de\ucda4\ud615 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \ubc30\uc6cc\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc694\uad6c\uc0ac\ud56d\n------------\n``\"torch>=1.4\"``\n"
       ]
     },
     {
@@ -33,7 +33,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ub525\ub7ec\ub2dd \ubaa8\ub378 \uc0dd\uc131\n-----------------------\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294, \uc580 \ub974\ucfe4 \uad50\uc218\ub2d8\uc758 \uc5f0\uad6c\uc9c4\ub4e4\uc774 1998\ub144\ub3c4\uc5d0 \ubc1c\ud45c\ud55c ``LeNet \n<http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf>`` \uc758 \ubaa8\ub378 \uad6c\uc870\ub97c \uc774\uc6a9\ud569\ub2c8\ub2e4.\n\n"
+        "\ub525\ub7ec\ub2dd \ubaa8\ub378 \uc0dd\uc131\n-----------------------\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294, \uc580 \ub974\ucfe4 \uad50\uc218\ub2d8\uc758 \uc5f0\uad6c\uc9c4\ub4e4\uc774 1998\ub144\ub3c4\uc5d0 \ubc1c\ud45c\ud55c ``LeNet\n<http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf>`` \uc758 \ubaa8\ub378 \uad6c\uc870\ub97c \uc774\uc6a9\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ubaa8\ub4c8 \uc810\uac80\n-----------------\n\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 LeNet \ubaa8\ub378\uc758 ``conv1`` \uce35\uc744 \uc810\uac80\ud574\ubd05\uc2dc\ub2e4. \n\uc5ec\uae30\uc5d0\ub294 2\uac1c\uc758 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc778 ``\uac00\uc911\uce58``\uac12\uacfc ``\ud3b8\ud5a5``\uac12\uc744\uc774 \ud3ec\ud568\ub420 \uac83\uc774\uba70, \ubc84\ud37c\ub294 \uc874\uc7ac\ud558\uc9c0 \uc54a\uc744 \uac83\uc785\ub2c8\ub2e4.\n\n"
+        "\ubaa8\ub4c8 \uc810\uac80\n-----------------\n\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 LeNet \ubaa8\ub378\uc758 ``conv1`` \uce35\uc744 \uc810\uac80\ud574\ubd05\uc2dc\ub2e4.\n\uc5ec\uae30\uc5d0\ub294 2\uac1c\uc758 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc778 ``\uac00\uc911\uce58`` \uac12\uacfc ``\ud3b8\ud5a5`` \uac12\uc774 \ud3ec\ud568\ub420 \uac83\uc774\uba70, \ubc84\ud37c\ub294 \uc874\uc7ac\ud558\uc9c0 \uc54a\uc744 \uac83\uc785\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -80,7 +80,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ubaa8\ub4c8 \uac00\uc9c0\uce58\uae30 \uae30\ubc95 \uc801\uc6a9 \uc608\uc81c\n-----------------------------------\n\n\ubaa8\ub4c8\uc5d0 \ub300\ud574 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574 (\uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294, LeNet \ubaa8\ub378\uc758 ``conv1`` \uce35)\n\uccab \ubc88\uc9f8\ub85c\ub294, ``torch.nn.utils.prune`` (\ub610\ub294 ``BasePruningMethod`` \uc758 \uc11c\ube0c \ud074\ub798\uc2a4\ub85c \uc9c1\uc811 `\uad6c\ud604\n<torch-nn-utils-prune>`_ )\n\ub0b4 \uc874\uc7ac\ud558\ub294 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.\n\uadf8 \ud6c4, \ud574\ub2f9 \ubaa8\ub4c8 \ub0b4\uc5d0\uc11c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uace0\uc790 \ud558\ub294 \ubaa8\ub4c8\uacfc \ud30c\ub77c\ubbf8\ud130\ub97c \uc9c0\uc815\ud569\ub2c8\ub2e4.\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc5d0 \uc801\ub2f9\ud55c \ud0a4\uc6cc\ub4dc \uc778\uc790\uac12\uc744 \uc774\uc6a9\ud558\uc5ec \uac00\uc9c0\uce58\uae30 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc9c0\uc815\ud569\ub2c8\ub2e4.\n\uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294, ``conv1`` \uce35\uc758 \uac00\uc911\uce58\uc758 30%\uac12\ub4e4\uc744 \ub79c\ub364\uc73c\ub85c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\ubaa8\ub4c8\uc740 \ud568\uc218\uc5d0 \ub300\ud55c \uccab \ubc88\uc9f8 \uc778\uc790\uac12\uc73c\ub85c \uc804\ub2ec\ub418\uba70, ``name`` \uc740 \ubb38\uc790\uc5f4 \uc2dd\ubcc4\uc790\ub97c \uc774\uc6a9\ud558\uc5ec \ud574\ub2f9 \ubaa8\ub4c8 \ub0b4 \ub9e4\uac1c\ubcc0\uc218\ub97c \uad6c\ubd84\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0, ``amount`` \ub294 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud55c \ub300\uc0c1 \uac00\uc911\uce58\uac12\ub4e4\uc758 \ubc31\ubd84\uc728 (0\uacfc 1\uc0ac\uc774\uc758 \uc2e4\uc218\uac12), \n\ud639\uc740 \uac00\uc911\uce58\uac12\uc758 \uc5f0\uacb0\uc758 \uac1c\uc218 (\uc74c\uc218\uac00 \uc544\ub2cc \uc815\uc218) \ub97c \uc9c0\uc815\ud569\ub2c8\ub2e4.\n\n"
+        "\ubaa8\ub4c8 \uac00\uc9c0\uce58\uae30 \uae30\ubc95 \uc801\uc6a9 \uc608\uc81c\n-----------------------------------\n\n\ubaa8\ub4c8\uc5d0 \ub300\ud574 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574 (\uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294, LeNet \ubaa8\ub378\uc758 ``conv1`` \uce35)\n\uccab \ubc88\uc9f8\ub85c\ub294, ``torch.nn.utils.prune`` (\ub610\ub294 ``BasePruningMethod`` \uc758 \uc11c\ube0c \ud074\ub798\uc2a4\ub85c \uc9c1\uc811\n`\uad6c\ud604 <torch-nn-utils-prune>`_ )\n\ub0b4 \uc874\uc7ac\ud558\ub294 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc120\ud0dd\ud569\ub2c8\ub2e4.\n\uadf8 \ud6c4, \ud574\ub2f9 \ubaa8\ub4c8 \ub0b4\uc5d0\uc11c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uace0\uc790 \ud558\ub294 \ubaa8\ub4c8\uacfc \ud30c\ub77c\ubbf8\ud130\ub97c \uc9c0\uc815\ud569\ub2c8\ub2e4.\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc5d0 \uc801\ub2f9\ud55c \ud0a4\uc6cc\ub4dc \uc778\uc790\uac12\uc744 \uc774\uc6a9\ud558\uc5ec \uac00\uc9c0\uce58\uae30 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc9c0\uc815\ud569\ub2c8\ub2e4.\n\uc774\ubc88 \uc608\uc81c\uc5d0\uc11c\ub294, ``conv1`` \uce35\uc758 \uac00\uc911\uce58\uc758 30%\uac12\ub4e4\uc744 \ub79c\ub364\uc73c\ub85c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\ubaa8\ub4c8\uc740 \ud568\uc218\uc5d0 \ub300\ud55c \uccab \ubc88\uc9f8 \uc778\uc790\uac12\uc73c\ub85c \uc804\ub2ec\ub418\uba70, ``name`` \uc740 \ubb38\uc790\uc5f4 \uc2dd\ubcc4\uc790\ub97c \uc774\uc6a9\ud558\uc5ec \ud574\ub2f9 \ubaa8\ub4c8 \ub0b4 \ub9e4\uac1c\ubcc0\uc218\ub97c \uad6c\ubd84\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0, ``amount`` \ub294 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud55c \ub300\uc0c1 \uac00\uc911\uce58\uac12\ub4e4\uc758 \ubc31\ubd84\uc728 (0\uacfc 1\uc0ac\uc774\uc758 \uc2e4\uc218\uac12),\n\ud639\uc740 \uac00\uc911\uce58\uac12\uc758 \uc5f0\uacb0\uc758 \uac1c\uc218 (\uc74c\uc218\uac00 \uc544\ub2cc \uc815\uc218) \ub97c \uc9c0\uc815\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -98,7 +98,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740 \uac00\uc911\uce58\uac12\ub4e4\uc744 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\ub85c\ubd80\ud130 \uc81c\uac70\ud558\uace0 ``weight_orig`` (\uc989, \ucd08\uae30 \uac00\uc911\uce58 \uc774\ub984\uc5d0 \"_orig\"\uc744 \ubd99\uc778) \uc774\ub77c\ub294 \n\uc0c8\ub85c\uc6b4 \ud30c\ub77c\ubbf8\ud130\uac12\uc73c\ub85c \ub300\uccb4\ud558\ub294 \uac83\uc73c\ub85c \uc2e4\ud589\ub429\ub2c8\ub2e4.\n``weight_orig`` \uc740 \ud150\uc11c\uac12\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 \uc0c1\ud0dc\ub97c \uc800\uc7a5\ud569\ub2c8\ub2e4. \n``bias`` \uc740 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc558\uae30 \ub54c\ubb38\uc5d0 \uadf8\ub300\ub85c \ub0a8\uc544 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740 \uac00\uc911\uce58\uac12\ub4e4\uc744 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\ub85c\ubd80\ud130 \uc81c\uac70\ud558\uace0 ``weight_orig`` (\uc989, \ucd08\uae30 \uac00\uc911\uce58 \uc774\ub984\uc5d0 \"_orig\"\uc744 \ubd99\uc778) \uc774\ub77c\ub294\n\uc0c8\ub85c\uc6b4 \ud30c\ub77c\ubbf8\ud130\uac12\uc73c\ub85c \ub300\uccb4\ud558\ub294 \uac83\uc73c\ub85c \uc2e4\ud589\ub429\ub2c8\ub2e4.\n``weight_orig`` \uc740 \ud150\uc11c\uac12\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 \uc0c1\ud0dc\ub97c \uc800\uc7a5\ud569\ub2c8\ub2e4.\n``bias`` \uc740 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc558\uae30 \ub54c\ubb38\uc5d0 \uadf8\ub300\ub85c \ub0a8\uc544 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -116,7 +116,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc704\uc5d0\uc11c \uc120\ud0dd\ud55c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc5d0 \uc758\ud574 \uc0dd\uc131\ub418\ub294 \uac00\uc9c0\uce58\uae30 \ub9c8\uc2a4\ud06c\ub294 \ucd08\uae30 \ud30c\ub77c\ubbf8\ud130  ``name`` \uc5d0 ``weight_mask`` \n(\uc989, \ucd08\uae30 \uac00\uc911\uce58 \uc774\ub984\uc5d0 \"_mask\"\ub97c \ubd99\uc778) \uc774\ub984\uc758 \ubaa8\ub4c8 \ubc84\ud37c\ub85c \uc800\uc7a5\ub429\ub2c8\ub2e4.\n\n"
+        "\uc704\uc5d0\uc11c \uc120\ud0dd\ud55c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc5d0 \uc758\ud574 \uc0dd\uc131\ub418\ub294 \uac00\uc9c0\uce58\uae30 \ub9c8\uc2a4\ud06c\ub294 \ucd08\uae30 \ud30c\ub77c\ubbf8\ud130  ``name`` \uc5d0 ``weight_mask``\n(\uc989, \ucd08\uae30 \uac00\uc911\uce58 \uc774\ub984\uc5d0 \"_mask\"\ub97c \ubd99\uc778) \uc774\ub984\uc758 \ubaa8\ub4c8 \ubc84\ud37c\ub85c \uc800\uc7a5\ub429\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -134,7 +134,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc218\uc815\uc774 \ub418\uc9c0 \uc54a\uc740 \uc0c1\ud0dc\uc5d0\uc11c \uc21c\uc804\ud30c\ub97c \uc9c4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 ``\uac00\uc911\uce58``\uac12 \uc18d\uc131\uc774 \uc874\uc7ac\ud574\uc57c \ud569\ub2c8\ub2e4.\n``torch.nn.utils.prune``  \ub0b4 \uad6c\ud604\ub41c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac00\uc911\uce58\uac12\ub4e4\uc744 \uc774\uc6a9\ud558\uc5ec \n(\uae30\uc874\uc758 \uac00\uc911\uce58\uac12\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c) \uc21c\uc804\ud30c\ub97c \uc9c4\ud589\ud558\uace0, ``weight`` \uc18d\uc131\uac12\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac00\uc911\uce58\uac12\ub4e4\uc744 \uc800\uc7a5\ud569\ub2c8\ub2e4.\n\uc774\uc81c \uac00\uc911\uce58\uac12\ub4e4\uc740 ``module`` \uc758 \ub9e4\uac1c\ubcc0\uc218\uac00 \uc544\ub2c8\ub77c \ud558\ub098\uc758 \uc18d\uc131\uac12\uc73c\ub85c \ucde8\uae09\ub418\ub294 \uc810\uc744 \uc8fc\uc758\ud558\uc138\uc694.\n\n"
+        "\uc218\uc815\uc774 \ub418\uc9c0 \uc54a\uc740 \uc0c1\ud0dc\uc5d0\uc11c \uc21c\uc804\ud30c\ub97c \uc9c4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 ``\uac00\uc911\uce58`` \uac12 \uc18d\uc131\uc774 \uc874\uc7ac\ud574\uc57c \ud569\ub2c8\ub2e4.\n``torch.nn.utils.prune`` \ub0b4 \uad6c\ud604\ub41c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac00\uc911\uce58\uac12\ub4e4\uc744 \uc774\uc6a9\ud558\uc5ec\n(\uae30\uc874\uc758 \uac00\uc911\uce58\uac12\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c) \uc21c\uc804\ud30c\ub97c \uc9c4\ud589\ud558\uace0, ``weight`` \uc18d\uc131\uac12\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac00\uc911\uce58\uac12\ub4e4\uc744 \uc800\uc7a5\ud569\ub2c8\ub2e4.\n\uc774\uc81c \uac00\uc911\uce58\uac12\ub4e4\uc740 ``module`` \uc758 \ub9e4\uac1c\ubcc0\uc218\uac00 \uc544\ub2c8\ub77c \ud558\ub098\uc758 \uc18d\uc131\uac12\uc73c\ub85c \ucde8\uae09\ub418\ub294 \uc810\uc744 \uc8fc\uc758\ud558\uc138\uc694.\n\n"
       ]
     },
     {
@@ -152,7 +152,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ucd5c\uc885\uc801\uc73c\ub85c, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740 \ud30c\uc774\ud1a0\uce58\uc758 ``forward_pre_hooks`` \ub97c \uc774\uc6a9\ud558\uc5ec \uac01 \uc21c\uc804\ud30c\uac00 \uc9c4\ud589\ub418\uae30 \uc804\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub429\ub2c8\ub2e4.\n\uad6c\uccb4\uc801\uc73c\ub85c, \uc9c0\uae08\uae4c\uc9c0 \uc9c4\ud589\ud55c \uac83 \ucc98\ub7fc, \ubaa8\ub4c8\uc774 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc5c8\uc744 \ub54c, \n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac01 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc774 ``forward_pre_hook`` \ub97c \uc5bb\uac8c\ub429\ub2c8\ub2e4.\n\uc774\ub7ec\ud55c \uacbd\uc6b0, ``weight`` \uc774\ub984\uc778 \uae30\uc874 \ud30c\ub77c\ubbf8\ud130\uac12\uc5d0 \ub300\ud574\uc11c\ub9cc \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uc600\uae30 \ub54c\ubb38\uc5d0, \n\ud6c5\uc740 \uc624\uc9c1 1\uac1c\ub9cc \uc874\uc7ac\ud560 \uac83\uc785\ub2c8\ub2e4.\n\n"
+        "\ucd5c\uc885\uc801\uc73c\ub85c, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740 \ud30c\uc774\ud1a0\uce58\uc758 ``forward_pre_hooks`` \ub97c \uc774\uc6a9\ud558\uc5ec \uac01 \uc21c\uc804\ud30c\uac00 \uc9c4\ud589\ub418\uae30 \uc804\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub429\ub2c8\ub2e4.\n\uad6c\uccb4\uc801\uc73c\ub85c, \uc9c0\uae08\uae4c\uc9c0 \uc9c4\ud589\ud55c \uac83 \ucc98\ub7fc, \ubaa8\ub4c8\uc774 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc5c8\uc744 \ub54c,\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac01 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc774 ``forward_pre_hook`` \ub97c \uc5bb\uac8c\ub429\ub2c8\ub2e4.\n\uc774\ub7ec\ud55c \uacbd\uc6b0, ``weight`` \uc774\ub984\uc778 \uae30\uc874 \ud30c\ub77c\ubbf8\ud130\uac12\uc5d0 \ub300\ud574\uc11c\ub9cc \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uc600\uae30 \ub54c\ubb38\uc5d0,\n\ud6c5\uc740 \uc624\uc9c1 1\uac1c\ub9cc \uc874\uc7ac\ud560 \uac83\uc785\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -170,7 +170,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc644\uacb0\uc131\uc744 \uc704\ud574, \ud3b8\ud5a5\uac12\uc5d0 \ub300\ud574\uc11c\ub3c4 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\uc73c\uba70, \n\ubaa8\ub4c8\uc758 \ud30c\ub77c\ubbf8\ud130, \ubc84\ud37c, \ud6c5, \uc18d\uc131\uac12\ub4e4\uc774 \uc5b4\ub5bb\uac8c \ubcc0\uacbd\ub418\ub294\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\ub610 \ub2e4\ub978 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud574\ubcf4\uae30 \uc704\ud574, ``l1_unstructured`` \uac00\uc9c0\uce58\uae30 \ud568\uc218\uc5d0\uc11c \uad6c\ud604\ub41c \ub0b4\uc6a9\uacfc \uac19\uc774, \nL1 Norm \uac12\uc774 \uac00\uc7a5 \uc791\uc740 \ud3b8\ud5a5\uac12 3\uac1c\ub97c \uac00\uc9c0\uce58\uae30\ub97c \uc2dc\ub3c4\ud574\ubd05\uc2dc\ub2e4.\n\n"
+        "\uc644\uacb0\uc131\uc744 \uc704\ud574, \ud3b8\ud5a5\uac12\uc5d0 \ub300\ud574\uc11c\ub3c4 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\uc73c\uba70,\n\ubaa8\ub4c8\uc758 \ud30c\ub77c\ubbf8\ud130, \ubc84\ud37c, \ud6c5, \uc18d\uc131\uac12\ub4e4\uc774 \uc5b4\ub5bb\uac8c \ubcc0\uacbd\ub418\ub294\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\ub610 \ub2e4\ub978 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud574\ubcf4\uae30 \uc704\ud574, ``l1_unstructured`` \uac00\uc9c0\uce58\uae30 \ud568\uc218\uc5d0\uc11c \uad6c\ud604\ub41c \ub0b4\uc6a9\uacfc \uac19\uc774,\nL1 Norm \uac12\uc774 \uac00\uc7a5 \uc791\uc740 \ud3b8\ud5a5\uac12 3\uac1c\ub97c \uac00\uc9c0\uce58\uae30\ub97c \uc2dc\ub3c4\ud574\ubd05\uc2dc\ub2e4.\n\n"
       ]
     },
     {
@@ -293,7 +293,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \ubaa8\ub378\uc758 \uc9c1\ub82c\ud654\n---------------------------------------------\n\ub9c8\uc2a4\ud06c \ubc84\ud37c\ub4e4\uacfc \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \ud150\uc11c \uacc4\uc0b0\uc5d0 \uc0ac\uc6a9\ub41c \uae30\uc874\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \ud3ec\ud568\ud558\uc5ec \uad00\ub828\ub41c \ubaa8\ub4e0 \ud150\uc11c\uac12\ub4e4\uc740 \n\ud544\uc694\ud55c \uacbd\uc6b0 \ubaa8\ub378\uc758 ``state_dict`` \uc5d0 \uc800\uc7a5\ub418\uae30 \ub584\ubb38\uc5d0, \uc27d\uac8c \uc9c1\ub82c\ud654\ud558\uc5ec \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \ubaa8\ub378\uc758 \uc9c1\ub82c\ud654\n---------------------------------------------\n\ub9c8\uc2a4\ud06c \ubc84\ud37c\ub4e4\uacfc \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \ud150\uc11c \uacc4\uc0b0\uc5d0 \uc0ac\uc6a9\ub41c \uae30\uc874\uc758 \ud30c\ub77c\ubbf8\ud130\ub97c \ud3ec\ud568\ud558\uc5ec \uad00\ub828\ub41c \ubaa8\ub4e0 \ud150\uc11c\uac12\ub4e4\uc740\n\ud544\uc694\ud55c \uacbd\uc6b0 \ubaa8\ub378\uc758 ``state_dict`` \uc5d0 \uc800\uc7a5\ub418\uae30 \ub584\ubb38\uc5d0, \uc27d\uac8c \uc9c1\ub82c\ud654\ud558\uc5ec \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -311,14 +311,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc758 \uc7ac-\ud30c\ub77c\ubbf8\ud130\ud654 \uc81c\uac70\n-----------------------------------------\n\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac83\uc744 \uc601\uad6c\uc801\uc73c\ub85c \ub9cc\ub4e4\uae30 \uc704\ud574\uc11c, \uc7ac-\ud30c\ub77c\ubbf8\ud130\ud654 \uad00\uc810\uc758 \n``weight_orig`` \uc640 ``weight_mask`` \uac12\uc744 \uc81c\uac70\ud558\uace0, ``forward_pre_hook`` \uac12\uc744 \uc81c\uac70\ud569\ub2c8\ub2e4.\n\uc81c\uac70\ud558\uae30 \uc704\ud574 ``torch.nn.utils.prune`` \ub0b4 ``remove`` \ud568\uc218\ub97c \uc774\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 \uac83\ucc98\ub7fc \uc2e4\ud589\ub418\ub294 \uac83\uc774 \uc544\ub2cc \uc810\uc744 \uc8fc\uc758\ud558\uc138\uc694.\n\uc774\ub294 \ub2e8\uc9c0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uc0c1\ud0dc\uc5d0\uc11c \uac00\uc911\uce58 \ud30c\ub77c\ubbf8\ud130\uac12\uc744 \ubaa8\ub378 \ud30c\ub77c\ubbf8\ud130\uac12\uc73c\ub85c \uc7ac\ud560\ub2f9\ud558\ub294 \uac83\uc744 \ud1b5\ud574 \uc601\uad6c\uc801\uc73c\ub85c \ub9cc\ub4dc\ub294 \uac83\uc77c \ubfd0\uc785\ub2c8\ub2e4.\n\n"
+        "\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc758 \uc7ac-\ud30c\ub77c\ubbf8\ud130\ud654 \uc81c\uac70\n-----------------------------------------\n\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac83\uc744 \uc601\uad6c\uc801\uc73c\ub85c \ub9cc\ub4e4\uae30 \uc704\ud574\uc11c, \uc7ac-\ud30c\ub77c\ubbf8\ud130\ud654 \uad00\uc810\uc758\n``weight_orig`` \uc640 ``weight_mask`` \uac12\uc744 \uc81c\uac70\ud558\uace0, ``forward_pre_hook`` \uac12\uc744 \uc81c\uac70\ud569\ub2c8\ub2e4.\n\uc81c\uac70\ud558\uae30 \uc704\ud574 ``torch.nn.utils.prune`` \ub0b4 ``remove`` \ud568\uc218\ub97c \uc774\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 \uac83\ucc98\ub7fc \uc2e4\ud589\ub418\ub294 \uac83\uc774 \uc544\ub2cc \uc810\uc744 \uc8fc\uc758\ud558\uc138\uc694.\n\uc774\ub294 \ub2e8\uc9c0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uc0c1\ud0dc\uc5d0\uc11c \uac00\uc911\uce58 \ud30c\ub77c\ubbf8\ud130\uac12\uc744 \ubaa8\ub378 \ud30c\ub77c\ubbf8\ud130\uac12\uc73c\ub85c \uc7ac\ud560\ub2f9\ud558\ub294 \uac83\uc744 \ud1b5\ud574 \uc601\uad6c\uc801\uc73c\ub85c \ub9cc\ub4dc\ub294 \uac83\uc77c \ubfd0\uc785\ub2c8\ub2e4.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc7ac-\ud30c\ub77c\ubbf8\ud130\ud654\ub97c \uc81c\uac70\ud558\uae30 \uc804 \uc0c1\ud0dc \n\n"
+        "\uc7ac-\ud30c\ub77c\ubbf8\ud130\ud654\ub97c \uc81c\uac70\ud558\uae30 \uc804 \uc0c1\ud0dc\n\n"
       ]
     },
     {
@@ -358,7 +358,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc7ac-\ud30c\ub77c\ubbf8\ud130\ub97c \uc81c\uac70\ud55c \ud6c4 \uc0c1\ud0dc \n\n"
+        "\uc7ac-\ud30c\ub77c\ubbf8\ud130\ub97c \uc81c\uac70\ud55c \ud6c4 \uc0c1\ud0dc\n\n"
       ]
     },
     {
@@ -387,7 +387,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ubaa8\ub378 \ub0b4 \uc5ec\ub7ec \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc5d0 \ub300\ud558\uc5ec \uac00\uc9c0\uce58\uae30 \uae30\ubc95 \uc801\uc6a9\n--------------------------------------\n\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uace0 \uc2f6\uc740 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc744 \uc9c0\uc815\ud568\uc73c\ub85c\uc368, \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c \ubcfc \uc218 \uc788\ub294 \uac83 \ucc98\ub7fc, \n\uc2e0\uacbd\ub9dd \ubaa8\ub378 \ub0b4 \uc5ec\ub7ec \ud150\uc11c\uac12\ub4e4\uc5d0 \ub300\ud574\uc11c \uc27d\uac8c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\ubaa8\ub378 \ub0b4 \uc5ec\ub7ec \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc5d0 \ub300\ud558\uc5ec \uac00\uc9c0\uce58\uae30 \uae30\ubc95 \uc801\uc6a9\n----------------------------------------------------------\n\n\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uace0 \uc2f6\uc740 \ud30c\ub77c\ubbf8\ud130\uac12\ub4e4\uc744 \uc9c0\uc815\ud568\uc73c\ub85c\uc368, \uc774\ubc88 \uc608\uc81c\uc5d0\uc11c \ubcfc \uc218 \uc788\ub294 \uac83 \ucc98\ub7fc,\n\uc2e0\uacbd\ub9dd \ubaa8\ub378 \ub0b4 \uc5ec\ub7ec \ud150\uc11c\uac12\ub4e4\uc5d0 \ub300\ud574\uc11c \uc27d\uac8c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -441,7 +441,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "``torch.nn.utils.prune`` \uc5d0\uc11c \ud655\uc7a5\ub41c \ub9de\ucda4\ud615 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\n------------------------------------------------------------------\n\ub9de\ucda4\ud615 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740, \ub2e4\ub978 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\ub294 \uac83\uacfc \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c, \n``BasePruningMethod`` \uc758 \uae30\ubcf8 \ud074\ub798\uc2a4\uc778 ``nn.utils.prune`` \ubaa8\ub4c8\uc744 \ud65c\uc6a9\ud558\uc5ec \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uae30\ubcf8 \ud074\ub798\uc2a4\ub294 ``__call__``, ``apply_mask``, ``apply``, ``prune``, ``remove`` \uba54\uc18c\ub4dc\ub4e4\uc744 \ub0b4\ud3ec\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\ud2b9\ubcc4\ud55c \ucf00\uc774\uc2a4\uac00 \uc544\ub2cc \uacbd\uc6b0, \uae30\ubcf8\uc801\uc73c\ub85c \uad6c\uc131\ub41c \uba54\uc18c\ub4dc\ub4e4\uc744 \uc7ac\uad6c\uc131\ud560 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098, ``__init__`` (\uad6c\uc131\uc694\uc18c), ``compute_mask`` \n(\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc758 \ub17c\ub9ac\uc5d0 \ub530\ub77c \uc8fc\uc5b4\uc9c4 \ud150\uc11c\uac12\uc5d0 \ub9c8\uc2a4\ud06c\ub97c \uc801\uc6a9\ud558\ub294 \ubc29\ubc95) \uc744 \uace0\ub824\ud558\uc5ec \uad6c\uc131\ud574\uc57c \ud569\ub2c8\ub2e4.\n\uac8c\ub2e4\uac00, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc5b4\ub5a0\ud55c \ubc29\uc2dd\uc73c\ub85c \uc801\uc6a9\ud558\ub294\uc9c0 \uba85\ud655\ud558\uac8c \uad6c\uc131\ud574\uc57c \ud569\ub2c8\ub2e4. \n(\uc9c0\uc6d0\ub418\ub294 \uc635\uc158\uc740 ``global``, ``structured``, ``unstructured`` \uc785\ub2c8\ub2e4.)\n\uc774\ub7ec\ud55c \ubc29\uc2dd\uc740, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \ubc18\ubcf5\uc801\uc73c\ub85c \uc801\uc6a9\ud574\uc57c \ud558\ub294 \uacbd\uc6b0 \ub9c8\uc2a4\ud06c\ub97c \uacb0\ud569\ud558\ub294 \ubc29\ubc95\uc744 \uacb0\uc815\ud558\uae30 \uc704\ud574 \ud544\uc694\ud569\ub2c8\ub2e4.\n\uc989, \uc774\ubbf8 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \ubaa8\ub378\uc5d0 \ub300\ud574\uc11c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud560 \ub54c, \n\uae30\uc874\uc758 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 \ud30c\ub77c\ubbf8\ud130 \uac12\uc5d0 \ub300\ud574 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc601\ud5a5\uc744 \ubbf8\uce60 \uac83\uc73c\ub85c \uc608\uc0c1\ub429\ub2c8\ub2e4.\n``PRUNING_TYPE``\uc744 \uc9c0\uc815\ud55c\ub2e4\uba74, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574 \ud30c\ub77c\ubbf8\ud130 \uac12\uc744 \uc62c\ubc14\ub974\uac8c \uc81c\uac70\ud558\ub294 \n``PruningContainer`` (\ub9c8\uc2a4\ud06c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \ubc18\ubcf5\uc801\uc73c\ub85c \uc801\uc6a9\ud558\ub294 \uac83\uc744 \ucc98\ub9ac\ud558\ub294)\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uc5b4, \ub2e4\ub978 \ubaa8\ub4e0 \ud56d\ubaa9\uc774 \uc874\uc7ac\ud558\ub294 \ud150\uc11c\ub97c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uad6c\ud604\ud558\uace0 \uc2f6\uc744 \ub54c,\n(\ub610\ub294, \ud150\uc11c\uac00 \uc774\uc804\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc5d0 \uc758\ud574 \uc81c\uac70\ub418\uc5c8\uac70\ub098 \ub0a8\uc544\uc788\ub294 \ud150\uc11c\uc5d0 \ub300\ud574)\n\ud55c \uce35\uc758 \uac1c\ubcc4 \uc5f0\uacb0\uc5d0 \uc791\uc6a9\ud558\uba70 \uc804\uccb4 \uc720\ub2db/\ucc44\ub110 (``'structured'``), \ub610\ub294 \ub2e4\ub978 \ud30c\ub77c\ubbf8\ud130 \uac04 \n(``'global'``) \uc5f0\uacb0\uc5d0\ub294 \uc791\uc6a9\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 ``PRUNING_TYPE='unstructured'`` \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4.\n\n"
+        "``torch.nn.utils.prune`` \uc5d0\uc11c \ud655\uc7a5\ub41c \ub9de\ucda4\ud615 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\n------------------------------------------------------------------\n\ub9de\ucda4\ud615 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc740, \ub2e4\ub978 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\ub294 \uac83\uacfc \uac19\uc740 \ubc29\uc2dd\uc73c\ub85c,\n``BasePruningMethod`` \uc758 \uae30\ubcf8 \ud074\ub798\uc2a4\uc778 ``nn.utils.prune`` \ubaa8\ub4c8\uc744 \ud65c\uc6a9\ud558\uc5ec \uad6c\ud604\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uae30\ubcf8 \ud074\ub798\uc2a4\ub294 ``__call__``, ``apply_mask``, ``apply``, ``prune``, ``remove`` \uba54\uc18c\ub4dc\ub4e4\uc744 \ub0b4\ud3ec\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\ud2b9\ubcc4\ud55c \ucf00\uc774\uc2a4\uac00 \uc544\ub2cc \uacbd\uc6b0, \uae30\ubcf8\uc801\uc73c\ub85c \uad6c\uc131\ub41c \uba54\uc18c\ub4dc\ub4e4\uc744 \uc7ac\uad6c\uc131\ud560 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098, ``__init__`` (\uad6c\uc131\uc694\uc18c), ``compute_mask``\n(\uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc758 \ub17c\ub9ac\uc5d0 \ub530\ub77c \uc8fc\uc5b4\uc9c4 \ud150\uc11c\uac12\uc5d0 \ub9c8\uc2a4\ud06c\ub97c \uc801\uc6a9\ud558\ub294 \ubc29\ubc95) \uc744 \uace0\ub824\ud558\uc5ec \uad6c\uc131\ud574\uc57c \ud569\ub2c8\ub2e4.\n\uac8c\ub2e4\uac00, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc5b4\ub5a0\ud55c \ubc29\uc2dd\uc73c\ub85c \uc801\uc6a9\ud558\ub294\uc9c0 \uba85\ud655\ud558\uac8c \uad6c\uc131\ud574\uc57c \ud569\ub2c8\ub2e4.\n(\uc9c0\uc6d0\ub418\ub294 \uc635\uc158\uc740 ``global``, ``structured``, ``unstructured`` \uc785\ub2c8\ub2e4.)\n\uc774\ub7ec\ud55c \ubc29\uc2dd\uc740, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \ubc18\ubcf5\uc801\uc73c\ub85c \uc801\uc6a9\ud574\uc57c \ud558\ub294 \uacbd\uc6b0 \ub9c8\uc2a4\ud06c\ub97c \uacb0\ud569\ud558\ub294 \ubc29\ubc95\uc744 \uacb0\uc815\ud558\uae30 \uc704\ud574 \ud544\uc694\ud569\ub2c8\ub2e4.\n\uc989, \uc774\ubbf8 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \ubaa8\ub378\uc5d0 \ub300\ud574\uc11c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud560 \ub54c,\n\uae30\uc874\uc758 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 \ud30c\ub77c\ubbf8\ud130 \uac12\uc5d0 \ub300\ud574 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc601\ud5a5\uc744 \ubbf8\uce60 \uac83\uc73c\ub85c \uc608\uc0c1\ub429\ub2c8\ub2e4.\n``PRUNING_TYPE`` \uc744 \uc9c0\uc815\ud55c\ub2e4\uba74, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud558\uae30 \uc704\ud574 \ud30c\ub77c\ubbf8\ud130 \uac12\uc744 \uc62c\ubc14\ub974\uac8c \uc81c\uac70\ud558\ub294\n``PruningContainer`` (\ub9c8\uc2a4\ud06c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \ubc18\ubcf5\uc801\uc73c\ub85c \uc801\uc6a9\ud558\ub294 \uac83\uc744 \ucc98\ub9ac\ud558\ub294)\ub97c \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uc5b4, \ub2e4\ub978 \ubaa8\ub4e0 \ud56d\ubaa9\uc774 \uc874\uc7ac\ud558\ub294 \ud150\uc11c\ub97c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uad6c\ud604\ud558\uace0 \uc2f6\uc744 \ub54c,\n(\ub610\ub294, \ud150\uc11c\uac00 \uc774\uc804\uc5d0 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc5d0 \uc758\ud574 \uc81c\uac70\ub418\uc5c8\uac70\ub098 \ub0a8\uc544\uc788\ub294 \ud150\uc11c\uc5d0 \ub300\ud574)\n\ud55c \uce35\uc758 \uac1c\ubcc4 \uc5f0\uacb0\uc5d0 \uc791\uc6a9\ud558\uba70 \uc804\uccb4 \uc720\ub2db/\ucc44\ub110 (``'structured'``), \ub610\ub294 \ub2e4\ub978 \ud30c\ub77c\ubbf8\ud130 \uac04\n(``'global'``) \uc5f0\uacb0\uc5d0\ub294 \uc791\uc6a9\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 ``PRUNING_TYPE='unstructured'`` \ubc29\uc2dd\uc73c\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -452,7 +452,7 @@
       },
       "outputs": [],
       "source": [
-        "class FooBarPruningMethod(prune.BasePruningMethod):\n    \"\"\"\n    \ud150\uc11c \ub0b4 \ub2e4\ub978 \ud56d\ubaa9\ub4e4\uc5d0 \ub300\ud574 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\n    \"\"\"\n    PRUNING_TYPE = 'unstructured'\n\n    def compute_mask(self, t, default_mask):\n        mask = default_mask.clone()\n        mask.view(-1)[::2] = 0 \n        return mask"
+        "class FooBarPruningMethod(prune.BasePruningMethod):\n    \"\"\"\n    \ud150\uc11c \ub0b4 \ub2e4\ub978 \ud56d\ubaa9\ub4e4\uc5d0 \ub300\ud574 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\n    \"\"\"\n    PRUNING_TYPE = 'unstructured'\n\n    def compute_mask(self, t, default_mask):\n        mask = default_mask.clone()\n        mask.view(-1)[::2] = 0\n        return mask"
       ]
     },
     {
@@ -470,7 +470,7 @@
       },
       "outputs": [],
       "source": [
-        "def foobar_unstructured(module, name):\n    \"\"\"\n    \ud150\uc11c \ub0b4 \ub2e4\ub978 \ubaa8\ub4e0 \ud56d\ubaa9\ub4e4\uc744 \uc81c\uac70\ud558\uc5ec `module` \uc5d0\uc11c `name` \uc774\ub77c\ub294 \ud30c\ub77c\ubbf8\ud130\uc5d0 \ub300\ud574 \uac00\uc790\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\n    \ub2e4\uc74c \ub0b4\uc6a9\uc5d0 \ub530\ub77c \ubaa8\ub4c8\uc744 \uc218\uc815 (\ub610\ub294 \uc218\uc815\ub41c \ubaa8\ub4c8\uc744 \ubc18\ud658):\n        1) \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc5d0 \uc758\ud574 \ub9e4\uac1c\ubcc0\uc218 `name` \uc5d0 \uc801\uc6a9\ub41c \uc774\uc9c4 \ub9c8\uc2a4\ud06c\uc5d0 \ud574\ub2f9\ud558\ub294 \uba85\uba85\ub41c \ubc84\ud37c `name+'_mask'` \ub97c \ucd94\uac00\ud569\ub2c8\ub2e4.\n        `name` \ud30c\ub77c\ubbf8\ud130\ub294 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac83\uc73c\ub85c \ub300\uccb4\ub418\uba70, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740 \n        \uae30\uc874\uc758 \ud30c\ub77c\ubbf8\ud130\ub294 `name+'_orig'` \ub77c\ub294 \uc774\ub984\uc758 \uc0c8\ub85c\uc6b4 \ub9e4\uac1c\ubcc0\uc218\uc5d0 \uc800\uc7a5\ub429\ub2c8\ub2e4.\n\n    \uc778\uc790\uac12:\n        module (nn.Module): \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud574\uc57c\ud558\ub294 \ud150\uc11c\ub97c \ud3ec\ud568\ud558\ub294 \ubaa8\ub4c8\n        name (string): \ubaa8\ub4c8 \ub0b4 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub420 \ud30c\ub77c\ubbf8\ud130\uc758 \uc774\ub984\n\n    \ubc18\ud658\uac12:\n        module (nn.Module): \uc785\ub825 \ubaa8\ub4c8\uc5d0 \ub300\ud574\uc11c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \ubaa8\ub4c8 \n    \n    \uc608\uc2dc:\n        >>> m = nn.Linear(3, 4)\n        >>> foobar_unstructured(m, name='bias')\n    \"\"\"\n    FooBarPruningMethod.apply(module, name)\n    return module"
+        "def foobar_unstructured(module, name):\n    \"\"\"\n    \ud150\uc11c \ub0b4 \ub2e4\ub978 \ubaa8\ub4e0 \ud56d\ubaa9\ub4e4\uc744 \uc81c\uac70\ud558\uc5ec `module` \uc5d0\uc11c `name` \uc774\ub77c\ub294 \ud30c\ub77c\ubbf8\ud130\uc5d0 \ub300\ud574 \uac00\uc790\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\n    \ub2e4\uc74c \ub0b4\uc6a9\uc5d0 \ub530\ub77c \ubaa8\ub4c8\uc744 \uc218\uc815 (\ub610\ub294 \uc218\uc815\ub41c \ubaa8\ub4c8\uc744 \ubc18\ud658):\n        1) \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc5d0 \uc758\ud574 \ub9e4\uac1c\ubcc0\uc218 `name` \uc5d0 \uc801\uc6a9\ub41c \uc774\uc9c4 \ub9c8\uc2a4\ud06c\uc5d0 \ud574\ub2f9\ud558\ub294 \uba85\uba85\ub41c \ubc84\ud37c `name+'_mask'` \ub97c \ucd94\uac00\ud569\ub2c8\ub2e4.\n        `name` \ud30c\ub77c\ubbf8\ud130\ub294 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \uac83\uc73c\ub85c \ub300\uccb4\ub418\uba70, \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub418\uc9c0 \uc54a\uc740\n        \uae30\uc874\uc758 \ud30c\ub77c\ubbf8\ud130\ub294 `name+'_orig'` \ub77c\ub294 \uc774\ub984\uc758 \uc0c8\ub85c\uc6b4 \ub9e4\uac1c\ubcc0\uc218\uc5d0 \uc800\uc7a5\ub429\ub2c8\ub2e4.\n\n    \uc778\uc790\uac12:\n        module (nn.Module): \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc744 \uc801\uc6a9\ud574\uc57c\ud558\ub294 \ud150\uc11c\ub97c \ud3ec\ud568\ud558\ub294 \ubaa8\ub4c8\n        name (string): \ubaa8\ub4c8 \ub0b4 \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub420 \ud30c\ub77c\ubbf8\ud130\uc758 \uc774\ub984\n\n    \ubc18\ud658\uac12:\n        module (nn.Module): \uc785\ub825 \ubaa8\ub4c8\uc5d0 \ub300\ud574\uc11c \uac00\uc9c0\uce58\uae30 \uae30\ubc95\uc774 \uc801\uc6a9\ub41c \ubaa8\ub4c8\n\n    \uc608\uc2dc:\n        >>> m = nn.Linear(3, 4)\n        >>> foobar_unstructured(m, name='bias')\n    \"\"\"\n    FooBarPruningMethod.apply(module, name)\n    return module"
       ]
     },
     {
@@ -508,7 +508,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/74249e7f9f1f398f57ccd094a4f3021b/transfer_learning_tutorial.ipynb b/docs/_downloads/74249e7f9f1f398f57ccd094a4f3021b/transfer_learning_tutorial.ipynb
index 4b540878d..074facaf0 100644
--- a/docs/_downloads/74249e7f9f1f398f57ccd094a4f3021b/transfer_learning_tutorial.ipynb
+++ b/docs/_downloads/74249e7f9f1f398f57ccd094a4f3021b/transfer_learning_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\ucef4\ud4e8\ud130 \ube44\uc804(Vision)\uc744 \uc704\ud55c \uc804\uc774\ud559\uc2b5(Transfer Learning)\n=======================================================\n**Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_\n  **\ubc88\uc5ed**: `\ubc15\uc815\ud658 <http://github.com/9bow>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc804\uc774\ud559\uc2b5(Transfer Learning)\uc744 \uc774\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \ubd84\ub958\ub97c \uc704\ud55c\n\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc744 \uc5b4\ub5bb\uac8c \ud559\uc2b5\uc2dc\ud0a4\ub294\uc9c0 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc804\uc774\ud559\uc2b5\uc5d0 \ub300\ud574\uc11c\ub294\n`CS231n \ub178\ud2b8 <http://cs231n.github.io/transfer-learning/>`__ \uc5d0\uc11c \ub354 \ub9ce\uc740 \ub0b4\uc6a9\uc744\n\uc77d\uc5b4\ubcf4\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc704 \ub178\ud2b8\ub97c \uc778\uc6a9\ud574\ubcf4\uba74,\n\n    \uc2e4\uc81c\ub85c \ucda9\ubd84\ud55c \ud06c\uae30\uc758 \ub370\uc774\ud130\uc14b\uc744 \uac16\ucd94\uae30\ub294 \uc0c1\ub300\uc801\uc73c\ub85c \ub4dc\ubb3c\uae30 \ub54c\ubb38\uc5d0,\n    (\ubb34\uc791\uc704 \ucd08\uae30\ud654\ub97c \ud1b5\ud574) \ub9e8 \ucc98\uc74c\ubd80\ud130 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolutional\n    Network) \uc804\uccb4\ub97c \ud559\uc2b5\ud558\ub294 \uc0ac\ub78c\uc740 \ub9e4\uc6b0 \uc801\uc2b5\ub2c8\ub2e4. \ub300\uc2e0, \ub9e4\uc6b0 \ud070 \ub370\uc774\ud130\uc14b(\uc608.\n    100\uac00\uc9c0 \ubd84\ub958\uc5d0 \ub300\ud574 120\ub9cc\uac1c\uc758 \uc774\ubbf8\uc9c0\uac00 \ud3ec\ud568\ub41c ImageNet)\uc5d0\uc11c \ud569\uc131\uacf1\n    \uc2e0\uacbd\ub9dd(ConvNet)\uc744 \ubbf8\ub9ac \ud559\uc2b5\ud55c \ud6c4, \uc774 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc744 \uad00\uc2ec\uc788\ub294 \uc791\uc5c5\n    \uc744 \uc704\ud55c \ucd08\uae30 \uc124\uc815 \ub610\ub294 \uace0\uc815\ub41c \ud2b9\uc9d5 \ucd94\ucd9c\uae30(fixed feature extractor)\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n\uc774\ub7ec\ud55c \uc804\uc774\ud559\uc2b5 \uc2dc\ub098\ub9ac\uc624\uc758 \uc8fc\uc694\ud55c 2\uac00\uc9c0\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n\n-  **\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc758 \ubbf8\uc138\uc870\uc815(finetuning)**: \ubb34\uc791\uc704 \ucd08\uae30\ud654 \ub300\uc2e0, \uc2e0\uacbd\ub9dd\uc744\n   ImageNet 1000 \ub370\uc774\ud130\uc14b \ub4f1\uc73c\ub85c \ubbf8\ub9ac \ud559\uc2b5\ud55c \uc2e0\uacbd\ub9dd\uc73c\ub85c \ucd08\uae30\ud654\ud569\ub2c8\ub2e4. \ud559\uc2b5\uc758 \ub098\uba38\uc9c0\n   \uacfc\uc815\ub4e4\uc740 \ud3c9\uc0c1\uc2dc\uc640 \uac19\uc2b5\ub2c8\ub2e4.\n-  **\uace0\uc815\ub41c \ud2b9\uc9d5 \ucd94\ucd9c\uae30\ub85c\uc368\uc758 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd**: \uc5ec\uae30\uc11c\ub294 \ub9c8\uc9c0\ub9c9\uc5d0 \uc644\uc804\ud788 \uc5f0\uacb0\n   \ub41c \uacc4\uce35\uc744 \uc81c\uc678\ud55c \ubaa8\ub4e0 \uc2e0\uacbd\ub9dd\uc758 \uac00\uc911\uce58\ub97c \uace0\uc815\ud569\ub2c8\ub2e4. \uc774 \ub9c8\uc9c0\ub9c9\uc758 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c\n   \uacc4\uce35\uc740 \uc0c8\ub85c\uc6b4 \ubb34\uc791\uc704\uc758 \uac00\uc911\uce58\ub97c \uac16\ub294 \uacc4\uce35\uc73c\ub85c \ub300\uccb4\ub418\uc5b4 \uc774 \uacc4\uce35\ub9cc \ud559\uc2b5\ud569\ub2c8\ub2e4.\n\n\n"
+        "\n\ucef4\ud4e8\ud130 \ube44\uc804(Vision)\uc744 \uc704\ud55c \uc804\uc774\ud559\uc2b5(Transfer Learning)\n=======================================================\n**Author**: `Sasank Chilamkurthy <https://chsasank.github.io>`_\n  **\ubc88\uc5ed**: `\ubc15\uc815\ud658 <http://github.com/9bow>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc804\uc774\ud559\uc2b5(Transfer Learning)\uc744 \uc774\uc6a9\ud558\uc5ec \uc774\ubbf8\uc9c0 \ubd84\ub958\ub97c \uc704\ud55c\n\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc744 \uc5b4\ub5bb\uac8c \ud559\uc2b5\uc2dc\ud0a4\ub294\uc9c0 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc804\uc774\ud559\uc2b5\uc5d0 \ub300\ud574\uc11c\ub294\n`CS231n \ub178\ud2b8 <http://cs231n.github.io/transfer-learning/>`__ \uc5d0\uc11c \ub354 \ub9ce\uc740 \ub0b4\uc6a9\uc744\n\uc77d\uc5b4\ubcf4\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc704 \ub178\ud2b8\ub97c \uc778\uc6a9\ud574\ubcf4\uba74,\n\n    \uc2e4\uc81c\ub85c \ucda9\ubd84\ud55c \ud06c\uae30\uc758 \ub370\uc774\ud130\uc14b\uc744 \uac16\ucd94\uae30\ub294 \uc0c1\ub300\uc801\uc73c\ub85c \ub4dc\ubb3c\uae30 \ub54c\ubb38\uc5d0,\n    (\ubb34\uc791\uc704 \ucd08\uae30\ud654\ub97c \ud1b5\ud574) \ub9e8 \ucc98\uc74c\ubd80\ud130 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(Convolutional\n    Network) \uc804\uccb4\ub97c \ud559\uc2b5\ud558\ub294 \uc0ac\ub78c\uc740 \ub9e4\uc6b0 \uc801\uc2b5\ub2c8\ub2e4. \ub300\uc2e0, \ub9e4\uc6b0 \ud070 \ub370\uc774\ud130\uc14b(\uc608.\n    100\uac00\uc9c0 \ubd84\ub958\uc5d0 \ub300\ud574 120\ub9cc\uac1c\uc758 \uc774\ubbf8\uc9c0\uac00 \ud3ec\ud568\ub41c ImageNet)\uc5d0\uc11c \ud569\uc131\uacf1\n    \uc2e0\uacbd\ub9dd(ConvNet)\uc744 \ubbf8\ub9ac \ud559\uc2b5\ud55c \ud6c4, \uc774 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc744 \uad00\uc2ec\uc788\ub294 \uc791\uc5c5\n    \uc744 \uc704\ud55c \ucd08\uae30 \uc124\uc815 \ub610\ub294 \uace0\uc815\ub41c \ud2b9\uc9d5 \ucd94\ucd9c\uae30(fixed feature extractor)\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n\uc774\ub7ec\ud55c \uc804\uc774\ud559\uc2b5 \uc2dc\ub098\ub9ac\uc624\uc758 \uc8fc\uc694\ud55c 2\uac00\uc9c0\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n\n-  **\ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc758 \ubbf8\uc138\uc870\uc815(finetuning)**: \ubb34\uc791\uc704 \ucd08\uae30\ud654 \ub300\uc2e0, \uc2e0\uacbd\ub9dd\uc744\n   ImageNet 1000 \ub370\uc774\ud130\uc14b \ub4f1\uc73c\ub85c \ubbf8\ub9ac \ud559\uc2b5\ud55c \uc2e0\uacbd\ub9dd\uc73c\ub85c \ucd08\uae30\ud654\ud569\ub2c8\ub2e4. \ud559\uc2b5\uc758 \ub098\uba38\uc9c0\n   \uacfc\uc815\ub4e4\uc740 \ud3c9\uc0c1\uc2dc\uc640 \uac19\uc2b5\ub2c8\ub2e4.\n-  **\uace0\uc815\ub41c \ud2b9\uc9d5 \ucd94\ucd9c\uae30\ub85c\uc368\uc758 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd**: \uc5ec\uae30\uc11c\ub294 \ub9c8\uc9c0\ub9c9\uc5d0 \uc644\uc804\ud788 \uc5f0\uacb0\n   \ub41c \uacc4\uce35\uc744 \uc81c\uc678\ud55c \ubaa8\ub4e0 \uc2e0\uacbd\ub9dd\uc758 \uac00\uc911\uce58\ub97c \uace0\uc815\ud569\ub2c8\ub2e4. \uc774 \ub9c8\uc9c0\ub9c9\uc758 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c\n   \uacc4\uce35\uc740 \uc0c8\ub85c\uc6b4 \ubb34\uc791\uc704\uc758 \uac00\uc911\uce58\ub97c \uac16\ub294 \uacc4\uce35\uc73c\ub85c \ub300\uccb4\ub418\uc5b4 \uc774 \uacc4\uce35\ub9cc \ud559\uc2b5\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -219,7 +219,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/786469bd4d28fe2528b92a6d12fb189e/flask_rest_api_tutorial.ipynb b/docs/_downloads/786469bd4d28fe2528b92a6d12fb189e/flask_rest_api_tutorial.ipynb
index bd22f62de..909dd0e66 100644
--- a/docs/_downloads/786469bd4d28fe2528b92a6d12fb189e/flask_rest_api_tutorial.ipynb
+++ b/docs/_downloads/786469bd4d28fe2528b92a6d12fb189e/flask_rest_api_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nFlask\ub97c \uc774\uc6a9\ud558\uc5ec Python\uc5d0\uc11c PyTorch\ub97c REST API\ub85c \ubc30\ud3ec\ud558\uae30\n===========================================================\n**Author**: `Avinash Sajjanshetty <https://avi.im>`_\n  **\ubc88\uc5ed**: `\ubc15\uc815\ud658 <http://github.com/9bow>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Flask\ub97c \uc774\uc6a9\ud558\uc5ec PyTorch \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uace0 \ubaa8\ub378 \ucd94\ub860(inference)\uc744\n\ud560 \uc218 \uc788\ub294 REST API\ub97c \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubbf8\ub9ac \ud6c8\ub828\ub41c DenseNet 121 \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uc5ec\n\uc774\ubbf8\uc9c0\ub97c \uc778\uc2dd\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n.. tip:: \uc5ec\uae30\uc11c \uc0ac\uc6a9\ud55c \ubaa8\ub4e0 \ucf54\ub4dc\ub294 MIT \ub77c\uc774\uc120\uc2a4\ub85c \ubc30\ud3ec\ub418\uba70,\n         `GitHub <https://github.com/avinassh/pytorch-flask-api>`_ \uc5d0\uc11c \ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774\uac83\uc740 PyTorch \ubaa8\ub378\uc744 \uc0c1\uc6a9(production)\uc73c\ub85c \ubc30\ud3ec\ud558\ub294 \ud29c\ud1a0\ub9ac\uc5bc \uc2dc\ub9ac\uc988\uc758 \uccab\ubc88\uc9f8\n\ud3b8\uc785\ub2c8\ub2e4. Flask\ub97c \uc5ec\uae30\uc5d0 \uc18c\uac1c\ub41c \uac83\ucc98\ub7fc \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 PyTorch \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ub294\n\uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95\uc774\uc9c0\ub9cc, \uace0\uc131\ub2a5\uc744 \uc694\uad6c\ud558\ub294 \ub54c\uc5d0\ub294 \uc801\ud569\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uadf8\uc5d0 \ub300\ud574\uc11c\ub294:\n\n    - TorchScript\uc5d0 \uc774\ubbf8 \uc775\uc219\ud558\ub2e4\uba74, \ubc14\ub85c `Loading a TorchScript Model in C++ <https://tutorials.pytorch.kr/advanced/cpp_export.html>`_\n      \ub97c \uc77d\uc5b4\ubcf4\uc138\uc694.\n\n    - TorchScript\uac00 \ubb34\uc5c7\uc778\uc9c0 \uc54c\uc544\ubcf4\ub294 \uac83\uc774 \ud544\uc694\ud558\ub2e4\uba74 `TorchScript \uc18c\uac1c <https://tutorials.pytorch.kr/beginner/Intro_to_TorchScript_tutorial.html>`_\n      \ubd80\ud130 \ubcf4\uc2dc\uae38 \ucd94\ucc9c\ud569\ub2c8\ub2e4.\n\n"
+        "\nFlask\ub97c \uc774\uc6a9\ud558\uc5ec Python\uc5d0\uc11c PyTorch\ub97c REST API\ub85c \ubc30\ud3ec\ud558\uae30\n===========================================================\n**Author**: `Avinash Sajjanshetty <https://avi.im>`_\n  **\ubc88\uc5ed**: `\ubc15\uc815\ud658 <http://github.com/9bow>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Flask\ub97c \uc774\uc6a9\ud558\uc5ec PyTorch \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uace0 \ubaa8\ub378 \ucd94\ub860(inference)\uc744\n\ud560 \uc218 \uc788\ub294 REST API\ub97c \ub9cc\ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubbf8\ub9ac \ud6c8\ub828\ub41c DenseNet 121 \ubaa8\ub378\uc744 \ubc30\ud3ec\ud558\uc5ec\n\uc774\ubbf8\uc9c0\ub97c \uc778\uc2dd\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n.. tip:: \uc5ec\uae30\uc11c \uc0ac\uc6a9\ud55c \ubaa8\ub4e0 \ucf54\ub4dc\ub294 MIT \ub77c\uc774\uc120\uc2a4\ub85c \ubc30\ud3ec\ub418\uba70,\n         `GitHub <https://github.com/avinassh/pytorch-flask-api>`_ \uc5d0\uc11c \ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774\uac83\uc740 PyTorch \ubaa8\ub378\uc744 \uc0c1\uc6a9(production)\uc73c\ub85c \ubc30\ud3ec\ud558\ub294 \ud29c\ud1a0\ub9ac\uc5bc \uc2dc\ub9ac\uc988\uc758 \uccab\ubc88\uc9f8\n\ud3b8\uc785\ub2c8\ub2e4. Flask\ub97c \uc5ec\uae30\uc5d0 \uc18c\uac1c\ub41c \uac83\ucc98\ub7fc \uc0ac\uc6a9\ud558\ub294 \uac83\uc774 PyTorch \ubaa8\ub378\uc744 \uc81c\uacf5\ud558\ub294\n\uac00\uc7a5 \uc26c\uc6b4 \ubc29\ubc95\uc774\uc9c0\ub9cc, \uace0\uc131\ub2a5\uc744 \uc694\uad6c\ud558\ub294 \ub54c\uc5d0\ub294 \uc801\ud569\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \uadf8\uc5d0 \ub300\ud574\uc11c\ub294:\n\n    - TorchScript\uc5d0 \uc774\ubbf8 \uc775\uc219\ud558\ub2e4\uba74, \ubc14\ub85c `Loading a TorchScript Model in C++ <https://tutorials.pytorch.kr/advanced/cpp_export.html>`_\n      \ub97c \uc77d\uc5b4\ubcf4\uc138\uc694.\n\n    - TorchScript\uac00 \ubb34\uc5c7\uc778\uc9c0 \uc54c\uc544\ubcf4\ub294 \uac83\uc774 \ud544\uc694\ud558\ub2e4\uba74 `TorchScript \uc18c\uac1c <https://tutorials.pytorch.kr/beginner/Intro_to_TorchScript_tutorial.html>`_\n      \ubd80\ud130 \ubcf4\uc2dc\uae38 \ucd94\ucc9c\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -29,7 +29,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc758\uc874\uc131(Dependencies)\n-------------------------\n\n\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\ud558\uc5ec \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub4e4\uc744 \uc124\uce58\ud569\ub2c8\ub2e4:\n\n::\n\n    $ pip install Flask==1.0.3 torchvision-0.3.0\n\n"
+        "\uc758\uc874\uc131(Dependencies)\n-------------------------\n\n\uc544\ub798 \uba85\ub839\uc5b4\ub97c \uc2e4\ud589\ud558\uc5ec \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub4e4\uc744 \uc124\uce58\ud569\ub2c8\ub2e4:\n\n::\n\n    $ pip install Flask==1.0.3 torchvision==0.3.0\n\n"
       ]
     },
     {
@@ -104,7 +104,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ucd94\ub860(Inference)\n-----------------\n\n\ub2e4\uc74c \uc139\uc158\uc5d0\uc11c\ub294 \ucd94\ub860 \ucf54\ub4dc \uc791\uc131\uc5d0 \uc9d1\uc911\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800 \uc774\ubbf8\uc9c0\ub97c DenseNet\uc5d0 \uacf5\uae09(feed)\ud560 \uc218 \n\uc788\ub3c4\ub85d \uc900\ube44\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf8 \ub4a4, \ubaa8\ub378\ub85c\ubd80\ud130 \uc608\uce21 \uacb0\uacfc\ub97c \uc5bb\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubbf8\uc9c0 \uc900\ube44\ud558\uae30\n~~~~~~~~~~~~~~~~~~~\n\nDenseNet \ubaa8\ub378\uc740 224 x 224\uc758 3\ucc44\ub110 RGB \uc774\ubbf8\uc9c0\ub97c \ud544\uc694\ub85c \ud569\ub2c8\ub2e4.\n\ub610\ud55c \uc774\ubbf8\uc9c0 \ud150\uc11c\ub97c \ud3c9\uaddc \ubc0f \ud45c\uc900\ud3b8\ucc28 \uac12\uc73c\ub85c \uc815\uaddc\ud654\ud569\ub2c8\ub2e4. \uc790\uc138\ud55c \ub0b4\uc6a9\uc740\n`\uc5ec\uae30 <https://pytorch.org/docs/stable/torchvision/models.html>`_ \ub97c \ucc38\uace0\ud558\uc138\uc694.\n\n``torchvision`` \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 ``transforms`` \uc744 \uc774\uc6a9\ud558\uc5ec \ubcc0\ud658 \ud30c\uc774\ud504\ub77c\uc778\n(transform pipeline)\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. Transforms\uc640 \uad00\ub828\ud55c \ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740\n`\uc5ec\uae30  <https://pytorch.org/docs/stable/torchvision/transforms.html>`_ \uc5d0\uc11c\n\uc77d\uc5b4\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\ucd94\ub860(Inference)\n-----------------\n\n\ub2e4\uc74c \uc139\uc158\uc5d0\uc11c\ub294 \ucd94\ub860 \ucf54\ub4dc \uc791\uc131\uc5d0 \uc9d1\uc911\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uba3c\uc800 \uc774\ubbf8\uc9c0\ub97c DenseNet\uc5d0 \uacf5\uae09(feed)\ud560 \uc218\n\uc788\ub3c4\ub85d \uc900\ube44\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf8 \ub4a4, \ubaa8\ub378\ub85c\ubd80\ud130 \uc608\uce21 \uacb0\uacfc\ub97c \uc5bb\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubbf8\uc9c0 \uc900\ube44\ud558\uae30\n~~~~~~~~~~~~~~~~~~~\n\nDenseNet \ubaa8\ub378\uc740 224 x 224\uc758 3\ucc44\ub110 RGB \uc774\ubbf8\uc9c0\ub97c \ud544\uc694\ub85c \ud569\ub2c8\ub2e4.\n\ub610\ud55c \uc774\ubbf8\uc9c0 \ud150\uc11c\ub97c \ud3c9\uaddc \ubc0f \ud45c\uc900\ud3b8\ucc28 \uac12\uc73c\ub85c \uc815\uaddc\ud654\ud569\ub2c8\ub2e4. \uc790\uc138\ud55c \ub0b4\uc6a9\uc740\n`\uc5ec\uae30 <https://pytorch.org/docs/stable/torchvision/models.html>`_ \ub97c \ucc38\uace0\ud558\uc138\uc694.\n\n``torchvision`` \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 ``transforms`` \uc744 \uc774\uc6a9\ud558\uc5ec \ubcc0\ud658 \ud30c\uc774\ud504\ub77c\uc778\n(transform pipeline)\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. Transforms\uc640 \uad00\ub828\ud55c \ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740\n`\uc5ec\uae30  <https://pytorch.org/docs/stable/torchvision/transforms.html>`_ \uc5d0\uc11c\n\uc77d\uc5b4\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -274,7 +274,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/78c2efd84ea24eb52e6d415e69187a36/two_layer_net_autograd.ipynb b/docs/_downloads/78c2efd84ea24eb52e6d415e69187a36/two_layer_net_autograd.ipynb
index 5491d34f2..4ff6c6895 100644
--- a/docs/_downloads/78c2efd84ea24eb52e6d415e69187a36/two_layer_net_autograd.ipynb
+++ b/docs/_downloads/78c2efd84ea24eb52e6d415e69187a36/two_layer_net_autograd.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch: Tensor\uc640 autograd\n-------------------------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88\uc5d0\ub294 PyTorch Tensor \uc5f0\uc0b0\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc21c\uc804\ud30c \ub2e8\uacc4\ub97c \uacc4\uc0b0\ud558\uace0, PyTorch autograd\ub97c\n\uc0ac\uc6a9\ud558\uc5ec \ubcc0\ud654\ub3c4(gradient)\ub97c \uacc4\uc0b0\ud558\ub294 \uac83\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch Tensor\ub294 \uc5f0\uc0b0 \uadf8\ub798\ud504\uc5d0\uc11c \ub178\ub4dc(node)\ub85c \ud45c\ud604\ub429\ub2c8\ub2e4. \ub9cc\uc57d ``x`` \uac00\n``x.requires_grad=True`` \uc778 Tensor\uba74 ``x.grad`` \ub294 \uc5b4\ub5a4 \uc2a4\uce7c\ub77c \uac12\uc5d0 \ub300\ud55c ``x`` \uc758\n\ubcc0\ud654\ub3c4\ub97c \uac16\ub294 \ub610 \ub2e4\ub978 Tensor\uc785\ub2c8\ub2e4.\n\n"
+        "\nPyTorch: Tensor\uc640 autograd\n-------------------------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88\uc5d0\ub294 PyTorch Tensor \uc5f0\uc0b0\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc21c\uc804\ud30c \ub2e8\uacc4\ub97c \uacc4\uc0b0\ud558\uace0, PyTorch autograd\ub97c\n\uc0ac\uc6a9\ud558\uc5ec \ubcc0\ud654\ub3c4(gradient)\ub97c \uacc4\uc0b0\ud558\ub294 \uac83\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch Tensor\ub294 \uc5f0\uc0b0 \uadf8\ub798\ud504\uc5d0\uc11c \ub178\ub4dc(node)\ub85c \ud45c\ud604\ub429\ub2c8\ub2e4. \ub9cc\uc57d ``x`` \uac00\n``x.requires_grad=True`` \uc778 Tensor\uba74 ``x.grad`` \ub294 \uc5b4\ub5a4 \uc2a4\uce7c\ub77c \uac12\uc5d0 \ub300\ud55c ``x`` \uc758\n\ubcc0\ud654\ub3c4\ub97c \uac16\ub294 \ub610 \ub2e4\ub978 Tensor\uc785\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/7eba5f912646e2d2239d4e1ce08de5ec/deploy_seq2seq_hybrid_frontend_tutorial.ipynb b/docs/_downloads/7eba5f912646e2d2239d4e1ce08de5ec/deploy_seq2seq_hybrid_frontend_tutorial.ipynb
index 9556abad6..8a6c3bcea 100644
--- a/docs/_downloads/7eba5f912646e2d2239d4e1ce08de5ec/deploy_seq2seq_hybrid_frontend_tutorial.ipynb
+++ b/docs/_downloads/7eba5f912646e2d2239d4e1ce08de5ec/deploy_seq2seq_hybrid_frontend_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nDeploying a Seq2Seq Model with TorchScript\n==================================================\n**Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_\n\n"
+        "\nDeploying a Seq2Seq Model with TorchScript\n==================================================\n**Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_\n"
       ]
     },
     {
@@ -272,7 +272,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/7f37028fb3517ca10f3388f4bb4889b8/data_parallel_tutorial.ipynb b/docs/_downloads/7f37028fb3517ca10f3388f4bb4889b8/data_parallel_tutorial.ipynb
index 918ff092c..990bbdbaf 100644
--- a/docs/_downloads/7f37028fb3517ca10f3388f4bb4889b8/data_parallel_tutorial.ipynb
+++ b/docs/_downloads/7f37028fb3517ca10f3388f4bb4889b8/data_parallel_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uc120\ud0dd \uc0ac\ud56d: \ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac (Data Parallelism)\n==========================\n**\uae00\uc4f4\uc774**: `Sung Kim <https://github.com/hunkim>`_ and `Jenny Kang <https://github.com/jennykang>`_\n**\ubc88\uc5ed**: '\uc815\uc544\uc9c4 <https://github.com/ajin-jng>'\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ``DataParallel`` (\ub370\uc774\ud130 \ubcd1\ub82c) \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc95\uc744 \ubc30\uc6b0\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch\ub97c \ud1b5\ud574 GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc27d\uc2b5\ub2c8\ub2e4. \uba3c\uc800, \ubaa8\ub378\uc744 GPU\uc5d0 \ub123\uc2b5\ub2c8\ub2e4:\n\n.. code:: python\n\n    device = torch.device(\"cuda:0\")\n    model.to(device)\n\n\uadf8 \ub2e4\uc74c\uc73c\ub85c\ub294 \ubaa8\ub4e0 Tensors \ub97c GPU\ub85c \ubcf5\uc0ac\ud569\ub2c8\ub2e4:\n\n.. code:: python\n\n    mytensor = my_tensor.to(device)\n\n''my_tensor.to(device)'' \ub97c \ud638\ucd9c \uc2dc \uc5d0\ub294 ''my_tensor'' \ub97c \ub2e4\uc2dc\uc4f0\ub294 \ub300\uc2e0 ''my_tensor'' \uc758 \ub610\ub2e4\ub978 \ubcf5\uc0ac\ubcf8\uc774 \uc0dd\uae34\ub2e4\ub294 \uc0ac\uc2e4\uc744 \uae30\uc5b5\ud558\uc2ed\uc2dc\uc624. \n\ub2f9\uc2e0\uc740 \uadf8\uac83\uc744 \uc0c8\ub85c\uc6b4 tensor \uc5d0 \uc18c\uc18d\uc2dc\ud0a4\uace0 GPU\uc5d0 \uadf8 tensor\ub97c \uc368\uc57c\ud569\ub2c8\ub2e4.\n\n\uc5ec\ub7ec GPU\ub97c \ud1b5\ud574 \uc55e\uacfc \ub4a4\uc758 \uc804\ud30c\ub97c \uc2e4\ud589\ud558\ub294 \uac83\uc740 \ub2f9\uc5f0\ud55c \uc77c \uc785\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 PyTorch\ub294 \uae30\ubcf8\uc801 \ud558\ub098\uc758 GPU\ub9cc \uc0ac\uc6a9\ud569\ub2c8\ub2e4. ``DataParallel`` \uc744 \uc774\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ubcd1\ub82c\ub85c \uc2e4\ud589\ud558\uc5ec \ub2e4\uc218\uc758 GPU \uc5d0\uc11c \uc27d\uac8c \uc791\uc5c5\uc744 \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n\n.. code:: python\n\n    model = nn.DataParallel(model)\n\n\uc774\uac83\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ud575\uc2ec\uc785\ub2c8\ub2e4. \uc790\uc138\ud55c \uc0ac\ud56d\ub4e4\uc5d0 \ub300\ud574\uc11c\ub294 \uc544\ub798\uc5d0\uc11c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n"
+        "\n\uc120\ud0dd \uc0ac\ud56d: \ub370\uc774\ud130 \ubcd1\ub82c \ucc98\ub9ac (Data Parallelism)\n====================================================\n\n**\uae00\uc4f4\uc774**: `Sung Kim <https://github.com/hunkim>`_ and `Jenny Kang <https://github.com/jennykang>`_\n**\ubc88\uc5ed**: '\uc815\uc544\uc9c4 <https://github.com/ajin-jng>'\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ``DataParallel`` (\ub370\uc774\ud130 \ubcd1\ub82c) \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc95\uc744 \ubc30\uc6b0\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch\ub97c \ud1b5\ud574 GPU\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc27d\uc2b5\ub2c8\ub2e4. \uba3c\uc800, \ubaa8\ub378\uc744 GPU\uc5d0 \ub123\uc2b5\ub2c8\ub2e4:\n\n.. code:: python\n\n    device = torch.device(\"cuda:0\")\n    model.to(device)\n\n\uadf8 \ub2e4\uc74c\uc73c\ub85c\ub294 \ubaa8\ub4e0 Tensors \ub97c GPU\ub85c \ubcf5\uc0ac\ud569\ub2c8\ub2e4:\n\n.. code:: python\n\n    mytensor = my_tensor.to(device)\n\n''my_tensor.to(device)'' \ub97c \ud638\ucd9c \uc2dc \uc5d0\ub294 ''my_tensor'' \ub97c \ub2e4\uc2dc\uc4f0\ub294 \ub300\uc2e0 ''my_tensor'' \uc758 \ub610\ub2e4\ub978 \ubcf5\uc0ac\ubcf8\uc774 \uc0dd\uae34\ub2e4\ub294 \uc0ac\uc2e4\uc744 \uae30\uc5b5\ud558\uc2ed\uc2dc\uc624.\n\ub2f9\uc2e0\uc740 \uadf8\uac83\uc744 \uc0c8\ub85c\uc6b4 tensor \uc5d0 \uc18c\uc18d\uc2dc\ud0a4\uace0 GPU\uc5d0 \uadf8 tensor\ub97c \uc368\uc57c\ud569\ub2c8\ub2e4.\n\n\uc5ec\ub7ec GPU\ub97c \ud1b5\ud574 \uc55e\uacfc \ub4a4\uc758 \uc804\ud30c\ub97c \uc2e4\ud589\ud558\ub294 \uac83\uc740 \ub2f9\uc5f0\ud55c \uc77c \uc785\ub2c8\ub2e4.\n\uadf8\ub7ec\ub098 PyTorch\ub294 \uae30\ubcf8\uc801 \ud558\ub098\uc758 GPU\ub9cc \uc0ac\uc6a9\ud569\ub2c8\ub2e4. ``DataParallel`` \uc744 \uc774\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ubcd1\ub82c\ub85c \uc2e4\ud589\ud558\uc5ec \ub2e4\uc218\uc758 GPU \uc5d0\uc11c \uc27d\uac8c \uc791\uc5c5\uc744 \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n\n.. code:: python\n\n    model = nn.DataParallel(model)\n\n\uc774\uac83\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ud575\uc2ec\uc785\ub2c8\ub2e4. \uc790\uc138\ud55c \uc0ac\ud56d\ub4e4\uc5d0 \ub300\ud574\uc11c\ub294 \uc544\ub798\uc5d0\uc11c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -58,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ub354\ubbf8(Dummy) \ub370\uc774\ud130\uc14b\n-------------\n\n\ub354\ubbf8(ramdom) \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4\uc5b4 \ubd05\uc2dc\ub2e4. Getitem \ub9cc \uad6c\ud604\ud558\uba74 \ub429\ub2c8\ub2e4.\n\n\n"
+        "\ub354\ubbf8(Dummy) \ub370\uc774\ud130\uc14b\n-----------------------\n\n\ub354\ubbf8(ramdom) \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4\uc5b4 \ubd05\uc2dc\ub2e4. Getitem \ub9cc \uad6c\ud604\ud558\uba74 \ub429\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -76,7 +76,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uac04\ub2e8\ud55c \ubaa8\ub378\n ------------\n\n \ub370\ubaa8\ub97c \uc704\ud574 \ubaa8\ub378\uc740 \uc785\ub825\uc744 \ubc1b\uace0 \uc120\ud615 \uc5f0\uc0b0\uc744 \uc218\ud589\ud558\uba70 \ucd9c\ub825\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 ``DataParallel`` \uc758 \uc5b4\ub5a4 \ubaa8\ub378 (CNN, RNN, Capsule Net \ub4f1) \uc5d0\uc11c\ub4e0 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc6b0\ub9ac\ub294 input\uacfc output\uc758 \ud06c\uae30\ub97c \ubaa8\ub2c8\ud130\ub9c1\ud558\uae30 \uc704\ud574 \ubaa8\ub378\uc548\uc5d0 print \ubb38\uc744 \ub123\uc5c8\uc2b5\ub2c8\ub2e4.\n \ubb34\uc5c7\uc774 \ubc30\uce58 \uc21c\uc704 (batch rank) 0 \uc5d0 \ud504\ub9b0\ud2b8 \ub418\ub294\uc9c0 \uc8fc\uc758 \uae4a\uac8c \ubd10\uc8fc\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n\n\n"
+        "\uac04\ub2e8\ud55c \ubaa8\ub378\n------------\n\n\ub370\ubaa8\ub97c \uc704\ud574 \ubaa8\ub378\uc740 \uc785\ub825\uc744 \ubc1b\uace0 \uc120\ud615 \uc5f0\uc0b0\uc744 \uc218\ud589\ud558\uba70 \ucd9c\ub825\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uadf8\ub7ec\ub098 ``DataParallel`` \uc758 \uc5b4\ub5a4 \ubaa8\ub378 (CNN, RNN, Capsule Net \ub4f1) \uc5d0\uc11c\ub4e0 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc6b0\ub9ac\ub294 input\uacfc output\uc758 \ud06c\uae30\ub97c \ubaa8\ub2c8\ud130\ub9c1\ud558\uae30 \uc704\ud574 \ubaa8\ub378\uc548\uc5d0 print \ubb38\uc744 \ub123\uc5c8\uc2b5\ub2c8\ub2e4.\n\ubb34\uc5c7\uc774 \ubc30\uce58 \uc21c\uc704 (batch rank) 0 \uc5d0 \ud504\ub9b0\ud2b8 \ub418\ub294\uc9c0 \uc8fc\uc758 \uae4a\uac8c \ubd10\uc8fc\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -94,7 +94,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ubaa8\ub378\uacfc \ub370\uc774\ud130 \ubcd1\ub82c\uc758 \uad6c\ud604\n-----------------------------\n\n\uc774\uac83\uc740 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ud575\uc2ec \ubd80\ubd84\uc785\ub2c8\ub2e4. \uba3c\uc800, model instance \ub97c \ub9cc\ub4e4\uace0 \uac00\uc9c0\uace0 \uc788\ub294 GPU\uac00 \uc5ec\ub7ec\uac1c\uc778\uc9c0 \ud655\uc778\ud574\uc57c\ud569\ub2c8\ub2e4.\n\ub9cc\uc57d \ub2e4\uc218\uc758 GPU\ub97c \ubcf4\uc720\uc911\uc774\ub77c\uba74, ``nn.DataParallel`` \uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ub798\ud551 (wrapping) \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \n\uadf8\ub7f0 \ub2e4\uc74c ``model.to(device)`` \ub97c \ud1b5\ud558\uc5ec \ubaa8\ub378\uc744 GPU\uc5d0 \ub123\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\n"
+        "\ubaa8\ub378\uacfc \ub370\uc774\ud130 \ubcd1\ub82c\uc758 \uad6c\ud604\n-----------------------------\n\n\uc774\uac83\uc740 \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \ud575\uc2ec \ubd80\ubd84\uc785\ub2c8\ub2e4. \uba3c\uc800, model instance \ub97c \ub9cc\ub4e4\uace0 \uac00\uc9c0\uace0 \uc788\ub294 GPU\uac00 \uc5ec\ub7ec\uac1c\uc778\uc9c0 \ud655\uc778\ud574\uc57c\ud569\ub2c8\ub2e4.\n\ub9cc\uc57d \ub2e4\uc218\uc758 GPU\ub97c \ubcf4\uc720\uc911\uc774\ub77c\uba74, ``nn.DataParallel`` \uc744 \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc744 \ub798\ud551 (wrapping) \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uadf8\ub7f0 \ub2e4\uc74c ``model.to(device)`` \ub97c \ud1b5\ud558\uc5ec \ubaa8\ub378\uc744 GPU\uc5d0 \ub123\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -157,7 +157,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/813e9f58ce3b4f2283d6c6cee9fee5a7/custom_dataset_transforms_loader.ipynb b/docs/_downloads/813e9f58ce3b4f2283d6c6cee9fee5a7/custom_dataset_transforms_loader.ipynb
index a6f129046..d07cd2073 100644
--- a/docs/_downloads/813e9f58ce3b4f2283d6c6cee9fee5a7/custom_dataset_transforms_loader.ipynb
+++ b/docs/_downloads/813e9f58ce3b4f2283d6c6cee9fee5a7/custom_dataset_transforms_loader.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uc0ac\uc6a9\uc790 \uc815\uc758 PyTorch Dataloader \uc791\uc131\ud558\uae30\n===========================================================\n\n\uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc5d0 \ub9ce\uc740 \ub178\ub825\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. PyTorch\ub294 \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud558\ub294\ub370 \uc27d\uace0 \uac00\ub2a5\ud558\ub2e4\uba74\n\ub354 \uc88b\uc740 \uac00\ub3c5\uc131\uc744 \uac00\uc9c4 \ucf54\ub4dc\ub97c \ub9cc\ub4e4\uae30\uc704\ud574 \ub9ce\uc740 \ub3c4\uad6c\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 \ub2e4\uc74c \uc138 \uac00\uc9c0\ub97c \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n 1. PyTorch \ub370\uc774\ud130\uc14b API\ub4e4\uc744 \uc774\uc6a9\ud558\uc5ec \uc0ac\uc6a9\uc790 \uc815\uc758 \ub370\uc774\ud130\uc14b \ub9cc\ub4e4\uae30.\n 2. \uad6c\uc131\uac00\ub2a5\ud558\uba70 \ud638\ucd9c \ub420 \uc218 \uc788\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 transform \ub9cc\ub4e4\uae30.\n 3. \uc774\ub7ec\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \ud569\uccd0\uc11c \uc0ac\uc6a9\uc790 \uc815\uc758 dataloader \ub9cc\ub4e4\uae30.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc2e4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc74c\uc758 \ud328\ud0a4\uc9c0\ub4e4\uc774 \uc124\uce58 \ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud574 \uc8fc\uc138\uc694.\n -  ``scikit-image``: \uc774\ubbf8\uc9c0 I/O\uc640 \uc774\ubbf8\uc9c0 \ubcc0\ud615\uc5d0 \ud544\uc694\ud569\ub2c8\ub2e4.\n -  ``pandas``: CSV\ub97c \ub354 \uc27d\uac8c \ud30c\uc2f1\ud558\uae30 \uc704\ud574 \ud544\uc694\ud569\ub2c8\ub2e4.\n\n\uc791\uc131\ub418\uace0 \uc788\ub294 \uc774 \uc2dc\uc810\uc5d0\uc11c, \uc774 \ub808\uc2dc\ud53c\ub294 `Sasank Chilamkurthy <https://chsasank.github.io>`__ \uc758 \uc624\ub9ac\uc9c0\ub110 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ubc14\ud0d5\uc73c\ub85c \ud558\uba70\n\ub098\uc911\uc5d0\ub294 `Joe Spisak <https://github.com/jspisak>`__ \uc5d0 \uc758\ud574 \uc218\uc815\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n\ud55c\uad6d\uc5b4\ub85c `Jae Joong Lee <https://https://github.com/JaeLee18>`__ \uc5d0 \uc758\ud574 \ubc88\uc5ed\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n\n"
+        "\n\uc0ac\uc6a9\uc790 \uc815\uc758 PyTorch Dataloader \uc791\uc131\ud558\uae30\n===========================================================\n\n\uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uac1c\ubc1c\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub370\uc774\ud130 \uc804\ucc98\ub9ac\uc5d0 \ub9ce\uc740 \ub178\ub825\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. PyTorch\ub294 \ub370\uc774\ud130\ub97c \ub85c\ub4dc\ud558\ub294\ub370 \uc27d\uace0 \uac00\ub2a5\ud558\ub2e4\uba74\n\ub354 \uc88b\uc740 \uac00\ub3c5\uc131\uc744 \uac00\uc9c4 \ucf54\ub4dc\ub97c \ub9cc\ub4e4\uae30\uc704\ud574 \ub9ce\uc740 \ub3c4\uad6c\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 \ub2e4\uc74c \uc138 \uac00\uc9c0\ub97c \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n 1. PyTorch \ub370\uc774\ud130\uc14b API\ub4e4\uc744 \uc774\uc6a9\ud558\uc5ec \uc0ac\uc6a9\uc790 \uc815\uc758 \ub370\uc774\ud130\uc14b \ub9cc\ub4e4\uae30.\n 2. \uad6c\uc131\uac00\ub2a5\ud558\uba70 \ud638\ucd9c \ub420 \uc218 \uc788\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 transform \ub9cc\ub4e4\uae30.\n 3. \uc774\ub7ec\ud55c \ucef4\ud3ec\ub10c\ud2b8\ub4e4\uc744 \ud569\uccd0\uc11c \uc0ac\uc6a9\uc790 \uc815\uc758 dataloader \ub9cc\ub4e4\uae30.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc2e4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 \ub2e4\uc74c\uc758 \ud328\ud0a4\uc9c0\ub4e4\uc774 \uc124\uce58 \ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud574 \uc8fc\uc138\uc694.\n -  ``scikit-image``: \uc774\ubbf8\uc9c0 I/O\uc640 \uc774\ubbf8\uc9c0 \ubcc0\ud615\uc5d0 \ud544\uc694\ud569\ub2c8\ub2e4.\n -  ``pandas``: CSV\ub97c \ub354 \uc27d\uac8c \ud30c\uc2f1\ud558\uae30 \uc704\ud574 \ud544\uc694\ud569\ub2c8\ub2e4.\n\n\uc791\uc131\ub418\uace0 \uc788\ub294 \uc774 \uc2dc\uc810\uc5d0\uc11c, \uc774 \ub808\uc2dc\ud53c\ub294 `Sasank Chilamkurthy <https://chsasank.github.io>`__ \uc758 \uc624\ub9ac\uc9c0\ub110 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ubc14\ud0d5\uc73c\ub85c \ud558\uba70\n\ub098\uc911\uc5d0\ub294 `Joe Spisak <https://github.com/jspisak>`__ \uc5d0 \uc758\ud574 \uc218\uc815\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n\ud55c\uad6d\uc5b4\ub85c `Jae Joong Lee <https://https://github.com/JaeLee18>`__ \uc5d0 \uc758\ud574 \ubc88\uc5ed\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -147,7 +147,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc6b0\ub9ac\ub294 \uc9c0\uae08\uae4c\uc9c0 \uc5b4\ub290\uc815\ub3c4 \uc0ac\uc6a9\uc790 \uc815\uc758 \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uc558\ub294\ub370 \uc774\uc81c\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 \ubcc0\ud615\uc744 \ub9cc\ub4e4 \ucc28\ub840 \uc785\ub2c8\ub2e4.\n\ucef4\ud4e8\ud130 \ube44\uc804\uc5d0\uc11c\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 \ubcc0\ud615\uc740 \uc54c\uace0\ub9ac\uc998\uc744 \uc77c\ubc18\ud654\uc2dc\ud0a4\uace0 \uc815\ud655\ub3c4\ub97c \uc62c\ub9ac\ub294\ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.\n\ubcc0\ud615\ub4e4\uc740 \ud6c8\ub828\uc2dc\uc5d0 \uc0ac\uc6a9\uc774 \ub418\uba70 \uc8fc\ub85c \ub370\uc774\ud130 \uc99d\uac15\uc73c\ub85c \ucc38\uc870\ub418\uba70 \ucd5c\uadfc\uc758 \ubaa8\ub378 \uac1c\ubc1c\uc5d0\uc120 \ud754\ud788 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n\n\ub370\uc774\ud130\uc14b\uc744 \ub2e4\ub8f0\ub54c \uc790\uc8fc \uc77c\uc5b4\ub098\ub294 \ubb38\uc81c\uc911 \ud558\ub098\ub294 \ubaa8\ub4e0 \uc0d8\ud50c\ub4e4\uc774 \uac19\uc740 \ud06c\uae30\ub97c \uac00\uc9c0\uace0 \uc788\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc785\ub2c8\ub2e4.\n\ub300\ubd80\ubd84\uc758 \uc2e0\uacbd\ub9dd\ub4e4\uc740 \ubbf8\ub9ac \uc815\ud574\uc9c4 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\ub4e4\uc744 \ubc1b\uc544\ub4dc\ub9bd\ub2c8\ub2e4.\n\uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \uc6b0\ub9ac\ub294 \uc804\ucc98\ub9ac \ucf54\ub4dc\ub97c \uc791\uc131\ud574\uc57c\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\uc81c \uc138\uac1c\uc758 \ubcc0\ud615\uc744 \ub9cc\ub4e4\uc5b4 \ubd05\uc2dc\ub2e4.\n\n-  ``Rescale``: \uc774\ubbf8\uc9c0 \ud06c\uae30\ub97c \ubcc0\uacbd\ud560\ub54c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n-  ``RandomCrop``: \ubb34\uc791\uc704\ub85c \uc774\ubbf8\uc9c0\ub97c \uc798\ub77c\ub0b4\uba70 \ub370\uc774\ud130 \uc99d\uac15\uc5d0 \uc4f0\uc785\ub2c8\ub2e4.\n-  ``ToTensor``: Numpy \uc774\ubbf8\uc9c0\ub4e4\uc744 \ud30c\uc774\ud1a0\uce58 \uc774\ubbf8\uc9c0\ub85c \ubcc0\ud658\ud560\ub54c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. (\uadf8\ub7ec\uae30 \uc704\ud574\uc11c\ub294 \uc774\ubbf8\uc9c0 \ucc28\uc6d0\uc758 \uc21c\uc11c\ub97c \ubc14\uafd4\uc57c\ud569\ub2c8\ub2e4.)\n\n\uc6b0\ub9ac\ub294 \uc704\uc758 \uc138\uac1c\uc758 \ubcc0\ud615\ub4e4\uc744 \ub2e8\uc21c\ud55c \ud568\uc218 \ub300\uc2e0\uc5d0 \ud638\ucd9c\uac00\ub2a5\ud55c \ud074\ub798\uc2a4\ub85c \ub9cc\ub4e4\uc5b4\uc11c \ub9e4\ubc88 \ubcc0\ud615\uc774 \ud638\ucd9c\ub420\ub54c \ud56d\uc0c1 \ub9e4\uac1c\ubcc0\uc218\uac00 \ub118\uaca8\uc9c0\uc9c0 \uc54a\ub3c4\ub85d \ud560\uac81\ub2c8\ub2e4.\n\uadf8\ub7ec\uae30 \uc704\ud574\uc11c\ub294 \uc6b0\ub9ac\ub294 \ub2e8\uc21c\ud788 ``__call__`` \uba54\uc18c\ub4dc\ub97c \ub9cc\ub4e4\uace0 \ud544\uc694\ud558\ub2e4\uba74 ``__init__`` \ub97c \ub9cc\ub4e4\uba74 \ub429\ub2c8\ub2e4.\n\uadf8\ub7ec\uba74 \uc6b0\ub9ac\ub294 \ubcc0\ud615\uc744 \uc774\ub7f0\uc2dd\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n::\n\n   tsfm = Transform(params)\n   transformed_sample = tsfm(sample)\n\n\uc5b4\ub5bb\uac8c \uc774\ub7f0 \ubcc0\ud615\ub4e4\uc774 \uc774\ubbf8\uc9c0\uc640 \ub79c\ub4dc\ub9c8\ud06c\uc5d0 \uc801\uc6a9\uc774 \ub418\uc5c8\ub294\uc9c0 \uc544\ub798\ub97c \ubd10\uc8fc\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n\n\n"
+        "\uc6b0\ub9ac\ub294 \uc9c0\uae08\uae4c\uc9c0 \uc5b4\ub290\uc815\ub3c4 \uc0ac\uc6a9\uc790 \uc815\uc758 \ub370\uc774\ud130\uc14b\uc744 \ub9cc\ub4e4\uc5b4 \ubcf4\uc558\ub294\ub370 \uc774\uc81c\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 \ubcc0\ud615\uc744 \ub9cc\ub4e4 \ucc28\ub840 \uc785\ub2c8\ub2e4.\n\ucef4\ud4e8\ud130 \ube44\uc804\uc5d0\uc11c\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 \ubcc0\ud615\uc740 \uc54c\uace0\ub9ac\uc998\uc744 \uc77c\ubc18\ud654\uc2dc\ud0a4\uace0 \uc815\ud655\ub3c4\ub97c \uc62c\ub9ac\ub294\ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.\n\ubcc0\ud615\ub4e4\uc740 \ud6c8\ub828\uc2dc\uc5d0 \uc0ac\uc6a9\uc774 \ub418\uba70 \uc8fc\ub85c \ub370\uc774\ud130 \uc99d\uac15\uc73c\ub85c \ucc38\uc870\ub418\uba70 \ucd5c\uadfc\uc758 \ubaa8\ub378 \uac1c\ubc1c\uc5d0\uc120 \ud754\ud788 \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n\n\ub370\uc774\ud130\uc14b\uc744 \ub2e4\ub8f0\ub54c \uc790\uc8fc \uc77c\uc5b4\ub098\ub294 \ubb38\uc81c\uc911 \ud558\ub098\ub294 \ubaa8\ub4e0 \uc0d8\ud50c\ub4e4\uc774 \uac19\uc740 \ud06c\uae30\ub97c \uac00\uc9c0\uace0 \uc788\uc9c0 \uc54a\uc744 \uacbd\uc6b0\uc785\ub2c8\ub2e4.\n\ub300\ubd80\ubd84\uc758 \uc2e0\uacbd\ub9dd\ub4e4\uc740 \ubbf8\ub9ac \uc815\ud574\uc9c4 \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\ub4e4\uc744 \ubc1b\uc544\ub4e4\uc785\ub2c8\ub2e4.\n\uadf8\ub807\uae30 \ub54c\ubb38\uc5d0 \uc6b0\ub9ac\ub294 \uc804\ucc98\ub9ac \ucf54\ub4dc\ub97c \uc791\uc131\ud574\uc57c\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\uc81c \uc138\uac1c\uc758 \ubcc0\ud615\uc744 \ub9cc\ub4e4\uc5b4 \ubd05\uc2dc\ub2e4.\n\n-  ``Rescale``: \uc774\ubbf8\uc9c0 \ud06c\uae30\ub97c \ubcc0\uacbd\ud560\ub54c \uc0ac\uc6a9\ub429\ub2c8\ub2e4.\n-  ``RandomCrop``: \ubb34\uc791\uc704\ub85c \uc774\ubbf8\uc9c0\ub97c \uc798\ub77c\ub0b4\uba70 \ub370\uc774\ud130 \uc99d\uac15\uc5d0 \uc4f0\uc785\ub2c8\ub2e4.\n-  ``ToTensor``: Numpy \uc774\ubbf8\uc9c0\ub4e4\uc744 \ud30c\uc774\ud1a0\uce58 \uc774\ubbf8\uc9c0\ub85c \ubcc0\ud658\ud560\ub54c \uc0ac\uc6a9\ub429\ub2c8\ub2e4. (\uadf8\ub7ec\uae30 \uc704\ud574\uc11c\ub294 \uc774\ubbf8\uc9c0 \ucc28\uc6d0\uc758 \uc21c\uc11c\ub97c \ubc14\uafd4\uc57c\ud569\ub2c8\ub2e4.)\n\n\uc6b0\ub9ac\ub294 \uc704\uc758 \uc138\uac1c\uc758 \ubcc0\ud615\ub4e4\uc744 \ub2e8\uc21c\ud55c \ud568\uc218 \ub300\uc2e0\uc5d0 \ud638\ucd9c\uac00\ub2a5\ud55c \ud074\ub798\uc2a4\ub85c \ub9cc\ub4e4\uc5b4\uc11c \ub9e4\ubc88 \ubcc0\ud615\uc774 \ud638\ucd9c\ub420\ub54c \ud56d\uc0c1 \ub9e4\uac1c\ubcc0\uc218\uac00 \ub118\uaca8\uc9c0\uc9c0 \uc54a\ub3c4\ub85d \ud560\uac81\ub2c8\ub2e4.\n\uadf8\ub7ec\uae30 \uc704\ud574\uc11c\ub294 \uc6b0\ub9ac\ub294 \ub2e8\uc21c\ud788 ``__call__`` \uba54\uc18c\ub4dc\ub97c \ub9cc\ub4e4\uace0 \ud544\uc694\ud558\ub2e4\uba74 ``__init__`` \ub97c \ub9cc\ub4e4\uba74 \ub429\ub2c8\ub2e4.\n\uadf8\ub7ec\uba74 \uc6b0\ub9ac\ub294 \ubcc0\ud615\uc744 \uc774\ub7f0\uc2dd\uc73c\ub85c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n::\n\n   tsfm = Transform(params)\n   transformed_sample = tsfm(sample)\n\n\uc5b4\ub5bb\uac8c \uc774\ub7f0 \ubcc0\ud615\ub4e4\uc774 \uc774\ubbf8\uc9c0\uc640 \ub79c\ub4dc\ub9c8\ud06c\uc5d0 \uc801\uc6a9\uc774 \ub418\uc5c8\ub294\uc9c0 \uc544\ub798\ub97c \ubd10\uc8fc\uc2dc\uae38 \ubc14\ub78d\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -253,7 +253,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/84ab670fda2216116ac8e3ecd5805f0b/model_parallel_tutorial.py b/docs/_downloads/84ab670fda2216116ac8e3ecd5805f0b/model_parallel_tutorial.py
index be56e9ec9..a99563c8a 100644
--- a/docs/_downloads/84ab670fda2216116ac8e3ecd5805f0b/model_parallel_tutorial.py
+++ b/docs/_downloads/84ab670fda2216116ac8e3ecd5805f0b/model_parallel_tutorial.py
@@ -5,10 +5,10 @@
 **저자** : `Shen Li <https://mrshenli.github.io/>`_
 **번역** : `안상준 <https://github.com/Justin-A>`_
 
-모델 병렬 처리는 분산 학습 기술에 범용적으로 사용되고 있습니다. 
+모델 병렬 처리는 분산 학습 기술에 범용적으로 사용되고 있습니다.
 이전 튜토리얼들에서는 'DataParallel' `<https://pytorch.org/tutorials/beginner/blitz/data_parallel_tutorial.html>`_
 여러 GPU를 사용하여 신경망 모델을 학습 시킬 때 어떻게 DataParallel을 사용하는지에 대해서 살펴보았습니다.
-이 방법은 각 GPU에 입력 데이터를 부분적으로 할당하고 동일한 신경망 모델을 복제하여 이용하는 방식이었습니다. 
+이 방법은 각 GPU에 입력 데이터를 부분적으로 할당하고 동일한 신경망 모델을 복제하여 이용하는 방식이었습니다.
 이 방법은 신경망 모델을 상당히 빠르게 학습시킬 수 있는 장점이 있지만, 신경망 모델이 너무 커서 단일 GPU에 할당이 되지 않는 경우에는 동작하지 않습니다.
 
 이번 튜토리얼에서는 ``데이터 병렬 처리`` 가 아닌 **모델 병렬 처리** 문제를 해결하는 방법을 소개합니다.
@@ -16,10 +16,10 @@
 구체적으로, 10개의 층으로 구성된 ``m`` 신경망 모델에 대해서 ``데이터 병렬 처리`` 방법은 10개의 층을 전부 복제하여 각 GPU에 할당하여 처리하지만,
 이와 반대로 2개의 GPU에 모델을 병렬 처리한다면, 각 GPU에 5개의 층씩 각각 할당하여 호스팅할 수 있습니다.
 
-모델 병렬 처리의 전반적인 아이디어는 모델의 서브 네트워크들을 각각 다른 GPU에 할당하고, 
-각 장비 별로 순전파를 진행하여 계산되는 출력값들을 각 장비 간 공유하여 이용하는 것입니다. 
+모델 병렬 처리의 전반적인 아이디어는 모델의 서브 네트워크들을 각각 다른 GPU에 할당하고,
+각 장비 별로 순전파를 진행하여 계산되는 출력값들을 각 장비 간 공유하여 이용하는 것입니다.
 이용하고자 하는 신경망 모델을 부분적으로 각 GPU에 할당하는 것이기 때문에, 여러 GPU를 이용하여 더 큰 신경망 모델을 할당하고 학습시킬 수 있습니다.
-이번 튜토리얼은 거대한 모델을 제한된 수의 GPU에 분할하여 할당하지 않고, 그 대신, 모델 병렬 처리의 아이디어를 이해하는 목적으로 작성되었습니다. 
+이번 튜토리얼은 거대한 모델을 제한된 수의 GPU에 분할하여 할당하지 않고, 그 대신, 모델 병렬 처리의 아이디어를 이해하는 목적으로 작성되었습니다.
 모델 병렬 처리의 아이디어를 활용하여 실제 어플리케이션에 적용하는 것은 여러분의 몫입니다.
 
 .. note::
@@ -49,7 +49,7 @@ def __init__(self):
         self.net2 = torch.nn.Linear(10, 5).to('cuda:1')  # 두 번째 층을 두 번째 GPU에 할당
 
     def forward(self, x):
-        x = self.relu(self.net1(x.to('cuda:0'))) 
+        x = self.relu(self.net1(x.to('cuda:0')))
         return self.net2(x.to('cuda:1')) # 첫 번째 층의 산출물을 두 번째 GPU에 할당하여 진행
 
 ######################################################################
@@ -71,10 +71,10 @@ def forward(self, x):
 
 ######################################################################
 # 기존에 존재하는 모듈에 모델 병렬 처리 적용해보기
-# ----------------------------------------
+# ---------------------------------------------------
 #
-# 기존에 단일 GPU에 존재하는 모듈을 여러 GPU에 할당하는 것은  단지 몇 줄의 코드를 수정하는 것으로도 쉽게 가능합니다. 
-# 아래에 있는 코드들은 ResNet50 모델을 분할하는 방법입니다. 이 아이디어는, 기존에 존재하는 ResNet 모듈을 상속받아 설계할 때, 2개의 GPU에 층을 나누어 설계하는 방식으로 진행됩니다. 
+# 기존에 단일 GPU에 존재하는 모듈을 여러 GPU에 할당하는 것은  단지 몇 줄의 코드를 수정하는 것으로도 쉽게 가능합니다.
+# 아래에 있는 코드들은 ResNet50 모델을 분할하는 방법입니다. 이 아이디어는, 기존에 존재하는 ResNet 모듈을 상속받아 설계할 때, 2개의 GPU에 층을 나누어 설계하는 방식으로 진행됩니다.
 # 그 후, 2개 GPU에서 계산되는 중간 산출물 텐서값을 적절히 배치하기 위헤 순전파 메소드를 수정합니다.
 
 
@@ -113,12 +113,12 @@ def forward(self, x):
 
 
 ######################################################################
-# 위의 예제에서는 단일 GPU에 신경망 모델을 할당하여 학습시키기에는 모델 크기가 너무 클 때 발생하는 문제를 해결하는 방법입니다. 
-# 하지만, 여러분은 단일 GPU를 이용할 때보다 학습 과정이 오래걸리며, 이는 여러분들이 이미 알고 있는 내용이었을 수 있습니다. 
+# 위의 예제에서는 단일 GPU에 신경망 모델을 할당하여 학습시키기에는 모델 크기가 너무 클 때 발생하는 문제를 해결하는 방법입니다.
+# 하지만, 여러분은 단일 GPU를 이용할 때보다 학습 과정이 오래걸리며, 이는 여러분들이 이미 알고 있는 내용이었을 수 있습니다.
 # 그 이유는, 두 개의 GPU가 동시에 계산하는 것이 아니라 1개의 GPU는 계산하지 않고 대기하고 있기 때문입니다.
 # 또한, 두 번째 층 (layer2)이 할당된 첫 번째 GPU에서 계산된 결과를 세 번째 층 (layer3)이 할당된 두 번째 GPU로 텐서값을 복사하기 때문에 계산 과정이 더 길어지게 됩니다.
 #
-# 코드 실행 시간을 정량적으로 살펴보기 위해 실험을 하나 해봅시다. 입력 텐서값과 레이블값을 랜덤으로 설정한 후, 
+# 코드 실행 시간을 정량적으로 살펴보기 위해 실험을 하나 해봅시다. 입력 텐서값과 레이블값을 랜덤으로 설정한 후,
 # 이미 존재하는 torchvision.models.reset50() 과, 모델 병렬 처리를 진행한 ``ModelParallelResNet50`` 을 통해 학습을 진행합니다.
 # 학습 진행을 완료한 후, 두 모델들은 랜덤으로 생성된 데이터로 학습을 진행했기 때문에 실용적인 예측을 하진 못하지만, 학습 진행 시간을 실용적으로 비교하여 할 수 있습니다.
 
@@ -157,8 +157,8 @@ def train(model):
 
 
 ######################################################################
-# 위에서 정의한 ``train(model)`` 메소드는 nn.MSELoss (Mean Squared Error ; 평균 제곱 오차) 로 손실 함수를 정의하여 신경망 모델을 학습하는 것을 의미합니다. 
-# 그리고, ``optim.SGD`` 메소드는 최적화 방식을 의미합니다. 위 방식은 128 * 128 크기의 이미지가 120개로 구성된 배치 데이터가 3개 존재하는 상황을 모방하기 위해 랜덤으로 생성하였습니다. 
+# 위에서 정의한 ``train(model)`` 메소드는 nn.MSELoss (Mean Squared Error ; 평균 제곱 오차) 로 손실 함수를 정의하여 신경망 모델을 학습하는 것을 의미합니다.
+# 그리고, ``optim.SGD`` 메소드는 최적화 방식을 의미합니다. 위 방식은 128 * 128 크기의 이미지가 120개로 구성된 배치 데이터가 3개 존재하는 상황을 모방하기 위해 랜덤으로 생성하였습니다.
 # 그리고나서, 우리는 ``timeit`` 을 이용하여 ``train(model)`` 메소드를 10회 실행하여 학습을 진행하고, 학습 실행 시간에 대해서 표준 편차값을 반영하는 이미지를 생성하여 저장합니다.
 
 
@@ -217,7 +217,7 @@ def plot(means, stds, labels, fig_name):
 # 실험 결과, 모델 병렬 철리하여 학습하는 시간이 단일 GPU로 학습하는 시간보다 약 7% ``4.02/3.75-1=7%``정도
 # 오래 걸리는 것을 확인할 수 있습니다. 그러므로, 순전파와 역전파를 진행하면서 GPU 간 텐서값들이
 # 복제되어 이용하는 시간이 약 7%정도 소요되는 것으로 결론지을 수 있습니다. 학습하는 과정 속에서
-# 2개의 GPU 중 1개의 GPU가 계산하지 않고 대기하고 있기 때문에, 이를 해결하여 
+# 2개의 GPU 중 1개의 GPU가 계산하지 않고 대기하고 있기 때문에, 이를 해결하여
 # 학습 시간을 빠르게 개선시킬 수 있습니다. 그 중 한 가지 방법은, 학습 단위인 미니 배치 1개의 데이터를
 # 2개로 분할하는 파이프라인을 생성하여, 분할된 첫 번째 데이터가 첫 번째 층을 통과하여 두 번째 층으로
 # 복제되고, 두 번째 층을 통과할 때, 두번재로 분할된 데이터가 첫 번쨰 층을 통해 계산되는 방식으로 설정하는 것입니다.
@@ -225,10 +225,10 @@ def plot(means, stds, labels, fig_name):
 
 ######################################################################
 # 입력 텐서값을 분할하는 파이프라인을 설계하여 학습 시간을 단축하는 방법에 대한 예제
-# -----------------------------
+# ---------------------------------------------------------------------------------------
 #
-# 아래에 있는 실험은, 120개의 이미지로 구성된 1개의 미니 배치 데이터를 20개씩 나누어 진행하는 
-# 과정입니다. 아래의 과정을 실행할 때, PyTorch가 CUDA 연산을 비동기적으로 이용하기 때문에, 
+# 아래에 있는 실험은, 120개의 이미지로 구성된 1개의 미니 배치 데이터를 20개씩 나누어 진행하는
+# 과정입니다. 아래의 과정을 실행할 때, PyTorch가 CUDA 연산을 비동기적으로 이용하기 때문에,
 # 프로세스를 실행하는 스레드를 여러개 생성할 필요가 없습니다.
 
 
@@ -248,7 +248,7 @@ def forward(self, x):
             s_prev = self.seq2(s_prev)
             ret.append(self.fc(s_prev.view(s_prev.size(0), -1)))
 
-            # B. s_next는 A.와 동시에 진행되면서 첫 번째 GPU에서 실행됩니다. 
+            # B. s_next는 A.와 동시에 진행되면서 첫 번째 GPU에서 실행됩니다.
             s_prev = self.seq1(s_next).to('cuda:1')
 
         s_prev = self.seq2(s_prev)
@@ -271,11 +271,11 @@ def forward(self, x):
 # GPU 간 텐서값이 복사되는 것은 현재 계산되고 있는 소스값과, 소스값의 목적지 GPU 간 연산되고 있는
 # 스트림과 동기화되는 것을 주의하세요. 만약 여러 스트림을 생성하여 진행하고 있다면, GPU 간 텐서값이
 # 정상적으로 복사되어 계산되고 있는지 꼭 확인해야 합니다. 만약 복사되는 과정 중에 소스값을 이용하거나,
-# GPU의 텐서값을 읽거나 쓰는 것은 올바르게 계산되지 않을 수 있습니다. 위의 예제에서는 소스값 및 GPU 
+# GPU의 텐서값을 읽거나 쓰는 것은 올바르게 계산되지 않을 수 있습니다. 위의 예제에서는 소스값 및 GPU
 # 텐서값을 기본 스트림만 이용하여 진행하므로 추가적인 동기화 과정을 진행할 필요는 없습니다.
 
 ######################################################################
-# 
+#
 # .. figure:: /_static/img/model-parallel-images/mp_vs_rn_vs_pp.png
 #    :alt:
 #
@@ -284,7 +284,7 @@ def forward(self, x):
 # 학습 시간이 2배 단축되는 것에 비해 다소 적게 학습 시간이 단축되었습니다. 파이프라인을 이용할 때,
 # ``split_sizes`` 매개변수를 도입하였기 때문에, 파이프라인을 이용하는 것이 학습 시간 단축에 얼마나
 # 영향을 미쳤는지 불분명합니다. 직관적으로 생각하였을 때, ``split_sizes`` 매개변수 값을 작게 설정한다면,
-# 아주 소규모의 CUDA 연산이 많이 진행되고, ``split_sizes`` 매개변수 값을 크게 설정한다면, 첫 번째와 
+# 아주 소규모의 CUDA 연산이 많이 진행되고, ``split_sizes`` 매개변수 값을 크게 설정한다면, 첫 번째와
 # 마지막 분리될 때 비교적 긴 시간 동안 CUDA 연산이 이루어지게 됩니다. 둘 다 최적의 설정이 아닙니다.
 # 따라서, ``split_sizes`` 매개변수 값을 최적으로 설정하였을 때, 학습 시간 과정이 단축될 수 있을 것이라
 # 기대됩니다. ``split_sizes`` 매개변수 값을 조정하여 실험하면서 최적의 값을 찾아봅시다.
@@ -317,20 +317,20 @@ def forward(self, x):
 # .. figure:: /_static/img/model-parallel-images/split_size_tradeoff.png
 #    :alt:
 #
-# 실험 결과, ``split_size`` 매개변수값을 12로 설정하였을 때, 학습 시간이 54% 수준으로 
+# 실험 결과, ``split_size`` 매개변수값을 12로 설정하였을 때, 학습 시간이 54% 수준으로
 # 가장 많이 단축되었습니다. 아직 학습 시간을 더 단축시킬 수 있는 방법은 다양하게 존재합니다.
 # 예를 들어, 첫 번째 GPU에서 모든 연산과정이 기본으로 설정되어 진행됩니다. 이는 미니배치 분할 과정 중,
 # 현재 진행되는 과정의 다음 단계는 현재 진행되는 과정과 동시에 복제가 이루어질 수 없는 것을 의미합니다.
 # 그러나, 이전과 다음 단계의 분할과정이 다른 텐서값을 이용하기 때문에, 다른 계산과 중복되어 진행되어도
-# 문제가 없습니다. 이에 대해서, 2개 GPU에 여러개의 스트림을 사용하는 것이 필요하며, 서로 다른 서브 네트워크 
-# 구조가 서로 다른 스트림을 관리하는 전략이 요구됩니다. 모델 병렬 처리에 대해서 여러 스트림을 사용하는 방법이 
+# 문제가 없습니다. 이에 대해서, 2개 GPU에 여러개의 스트림을 사용하는 것이 필요하며, 서로 다른 서브 네트워크
+# 구조가 서로 다른 스트림을 관리하는 전략이 요구됩니다. 모델 병렬 처리에 대해서 여러 스트림을 사용하는 방법이
 # 일반적을로 존재하지 않기 때문에 이번 튜토리얼에서는 설명하지 않습니다.
 
 """
 .. note::
    이번 게시물에서는 다양한 성능 측정값을 확인할 수 있습니다. 여러분은 위의 예제를 실행할 때 마다 매번
-   다른 결과를 확인할 수 있습니다. 그 이유는, 이용하는 소프트웨어 및 하드웨어에 따라 결과가 
+   다른 결과를 확인할 수 있습니다. 그 이유는, 이용하는 소프트웨어 및 하드웨어에 따라 결과가
    다르게 나타나기 때문입니다. 여러분이 이용하고 있는 환경 내에서 가장 좋은 성능을 얻기 위해서는, 곡선을 그려서
-   최적의 ``split_size`` 값을 도출한 후, 해당 값을 이용하여 미니 배치 내 데이터를 분리하는 파이프라인을 
+   최적의 ``split_size`` 값을 도출한 후, 해당 값을 이용하여 미니 배치 내 데이터를 분리하는 파이프라인을
    생성하는 것입니다.
 """
\ No newline at end of file
diff --git a/docs/_downloads/8eed7e178f8fa30798f280ea82ff468b/autograd_tutorial.ipynb b/docs/_downloads/8eed7e178f8fa30798f280ea82ff468b/autograd_tutorial.ipynb
index a2a75f0d5..92ac81646 100644
--- a/docs/_downloads/8eed7e178f8fa30798f280ea82ff468b/autograd_tutorial.ipynb
+++ b/docs/_downloads/8eed7e178f8fa30798f280ea82ff468b/autograd_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nAutograd: \uc790\ub3d9 \ubbf8\ubd84\n===================================\n\nPyTorch\uc758 \ubaa8\ub4e0 \uc2e0\uacbd\ub9dd\uc758 \uc911\uc2ec\uc5d0\ub294 ``autograd`` \ud328\ud0a4\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uba3c\uc800 \uc774\uac83\uc744 \uac00\ubccd\uac8c \uc0b4\ud3b4\ubcf8 \ub4a4, \uccab\ubc88\uc9f8 \uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\uc2dc\ucf1c\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n``autograd`` \ud328\ud0a4\uc9c0\ub294 Tensor\uc758 \ubaa8\ub4e0 \uc5f0\uc0b0\uc5d0 \ub300\ud574 \uc790\ub3d9 \ubbf8\ubd84\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774\ub294 \uc2e4\ud589-\uae30\ubc18-\uc815\uc758(define-by-run) \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \uc774\ub294 \ucf54\ub4dc\ub97c \uc5b4\ub5bb\uac8c \uc791\uc131\ud558\uc5ec\n\uc2e4\ud589\ud558\ub290\ub0d0\uc5d0 \ub530\ub77c \uc5ed\uc804\ud30c\uac00 \uc815\uc758\ub41c\ub2e4\ub294 \ub73b\uc774\uba70, \uc5ed\uc804\ud30c\ub294 \ud559\uc2b5 \uacfc\uc815\uc758 \ub9e4 \ub2e8\uacc4\ub9c8\ub2e4\n\ub2ec\ub77c\uc9d1\ub2c8\ub2e4.\n\n\ub354 \uac04\ub2e8\ud55c \uc6a9\uc5b4\ub85c \uba87 \uac00\uc9c0 \uc608\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nTensor\n--------\n\n\ud328\ud0a4\uc9c0\uc758 \uc911\uc2ec\uc5d0\ub294 ``torch.Tensor`` \ud074\ub798\uc2a4\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ub9cc\uc57d ``.requires_grad``\n\uc18d\uc131\uc744 ``True`` \ub85c \uc124\uc815\ud558\uba74, \uadf8 tensor\uc5d0\uc11c \uc774\ub904\uc9c4 \ubaa8\ub4e0 \uc5f0\uc0b0\ub4e4\uc744 \ucd94\uc801(track)\ud558\uae30\n\uc2dc\uc791\ud569\ub2c8\ub2e4. \uacc4\uc0b0\uc774 \uc644\ub8cc\ub41c \ud6c4 ``.backward()`` \ub97c \ud638\ucd9c\ud558\uc5ec \ubaa8\ub4e0 \ubcc0\ud654\ub3c4(gradient)\ub97c\n\uc790\ub3d9\uc73c\ub85c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 Tensor\uc758 \ubcc0\ud654\ub3c4\ub294 ``.grad`` \uc18d\uc131\uc5d0 \ub204\uc801\ub429\ub2c8\ub2e4.\n\nTensor\uac00 \uae30\ub85d\uc744 \ucd94\uc801\ud558\ub294 \uac83\uc744 \uc911\ub2e8\ud558\uac8c \ud558\ub824\uba74, ``.detach()`` \ub97c \ud638\ucd9c\ud558\uc5ec \uc5f0\uc0b0\n\uae30\ub85d\uc73c\ub85c\ubd80\ud130 \ubd84\ub9ac(detach)\ud558\uc5ec \uc774\ud6c4 \uc5f0\uc0b0\ub4e4\uc774 \ucd94\uc801\ub418\ub294 \uac83\uc744 \ubc29\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uae30\ub85d\uc744 \ucd94\uc801\ud558\ub294 \uac83(\uacfc \uba54\ubaa8\ub9ac\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83)\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud574, \ucf54\ub4dc \ube14\ub7ed\uc744\n``with torch.no_grad():`` \ub85c \uac10\uc300 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \ubcc0\ud654\ub3c4(gradient)\ub294\n\ud544\uc694\uc5c6\uc9c0\ub9cc, `requires_grad=True` \uac00 \uc124\uc815\ub418\uc5b4 \ud559\uc2b5 \uac00\ub2a5\ud55c \ub9e4\uac1c\ubcc0\uc218\ub97c \uac16\ub294 \ubaa8\ub378\uc744\n\ud3c9\uac00(evaluate)\ud560 \ub54c \uc720\uc6a9\ud569\ub2c8\ub2e4.\n\nAutograd \uad6c\ud604\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud55c \ud074\ub798\uc2a4\uac00 \ud558\ub098 \ub354 \uc788\ub294\ub370, \uc774\uac83\uc740 \ubc14\ub85c ``Function``\n\ud074\ub798\uc2a4\uc785\ub2c8\ub2e4.\n\n``Tensor`` \uc640 ``Function`` \uc740 \uc11c\ub85c \uc5f0\uacb0\ub418\uc5b4 \uc788\uc73c\uba70, \ubaa8\ub4e0 \uc5f0\uc0b0 \uacfc\uc815\uc744\n\ubd80\ud638\ud654(encode)\ud558\uc5ec \uc21c\ud658\ud558\uc9c0 \uc54a\ub294 \uadf8\ub798\ud504(acyclic graph)\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uac01 tensor\ub294\n``.grad_fn`` \uc18d\uc131\uc744 \uac16\uace0 \uc788\ub294\ub370, \uc774\ub294 ``Tensor`` \ub97c \uc0dd\uc131\ud55c ``Function`` \uc744\n\ucc38\uc870\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\ub2e8, \uc0ac\uc6a9\uc790\uac00 \ub9cc\ub4e0 Tensor\ub294 \uc608\uc678\ub85c, \uc774 \ub54c ``grad_fn`` \uc740\n``None`` \uc785\ub2c8\ub2e4.)\n\n\ub3c4\ud568\uc218\ub97c \uacc4\uc0b0\ud558\uae30 \uc704\ud574\uc11c\ub294 ``Tensor`` \uc758 ``.backward()`` \ub97c \ud638\ucd9c\ud558\uba74\n\ub429\ub2c8\ub2e4. \ub9cc\uc57d ``Tensor`` \uac00 \uc2a4\uce7c\ub77c(scalar)\uc778 \uacbd\uc6b0(\uc608. \ud558\ub098\uc758 \uc694\uc18c \uac12\ub9cc \uac16\ub294 \ub4f1)\uc5d0\ub294\n``backward`` \uc5d0 \uc778\uc790\ub97c \uc815\ud574\uc904 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc5ec\ub7ec \uac1c\uc758 \uc694\uc18c\ub97c \uac16\uace0 \uc788\uc744\n\ub54c\ub294 tensor\uc758 \ubaa8\uc591\uc744 ``gradient`` \uc758 \uc778\uc790\ub85c \uc9c0\uc815\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\nAutograd: \uc790\ub3d9 \ubbf8\ubd84\n===================================\n\nPyTorch\uc758 \ubaa8\ub4e0 \uc2e0\uacbd\ub9dd\uc758 \uc911\uc2ec\uc5d0\ub294 ``autograd`` \ud328\ud0a4\uc9c0\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uba3c\uc800 \uc774\uac83\uc744 \uac00\ubccd\uac8c \uc0b4\ud3b4\ubcf8 \ub4a4, \uccab\ubc88\uc9f8 \uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\uc2dc\ucf1c\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n``autograd`` \ud328\ud0a4\uc9c0\ub294 Tensor\uc758 \ubaa8\ub4e0 \uc5f0\uc0b0\uc5d0 \ub300\ud574 \uc790\ub3d9 \ubbf8\ubd84\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774\ub294 \uc2e4\ud589-\uae30\ubc18-\uc815\uc758(define-by-run) \ud504\ub808\uc784\uc6cc\ud06c\ub85c, \uc774\ub294 \ucf54\ub4dc\ub97c \uc5b4\ub5bb\uac8c \uc791\uc131\ud558\uc5ec\n\uc2e4\ud589\ud558\ub290\ub0d0\uc5d0 \ub530\ub77c \uc5ed\uc804\ud30c\uac00 \uc815\uc758\ub41c\ub2e4\ub294 \ub73b\uc774\uba70, \uc5ed\uc804\ud30c\ub294 \ud559\uc2b5 \uacfc\uc815\uc758 \ub9e4 \ub2e8\uacc4\ub9c8\ub2e4\n\ub2ec\ub77c\uc9d1\ub2c8\ub2e4.\n\n\ub354 \uac04\ub2e8\ud55c \uc6a9\uc5b4\ub85c \uba87 \uac00\uc9c0 \uc608\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nTensor\n--------\n\n\ud328\ud0a4\uc9c0\uc758 \uc911\uc2ec\uc5d0\ub294 ``torch.Tensor`` \ud074\ub798\uc2a4\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ub9cc\uc57d ``.requires_grad``\n\uc18d\uc131\uc744 ``True`` \ub85c \uc124\uc815\ud558\uba74, \uadf8 tensor\uc5d0\uc11c \uc774\ub904\uc9c4 \ubaa8\ub4e0 \uc5f0\uc0b0\ub4e4\uc744 \ucd94\uc801(track)\ud558\uae30\n\uc2dc\uc791\ud569\ub2c8\ub2e4. \uacc4\uc0b0\uc774 \uc644\ub8cc\ub41c \ud6c4 ``.backward()`` \ub97c \ud638\ucd9c\ud558\uc5ec \ubaa8\ub4e0 \ubcc0\ud654\ub3c4(gradient)\ub97c\n\uc790\ub3d9\uc73c\ub85c \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 Tensor\uc758 \ubcc0\ud654\ub3c4\ub294 ``.grad`` \uc18d\uc131\uc5d0 \ub204\uc801\ub429\ub2c8\ub2e4.\n\nTensor\uac00 \uae30\ub85d\uc744 \ucd94\uc801\ud558\ub294 \uac83\uc744 \uc911\ub2e8\ud558\uac8c \ud558\ub824\uba74, ``.detach()`` \ub97c \ud638\ucd9c\ud558\uc5ec \uc5f0\uc0b0\n\uae30\ub85d\uc73c\ub85c\ubd80\ud130 \ubd84\ub9ac(detach)\ud558\uc5ec \uc774\ud6c4 \uc5f0\uc0b0\ub4e4\uc774 \ucd94\uc801\ub418\ub294 \uac83\uc744 \ubc29\uc9c0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uae30\ub85d\uc744 \ucd94\uc801\ud558\ub294 \uac83(\uacfc \uba54\ubaa8\ub9ac\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83)\uc744 \ubc29\uc9c0\ud558\uae30 \uc704\ud574, \ucf54\ub4dc \ube14\ub7ed\uc744\n``with torch.no_grad():`` \ub85c \uac10\uc300 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub294 \ud2b9\ud788 \ubcc0\ud654\ub3c4(gradient)\ub294\n\ud544\uc694\uc5c6\uc9c0\ub9cc, `requires_grad=True` \uac00 \uc124\uc815\ub418\uc5b4 \ud559\uc2b5 \uac00\ub2a5\ud55c \ub9e4\uac1c\ubcc0\uc218\ub97c \uac16\ub294 \ubaa8\ub378\uc744\n\ud3c9\uac00(evaluate)\ud560 \ub54c \uc720\uc6a9\ud569\ub2c8\ub2e4.\n\nAutograd \uad6c\ud604\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud55c \ud074\ub798\uc2a4\uac00 \ud558\ub098 \ub354 \uc788\ub294\ub370, \uc774\uac83\uc740 \ubc14\ub85c ``Function``\n\ud074\ub798\uc2a4\uc785\ub2c8\ub2e4.\n\n``Tensor`` \uc640 ``Function`` \uc740 \uc11c\ub85c \uc5f0\uacb0\ub418\uc5b4 \uc788\uc73c\uba70, \ubaa8\ub4e0 \uc5f0\uc0b0 \uacfc\uc815\uc744\n\ubd80\ud638\ud654(encode)\ud558\uc5ec \uc21c\ud658\ud558\uc9c0 \uc54a\ub294 \uadf8\ub798\ud504(acyclic graph)\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uac01 tensor\ub294\n``.grad_fn`` \uc18d\uc131\uc744 \uac16\uace0 \uc788\ub294\ub370, \uc774\ub294 ``Tensor`` \ub97c \uc0dd\uc131\ud55c ``Function`` \uc744\n\ucc38\uc870\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\ub2e8, \uc0ac\uc6a9\uc790\uac00 \ub9cc\ub4e0 Tensor\ub294 \uc608\uc678\ub85c, \uc774 \ub54c ``grad_fn`` \uc740\n``None`` \uc785\ub2c8\ub2e4.)\n\n\ub3c4\ud568\uc218\ub97c \uacc4\uc0b0\ud558\uae30 \uc704\ud574\uc11c\ub294 ``Tensor`` \uc758 ``.backward()`` \ub97c \ud638\ucd9c\ud558\uba74\n\ub429\ub2c8\ub2e4. \ub9cc\uc57d ``Tensor`` \uac00 \uc2a4\uce7c\ub77c(scalar)\uc778 \uacbd\uc6b0(\uc608. \ud558\ub098\uc758 \uc694\uc18c \uac12\ub9cc \uac16\ub294 \ub4f1)\uc5d0\ub294\n``backward`` \uc5d0 \uc778\uc790\ub97c \uc815\ud574\uc904 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc5ec\ub7ec \uac1c\uc758 \uc694\uc18c\ub97c \uac16\uace0 \uc788\uc744\n\ub54c\ub294 tensor\uc758 \ubaa8\uc591\uc744 ``gradient`` \uc758 \uc778\uc790\ub85c \uc9c0\uc815\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -265,7 +265,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/988d3733a780db0912efb0d6f34d0ec7/two_layer_net_nn.ipynb b/docs/_downloads/988d3733a780db0912efb0d6f34d0ec7/two_layer_net_nn.ipynb
index a19e21e72..661c99589 100644
--- a/docs/_downloads/988d3733a780db0912efb0d6f34d0ec7/two_layer_net_nn.ipynb
+++ b/docs/_downloads/988d3733a780db0912efb0d6f34d0ec7/two_layer_net_nn.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch: nn\n-----------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88\uc5d0\ub294 PyTorch\uc758 nn \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4.\nPyTorch autograd\ub294 \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c \uc815\uc758\ud558\uace0 \ubcc0\ud654\ub3c4\ub97c \uacc4\uc0b0\ud558\ub294 \uac83\uc744 \uc190\uc27d\uac8c \ub9cc\ub4e4\uc5b4\uc8fc\uc9c0\ub9cc,\nautograd \uadf8 \uc790\uccb4\ub9cc\uc73c\ub85c\ub294 \ubcf5\uc7a1\ud55c \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\uae30\uc5d0\ub294 \ub108\ubb34 \uc800\uc218\uc900(low-level)\uc77c \uc218\n\uc788\uc2b5\ub2c8\ub2e4; \uc774\uac83\uc774 nn \ud328\ud0a4\uc9c0\uac00 \ud544\uc694\ud55c \uc774\uc720\uc785\ub2c8\ub2e4. nn \ud328\ud0a4\uc9c0\ub294 Module\uc758 \uc9d1\ud569\uc744\n\uc815\uc758\ud558\ub294\ub370, \uc774\ub294 \uc785\ub825\uc73c\ub85c\ubd80\ud130 \ucd9c\ub825\uc744 \uc0dd\uc131\ud558\uace0 \ud559\uc2b5 \uac00\ub2a5\ud55c \uac00\uc911\uce58\ub97c \uac16\ub294\n\uc2e0\uacbd\ub9dd \uacc4\uce35(neural network layer)\uc774\ub77c\uace0 \uc0dd\uac01\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\nPyTorch: nn\n-----------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88\uc5d0\ub294 PyTorch\uc758 nn \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4.\nPyTorch autograd\ub294 \uc5f0\uc0b0 \uadf8\ub798\ud504\ub97c \uc815\uc758\ud558\uace0 \ubcc0\ud654\ub3c4\ub97c \uacc4\uc0b0\ud558\ub294 \uac83\uc744 \uc190\uc27d\uac8c \ub9cc\ub4e4\uc5b4\uc8fc\uc9c0\ub9cc,\nautograd \uadf8 \uc790\uccb4\ub9cc\uc73c\ub85c\ub294 \ubcf5\uc7a1\ud55c \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\uae30\uc5d0\ub294 \ub108\ubb34 \uc800\uc218\uc900(low-level)\uc77c \uc218\n\uc788\uc2b5\ub2c8\ub2e4; \uc774\uac83\uc774 nn \ud328\ud0a4\uc9c0\uac00 \ud544\uc694\ud55c \uc774\uc720\uc785\ub2c8\ub2e4. nn \ud328\ud0a4\uc9c0\ub294 Module\uc758 \uc9d1\ud569\uc744\n\uc815\uc758\ud558\ub294\ub370, \uc774\ub294 \uc785\ub825\uc73c\ub85c\ubd80\ud130 \ucd9c\ub825\uc744 \uc0dd\uc131\ud558\uace0 \ud559\uc2b5 \uac00\ub2a5\ud55c \uac00\uc911\uce58\ub97c \uac16\ub294\n\uc2e0\uacbd\ub9dd \uacc4\uce35(neural network layer)\uc774\ub77c\uace0 \uc0dd\uac01\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/99fdc80b1d3c74fcdb1432b4e7df20f7/advanced_tutorial.ipynb b/docs/_downloads/99fdc80b1d3c74fcdb1432b4e7df20f7/advanced_tutorial.ipynb
index d29aff0a0..14b7ef70f 100644
--- a/docs/_downloads/99fdc80b1d3c74fcdb1432b4e7df20f7/advanced_tutorial.ipynb
+++ b/docs/_downloads/99fdc80b1d3c74fcdb1432b4e7df20f7/advanced_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nAdvanced: Making Dynamic Decisions and the Bi-LSTM CRF\n======================================================\n\nDynamic versus Static Deep Learning Toolkits\n--------------------------------------------\n\nPytorch is a *dynamic* neural network kit. Another example of a dynamic\nkit is `Dynet <https://github.com/clab/dynet>`__ (I mention this because\nworking with Pytorch and Dynet is similar. If you see an example in\nDynet, it will probably help you implement it in Pytorch). The opposite\nis the *static* tool kit, which includes Theano, Keras, TensorFlow, etc.\nThe core difference is the following:\n\n* In a static toolkit, you define\n  a computation graph once, compile it, and then stream instances to it.\n* In a dynamic toolkit, you define a computation graph *for each\n  instance*. It is never compiled and is executed on-the-fly\n\nWithout a lot of experience, it is difficult to appreciate the\ndifference. One example is to suppose we want to build a deep\nconstituent parser. Suppose our model involves roughly the following\nsteps:\n\n* We build the tree bottom up\n* Tag the root nodes (the words of the sentence)\n* From there, use a neural network and the embeddings\n  of the words to find combinations that form constituents. Whenever you\n  form a new constituent, use some sort of technique to get an embedding\n  of the constituent. In this case, our network architecture will depend\n  completely on the input sentence. In the sentence \"The green cat\n  scratched the wall\", at some point in the model, we will want to combine\n  the span $(i,j,r) = (1, 3, \\text{NP})$ (that is, an NP constituent\n  spans word 1 to word 3, in this case \"The green cat\").\n\nHowever, another sentence might be \"Somewhere, the big fat cat scratched\nthe wall\". In this sentence, we will want to form the constituent\n$(2, 4, NP)$ at some point. The constituents we will want to form\nwill depend on the instance. If we just compile the computation graph\nonce, as in a static toolkit, it will be exceptionally difficult or\nimpossible to program this logic. In a dynamic toolkit though, there\nisn't just 1 pre-defined computation graph. There can be a new\ncomputation graph for each instance, so this problem goes away.\n\nDynamic toolkits also have the advantage of being easier to debug and\nthe code more closely resembling the host language (by that I mean that\nPytorch and Dynet look more like actual Python code than Keras or\nTheano).\n\nBi-LSTM Conditional Random Field Discussion\n-------------------------------------------\n\nFor this section, we will see a full, complicated example of a Bi-LSTM\nConditional Random Field for named-entity recognition. The LSTM tagger\nabove is typically sufficient for part-of-speech tagging, but a sequence\nmodel like the CRF is really essential for strong performance on NER.\nFamiliarity with CRF's is assumed. Although this name sounds scary, all\nthe model is is a CRF but where an LSTM provides the features. This is\nan advanced model though, far more complicated than any earlier model in\nthis tutorial. If you want to skip it, that is fine. To see if you're\nready, see if you can:\n\n-  Write the recurrence for the viterbi variable at step i for tag k.\n-  Modify the above recurrence to compute the forward variables instead.\n-  Modify again the above recurrence to compute the forward variables in\n   log-space (hint: log-sum-exp)\n\nIf you can do those three things, you should be able to understand the\ncode below. Recall that the CRF computes a conditional probability. Let\n$y$ be a tag sequence and $x$ an input sequence of words.\nThen we compute\n\n\\begin{align}P(y|x) = \\frac{\\exp{(\\text{Score}(x, y)})}{\\sum_{y'} \\exp{(\\text{Score}(x, y')})}\\end{align}\n\nWhere the score is determined by defining some log potentials\n$\\log \\psi_i(x,y)$ such that\n\n\\begin{align}\\text{Score}(x,y) = \\sum_i \\log \\psi_i(x,y)\\end{align}\n\nTo make the partition function tractable, the potentials must look only\nat local features.\n\nIn the Bi-LSTM CRF, we define two kinds of potentials: emission and\ntransition. The emission potential for the word at index $i$ comes\nfrom the hidden state of the Bi-LSTM at timestep $i$. The\ntransition scores are stored in a $|T|x|T|$ matrix\n$\\textbf{P}$, where $T$ is the tag set. In my\nimplementation, $\\textbf{P}_{j,k}$ is the score of transitioning\nto tag $j$ from tag $k$. So:\n\n\\begin{align}\\text{Score}(x,y) = \\sum_i \\log \\psi_\\text{EMIT}(y_i \\rightarrow x_i) + \\log \\psi_\\text{TRANS}(y_{i-1} \\rightarrow y_i)\\end{align}\n\n\\begin{align}= \\sum_i h_i[y_i] + \\textbf{P}_{y_i, y_{i-1}}\\end{align}\n\nwhere in this second expression, we think of the tags as being assigned\nunique non-negative indices.\n\nIf the above discussion was too brief, you can check out\n`this <http://www.cs.columbia.edu/%7Emcollins/crf.pdf>`__ write up from\nMichael Collins on CRFs.\n\nImplementation Notes\n--------------------\n\nThe example below implements the forward algorithm in log space to\ncompute the partition function, and the viterbi algorithm to decode.\nBackpropagation will compute the gradients automatically for us. We\ndon't have to do anything by hand.\n\nThe implementation is not optimized. If you understand what is going on,\nyou'll probably quickly see that iterating over the next tag in the\nforward algorithm could probably be done in one big operation. I wanted\nto code to be more readable. If you want to make the relevant change,\nyou could probably use this tagger for real tasks.\n\n"
+        "\n\uc2ec\ud654 \uacfc\uc815 : Bi-LSTM CRF\uc640 \ub3d9\uc801 \uacb0\uc815\n======================================================\n\n\ub3d9\uc801, \uc815\uc801 \ub525 \ub7ec\ub2dd \ud234\ud0b7(toolkits) \ube44\uad50\n--------------------------------------------\n\nPytorch\ub294 *\ub3d9\uc801* \uc2e0\uacbd\ub9dd \ud234\ud0b7\uc785\ub2c8\ub2e4. \ub2e4\ub978 \ub3d9\uc801 \uc2e0\uacbd\ub9dd \ud234\ud0b7\uc73c\ub85c\ub294\n`Dynet <https://github.com/clab/dynet>`_ \uc774 \uc788\uc2b5\ub2c8\ub2e4.(\uc774 \ud234\ud0b7\uc744\n\uc608\ub85c \ub4e0 \uc774\uc720\ub294 \uc0ac\uc6a9\ud558\ub294 \ubc95\uc774 Pytorch\uc640 \ube44\uc2b7\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. Dynet\uc758 \uc608\uc81c\ub97c \ubcf4\uba74\nPytorch\ub85c \uad6c\ud604\ud560 \ub54c\ub3c4 \ub3c4\uc6c0\uc774 \ub420 \uac83\uc785\ub2c8\ub2e4.) \ubc18\ub300\ub85c *\uc815\uc801* \ud234\ud0b7\ub4e4\ub85c\nTheano, Keras, TensorFlow \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\uc694 \ucc28\uc774\uc810\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n\n* \uc815\uc801 \ud234\ud0b7\uc744 \uc4f8 \ub54c\ub294 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \ud55c \ubc88\ub9cc \uc815\uc758\ud558\uace0, \ucef4\ud30c\uc77c \ud55c \ud6c4,\n  \ub370\uc774\ud130\ub97c \uacc4\uc0b0 \uadf8\ub798\ud504\uc5d0 \ub118\uae41\ub2c8\ub2e4.\n* \ub3d9\uc801 \ud234\ud0b7\uc5d0\uc11c\ub294 *\uac01 \ub370\uc774\ud130* \uc758 \uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \uc815\uc758\ud558\uba70 \ucef4\ud30c\uc77c\ud558\uc9c0\n  \uc54a\uace0 \uc989\uac01 \uc2e4\ud589\ub429\ub2c8\ub2e4.\n\n\uacbd\ud5d8\uc774 \ub9ce\uc9c0 \uc54a\ub2e4\uba74 \ub450 \ubc29\uc2dd\uc758 \ucc28\uc774\ub97c \uc54c\uae30 \uc5b4\ub835\uc2b5\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd \uae30\ubc18\uc758\n\uad6c\uad6c\uc870 \ubd84\uc11d\uae30(constituent parser)\ub97c \uc608\ub85c \ub4e4\uc5b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\uc740 \ub300\ub7b5\n\ub2e4\uc74c\uacfc \uac19\uc740 \uacfc\uc815\uc744 \uc218\ud589\ud569\ub2c8\ub2e4:\n\n* \ud2b8\ub9ac\ub97c \uc0c1\ud5a5\uc2dd(bottom-up)\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \ub098\uac11\ub2c8\ub2e4.\n* \ucd5c\uc0c1\uc704 \ub178\ub4dc\ub97c \ud0dc\uae45\ud569\ub2c8\ub2e4. (\ubb38\uc7a5\uc758 \uac01 \ub2e8\uc5b4)\n* \uac70\uae30\uc11c\ubd80\ud130 \uc2e0\uacbd\ub9dd\uacfc \ub2e8\uc5b4\ub4e4\uc758 \uc784\ubca0\ub529\uc744 \uc774\uc6a9\ud574 \uad6c\uad6c\uc870\ub97c \uc774\ub8e8\ub294 \uc870\ud569\uc744\n  \ucc3e\uc544\ub0c5\ub2c8\ub2e4. \uc0c8\ub85c\uc6b4 \uad6c\uad6c\uc870\ub97c \uc0dd\uc131\ud560 \ub54c\ub9c8\ub2e4 \uad6c\uad6c\uc870\uc758 \uc784\ubca0\ub529\uc744 \uc5bb\uae30 \uc704\ud55c\n  \uc5b4\ub5a4 \uae30\uc220\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc9c0\uae08\uc740 \uc2e0\uacbd\ub9dd\uc774 \uc624\uc9c1 \uc785\ub825 \ubb38\uc7a5\ub9cc \ucc38\uace0\ud560\n  \uac83\uc785\ub2c8\ub2e4. \"The green cat scratched the wall\"\uc774\ub780 \ubb38\uc7a5\uc5d0\uc11c, \ubaa8\ub378\uc758 \uc5b4\ub290 \uc2dc\uc810\uc5d0\n  $(i,j,r) = (1, 3, \\text{NP})$ \ubc94\uc704 (\ub2e8\uc5b4 1\uc5d0\uc11c\ubd80\ud130 \ub2e8\uc5b4 3\uae4c\uc9c0\uac00\n  NP \uad6c\uad6c\uc870\ub77c\ub294 \ub73b\uc774\uba70, \uc774 \ubb38\uc7a5\uc5d0\uc11c\ub294 \"The green cat\") \ub97c \uacb0\ud569\ud558\uae38 \uc6d0\ud560\n  \uac83\uc785\ub2c8\ub2e4.\n\n\uadf8\ub7f0\ub370, \ub610\ub2e4\ub978 \ubb38\uc7a5 \"Somewhere, the big fat cat scratched the wall\" \uc5d0\uc11c\ub294\n\uc5b4\ub290 \uc2dc\uc810\uc5d0 $(2, 4, NP)$ \uad6c\uad6c\uc870\ub97c \ub9cc\ub4e4\uae30\ub97c \uc6d0\ud560 \uac83\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\uac00\n\ub9cc\ub4e4\uae30 \uc6d0\ud558\ub294 \uad6c\uad6c\uc870\ub4e4\uc740 \ubb38\uc7a5\uc5d0 \ub530\ub77c \ub2e4\ub985\ub2c8\ub2e4. \ub9cc\uc57d \uc815\uc801 \ud234\ud0b7\uc5d0\uc11c\ucc98\ub7fc\n\uacc4\uc0b0 \uadf8\ub798\ud504\ub97c \ud55c \ubc88\ub9cc \ucef4\ud30c\uc77c\ud55c\ub2e4\uba74, \uc774 \uacfc\uc815\uc744 \ud504\ub85c\uadf8\ub798\ubc0d\ud558\uae30 \ub9e4\uc6b0 \uc5b4\ub835\uac70\ub098\n\ubd88\uac00\ub2a5\ud560 \uac83\uc785\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ub3d9\uc801 \ud234\ud0b7\uc5d0\uc11c\ub294 \ud558\ub098\uc758 \uacc4\uc0b0 \uadf8\ub798\ud504\ub9cc \uc788\uc9c0\n\uc54a\uc2b5\ub2c8\ub2e4. \uac01 \ubb38\uc7a5\ub4e4\ub9c8\ub2e4 \uc0c8\ub85c\uc6b4 \uacc4\uc0b0 \uadf8\ub798\ud504\uac00 \uc788\uc744 \uc218 \uc788\uae30 \ub54c\ubb38\uc5d0 \uc774\ub7f0\n\ubb38\uc81c\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n\ub3d9\uc801 \ud2c0\ud0b7\uc740 \ub514\ubc84\uae45 \ud558\uae30 \ub354 \uc27d\uace0, \ucf54\ub4dc\uac00 \uae30\ubc18 \uc5b8\uc5b4\uc640 \ub354 \ube44\uc2b7\ud569\ub2c8\ub2e4\n(Pytorch\uc640 Dynet\uc774 Keras \ub610\ub294 Theano \ubcf4\ub2e4 Python \ucf54\ub4dc\uc640 \ub354 \ube44\uc2b7\ud569\ub2c8\ub2e4).\n\nBi-LSTM Conditional Rnadom Field \uc124\uba85\n-------------------------------------------\n\n\uc774 \uc601\uc5ed\uc5d0\uc11c\ub294 \uac1c\uccb4\uba85 \uc778\uc2dd\uc744 \uc218\ud589\ud558\ub294 \uc644\uc131\ub41c Bi-LSTM Conditional Random\nField \uc608\uc2dc\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc704\uc5d0 \ub098\uc628 LSTM \ud0dc\uac70(tagger)\ub294 \uc77c\ubc18\uc801\uc73c\ub85c\n\ud488\uc0ac \ud0dc\uae45\uc744 \ud558\uae30\uc5d0 \ucda9\ubd84\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc CRF \uac19\uc740 \uc5f0\uc18d\ub41c \ub370\uc774\ud130\ub97c \ub2e4\ub8e8\ub294\n\ubaa8\ub378\uc740 \uc88b\uc740 \uac1c\uccb4\uba85 \uc778\uc2dd \ubaa8\ub378(NER)\uc5d0 \uaf2d \ud544\uc694\ud569\ub2c8\ub2e4. \uc5ec\ub7ec\ubd84\uc774 CRF\ub97c \uc798 \uc54c\uace0\n\uc788\ub2e4\uace0 \uac00\uc815\ud558\uaca0\uc2b5\ub2c8\ub2e4. \uc774\ub984\uc774 \ubb34\uc12d\uac8c \ub4e4\ub9b4 \uc218\ub3c4 \uc788\uc9c0\ub9cc, LSTM\uc774 \ud2b9\uc9d5\uc744\n\uc81c\uacf5\ud558\ub294 \uc810\uc744 \uc81c\uc678\ud558\uba74 \uc774 \ubaa8\ub378\uc740 CRF \uc785\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ub354 \ubc1c\uc804\ub41c \ubaa8\ub378\uc774\uba70,\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 \uc55e\ubd80\ubd84\uc5d0 \ub098\uc654\ub358 \ubaa8\ub378\ubcf4\ub2e4 \ud6e8\uc52c \ubcf5\uc7a1\ud569\ub2c8\ub2e4. \ub118\uc5b4\uac00\uace0 \uc2f6\ub2e4\uba74\n\ub118\uc5b4\uac00\ub3c4 \uad1c\ucc2e\uc2b5\ub2c8\ub2e4. \uc774\ud574\ud560 \uc218 \uc788\ub2e4\uace0 \uc0dd\uac01\ud55c\ub2e4\uba74, \uc544\ub798\ub97c \uc77d\uc5b4\ubcf4\uc138\uc694:\n\n-  \ud0dc\uadf8 k\uc5d0 \ub300\ud55c i\ubc88\uc9f8 \ub2e8\uacc4\uc758 \ube44\ud130\ube44(viterbi) \ubcc0\uc218\ub97c \uc704\ud574 \uc21c\ud658 \ud750\ub984\uc744 \ub9cc\ub4e0\ub2e4.\n-  \uc21c\ubc29\ud5a5 \ubcc0\uc218\ub97c \uacc4\uc0b0\ud558\uae30 \uc704\ud574 \uc704\uc758 \uc21c\ud55c \ud750\ub984\uc744 \uc870\uc815\ud55c\ub2e4.\n-  \uc21c\ubc29\ud5a5 \ubcc0\uc218\ub97c \ub85c\uadf8 \uacf5\uac04\uc5d0\uc11c \uacc4\uc0b0\ud558\uae30 \uc704\ud574 \ub2e4\uc2dc \ud55c \ubc88 \uc870\uc815\ud55c\ub2e4.\n   (\ud78c\ud2b8 : \ub85c\uadf8-\ud569-\uc9c0\uc218\uc2b9)\n\n\uc704\uc758 \uc138\uac00\uc9c0\ub97c \ud560 \uc218 \uc788\ub2e4\uba74, \uc544\ub798\uc758 \ucf54\ub4dc\ub97c \uc774\ud574\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\nCRF\ub294 \uc870\uac74\ubd80 \ud655\ub960\uc744 \uacc4\uc0b0\ud55c\ub2e4\ub294 \uc810\uc744 \uae30\uc5b5\ud558\uc138\uc694. $y$ \ub97c \uc5f0\uc18d\ub41c\n\ud0dc\uadf8\ub77c \ud558\uace0, $x$ \ub97c \uc5f0\uc18d\ub41c \uc785\ub825 \ub2e8\uc5b4\ub77c \ud558\uaca0\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\uba74 \uc544\ub798\uc758\n\uc2dd\uc744 \uacc4\uc0b0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\\begin{align}P(y|x) = \\frac{\\exp{(\\text{Score}(x, y)})}{\\sum_{y'} \\exp{(\\text{Score}(x, y')})}\\end{align}\n\n\uc810\uc218(score) \ud568\uc218\ub294 \uc544\ub798\uc640 \uac19\uc774 \uc815\uc758\ub41c \ub85c\uadf8 \ud3ec\ud150\uc15c(potential) $\\log \\psi_i(x,y)$\n\ud568\uc218\uc5d0 \uc758\ud574 \uacb0\uc815\ub429\ub2c8\ub2e4.\n\n\\begin{align}\\text{Score}(x,y) = \\sum_i \\log \\psi_i(x,y)\\end{align}\n\n\ubd84\ubc30 \ud568\uc218(partition function)\ub97c \ub2e8\uc21c\ud654\ud558\uae30 \uc704\ud574\uc11c, \ud3ec\ud150\uc15c\uc774 \uc8fc\ubcc0\uc758\n\ud2b9\uc9d5\ub4e4\ub9cc \ubc18\uc601\ud55c\ub2e4\uace0 \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\nBi-LSTM CRF \uc548\uc5d0 \ubc30\ucd9c(emission), \uc804\uc774(transition) \ub450 \uc885\ub958\uc758 \ud3ec\ud150\uc15c\uc744\n\uc815\uc758\ud569\ub2c8\ub2e4. $i$ \ubc88\uc9f8 \ub2e8\uc5b4\uc5d0 \ub300\ud55c \ubc30\ucd9c \ud3ec\ud150\uc15c\uc740 Bi-LSTM\uc758\n$i$ \ubc88\uc9f8 \uc2dc\uc810\uc758 \uc740\ub2c9 \uc0c1\ud0dc\uac00 \uacb0\uc815\ud569\ub2c8\ub2e4. \uc804\uc774 \uc810\uc218\ub294 $|T|x|T|$\n\ud615\ud0dc\uc778 \ud589\ub82c $\\textbf{P}$ \uc5d0 \uc800\uc7a5\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. $T$ \ub294\n\ud0dc\uadf8\uc758 \uc9d1\ud569\uc785\ub2c8\ub2e4. \uc774 \uad6c\ud604\uc5d0\uc11c, $\\textbf{P}_{j,k}$ \ub294 tag $j$ \uc5d0\uc11c\ntag $k$ \ub85c\uc758 \uc804\uc774 \uc810\uc218\ub97c \uc758\ubbf8\ud569\ub2c8\ub2e4. \ub530\ub77c\uc11c:\n\n\\begin{align}\\text{Score}(x,y) = \\sum_i \\log \\psi_\\text{EMIT}(y_i \\rightarrow x_i) + \\log \\psi_\\text{TRANS}(y_{i-1} \\rightarrow y_i)\\end{align}\n\n\\begin{align}= \\sum_i h_i[y_i] + \\textbf{P}_{y_i, y_{i-1}}\\end{align}\n\n\ub450 \ubc88\uc9f8 \uc2dd\uc5d0\uc11c \uace0\uc720\ud558\uace0 \uc74c\uc218\uac00 \uc544\ub2cc \uc778\ub371\uc2a4\uc5d0 \uc758\ud574 \ud0dc\uadf8\uac00 \ubd80\uc5ec\ub410\ub2e4\uace0\n\uac04\uc8fc\ud569\ub2c8\ub2e4.\n\n\uc704\uc758 \uc124\uba85\uc774 \ub108\ubb34 \uac04\ub2e8\ud558\ub2e4\uace0 \uc0dd\uac01\ud55c\ub2e4\uba74 CRF\uc5d0 \ub300\ud55c Michael Collins\uc758\n\uae00\uc744 `\uc5ec\uae30 <http://www.cs.columbia.edu/%7Emcollins/crf.pdf>`__ \uc5d0\uc11c\n\uc77d\uc5b4\ubcf4\uc138\uc694.\n\n\uad6c\ud604 \ubb38\uc11c\n--------------------\n\n\uc544\ub798\uc758 \uc608\uc2dc\ub294 \ub85c\uadf8 \uacf5\uac04\uc5d0\uc11c \ubd84\ubc30 \ud568\uc218\ub97c \uacc4\uc0b0\ud558\uae30 \uc704\ud55c \uc21c\ubc29\ud5a5 \uc54c\uace0\ub9ac\uc998\uacfc\n\ubcf5\ud638\ud654\ud558\uae30 \uc704\ud55c \ube44\ud130\ube44 \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud55c \uac83\uc785\ub2c8\ub2e4. \uc5ed\uc804\ud30c\n\ub2e8\uacc4\uc5d0\uc11c \ubcc0\ud654\ub3c4\ub294 \uc790\ub3d9\uc73c\ub85c \uacc4\uc0b0\ub420 \uac83\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\uac00 \uc9c1\uc811 \ud560 \uc77c\uc740\n\uc5c6\uc2b5\ub2c8\ub2e4.\n\n\uc774 \uad6c\ud604\uc740 \ucd5c\uc801\uc758 \uc0c1\ud0dc\uac00 \uc544\ub2d9\ub2c8\ub2e4. \uacfc\uc815\uc744 \uc774\ud574\ud588\ub2e4\uba74, \uc21c\ubc29\ud5a5 \uc54c\uace0\ub9ac\uc998\n\uc0c1\uc5d0\uc11c \ub2e4\uc74c \ud0dc\uadf8\ub97c \uc21c\ucc28\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\ub294 \uacfc\uc815\uc744 \ud558\ub098\uc758 \ud070 \uc5f0\uc0b0\uc73c\ub85c \uc904\uc77c\n\uc218 \uc788\ub2e4\ub294 \uac83\uc744 \uc544\ub9c8 \ube60\ub974\uac8c \uc54c \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4. \uc774 \ucf54\ub4dc\ub294 \uac00\ub2a5\ud55c \uc77d\uae30\n\uc27d\uac8c \uc791\uc131\ud588\uc2b5\ub2c8\ub2e4. \uc801\uc808\ud558\uac8c \uc218\uc815\ud558\uba74, \uc774 \ud0dc\uac70\ub97c \uc2e4\uc81c \ubb38\uc81c\ub4e4\uc5d0 \uc0ac\uc6a9\ud560\n\uc218\ub3c4 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -26,14 +26,14 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Robert Guthrie\n\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.optim as optim\n\ntorch.manual_seed(1)"
+        "# \uc791\uc131\uc790: Robert Guthrie\n\nimport torch\nimport torch.autograd as autograd\nimport torch.nn as nn\nimport torch.optim as optim\n\ntorch.manual_seed(1)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Helper functions to make the code more readable.\n\n"
+        "\ucf54\ub4dc \uac00\ub3c5\uc131\uc744 \ub192\uc5ec\uc8fc\ub294 \ubcf4\uc870 \ud568\uc218\ub4e4\n\n"
       ]
     },
     {
@@ -44,14 +44,14 @@
       },
       "outputs": [],
       "source": [
-        "def argmax(vec):\n    # return the argmax as a python int\n    _, idx = torch.max(vec, 1)\n    return idx.item()\n\n\ndef prepare_sequence(seq, to_ix):\n    idxs = [to_ix[w] for w in seq]\n    return torch.tensor(idxs, dtype=torch.long)\n\n\n# Compute log sum exp in a numerically stable way for the forward algorithm\ndef log_sum_exp(vec):\n    max_score = vec[0, argmax(vec)]\n    max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])\n    return max_score + \\\n        torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))"
+        "def argmax(vec):\n    # argmax\ub97c \ud30c\uc774\uc36c \uc815\uc218\ud615\uc73c\ub85c \ubc18\ud658\ud569\ub2c8\ub2e4.\n    _, idx = torch.max(vec, 1)\n    return idx.item()\n\n\ndef prepare_sequence(seq, to_ix):\n    idxs = [to_ix[w] for w in seq]\n    return torch.tensor(idxs, dtype=torch.long)\n\n\n# \uc21c\ubc29\ud5a5 \uc54c\uace0\ub9ac\uc998\uc744 \uc704\ud574 \uc218\uce58\uc801\uc73c\ub85c \uc548\uc815\uc801\uc778 \ubc29\ubc95\uc73c\ub85c \ub85c\uadf8 \ud569 \uc9c0\uc218\uc2b9\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.\ndef log_sum_exp(vec):\n    max_score = vec[0, argmax(vec)]\n    max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])\n    return max_score + \\\n        torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Create model\n\n"
+        "\ubaa8\ub378 \uc0dd\uc131\n\n"
       ]
     },
     {
@@ -62,14 +62,14 @@
       },
       "outputs": [],
       "source": [
-        "class BiLSTM_CRF(nn.Module):\n\n    def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):\n        super(BiLSTM_CRF, self).__init__()\n        self.embedding_dim = embedding_dim\n        self.hidden_dim = hidden_dim\n        self.vocab_size = vocab_size\n        self.tag_to_ix = tag_to_ix\n        self.tagset_size = len(tag_to_ix)\n\n        self.word_embeds = nn.Embedding(vocab_size, embedding_dim)\n        self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,\n                            num_layers=1, bidirectional=True)\n\n        # Maps the output of the LSTM into tag space.\n        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)\n\n        # Matrix of transition parameters.  Entry i,j is the score of\n        # transitioning *to* i *from* j.\n        self.transitions = nn.Parameter(\n            torch.randn(self.tagset_size, self.tagset_size))\n\n        # These two statements enforce the constraint that we never transfer\n        # to the start tag and we never transfer from the stop tag\n        self.transitions.data[tag_to_ix[START_TAG], :] = -10000\n        self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000\n\n        self.hidden = self.init_hidden()\n\n    def init_hidden(self):\n        return (torch.randn(2, 1, self.hidden_dim // 2),\n                torch.randn(2, 1, self.hidden_dim // 2))\n\n    def _forward_alg(self, feats):\n        # Do the forward algorithm to compute the partition function\n        init_alphas = torch.full((1, self.tagset_size), -10000.)\n        # START_TAG has all of the score.\n        init_alphas[0][self.tag_to_ix[START_TAG]] = 0.\n\n        # Wrap in a variable so that we will get automatic backprop\n        forward_var = init_alphas\n\n        # Iterate through the sentence\n        for feat in feats:\n            alphas_t = []  # The forward tensors at this timestep\n            for next_tag in range(self.tagset_size):\n                # broadcast the emission score: it is the same regardless of\n                # the previous tag\n                emit_score = feat[next_tag].view(\n                    1, -1).expand(1, self.tagset_size)\n                # the ith entry of trans_score is the score of transitioning to\n                # next_tag from i\n                trans_score = self.transitions[next_tag].view(1, -1)\n                # The ith entry of next_tag_var is the value for the\n                # edge (i -> next_tag) before we do log-sum-exp\n                next_tag_var = forward_var + trans_score + emit_score\n                # The forward variable for this tag is log-sum-exp of all the\n                # scores.\n                alphas_t.append(log_sum_exp(next_tag_var).view(1))\n            forward_var = torch.cat(alphas_t).view(1, -1)\n        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]\n        alpha = log_sum_exp(terminal_var)\n        return alpha\n\n    def _get_lstm_features(self, sentence):\n        self.hidden = self.init_hidden()\n        embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)\n        lstm_out, self.hidden = self.lstm(embeds, self.hidden)\n        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)\n        lstm_feats = self.hidden2tag(lstm_out)\n        return lstm_feats\n\n    def _score_sentence(self, feats, tags):\n        # Gives the score of a provided tag sequence\n        score = torch.zeros(1)\n        tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])\n        for i, feat in enumerate(feats):\n            score = score + \\\n                self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]\n        score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]\n        return score\n\n    def _viterbi_decode(self, feats):\n        backpointers = []\n\n        # Initialize the viterbi variables in log space\n        init_vvars = torch.full((1, self.tagset_size), -10000.)\n        init_vvars[0][self.tag_to_ix[START_TAG]] = 0\n\n        # forward_var at step i holds the viterbi variables for step i-1\n        forward_var = init_vvars\n        for feat in feats:\n            bptrs_t = []  # holds the backpointers for this step\n            viterbivars_t = []  # holds the viterbi variables for this step\n\n            for next_tag in range(self.tagset_size):\n                # next_tag_var[i] holds the viterbi variable for tag i at the\n                # previous step, plus the score of transitioning\n                # from tag i to next_tag.\n                # We don't include the emission scores here because the max\n                # does not depend on them (we add them in below)\n                next_tag_var = forward_var + self.transitions[next_tag]\n                best_tag_id = argmax(next_tag_var)\n                bptrs_t.append(best_tag_id)\n                viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))\n            # Now add in the emission scores, and assign forward_var to the set\n            # of viterbi variables we just computed\n            forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)\n            backpointers.append(bptrs_t)\n\n        # Transition to STOP_TAG\n        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]\n        best_tag_id = argmax(terminal_var)\n        path_score = terminal_var[0][best_tag_id]\n\n        # Follow the back pointers to decode the best path.\n        best_path = [best_tag_id]\n        for bptrs_t in reversed(backpointers):\n            best_tag_id = bptrs_t[best_tag_id]\n            best_path.append(best_tag_id)\n        # Pop off the start tag (we dont want to return that to the caller)\n        start = best_path.pop()\n        assert start == self.tag_to_ix[START_TAG]  # Sanity check\n        best_path.reverse()\n        return path_score, best_path\n\n    def neg_log_likelihood(self, sentence, tags):\n        feats = self._get_lstm_features(sentence)\n        forward_score = self._forward_alg(feats)\n        gold_score = self._score_sentence(feats, tags)\n        return forward_score - gold_score\n\n    def forward(self, sentence):  # dont confuse this with _forward_alg above.\n        # Get the emission scores from the BiLSTM\n        lstm_feats = self._get_lstm_features(sentence)\n\n        # Find the best path, given the features.\n        score, tag_seq = self._viterbi_decode(lstm_feats)\n        return score, tag_seq"
+        "class BiLSTM_CRF(nn.Module):\n\n    def __init__(self, vocab_size, tag_to_ix, embedding_dim, hidden_dim):\n        super(BiLSTM_CRF, self).__init__()\n        self.embedding_dim = embedding_dim\n        self.hidden_dim = hidden_dim\n        self.vocab_size = vocab_size\n        self.tag_to_ix = tag_to_ix\n        self.tagset_size = len(tag_to_ix)\n\n        self.word_embeds = nn.Embedding(vocab_size, embedding_dim)\n        self.lstm = nn.LSTM(embedding_dim, hidden_dim // 2,\n                            num_layers=1, bidirectional=True)\n\n        # LSTM\uc758 \ucd9c\ub825\uc744 \ud0dc\uadf8 \uacf5\uac04\uc73c\ub85c \ub300\uc751\uc2dc\ud0b5\ub2c8\ub2e4.\n        self.hidden2tag = nn.Linear(hidden_dim, self.tagset_size)\n\n        # \uc804\uc774 \ub9e4\uac1c\ubcc0\uc218 \ud589\ub82c. i, j \uc131\ubd84\uc740 i\uc5d0\uc11c j\ub85c \ubcc0\ud560 \ub54c\uc758 \uc810\uc218\uc785\ub2c8\ub2e4.\n        self.transitions = nn.Parameter(\n            torch.randn(self.tagset_size, self.tagset_size))\n\n        # \uc774 \ub450 \ucf54\ub4dc\ub294 \uc2dc\uc791 \ud0dc\uadf8\ub85c \uc804\uc774\ud558\uc9c0 \uc54a\uace0, \uc815\uc9c0 \ud0dc\uadf8\uc5d0\uc11c\ubd80\ud130\n        # \uc804\uc774\ud558\uc9c0 \uc54a\ub3c4\ub85d \uac15\uc81c\ud569\ub2c8\ub2e4.\n        self.transitions.data[tag_to_ix[START_TAG], :] = -10000\n        self.transitions.data[:, tag_to_ix[STOP_TAG]] = -10000\n\n        self.hidden = self.init_hidden()\n\n    def init_hidden(self):\n        return (torch.randn(2, 1, self.hidden_dim // 2),\n                torch.randn(2, 1, self.hidden_dim // 2))\n\n    def _forward_alg(self, feats):\n        # \ubd84\ubc30 \ud568\uc218\ub97c \uacc4\uc0b0\ud558\uae30 \uc704\ud574 \uc21c\ubc29\ud5a5 \uc54c\uace0\ub9ac\uc998\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n        init_alphas = torch.full((1, self.tagset_size), -10000.)\n        # START_TAG\ub294 \ubaa8\ub4e0 \uc810\uc218\ub97c \uac16\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n        init_alphas[0][self.tag_to_ix[START_TAG]] = 0.\n\n        # \uc790\ub3d9\uc73c\ub85c \uc5ed\uc804\ud30c \ub418\ub3c4\ub85d \ubcc0\uc218\ub85c \uac10\uc309\ub2c8\ub2e4.\n        forward_var = init_alphas\n\n        # \ubb38\uc7a5\uc758 \uac01 \uc131\ubd84\uc744 \ubc18\ubcf5 \ucc98\ub9ac\ud569\ub2c8\ub2e4.\n        for feat in feats:\n            alphas_t = []  # \ud604\uc7ac \uc2dc\uc810\uc758 \uc21c\ubc29\ud5a5 \ud150\uc11c\n            for next_tag in range(self.tagset_size):\n                # \uc774\uc804\uc758 \ud0dc\uadf8\uc640 \uc0c1\uad00\uc5c6\uc774 \ubc30\ucd9c \uc810\uc218\ub97c \uc804\ud30c\ud569\ub2c8\ub2e4.\n                emit_score = feat[next_tag].view(\n                    1, -1).expand(1, self.tagset_size)\n                # trans_score\uc758 i\ubc88\uc9f8 \uc131\ubd84\uc740 i\ub85c\ubd80\ud130 next_tag\ub85c \uc804\uc774\ud560 \uc810\uc218\uc785\ub2c8\ub2e4.\n                trans_score = self.transitions[next_tag].view(1, -1)\n                # next_tag_var\uc758 i\ubc88\uc9f8 \uc131\ubd84\uc740 \ub85c\uadf8-\ud569-\uc9c0\uc218\uc2b9\uc744 \uacc4\uc0b0\ud558\uae30 \uc804\n                # i\uc5d0\uc11c next_tag\ub85c \uac00\ub294 \uac04\uc120\uc758 \uac12\uc785\ub2c8\ub2e4.\n                next_tag_var = forward_var + trans_score + emit_score\n                # \uc774 \ud0dc\uadf8\uc758 \uc21c\ubc29\ud5a5 \ubcc0\uc218\ub294 \ubaa8\ub4e0 \uc810\uc218\ub4e4\uc758 \ub85c\uadf8-\ud569-\uc9c0\uc218\uc2b9 \uacc4\uc0b0\n                # \uacb0\uacfc\uc785\ub2c8\ub2e4.\n                alphas_t.append(log_sum_exp(next_tag_var).view(1))\n            forward_var = torch.cat(alphas_t).view(1, -1)\n        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]\n        alpha = log_sum_exp(terminal_var)\n        return alpha\n\n    def _get_lstm_features(self, sentence):\n        self.hidden = self.init_hidden()\n        embeds = self.word_embeds(sentence).view(len(sentence), 1, -1)\n        lstm_out, self.hidden = self.lstm(embeds, self.hidden)\n        lstm_out = lstm_out.view(len(sentence), self.hidden_dim)\n        lstm_feats = self.hidden2tag(lstm_out)\n        return lstm_feats\n\n    def _score_sentence(self, feats, tags):\n        # \uc8fc\uc5b4\uc9c4 \ud0dc\uadf8 \uc21c\uc5f4\uc5d0 \uc810\uc218\ub97c \ub9e4\uae41\ub2c8\ub2e4.\n        score = torch.zeros(1)\n        tags = torch.cat([torch.tensor([self.tag_to_ix[START_TAG]], dtype=torch.long), tags])\n        for i, feat in enumerate(feats):\n            score = score + \\\n                self.transitions[tags[i + 1], tags[i]] + feat[tags[i + 1]]\n        score = score + self.transitions[self.tag_to_ix[STOP_TAG], tags[-1]]\n        return score\n\n    def _viterbi_decode(self, feats):\n        backpointers = []\n\n        # \ube44\ud130\ube44 \ubcc0\uc218\ub97c \ub85c\uadf8 \uacf5\uac04 \uc0c1\uc5d0 \ucd08\uae30\ud654\ud569\ub2c8\ub2e4.\n        init_vvars = torch.full((1, self.tagset_size), -10000.)\n        init_vvars[0][self.tag_to_ix[START_TAG]] = 0\n\n        # i \ub2e8\uacc4\uc758 forward_var\ub294 i-1 \ub2e8\uacc4\uc758 \ube44\ud130\ube44 \ubcc0\uc218\ub97c \uac16\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n        forward_var = init_vvars\n        for feat in feats:\n            bptrs_t = []  # \ud604\uc7ac \ub2e8\uacc4\uc758 backpointer\ub97c \uac16\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n            viterbivars_t = []  # \ud604\uc7ac \ub2e8\uacc4\uc758 \ube44\ud130\ube44 \ubcc0\uc218\ub97c \uac16\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n            for next_tag in range(self.tagset_size):\n                # next_tag_var[i]\ub294 \uc774\uc804 \ub2e8\uacc4\uc758 \ud0dc\uadf8 i\uc5d0 \ub300\ud55c \ube44\ud130\ube44 \ubcc0\uc218\uc640,\n                # \ud0dc\uadf8 i\uc5d0\uc11c next_tag\ub85c \uc804\uc774\ud560 \uc810\uc218\ub97c \ub354\ud55c \uac12\uc744 \uac16\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n                # \ubc30\ucd9c \uc810\uc218\ub294 argmax\uc640 \uc0c1\uad00 \uc5c6\uae30 \ub54c\ubb38\uc5d0(\uc544\ub798 \ucf54\ub4dc\uc5d0\uc11c \ucd94\uac00\ud560 \uac83\uc785\ub2c8\ub2e4)\n                # \uc5ec\uae30\uc5d0 \ud3ec\ud568\ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n                next_tag_var = forward_var + self.transitions[next_tag]\n                best_tag_id = argmax(next_tag_var)\n                bptrs_t.append(best_tag_id)\n                viterbivars_t.append(next_tag_var[0][best_tag_id].view(1))\n            # \uc774\uc81c \ubc30\ucd9c \uc810\uc218\ub97c \ub354\ud569\ub2c8\ub2e4. \uadf8\ub9ac\uace0 \ubc29\uae08 \uacc4\uc0b0\ud55c \ube44\ud130\ube44 \ubcc0\uc218\uc758\n            # \uc9d1\ud569\uc744 forward_var\uc5d0 \ud560\ub2f9\ud569\ub2c8\ub2e4.\n            forward_var = (torch.cat(viterbivars_t) + feat).view(1, -1)\n            backpointers.append(bptrs_t)\n\n        # STAP_TAG\ub85c\uc758 \uc804\uc774\n        terminal_var = forward_var + self.transitions[self.tag_to_ix[STOP_TAG]]\n        best_tag_id = argmax(terminal_var)\n        path_score = terminal_var[0][best_tag_id]\n\n        # \ucd5c\uc801\uc758 \uacbd\ub85c\ub97c \uad6c\ud558\uae30 \uc704\ud574 back pointer\ub97c \ub530\ub77c\uac11\ub2c8\ub2e4.\n        best_path = [best_tag_id]\n        for bptrs_t in reversed(backpointers):\n            best_tag_id = bptrs_t[best_tag_id]\n            best_path.append(best_tag_id)\n        # \uc2dc\uc791 \ud0dc\uadf8\ub97c \ube7c\ub0c5\ub2c8\ub2e4 (\uc2dc\uc791 \ud0dc\uadf8\ub294 \ubc18\ud658\ub41c \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4)\n        start = best_path.pop()\n        assert start == self.tag_to_ix[START_TAG]  # \uc644\uacb0\uc131 \uac80\uc0ac (Sanity check)\n        best_path.reverse()\n        return path_score, best_path\n\n    def neg_log_likelihood(self, sentence, tags):\n        feats = self._get_lstm_features(sentence)\n        forward_score = self._forward_alg(feats)\n        gold_score = self._score_sentence(feats, tags)\n        return forward_score - gold_score\n\n    def forward(self, sentence):  # \uc774 \ud568\uc218\uc640 \uc704\uc758 _forward_alg\ub97c \ud5f7\uac08\ub9ac\uc9c0 \ub9c8\uc138\uc694.\n        # Bi-LSTM\uc73c\ub85c\ubd80\ud130 \ubc30\ucd9c \uc810\uc218\ub97c \uc5bb\uc2b5\ub2c8\ub2e4.\n        lstm_feats = self._get_lstm_features(sentence)\n\n        # \uc8fc\uc5b4\uc9c4 \ud2b9\uc9d5(\ubc30\ucd9c \uc810\uc218)\ub4e4\ub85c \ucd5c\uc801\uc758 \uacbd\ub85c\ub97c \ucc3e\uc544\ub0c5\ub2c8\ub2e4.\n        score, tag_seq = self._viterbi_decode(lstm_feats)\n        return score, tag_seq"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Run training\n\n"
+        "\ud6c8\ub828 \uc2e4\ud589\n\n"
       ]
     },
     {
@@ -80,14 +80,14 @@
       },
       "outputs": [],
       "source": [
-        "START_TAG = \"<START>\"\nSTOP_TAG = \"<STOP>\"\nEMBEDDING_DIM = 5\nHIDDEN_DIM = 4\n\n# Make up some training data\ntraining_data = [(\n    \"the wall street journal reported today that apple corporation made money\".split(),\n    \"B I I I O O O B I O O\".split()\n), (\n    \"georgia tech is a university in georgia\".split(),\n    \"B I O O O O B\".split()\n)]\n\nword_to_ix = {}\nfor sentence, tags in training_data:\n    for word in sentence:\n        if word not in word_to_ix:\n            word_to_ix[word] = len(word_to_ix)\n\ntag_to_ix = {\"B\": 0, \"I\": 1, \"O\": 2, START_TAG: 3, STOP_TAG: 4}\n\nmodel = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)\noptimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)\n\n# Check predictions before training\nwith torch.no_grad():\n    precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)\n    precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long)\n    print(model(precheck_sent))\n\n# Make sure prepare_sequence from earlier in the LSTM section is loaded\nfor epoch in range(\n        300):  # again, normally you would NOT do 300 epochs, it is toy data\n    for sentence, tags in training_data:\n        # Step 1. Remember that Pytorch accumulates gradients.\n        # We need to clear them out before each instance\n        model.zero_grad()\n\n        # Step 2. Get our inputs ready for the network, that is,\n        # turn them into Tensors of word indices.\n        sentence_in = prepare_sequence(sentence, word_to_ix)\n        targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)\n\n        # Step 3. Run our forward pass.\n        loss = model.neg_log_likelihood(sentence_in, targets)\n\n        # Step 4. Compute the loss, gradients, and update the parameters by\n        # calling optimizer.step()\n        loss.backward()\n        optimizer.step()\n\n# Check predictions after training\nwith torch.no_grad():\n    precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)\n    print(model(precheck_sent))\n# We got it!"
+        "START_TAG = \"<START>\"\nSTOP_TAG = \"<STOP>\"\nEMBEDDING_DIM = 5\nHIDDEN_DIM = 4\n\n# \ud6c8\ub828\uc6a9 \ub370\uc774\ud130\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.\ntraining_data = [(\n    \"the wall street journal reported today that apple corporation made money\".split(),\n    \"B I I I O O O B I O O\".split()\n), (\n    \"georgia tech is a university in georgia\".split(),\n    \"B I O O O O B\".split()\n)]\n\nword_to_ix = {}\nfor sentence, tags in training_data:\n    for word in sentence:\n        if word not in word_to_ix:\n            word_to_ix[word] = len(word_to_ix)\n\ntag_to_ix = {\"B\": 0, \"I\": 1, \"O\": 2, START_TAG: 3, STOP_TAG: 4}\n\nmodel = BiLSTM_CRF(len(word_to_ix), tag_to_ix, EMBEDDING_DIM, HIDDEN_DIM)\noptimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=1e-4)\n\n# \ud6c8\ub828 \uc804 \uc608\uce21 \uacb0\uacfc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4.\nwith torch.no_grad():\n    precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)\n    precheck_tags = torch.tensor([tag_to_ix[t] for t in training_data[0][1]], dtype=torch.long)\n    print(model(precheck_sent))\n\n# Make sure prepare_sequence from earlier in the LSTM section is loaded\n# \uc704\uc758 \ubcf4\uc870 \ud568\uc218 \uc601\uc5ed\uc5d0 \uc788\ub294 prepare_sequence \ud568\uc218\uac00 \ubd88\ub7ec\uc640 \uc84c\ub294\uc9c0 \ud655\uc778\ud569\ub2c8\ub2e4.\nfor epoch in range(\n        300):  # \ub2e4\uc2dc \ub9d0\ud558\uc9c0\ub9cc, \uc544\ub9c8 300 \uc5d0\ud3ed\uc744 \uc2e4\ud589\ud558\uc9c4 \uc54a\uc744 \uac83\uc785\ub2c8\ub2e4. \uc774\uac83\uc740 \uc5f0\uc2b5\uc6a9 \ub370\uc774\ud130\uc785\ub2c8\ub2e4.\n    for sentence, tags in training_data:\n        # 1\ub2e8\uacc4. Pytorch\uac00 \ubcc0\ud654\ub3c4\ub97c \ub204\uc801\ud55c\ub2e4\ub294 \uac83\uc744 \uae30\uc5b5\ud558\uc138\uc694.\n        # \uadf8\uac83\ub4e4\uc744 \uc81c\uac70\ud569\ub2c8\ub2e4.\n        model.zero_grad()\n\n        # 2\ub2e8\uacc4. \uc785\ub825 \ub370\uc774\ud130\ub97c \uc2e0\uacbd\ub9dd\uc5d0 \uc0ac\uc6a9\ub420 \uc218 \uc788\ub3c4\ub85d \ub2e8\uc5b4\n        # \uc778\ub371\uc2a4\ub4e4\uc758 \ud150\uc11c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.\n        sentence_in = prepare_sequence(sentence, word_to_ix)\n        targets = torch.tensor([tag_to_ix[t] for t in tags], dtype=torch.long)\n\n        # 3\ub2e8\uacc4. \uc21c\ubc29\ud5a5 \uacc4\uc0b0\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n        loss = model.neg_log_likelihood(sentence_in, targets)\n\n        # 4\ub2e8\uacc4. \uc190\uc2e4\uac12, \ubcc0\ud654\ub3c4\ub97c \uacc4\uc0b0\ud558\uace0 optimizer.step()\uc744 \ud638\ucd9c\ud558\uc5ec\n        # \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc744 \uac31\uc2e0\ud569\ub2c8\ub2e4.\n        loss.backward()\n        optimizer.step()\n\n# \ud6c8\ub828\uc774 \ub05d\ub09c \ud6c4 \uc608\uce21 \uacb0\uacfc\ub97c \ud655\uc778\ud569\ub2c8\ub2e4.\nwith torch.no_grad():\n    precheck_sent = prepare_sequence(training_data[0][0], word_to_ix)\n    print(model(precheck_sent))\n# \ub2e4 \ud588\uc2b5\ub2c8\ub2e4!"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Exercise: A new loss function for discriminative tagging\n--------------------------------------------------------\n\nIt wasn't really necessary for us to create a computation graph when\ndoing decoding, since we do not backpropagate from the viterbi path\nscore. Since we have it anyway, try training the tagger where the loss\nfunction is the difference between the Viterbi path score and the score\nof the gold-standard path. It should be clear that this function is\nnon-negative and 0 when the predicted tag sequence is the correct tag\nsequence. This is essentially *structured perceptron*.\n\nThis modification should be short, since Viterbi and score\\_sentence are\nalready implemented. This is an example of the shape of the computation\ngraph *depending on the training instance*. Although I haven't tried\nimplementing this in a static toolkit, I imagine that it is possible but\nmuch less straightforward.\n\nPick up some real data and do a comparison!\n\n\n"
+        "\uc5f0\uc2b5 : \ud310\ubcc4\uc801(discriminative) \ud0dc\uae45\uc744 \uc704\ud55c \uc0c8\ub85c\uc6b4 \uc190\uc2e4 \ud568\uc218\n------------------------------------------------------------\n\n\uc0ac\uc2e4 \ubcf5\ud638\ud654 \ud560 \ub54c\ub294 \ube44\ud130\ube44 \uacbd\ub85c \uc810\uc218\ub85c \uc5ed\uc804\ud30c\ub97c \ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0 \uacc4\uc0b0\n\uadf8\ub798\ud504\ub97c \ub9cc\ub4e4 \ud544\uc694\uac00 \uc5c6\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc774\ubbf8 \ub9cc\ub4e4\uc5c8\uc73c\ub2c8, \ube44\ud130\ube44 \uacbd\ub85c\n\uc810\uc218\uc640 \uc2e4\uc81c \uc815\ub2f5 \uacbd\ub85c \uc810\uc218\uc758 \ucc28\uc774\ub97c \uc190\uc2e4 \ud568\uc218\ub85c \uc0ac\uc6a9\ud574\uc11c \ud0dc\uac70\ub97c\n\ud559\uc2b5\uc2dc\ucf1c \ubcf4\uc138\uc694. \uc190\uc2e4 \ud568\uc218\uc758 \uac12\uc740 \uc74c\uc218\uac00 \uc544\ub2c8\uc5b4\uc57c \ud558\uba70, \uc608\uce21\ub41c \ud0dc\uadf8\n\uc21c\uc5f4\uc774 \uc815\ub2f5\uc774\ub77c\uba74 \uc190\uc2e4 \ud568\uc218\uc758 \uac12\uc740 0\uc774\uc5b4\uc57c \ud569\ub2c8\ub2e4. \uc774\uac83\uc740 \ubcf8\uc9c8\uc801\uc73c\ub85c\n*\uad6c\uc870\ud654\ub41c \ud37c\uc149\ud2b8\ub860* \uc785\ub2c8\ub2e4.\n\n\uc774\ubbf8 \ube44\ud130\ube44\uc640 score_sentence \ud568\uc218\uac00 \uad6c\ud604\ub418\uc5b4 \uc788\uae30 \ub54c\ubb38\uc5d0 \uac04\ub2e8\ud788 \uc218\uc815\ud560\n\uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc740 *\ud559\uc2b5 \ub370\uc774\ud130\uc5d0 \ub530\ub77c \ubcc0\ud558\ub294* \uacc4\uc0b0 \uadf8\ub798\ud504\uc758 \ud55c\n\uc608\uc2dc\uc785\ub2c8\ub2e4. \uc774 \ubaa8\ub378\uc744 \uc815\uc801 \ud234\ud0b7\uc5d0\uc11c \uad6c\ud604\ud574 \ubcf4\uc9c0\ub294 \uc54a\uc558\ub294\ub370, \uad6c\ud604\uc774\n\uac00\ub2a5\ud558\uc9c0\ub9cc \ub35c \uc9c1\uad00\uc801\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc2e4\uc81c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud574\ubcf4\uace0 \ube44\uad50\ud574\ubcf4\uc138\uc694!\n\n"
       ]
     }
   ],
@@ -107,7 +107,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/9a0e851f5cb70c78bfde07b9bd268569/dynamic_quantization_tutorial.ipynb b/docs/_downloads/9a0e851f5cb70c78bfde07b9bd268569/dynamic_quantization_tutorial.ipynb
index f708f3859..4d0efb1b0 100644
--- a/docs/_downloads/9a0e851f5cb70c78bfde07b9bd268569/dynamic_quantization_tutorial.ipynb
+++ b/docs/_downloads/9a0e851f5cb70c78bfde07b9bd268569/dynamic_quantization_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n(beta) Dynamic Quantization on an LSTM Word Language Model\n==================================================================\n\n**Author**: `James Reed <https://github.com/jamesr66a>`_\n\n**Edited by**: `Seth Weidman <https://github.com/SethHWeidman/>`_\n\nIntroduction\n------------\n\nQuantization involves converting the weights and activations of your model from float\nto int, which can result in smaller model size and faster inference with only a small\nhit to accuracy.\n\nIn this tutorial, we'll apply the easiest form of quantization -\n`dynamic quantization <https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic>`_ -\nto an LSTM-based next word-prediction model, closely following the\n`word language model <https://github.com/pytorch/examples/tree/master/word_language_model>`_\nfrom the PyTorch examples.\n\n"
+        "\n(\ubca0\ud0c0) LSTM \uae30\ubc18 \ub2e8\uc5b4 \ub2e8\uc704 \uc5b8\uc5b4 \ubaa8\ub378\uc758 \ub3d9\uc801 \uc591\uc790\ud654\n==================================================================\n\n**Author**: `James Reed <https://github.com/jamesr66a>`_\n\n**Edited by**: `Seth Weidman <https://github.com/SethHWeidman/>`_\n\n**\ubc88\uc5ed**: `\ubc15\uacbd\ub9bc <https://github.com/kypark7/>`_ `Myungha Kwon <https://github.com/kwonmha/>`_ \n\n\uc2dc\uc791\ud558\uae30\n------------\n\n\uc591\uc790\ud654\ub294 \ubaa8\ub378\uc758 \ud06c\uae30\ub97c \uc904\uc774\uace0 \ucd94\ub860 \uc18d\ub3c4\ub97c \ub192\uc774\uba74\uc11c\ub3c4 \uc815\ud655\ub3c4\ub294 \ubcc4\ub85c \ub0ae\uc544\uc9c0\uc9c0 \uc54a\ub3c4\ub85d,\n\ubaa8\ub378\uc758 \uac00\uc911\uce58\uc640 \ud65c\uc131 \ud568\uc218\ub97c \uc2e4\uc218\ud615\uc5d0\uc11c \uc815\uc218\ud615\uc73c\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 PyTorch\uc758 `\ub2e8\uc5b4 \ub2e8\uc704 \uc5b8\uc5b4 \ubaa8\ub378 <https://github.com/pytorch/examples/tree/master/word_language_model>`_\n\uc608\uc81c\ub97c \ub530\ub77c\ud558\uba74\uc11c, LSTM \uae30\ubc18\uc758 \ub2e8\uc5b4 \uc608\uce21 \ubaa8\ub378\uc5d0 \uac00\uc7a5 \uac04\ub2e8\ud55c \uc591\uc790\ud654 \uae30\ubc95\uc778\n`\ub3d9\uc801 \uc591\uc790\ud654 <https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic>`_\n\ub97c \uc801\uc6a9\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -26,14 +26,14 @@
       },
       "outputs": [],
       "source": [
-        "# imports\nimport os\nfrom io import open\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F"
+        "# \ubd88\ub7ec\uc624\uae30\nimport os\nfrom io import open\nimport time\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "1. Define the model\n-------------------\n\nHere we define the LSTM model architecture, following the\n`model <https://github.com/pytorch/examples/blob/master/word_language_model/model.py>`_\nfrom the word language model example.\n\n"
+        "1. \ubaa8\ub378 \uc815\uc758\ud558\uae30\n-------------------\n\n\ub2e8\uc5b4 \ub2e8\uc704 \uc5b8\uc5b4 \ubaa8\ub378 \uc608\uc81c\uc5d0\uc11c \uc0ac\uc6a9\ub41c `\ubaa8\ub378 <https://github.com/pytorch/examples/blob/master/word_language_model/model.py>`_ \uc744\n\ub530\ub77c LSTM \ubaa8\ub378 \uc544\ud0a4\ud14d\ucc98\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -44,14 +44,14 @@
       },
       "outputs": [],
       "source": [
-        "class LSTMModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n    def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):\n        super(LSTMModel, self).__init__()\n        self.drop = nn.Dropout(dropout)\n        self.encoder = nn.Embedding(ntoken, ninp)\n        self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout)\n        self.decoder = nn.Linear(nhid, ntoken)\n\n        self.init_weights()\n\n        self.nhid = nhid\n        self.nlayers = nlayers\n\n    def init_weights(self):\n        initrange = 0.1\n        self.encoder.weight.data.uniform_(-initrange, initrange)\n        self.decoder.bias.data.zero_()\n        self.decoder.weight.data.uniform_(-initrange, initrange)\n\n    def forward(self, input, hidden):\n        emb = self.drop(self.encoder(input))\n        output, hidden = self.rnn(emb, hidden)\n        output = self.drop(output)\n        decoded = self.decoder(output)\n        return decoded, hidden\n\n    def init_hidden(self, bsz):\n        weight = next(self.parameters())\n        return (weight.new_zeros(self.nlayers, bsz, self.nhid),\n                weight.new_zeros(self.nlayers, bsz, self.nhid))"
+        "class LSTMModel(nn.Module):\n    \"\"\"\uc778\ucf54\ub354, \ubc18\ubcf5 \ubaa8\ub4c8 \ubc0f \ub514\ucf54\ub354\uac00 \uc788\ub294 \ucee8\ud14c\uc774\ub108 \ubaa8\ub4c8.\"\"\"\n\n    def __init__(self, ntoken, ninp, nhid, nlayers, dropout=0.5):\n        super(LSTMModel, self).__init__()\n        self.drop = nn.Dropout(dropout)\n        self.encoder = nn.Embedding(ntoken, ninp)\n        self.rnn = nn.LSTM(ninp, nhid, nlayers, dropout=dropout)\n        self.decoder = nn.Linear(nhid, ntoken)\n\n        self.init_weights()\n\n        self.nhid = nhid\n        self.nlayers = nlayers\n\n    def init_weights(self):\n        initrange = 0.1\n        self.encoder.weight.data.uniform_(-initrange, initrange)\n        self.decoder.bias.data.zero_()\n        self.decoder.weight.data.uniform_(-initrange, initrange)\n\n    def forward(self, input, hidden):\n        emb = self.drop(self.encoder(input))\n        output, hidden = self.rnn(emb, hidden)\n        output = self.drop(output)\n        decoded = self.decoder(output)\n        return decoded, hidden\n\n    def init_hidden(self, bsz):\n        weight = next(self.parameters())\n        return (weight.new_zeros(self.nlayers, bsz, self.nhid),\n                weight.new_zeros(self.nlayers, bsz, self.nhid))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "2. Load in the text data\n------------------------\n\nNext, we load the\n`Wikitext-2 dataset <https://www.google.com/search?q=wikitext+2+data>`_ into a `Corpus`,\nagain following the\n`preprocessing <https://github.com/pytorch/examples/blob/master/word_language_model/data.py>`_\nfrom the word language model example.\n\n"
+        "2. \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\n------------------------\n\n\ub2e4\uc74c\uc73c\ub85c, \ub2e8\uc5b4 \ub2e8\uc704 \uc5b8\uc5b4 \ubaa8\ub378 \uc608\uc81c\uc758 `\uc804\ucc98\ub9ac <https://github.com/pytorch/examples/blob/master/word_language_model/data.py>`_\n\uacfc\uc815\uc744 \ub530\ub77c `Wikitext-2 \ub370\uc774\ud130\uc14b <https://www.google.com/search?q=wikitext+2+data>`_ \uc744 `Corpus` \uc778\uc2a4\ud134\uc2a4\uc5d0 \ubd88\ub7ec\uc635\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -62,14 +62,14 @@
       },
       "outputs": [],
       "source": [
-        "class Dictionary(object):\n    def __init__(self):\n        self.word2idx = {}\n        self.idx2word = []\n\n    def add_word(self, word):\n        if word not in self.word2idx:\n            self.idx2word.append(word)\n            self.word2idx[word] = len(self.idx2word) - 1\n        return self.word2idx[word]\n\n    def __len__(self):\n        return len(self.idx2word)\n\n\nclass Corpus(object):\n    def __init__(self, path):\n        self.dictionary = Dictionary()\n        self.train = self.tokenize(os.path.join(path, 'train.txt'))\n        self.valid = self.tokenize(os.path.join(path, 'valid.txt'))\n        self.test = self.tokenize(os.path.join(path, 'test.txt'))\n\n    def tokenize(self, path):\n        \"\"\"Tokenizes a text file.\"\"\"\n        assert os.path.exists(path)\n        # Add words to the dictionary\n        with open(path, 'r', encoding=\"utf8\") as f:\n            for line in f:\n                words = line.split() + ['<eos>']\n                for word in words:\n                    self.dictionary.add_word(word)\n\n        # Tokenize file content\n        with open(path, 'r', encoding=\"utf8\") as f:\n            idss = []\n            for line in f:\n                words = line.split() + ['<eos>']\n                ids = []\n                for word in words:\n                    ids.append(self.dictionary.word2idx[word])\n                idss.append(torch.tensor(ids).type(torch.int64))\n            ids = torch.cat(idss)\n\n        return ids\n\nmodel_data_filepath = 'data/'\n\ncorpus = Corpus(model_data_filepath + 'wikitext-2')"
+        "class Dictionary(object):\n    def __init__(self):\n        self.word2idx = {}\n        self.idx2word = []\n\n    def add_word(self, word):\n        if word not in self.word2idx:\n            self.idx2word.append(word)\n            self.word2idx[word] = len(self.idx2word) - 1\n        return self.word2idx[word]\n\n    def __len__(self):\n        return len(self.idx2word)\n\n\nclass Corpus(object):\n    def __init__(self, path):\n        self.dictionary = Dictionary()\n        self.train = self.tokenize(os.path.join(path, 'train.txt'))\n        self.valid = self.tokenize(os.path.join(path, 'valid.txt'))\n        self.test = self.tokenize(os.path.join(path, 'test.txt'))\n\n    def tokenize(self, path):\n        assert os.path.exists(path)\n        \"\"\"\ud14d\uc2a4\ud2b8 \ud30c\uc77c \ud1a0\ud070\ud654\"\"\"\n        assert os.path.exists(path)\n        # \uc0ac\uc804\uc5d0 \ub2e8\uc5b4 \ucd94\uac00\n        with open(path, 'r', encoding=\"utf8\") as f:\n            for line in f:\n                words = line.split() + ['<eos>']\n                for word in words:\n                    self.dictionary.add_word(word)\n\n        # \ud30c\uc77c \ub0b4\uc6a9 \ud1a0\ud070\ud654\n        with open(path, 'r', encoding=\"utf8\") as f:\n            idss = []\n            for line in f:\n                words = line.split() + ['<eos>']\n                ids = []\n                for word in words:\n                    ids.append(self.dictionary.word2idx[word])\n                idss.append(torch.tensor(ids).type(torch.int64))\n            ids = torch.cat(idss)\n\n        return ids\n\nmodel_data_filepath = 'data/'\n\ncorpus = Corpus(model_data_filepath + 'wikitext-2')"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "3. Load the pre-trained model\n-----------------------------\n\nThis is a tutorial on dynamic quantization, a quantization technique\nthat is applied after a model has been trained. Therefore, we'll simply load some\npre-trained weights into this model architecture; these weights were obtained\nby training for five epochs using the default settings in the word language model\nexample.\n\n"
+        "3. \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378 \ubd88\ub7ec\uc624\uae30\n-----------------------------\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 \ubaa8\ub378\uc774 \ud559\uc2b5\ub41c \ud6c4 \uc801\uc6a9\ub418\ub294 \uc591\uc790\ud654 \uae30\uc220\uc778 \ub3d9\uc801 \uc591\uc790\ud654\uc5d0 \ub300\ud55c \ud29c\ud1a0\ub9ac\uc5bc\uc785\ub2c8\ub2e4.\n\ub530\ub77c\uc11c \uc6b0\ub9ac\ub294 \ubbf8\ub9ac \ud559\uc2b5\ub41c \uac00\uc911\uce58\ub97c \ubaa8\ub378 \uc544\ud0a4\ud14d\ucc98\uc5d0 \ub85c\ub4dc\ud560 \uac83 \uc785\ub2c8\ub2e4. \uc774 \uac00\uc911\uce58\ub294 word \nlanguage \ubaa8\ub378 \uc608\uc81c\uc758 \uae30\ubcf8 \uc124\uc815\uc744 \uc0ac\uc6a9\ud558\uc5ec 5\uac1c\uc758 epoch \ub3d9\uc548 \ud559\uc2b5\ud558\uc5ec \uc5bb\uc740 \uac83\uc785\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -87,7 +87,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Now let's generate some text to ensure that the pre-trained model is working\nproperly - similarly to before, we follow\n`here <https://github.com/pytorch/examples/blob/master/word_language_model/generate.py>`_\n\n"
+        "\uc774\uc81c \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc774 \uc798 \ub3d9\uc791\ud558\ub294\uc9c0 \ud655\uc778\ud574\ubcf4\uae30 \uc704\ud574 \ud14d\uc2a4\ud2b8\ub97c \uc0dd\uc131\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\uc9c0\uae08\uae4c\uc9c0 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc9c4\ud589\ud588\ub358 \ubc29\uc2dd\ucc98\ub7fc `\uc774 \uc608\uc81c <https://github.com/pytorch/examples/blob/master/word_language_model/generate.py>`_ \ub97c \ub530\ub77c \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -98,14 +98,14 @@
       },
       "outputs": [],
       "source": [
-        "input_ = torch.randint(ntokens, (1, 1), dtype=torch.long)\nhidden = model.init_hidden(1)\ntemperature = 1.0\nnum_words = 1000\n\nwith open(model_data_filepath + 'out.txt', 'w') as outf:\n    with torch.no_grad():  # no tracking history\n        for i in range(num_words):\n            output, hidden = model(input_, hidden)\n            word_weights = output.squeeze().div(temperature).exp().cpu()\n            word_idx = torch.multinomial(word_weights, 1)[0]\n            input_.fill_(word_idx)\n\n            word = corpus.dictionary.idx2word[word_idx]\n\n            outf.write(str(word.encode('utf-8')) + ('\\n' if i % 20 == 19 else ' '))\n\n            if i % 100 == 0:\n                print('| Generated {}/{} words'.format(i, 1000))\n\nwith open(model_data_filepath + 'out.txt', 'r') as outf:\n    all_output = outf.read()\n    print(all_output)"
+        "input_ = torch.randint(ntokens, (1, 1), dtype=torch.long)\nhidden = model.init_hidden(1)\ntemperature = 1.0\nnum_words = 1000\n\nwith open(model_data_filepath + 'out.txt', 'w') as outf:\n    with torch.no_grad():  # \uae30\ub85d\uc744 \ucd94\uc801\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n        for i in range(num_words):\n            output, hidden = model(input_, hidden)\n            word_weights = output.squeeze().div(temperature).exp().cpu()\n            word_idx = torch.multinomial(word_weights, 1)[0]\n            input_.fill_(word_idx)\n\n            word = corpus.dictionary.idx2word[word_idx]\n\n            outf.write(str(word.encode('utf-8')) + ('\\n' if i % 20 == 19 else ' '))\n\n            if i % 100 == 0:\n                print('| Generated {}/{} words'.format(i, 1000))\n\nwith open(model_data_filepath + 'out.txt', 'r') as outf:\n    all_output = outf.read()\n    print(all_output)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "It's no GPT-2, but it looks like the model has started to learn the structure of\nlanguage!\n\nWe're almost ready to demonstrate dynamic quantization. We just need to define a few more\nhelper functions:\n\n"
+        "\uc774 \ubaa8\ub378\uc774 GPT-2\ub294 \uc544\ub2c8\uc9c0\ub9cc, \uc5b8\uc5b4\uc758 \uad6c\uc870\ub97c \ubc30\uc6b0\uae30 \uc2dc\uc791\ud55c \uac83\ucc98\ub7fc \ubcf4\uc785\ub2c8\ub2e4!\n\n\n\ub3d9\uc801 \uc591\uc790\ud654\ub97c \uc2dc\uc5f0\ud560 \uc900\ube44\uac00 \uac70\uc758 \ub05d\ub0ac\uc2b5\ub2c8\ub2e4. \uba87 \uac00\uc9c0 helper \ud568\uc218\ub97c \uc815\uc758\ud558\uae30\ub9cc \ud558\uba74 \ub429\ub2c8\ub2e4:\n\n"
       ]
     },
     {
@@ -116,14 +116,14 @@
       },
       "outputs": [],
       "source": [
-        "bptt = 25\ncriterion = nn.CrossEntropyLoss()\neval_batch_size = 1\n\n# create test data set\ndef batchify(data, bsz):\n    # Work out how cleanly we can divide the dataset into bsz parts.\n    nbatch = data.size(0) // bsz\n    # Trim off any extra elements that wouldn't cleanly fit (remainders).\n    data = data.narrow(0, 0, nbatch * bsz)\n    # Evenly divide the data across the bsz batches.\n    return data.view(bsz, -1).t().contiguous()\n\ntest_data = batchify(corpus.test, eval_batch_size)\n\n# Evaluation functions\ndef get_batch(source, i):\n    seq_len = min(bptt, len(source) - 1 - i)\n    data = source[i:i+seq_len]\n    target = source[i+1:i+1+seq_len].view(-1)\n    return data, target\n\ndef repackage_hidden(h):\n  \"\"\"Wraps hidden states in new Tensors, to detach them from their history.\"\"\"\n\n  if isinstance(h, torch.Tensor):\n      return h.detach()\n  else:\n      return tuple(repackage_hidden(v) for v in h)\n\ndef evaluate(model_, data_source):\n    # Turn on evaluation mode which disables dropout.\n    model_.eval()\n    total_loss = 0.\n    hidden = model_.init_hidden(eval_batch_size)\n    with torch.no_grad():\n        for i in range(0, data_source.size(0) - 1, bptt):\n            data, targets = get_batch(data_source, i)\n            output, hidden = model_(data, hidden)\n            hidden = repackage_hidden(hidden)\n            output_flat = output.view(-1, ntokens)\n            total_loss += len(data) * criterion(output_flat, targets).item()\n    return total_loss / (len(data_source) - 1)"
+        "bptt = 25\ncriterion = nn.CrossEntropyLoss()\neval_batch_size = 1\n\n# \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc14b \ub9cc\ub4e4\uae30\ndef batchify(data, bsz):\n    # \ub370\uc774\ud130\uc14b\uc744 bsz \ubd80\ubd84\uc73c\ub85c \uc5bc\ub9c8\ub098 \uae54\ub054\ud558\uac8c \ub098\ub20c \uc218 \uc788\ub294\uc9c0 \uacc4\uc0b0\ud569\ub2c8\ub2e4.\n    nbatch = data.size(0) // bsz\n    # \uae54\ub054\ud558\uac8c \ub9de\uc9c0 \uc54a\ub294 \ucd94\uac00\uc801\uc778 \ubd80\ubd84(\ub098\uba38\uc9c0\ub4e4)\uc744 \uc798\ub77c\ub0c5\ub2c8\ub2e4.\n    data = data.narrow(0, 0, nbatch * bsz)\n    # \ub370\uc774\ud130\uc5d0 \ub300\ud558\uc5ec bsz \ubc30\uce58\ub4e4\ub85c \ub3d9\ub4f1\ud558\uac8c \ub098\ub215\ub2c8\ub2e4.\n    return data.view(bsz, -1).t().contiguous()\n\ntest_data = batchify(corpus.test, eval_batch_size)\n\n# \ud3c9\uac00 \ud568\uc218\ub4e4\ndef get_batch(source, i):\n    seq_len = min(bptt, len(source) - 1 - i)\n    data = source[i:i+seq_len]\n    target = source[i+1:i+1+seq_len].view(-1)\n    return data, target\n\ndef repackage_hidden(h):\n  \"\"\"\uc740\ub2c9 \uc0c1\ud0dc\ub97c \ubcc0\ud654\ub3c4 \uae30\ub85d\uc5d0\uc11c \uc81c\uac70\ub41c \uc0c8\ub85c\uc6b4 tensor\ub85c \ub9cc\ub4ed\ub2c8\ub2e4.\"\"\"\n\n  if isinstance(h, torch.Tensor):\n      return h.detach()\n  else:\n      return tuple(repackage_hidden(v) for v in h)\n\ndef evaluate(model_, data_source):\n    # Dropout\uc744 \uc911\uc9c0\uc2dc\ud0a4\ub294 \ud3c9\uac00 \ubaa8\ub4dc\ub85c \uc2e4\ud589\ud569\ub2c8\ub2e4.\n    model_.eval()\n    total_loss = 0.\n    hidden = model_.init_hidden(eval_batch_size)\n    with torch.no_grad():\n        for i in range(0, data_source.size(0) - 1, bptt):\n            data, targets = get_batch(data_source, i)\n            output, hidden = model_(data, hidden)\n            hidden = repackage_hidden(hidden)\n            output_flat = output.view(-1, ntokens)\n            total_loss += len(data) * criterion(output_flat, targets).item()\n    return total_loss / (len(data_source) - 1)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "4. Test dynamic quantization\n----------------------------\n\nFinally, we can call ``torch.quantization.quantize_dynamic`` on the model!\nSpecifically,\n\n- We specify that we want the ``nn.LSTM`` and ``nn.Linear`` modules in our\n  model to be quantized\n- We specify that we want weights to be converted to ``int8`` values\n\n"
+        "4. \ub3d9\uc801 \uc591\uc790\ud654 \ud14c\uc2a4\ud2b8\ud558\uae30\n----------------------------\n\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \ubaa8\ub378\uc5d0\uc11c ``torch.quantization.quantize_dynamic`` \uc744 \ud638\ucd9c \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4!\n\uad6c\uccb4\uc801\uc73c\ub85c,\n\n- \ubaa8\ub378\uc758 ``nn.LSTM`` \uacfc ``nn.Linear`` \ubaa8\ub4c8\uc744 \uc591\uc790\ud654 \ud558\ub3c4\ub85d \uba85\uc2dc\ud569\ub2c8\ub2e4.\n- \uac00\uc911\uce58\ub4e4\uc774 ``int8`` \uac12\uc73c\ub85c \ubcc0\ud658\ub418\ub3c4\ub85d \uba85\uc2dc\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -141,7 +141,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The model looks the same; how has this benefited us? First, we see a\nsignificant reduction in model size:\n\n"
+        "\ubaa8\ub378\uc740 \ub3d9\uc77c\ud558\uac8c \ubcf4\uc785\ub2c8\ub2e4. \uc774\uac83\uc774 \uc5b4\ub5bb\uac8c \uc774\ub4dd\uc744 \uc8fc\ub294 \uac83\uc77c\uae4c\uc694? \uccab\uc9f8, \ubaa8\ub378 \ud06c\uae30\uac00\n\uc0c1\ub2f9\ud788 \uc904\uc5b4 \ub4ed\ub2c8\ub2e4:\n\n"
       ]
     },
     {
@@ -159,7 +159,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Second, we see faster inference time, with no difference in evaluation loss:\n\nNote: we number of threads to one for single threaded comparison, since quantized\nmodels run single threaded.\n\n"
+        "\ub450 \ubc88\uc9f8\ub85c, \ud3c9\uac00 \uc190\uc2e4\uac12\uc740 \uac19\uc73c\ub098 \ucd94\ub860(inference) \uc18d\ub3c4\uac00 \ube68\ub77c\uc84c\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -170,14 +170,14 @@
       },
       "outputs": [],
       "source": [
-        "torch.set_num_threads(1)\n\ndef time_model_evaluation(model, test_data):\n    s = time.time()\n    loss = evaluate(model, test_data)\n    elapsed = time.time() - s\n    print('''loss: {0:.3f}\\nelapsed time (seconds): {1:.1f}'''.format(loss, elapsed))\n\ntime_model_evaluation(model, test_data)\ntime_model_evaluation(quantized_model, test_data)"
+        "# \uba54\ubaa8: \uc591\uc790\ud654 \ub41c \ubaa8\ub378\uc740 \ub2e8\uc77c \uc2a4\ub808\ub4dc\ub85c \uc2e4\ud589\ub418\uae30 \ub54c\ubb38\uc5d0 \ub2e8\uc77c \uc2a4\ub808\ub4dc \ube44\uad50\ub97c \uc704\ud574\n# \uc2a4\ub808\ub4dc \uc218\ub97c 1\ub85c \uc124\uc815\ud588\uc2b5\ub2c8\ub2e4.\n\ntorch.set_num_threads(1)\n\ndef time_model_evaluation(model, test_data):\n    s = time.time()\n    loss = evaluate(model, test_data)\n    elapsed = time.time() - s\n    print('''loss: {0:.3f}\\nelapsed time (seconds): {1:.1f}'''.format(loss, elapsed))\n\ntime_model_evaluation(model, test_data)\ntime_model_evaluation(quantized_model, test_data)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Running this locally on a MacBook Pro, without quantization, inference takes about 200 seconds,\nand with quantization it takes just about 100 seconds.\n\nConclusion\n----------\n\nDynamic quantization can be an easy way to reduce model size while only\nhaving a limited effect on accuracy.\n\nThanks for reading! As always, we welcome any feedback, so please create an issue\n`here <https://github.com/pytorch/pytorch/issues>`_ if you have any.\n\n"
+        "MacBook Pro\uc5d0\uc11c \ub85c\uceec\ub85c \uc2e4\ud589\ud558\ub294 \uacbd\uc6b0, \uc591\uc790\ud654 \uc5c6\uc774\ub294 \ucd94\ub860(inference)\uc5d0 \uc57d 200\ucd08\uac00 \uac78\ub9ac\uace0 \n\uc591\uc790\ud654\ub97c \uc0ac\uc6a9\ud558\uba74 \uc57d 100\ucd08\uac00 \uac78\ub9bd\ub2c8\ub2e4.\n\n\ub9c8\uce58\uba70\n----------\n\n\ub3d9\uc801 \uc591\uc790\ud654\ub294 \uc815\ud655\ub3c4\uc5d0 \uc81c\ud55c\uc801\uc778 \uc601\ud5a5\uc744 \ubbf8\uce58\uba74\uc11c \ubaa8\ub378 \ud06c\uae30\ub97c \uc904\uc774\ub294\n\uc26c\uc6b4 \ubc29\ubc95\uc774 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc77d\uc5b4\uc8fc\uc154\uc11c \uac10\uc0ac\ud569\ub2c8\ub2e4. \uc5b8\uc81c\ub098\ucc98\ub7fc \uc5b4\ub5a0\ud55c \ud53c\ub4dc\ubc31\ub3c4 \ud658\uc601\uc774\ub2c8, \uc758\uacac\uc774 \uc788\ub2e4\uba74\n`\uc5ec\uae30 <https://github.com/pytorch/pytorch/issues>`_ \uc5d0 \uc774\uc288\ub97c \ub0a8\uaca8 \uc8fc\uc138\uc694.\n\n"
       ]
     }
   ],
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/9a7fec783882a14243d1253bf1335d36/warmstarting_model_using_parameters_from_a_different_model.ipynb b/docs/_downloads/9a7fec783882a14243d1253bf1335d36/warmstarting_model_using_parameters_from_a_different_model.ipynb
index 3e24d821a..45f69f96b 100644
--- a/docs/_downloads/9a7fec783882a14243d1253bf1335d36/warmstarting_model_using_parameters_from_a_different_model.ipynb
+++ b/docs/_downloads/9a7fec783882a14243d1253bf1335d36/warmstarting_model_using_parameters_from_a_different_model.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nWarmstarting model using parameters from a different model in PyTorch\n=====================================================================\nPartially loading a model or loading a partial model are common\nscenarios when transfer learning or training a new complex model.\nLeveraging trained parameters, even if only a few are usable, will help\nto warmstart the training process and hopefully help your model converge\nmuch faster than training from scratch.\n\nIntroduction\n------------\nWhether you are loading from a partial ``state_dict``, which is missing\nsome keys, or loading a ``state_dict`` with more keys than the model\nthat you are loading into, you can set the strict argument to ``False``\nin the ``load_state_dict()`` function to ignore non-matching keys.\nIn this recipe, we will experiment with warmstarting a model using\nparameters of a different model.\n\nSetup\n-----\nBefore we begin, we need to install ``torch`` if it isn\u2019t already\navailable.\n\n::\n\n   pip install torch\n   \n\n"
+        "\nWarmstarting model using parameters from a different model in PyTorch\n=====================================================================\nPartially loading a model or loading a partial model are common\nscenarios when transfer learning or training a new complex model.\nLeveraging trained parameters, even if only a few are usable, will help\nto warmstart the training process and hopefully help your model converge\nmuch faster than training from scratch.\n\nIntroduction\n------------\nWhether you are loading from a partial ``state_dict``, which is missing\nsome keys, or loading a ``state_dict`` with more keys than the model\nthat you are loading into, you can set the strict argument to ``False``\nin the ``load_state_dict()`` function to ignore non-matching keys.\nIn this recipe, we will experiment with warmstarting a model using\nparameters of a different model.\n\nSetup\n-----\nBefore we begin, we need to install ``torch`` if it isn\u2019t already\navailable.\n\n::\n\n   pip install torch\n   \n"
       ]
     },
     {
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/9b89023ea3fb5bf2511a9c08a4311cce/saving_multiple_models_in_one_file.ipynb b/docs/_downloads/9b89023ea3fb5bf2511a9c08a4311cce/saving_multiple_models_in_one_file.ipynb
index 97c6dcffb..9a56153c9 100644
--- a/docs/_downloads/9b89023ea3fb5bf2511a9c08a4311cce/saving_multiple_models_in_one_file.ipynb
+++ b/docs/_downloads/9b89023ea3fb5bf2511a9c08a4311cce/saving_multiple_models_in_one_file.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\uc5d0\uc11c \uc5ec\ub7ec \ubaa8\ub378\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n============================================================\n\uc5ec\ub7ec \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \uac83\uc740 \uc774\uc804\uc5d0 \ud559\uc2b5\ud588\ub358 \ubaa8\ub378\ub4e4\uc744 \uc7ac\uc0ac\uc6a9\ud558\ub294\ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\nGAN\uc774\ub098 \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4(sequence-to-sequence model), \uc559\uc0c1\ube14 \ubaa8\ub378(ensemble of models)\uacfc\n\uac19\uc774 \uc5ec\ub7ec ``torch.nn.Modules`` \ub85c \uad6c\uc131\ub41c \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c\ub294 \uac01 \ubaa8\ub378\uc758 state_dict\uc640\n\ud574\ub2f9 \uc635\ud2f0\ub9c8\uc774\uc800(optimizer)\uc758 \uc0ac\uc804\uc744 \uc800\uc7a5\ud574\uc57c \ud569\ub2c8\ub2e4. \ub610\ud55c, \ud559\uc2b5 \ud559\uc2b5\uc744 \uc7ac\uac1c\ud558\ub294\ub370\n\ud544\uc694\ud55c \ub2e4\ub978 \ud56d\ubaa9\ub4e4\uc744 \uc0ac\uc804\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\ub4e4\uc744 \ubd88\ub7ec\uc62c \ub54c\uc5d0\ub294, \uba3c\uc800\n\ubaa8\ub378\ub4e4\uacfc \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \ucd08\uae30\ud654\ud558\uace0, ``torch.load()`` \ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.\n\uc774\ud6c4 \uc6d0\ud558\ub294\ub300\ub85c \uc800\uc7a5\ud55c \ud56d\ubaa9\ub4e4\uc744 \uc0ac\uc804\uc5d0 \uc870\ud68c\ud558\uc5ec \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \ubaa8\ub378\ub4e4\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc5b4\ub5bb\uac8c \uc800\uc7a5\ud558\uace0\n\ubd88\ub7ec\uc624\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815\n---------\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0 ``torch`` \uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n::\n\n   pip install torch\n\n\n"
+        "\nPyTorch\uc5d0\uc11c \uc5ec\ub7ec \ubaa8\ub378\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n============================================================\n\uc5ec\ub7ec \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \uac83\uc740 \uc774\uc804\uc5d0 \ud559\uc2b5\ud588\ub358 \ubaa8\ub378\ub4e4\uc744 \uc7ac\uc0ac\uc6a9\ud558\ub294\ub370 \ub3c4\uc6c0\uc774 \ub429\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\nGAN\uc774\ub098 \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4(sequence-to-sequence model), \uc559\uc0c1\ube14 \ubaa8\ub378(ensemble of models)\uacfc\n\uac19\uc774 \uc5ec\ub7ec ``torch.nn.Modules`` \ub85c \uad6c\uc131\ub41c \ubaa8\ub378\uc744 \uc800\uc7a5\ud560 \ub54c\ub294 \uac01 \ubaa8\ub378\uc758 state_dict\uc640\n\ud574\ub2f9 \uc635\ud2f0\ub9c8\uc774\uc800(optimizer)\uc758 \uc0ac\uc804\uc744 \uc800\uc7a5\ud574\uc57c \ud569\ub2c8\ub2e4. \ub610\ud55c, \ud559\uc2b5 \ud559\uc2b5\uc744 \uc7ac\uac1c\ud558\ub294\ub370\n\ud544\uc694\ud55c \ub2e4\ub978 \ud56d\ubaa9\ub4e4\uc744 \uc0ac\uc804\uc5d0 \ucd94\uac00\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubaa8\ub378\ub4e4\uc744 \ubd88\ub7ec\uc62c \ub54c\uc5d0\ub294, \uba3c\uc800\n\ubaa8\ub378\ub4e4\uacfc \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \ucd08\uae30\ud654\ud558\uace0, ``torch.load()`` \ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.\n\uc774\ud6c4 \uc6d0\ud558\ub294\ub300\ub85c \uc800\uc7a5\ud55c \ud56d\ubaa9\ub4e4\uc744 \uc0ac\uc804\uc5d0 \uc870\ud68c\ud558\uc5ec \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc5ec\ub7ec \ubaa8\ub378\ub4e4\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc5b4\ub5bb\uac8c \uc800\uc7a5\ud558\uace0\n\ubd88\ub7ec\uc624\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815\n---------\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0 ``torch`` \uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n::\n\n   pip install torch\n"
       ]
     },
     {
@@ -143,7 +143,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/9cf2d4ead514e661e20d2070c9bf7324/transformer_tutorial.ipynb b/docs/_downloads/9cf2d4ead514e661e20d2070c9bf7324/transformer_tutorial.ipynb
index bf784604b..459b02d27 100644
--- a/docs/_downloads/9cf2d4ead514e661e20d2070c9bf7324/transformer_tutorial.ipynb
+++ b/docs/_downloads/9cf2d4ead514e661e20d2070c9bf7324/transformer_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nnn.Transformer \uc640 TorchText \ub85c \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4(Sequence-to-Sequence) \ubaa8\ub378\ub9c1\ud558\uae30\n==========================================================================\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294\n`nn.Transformer <https://pytorch.org/docs/master/nn.html?highlight=nn%20transformer#torch.nn.Transformer>`__ \ubaa8\ub4c8\uc744\n\uc774\uc6a9\ud558\ub294 \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4(Sequence-to-Sequence) \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch 1.2 \ubc84\uc83c\uc5d0\ub294\n`Attention is All You Need <https://arxiv.org/pdf/1706.03762.pdf>`__ \ub17c\ubb38\uc5d0\n\uae30\ubc18\ud55c \ud45c\uc900 \ud2b8\ub79c\uc2a4\ud3ec\uba38(transformer) \ubaa8\ub4c8\uc744 \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc740 \ub354 \ub192\uc740 \uc218\uc900\uc758 \ubcd1\ub82c\ud654(parallelization)\uac00 \uac00\ub2a5\ud558\uba74\uc11c\ub3c4\n\ub9ce\uc740 \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4 \ubb38\uc81c\ub4e4\uc5d0\uc11c \ud488\uc9c8\uc774 \uc6b0\uc218\ud568\uc774 \uc785\uc99d\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n``nn.Transformer`` \ubaa8\ub4c8\uc740 \uc785\ub825(input) \uacfc \ucd9c\ub825(output) \uc0ac\uc774\uc758 \uc804\uc5ed\uc801\uc778 \uc758\uc874\uc131(global dependencies)\n\uc744 \ub098\ud0c0\ub0b4\uae30 \uc704\ud558\uc5ec \uc804\uc801\uc73c\ub85c \uc5b4\ud150\uc158(attention) \uba54\ucee4\ub2c8\uc998\uc5d0 \uc758\uc874\ud569\ub2c8\ub2e4.\n(\ucd5c\uadfc\uc5d0 \ub610 \ub2e4\ub978 \ubaa8\ub4c8\uc774 `nn.MultiheadAttention <https://pytorch.org/docs/master/nn.html?highlight=multiheadattention#torch.nn.MultiheadAttention>`__ \uc73c\ub85c \uad6c\ud604\ub418\uc5c8\uc2b5\ub2c8\ub2e4.)\n``nn.Transformer`` \ubaa8\ub4c8\uc740 \ud604\uc7ac \ubaa8\ub4c8\ud654\uac00 \ub9e4\uc6b0 \uc798 \ub418\uc5b4 \uc788\uc5b4,\n\ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uc77c \ucef4\ud3ec\ub10c\ud2b8 (\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 `nn.TransformerEncoder <https://pytorch.org/docs/master/nn.html?highlight=nn%20transformerencoder#torch.nn.TransformerEncoder>`__ \uc640 \uac19\uc740)\n\ub294 \uc27d\uac8c \uc801\uc6a9 \ubc0f \uad6c\uc131\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n![](../_static/img/transformer_architecture.jpg)\n\n\n\n"
+        "\nnn.Transformer \uc640 TorchText \ub85c \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4(Sequence-to-Sequence) \ubaa8\ub378\ub9c1\ud558\uae30\n=================================================================================\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294\n`nn.Transformer <https://pytorch.org/docs/master/nn.html?highlight=nn%20transformer#torch.nn.Transformer>`__ \ubaa8\ub4c8\uc744\n\uc774\uc6a9\ud558\ub294 \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4(Sequence-to-Sequence) \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch 1.2 \ubc84\uc83c\uc5d0\ub294\n`Attention is All You Need <https://arxiv.org/pdf/1706.03762.pdf>`__ \ub17c\ubb38\uc5d0\n\uae30\ubc18\ud55c \ud45c\uc900 \ud2b8\ub79c\uc2a4\ud3ec\uba38(transformer) \ubaa8\ub4c8\uc744 \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\ud2b8\ub79c\uc2a4\ud3ec\uba38 \ubaa8\ub378\uc740 \ub354 \ub192\uc740 \uc218\uc900\uc758 \ubcd1\ub82c\ud654(parallelization)\uac00 \uac00\ub2a5\ud558\uba74\uc11c\ub3c4\n\ub9ce\uc740 \uc2dc\ud000\uc2a4-\ud22c-\uc2dc\ud000\uc2a4 \ubb38\uc81c\ub4e4\uc5d0\uc11c \ud488\uc9c8\uc774 \uc6b0\uc218\ud568\uc774 \uc785\uc99d\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n``nn.Transformer`` \ubaa8\ub4c8\uc740 \uc785\ub825(input) \uacfc \ucd9c\ub825(output) \uc0ac\uc774\uc758 \uc804\uc5ed\uc801\uc778 \uc758\uc874\uc131(global dependencies)\n\uc744 \ub098\ud0c0\ub0b4\uae30 \uc704\ud558\uc5ec \uc804\uc801\uc73c\ub85c \uc5b4\ud150\uc158(attention) \uba54\ucee4\ub2c8\uc998\uc5d0 \uc758\uc874\ud569\ub2c8\ub2e4.\n(\ucd5c\uadfc\uc5d0 \ub610 \ub2e4\ub978 \ubaa8\ub4c8\uc774 `nn.MultiheadAttention <https://pytorch.org/docs/master/nn.html?highlight=multiheadattention#torch.nn.MultiheadAttention>`__ \uc73c\ub85c \uad6c\ud604\ub418\uc5c8\uc2b5\ub2c8\ub2e4.)\n``nn.Transformer`` \ubaa8\ub4c8\uc740 \ud604\uc7ac \ubaa8\ub4c8\ud654\uac00 \ub9e4\uc6b0 \uc798 \ub418\uc5b4 \uc788\uc5b4,\n\ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uc77c \ucef4\ud3ec\ub10c\ud2b8 (\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc758 `nn.TransformerEncoder <https://pytorch.org/docs/master/nn.html?highlight=nn%20transformerencoder#torch.nn.TransformerEncoder>`__ \uc640 \uac19\uc740)\n\ub294 \uc27d\uac8c \uc801\uc6a9 \ubc0f \uad6c\uc131\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n![](../_static/img/transformer_architecture.jpg)\n\n"
       ]
     },
     {
@@ -65,7 +65,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ub370\uc774\ud130 \ub85c\ub4dc\ud558\uace0 \ubc30\uce58 \ub9cc\ub4e4\uae30\n-------------------\n\n\n"
+        "\ub370\uc774\ud130 \ub85c\ub4dc\ud558\uace0 \ubc30\uce58 \ub9cc\ub4e4\uae30\n-----------------------------\n\n\n"
       ]
     },
     {
@@ -90,7 +90,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc785\ub825(input) \uacfc \ud0c0\uac9f(target) \uc2dc\ud000\uc2a4\ub97c \uc0dd\uc131\ud558\uae30 \uc704\ud55c \ud568\uc218\ub4e4\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\n"
+        "\uc785\ub825(input) \uacfc \ud0c0\uac9f(target) \uc2dc\ud000\uc2a4\ub97c \uc0dd\uc131\ud558\uae30 \uc704\ud55c \ud568\uc218\ub4e4\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\n"
       ]
     },
     {
@@ -115,7 +115,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc778\uc2a4\ud134\uc2a4(instance) \ucd08\uae30\ud654\ud558\uae30\n--------------------\n\n\n"
+        "\uc778\uc2a4\ud134\uc2a4(instance) \ucd08\uae30\ud654\ud558\uae30\n--------------------------------\n\n\n"
       ]
     },
     {
@@ -183,7 +183,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ud3c9\uac00 \ub370\uc774\ud130\uc14b(test dataset) \uc73c\ub85c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30\n-------------------------------------\n\n\ud3c9\uac00 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \uacb0\uacfc\ub97c \ud655\uc778\ud558\uae30 \uc704\ud574\uc11c \ucd5c\uace0\uc758 \ubaa8\ub378\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4.\n\n"
+        "\ud3c9\uac00 \ub370\uc774\ud130\uc14b(test dataset)\uc73c\ub85c \ubaa8\ub378\uc744 \ud3c9\uac00\ud558\uae30\n-------------------------------------------------\n\n\ud3c9\uac00 \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \uacb0\uacfc\ub97c \ud655\uc778\ud558\uae30 \uc704\ud574\uc11c \ucd5c\uace0\uc758 \ubaa8\ub378\uc744 \uc801\uc6a9\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -214,7 +214,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/9da0471a9eeb2351a488cd4b44fc6bbf/reinforcement_q_learning.ipynb b/docs/_downloads/9da0471a9eeb2351a488cd4b44fc6bbf/reinforcement_q_learning.ipynb
index 7bcda26b6..cc37362b0 100644
--- a/docs/_downloads/9da0471a9eeb2351a488cd4b44fc6bbf/reinforcement_q_learning.ipynb
+++ b/docs/_downloads/9da0471a9eeb2351a488cd4b44fc6bbf/reinforcement_q_learning.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uac15\ud654 \ud559\uc2b5 (DQN) \ud29c\ud1a0\ub9ac\uc5bc\n=====================================\n**Author**: `Adam Paszke <https://github.com/apaszke>`_\n  **\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 `OpenAI Gym <https://gym.openai.com/>`__ \uc758\nCartPole-v0 \ud0dc\uc2a4\ud06c\uc5d0\uc11c DQN (Deep Q Learning) \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\ud558\ub294\ub370\nPyTorch\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\ub4dc\ub9bd\ub2c8\ub2e4.\n\n**\ud0dc\uc2a4\ud06c**\n\n\uc5d0\uc774\uc804\ud2b8\ub294 \uc5f0\uacb0\ub41c \ub9c9\ub300\uac00 \ub611\ubc14\ub85c \uc11c \uc788\ub3c4\ub85d \uce74\ud2b8\ub97c \uc67c\ucabd\uc774\ub098 \uc624\ub978\ucabd\uc73c\ub85c\n\uc6c0\uc9c1\uc774\ub294 \ub450 \uac00\uc9c0 \ub3d9\uc791 \uc911 \ud558\ub098\ub97c \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4.\n\ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uacfc \uc2dc\uac01\ud654 \uae30\ub2a5\uc744 \uac16\ucd98 \uacf5\uc2dd \uc21c\uc704\ud45c\ub97c\n`Gym \uc6f9\uc0ac\uc774\ud2b8 <https://gym.openai.com/envs/CartPole-v0>`__ \uc5d0\uc11c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n.. figure:: /_static/img/cartpole.gif\n   :alt: cartpole\n\n   cartpole\n\n\uc5d0\uc774\uc804\ud2b8\uac00 \ud604\uc7ac \ud658\uacbd \uc0c1\ud0dc\ub97c \uad00\ucc30\ud558\uace0 \ud589\ub3d9\uc744 \uc120\ud0dd\ud558\uba74,\n\ud658\uacbd\uc774 \uc0c8\ub85c\uc6b4 \uc0c1\ud0dc\ub85c *\uc804\ud658* \ub418\uace0 \uc791\uc5c5\uc758 \uacb0\uacfc\ub97c \ub098\ud0c0\ub0b4\ub294 \ubcf4\uc0c1\ub3c4 \ubc18\ud658\ub429\ub2c8\ub2e4.\n\uc774 \ud0dc\uc2a4\ud06c\uc5d0\uc11c \ub9e4 \ud0c0\uc784\uc2a4\ud15d \uc99d\uac00\ub9c8\ub2e4 \ubcf4\uc0c1\uc774 +1\uc774 \ub418\uace0, \ub9cc\uc57d \ub9c9\ub300\uac00 \ub108\ubb34 \uba40\ub9ac\n\ub5a8\uc5b4\uc9c0\uac70\ub098 \uce74\ud2b8\uac00 \uc911\uc2ec\uc5d0\uc11c 2.4 \uc720\ub2db \uc774\uc0c1 \uba40\uc5b4\uc9c0\uba74 \ud658\uacbd\uc774 \uc911\ub2e8\ub429\ub2c8\ub2e4.\n\uc774\uac83\uc740 \ub354 \uc88b\uc740 \uc2dc\ub098\ub9ac\uc624\uac00 \ub354 \uc624\ub7ab\ub3d9\uc548 \ub354 \ub9ce\uc740 \ubcf4\uc0c1\uc744 \ucd95\uc801\ud558\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.\n\n\uce74\ud2b8\ud3f4 \ud0dc\uc2a4\ud06c\ub294 \uc5d0\uc774\uc804\ud2b8\uc5d0 \ub300\ud55c \uc785\ub825\uc774 \ud658\uacbd \uc0c1\ud0dc(\uc704\uce58, \uc18d\ub3c4 \ub4f1)\ub97c \ub098\ud0c0\ub0b4\ub294\n4\uac1c\uc758 \uc2e4\uc81c \uac12\uc774 \ub418\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc2e0\uacbd\ub9dd\uc740 \uc21c\uc218\ud558\uac8c \uadf8 \uc7a5\uba74\uc744 \ubcf4\uace0\n\ud0dc\uc2a4\ud06c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4 \ub530\ub77c\uc11c \uce74\ud2b8 \uc911\uc2ec\uc758 \ud654\uba74 \ud328\uce58\ub97c \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\uc774 \ub54c\ubb38\uc5d0 \uc6b0\ub9ac\uc758 \uacb0\uacfc\ub294 \uacf5\uc2dd \uc21c\uc704\ud45c\uc758 \uacb0\uacfc\uc640 \uc9c1\uc811\uc801\uc73c\ub85c \ube44\uad50\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.\n\uc6b0\ub9ac\uc758 \ud0dc\uc2a4\ud06c\ub294 \ud6e8\uc52c \ub354 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.\n\ubd88\ud589\ud788\ub3c4 \ubaa8\ub4e0 \ud504\ub808\uc784\uc744 \ub80c\ub354\ub9c1\ud574\uc57c\ub418\ubbc0\ub85c \uc774\uac83\uc740 \ud559\uc2b5 \uc18d\ub3c4\ub97c \ub2a6\ucd94\uac8c\ub429\ub2c8\ub2e4.\n\n\uc5c4\ubc00\ud788 \ub9d0\ud558\uba74, \ud604\uc7ac \uc2a4\ud06c\ub9b0 \ud328\uce58\uc640 \uc774\uc804 \uc2a4\ud06c\ub9b0 \ud328\uce58 \uc0ac\uc774\uc758 \ucc28\uc774\ub85c \uc0c1\ud0dc\ub97c \ud45c\uc2dc\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774\ub807\uac8c\ud558\uba74 \uc5d0\uc774\uc804\ud2b8\uac00 \ub9c9\ub300\uc758 \uc18d\ub3c4\ub97c \ud55c \uc774\ubbf8\uc9c0\uc5d0\uc11c \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n**\ud328\ud0a4\uc9c0**\n\n\uba3c\uc800 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uac00\uc838\uc635\ub2c8\ub2e4. \uccab\uc9f8, \ud658\uacbd\uc744 \uc704\ud574\n`gym <https://gym.openai.com/docs>`__ \uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\n(`pip install gym` \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud558\uc2ed\uc2dc\uc624).\n\ub610\ud55c PyTorch\uc5d0\uc11c \ub2e4\uc74c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4:\n\n-  \uc2e0\uacbd\ub9dd (``torch.nn``)\n-  \ucd5c\uc801\ud654 (``torch.optim``)\n-  \uc790\ub3d9 \ubbf8\ubd84 (``torch.autograd``)\n-  \uc2dc\uac01 \ud0dc\uc2a4\ud06c\ub97c \uc704\ud55c \uc720\ud2f8\ub9ac\ud2f0\ub4e4 (``torchvision`` - `a separate\n   package <https://github.com/pytorch/vision>`__).\n\n\n"
+        "\n\uac15\ud654 \ud559\uc2b5 (DQN) \ud29c\ud1a0\ub9ac\uc5bc\n=====================================\n**Author**: `Adam Paszke <https://github.com/apaszke>`_\n  **\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 `OpenAI Gym <https://gym.openai.com/>`__ \uc758\nCartPole-v0 \ud0dc\uc2a4\ud06c\uc5d0\uc11c DQN (Deep Q Learning) \uc5d0\uc774\uc804\ud2b8\ub97c \ud559\uc2b5\ud558\ub294\ub370\nPyTorch\ub97c \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\ub4dc\ub9bd\ub2c8\ub2e4.\n\n**\ud0dc\uc2a4\ud06c**\n\n\uc5d0\uc774\uc804\ud2b8\ub294 \uc5f0\uacb0\ub41c \ub9c9\ub300\uac00 \ub611\ubc14\ub85c \uc11c \uc788\ub3c4\ub85d \uce74\ud2b8\ub97c \uc67c\ucabd\uc774\ub098 \uc624\ub978\ucabd\uc73c\ub85c\n\uc6c0\uc9c1\uc774\ub294 \ub450 \uac00\uc9c0 \ub3d9\uc791 \uc911 \ud558\ub098\ub97c \uc120\ud0dd\ud574\uc57c \ud569\ub2c8\ub2e4.\n\ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uacfc \uc2dc\uac01\ud654 \uae30\ub2a5\uc744 \uac16\ucd98 \uacf5\uc2dd \uc21c\uc704\ud45c\ub97c\n`Gym \uc6f9\uc0ac\uc774\ud2b8 <https://gym.openai.com/envs/CartPole-v0>`__ \uc5d0\uc11c \ucc3e\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n.. figure:: /_static/img/cartpole.gif\n   :alt: cartpole\n\n   cartpole\n\n\uc5d0\uc774\uc804\ud2b8\uac00 \ud604\uc7ac \ud658\uacbd \uc0c1\ud0dc\ub97c \uad00\ucc30\ud558\uace0 \ud589\ub3d9\uc744 \uc120\ud0dd\ud558\uba74,\n\ud658\uacbd\uc774 \uc0c8\ub85c\uc6b4 \uc0c1\ud0dc\ub85c *\uc804\ud658* \ub418\uace0 \uc791\uc5c5\uc758 \uacb0\uacfc\ub97c \ub098\ud0c0\ub0b4\ub294 \ubcf4\uc0c1\ub3c4 \ubc18\ud658\ub429\ub2c8\ub2e4.\n\uc774 \ud0dc\uc2a4\ud06c\uc5d0\uc11c \ub9e4 \ud0c0\uc784\uc2a4\ud15d \uc99d\uac00\ub9c8\ub2e4 \ubcf4\uc0c1\uc774 +1\uc774 \ub418\uace0, \ub9cc\uc57d \ub9c9\ub300\uac00 \ub108\ubb34 \uba40\ub9ac\n\ub5a8\uc5b4\uc9c0\uac70\ub098 \uce74\ud2b8\uac00 \uc911\uc2ec\uc5d0\uc11c 2.4 \uc720\ub2db \uc774\uc0c1 \uba40\uc5b4\uc9c0\uba74 \ud658\uacbd\uc774 \uc911\ub2e8\ub429\ub2c8\ub2e4.\n\uc774\uac83\uc740 \ub354 \uc88b\uc740 \uc2dc\ub098\ub9ac\uc624\uac00 \ub354 \uc624\ub7ab\ub3d9\uc548 \ub354 \ub9ce\uc740 \ubcf4\uc0c1\uc744 \ucd95\uc801\ud558\ub294 \uac83\uc744 \uc758\ubbf8\ud569\ub2c8\ub2e4.\n\n\uce74\ud2b8\ud3f4 \ud0dc\uc2a4\ud06c\ub294 \uc5d0\uc774\uc804\ud2b8\uc5d0 \ub300\ud55c \uc785\ub825\uc774 \ud658\uacbd \uc0c1\ud0dc(\uc704\uce58, \uc18d\ub3c4 \ub4f1)\ub97c \ub098\ud0c0\ub0b4\ub294\n4\uac1c\uc758 \uc2e4\uc81c \uac12\uc774 \ub418\ub3c4\ub85d \uc124\uacc4\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8\ub7ec\ub098 \uc2e0\uacbd\ub9dd\uc740 \uc21c\uc218\ud558\uac8c \uadf8 \uc7a5\uba74\uc744 \ubcf4\uace0\n\ud0dc\uc2a4\ud06c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4 \ub530\ub77c\uc11c \uce74\ud2b8 \uc911\uc2ec\uc758 \ud654\uba74 \ud328\uce58\ub97c \uc785\ub825\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\uc774 \ub54c\ubb38\uc5d0 \uc6b0\ub9ac\uc758 \uacb0\uacfc\ub294 \uacf5\uc2dd \uc21c\uc704\ud45c\uc758 \uacb0\uacfc\uc640 \uc9c1\uc811\uc801\uc73c\ub85c \ube44\uad50\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4.\n\uc6b0\ub9ac\uc758 \ud0dc\uc2a4\ud06c\ub294 \ud6e8\uc52c \ub354 \uc5b4\ub835\uc2b5\ub2c8\ub2e4.\n\ubd88\ud589\ud788\ub3c4 \ubaa8\ub4e0 \ud504\ub808\uc784\uc744 \ub80c\ub354\ub9c1\ud574\uc57c\ub418\ubbc0\ub85c \uc774\uac83\uc740 \ud559\uc2b5 \uc18d\ub3c4\ub97c \ub2a6\ucd94\uac8c\ub429\ub2c8\ub2e4.\n\n\uc5c4\ubc00\ud788 \ub9d0\ud558\uba74, \ud604\uc7ac \uc2a4\ud06c\ub9b0 \ud328\uce58\uc640 \uc774\uc804 \uc2a4\ud06c\ub9b0 \ud328\uce58 \uc0ac\uc774\uc758 \ucc28\uc774\ub85c \uc0c1\ud0dc\ub97c \ud45c\uc2dc\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774\ub807\uac8c\ud558\uba74 \uc5d0\uc774\uc804\ud2b8\uac00 \ub9c9\ub300\uc758 \uc18d\ub3c4\ub97c \ud55c \uc774\ubbf8\uc9c0\uc5d0\uc11c \uace0\ub824\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n**\ud328\ud0a4\uc9c0**\n\n\uba3c\uc800 \ud544\uc694\ud55c \ud328\ud0a4\uc9c0\ub97c \uac00\uc838\uc635\ub2c8\ub2e4. \uccab\uc9f8, \ud658\uacbd\uc744 \uc704\ud574\n`gym <https://gym.openai.com/docs>`__ \uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\n(`pip install gym` \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc124\uce58\ud558\uc2ed\uc2dc\uc624).\n\ub610\ud55c PyTorch\uc5d0\uc11c \ub2e4\uc74c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4:\n\n-  \uc2e0\uacbd\ub9dd (``torch.nn``)\n-  \ucd5c\uc801\ud654 (``torch.optim``)\n-  \uc790\ub3d9 \ubbf8\ubd84 (``torch.autograd``)\n-  \uc2dc\uac01 \ud0dc\uc2a4\ud06c\ub97c \uc704\ud55c \uc720\ud2f8\ub9ac\ud2f0\ub4e4 (``torchvision`` - `a separate\n   package <https://github.com/pytorch/vision>`__).\n"
       ]
     },
     {
@@ -161,7 +161,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/9dfafb625d821ccfaa723af12eed7ce4/nnft_tutorial.ipynb b/docs/_downloads/9dfafb625d821ccfaa723af12eed7ce4/nnft_tutorial.ipynb
index b5703bfb0..1761dcb29 100644
--- a/docs/_downloads/9dfafb625d821ccfaa723af12eed7ce4/nnft_tutorial.ipynb
+++ b/docs/_downloads/9dfafb625d821ccfaa723af12eed7ce4/nnft_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nnn \ud328\ud0a4\uc9c0\n==========\n\nautograd\uc5d0 \uc644\ubcbd\ud788 \ud1b5\ud569\ub418\uac8c \ud558\uae30 \uc704\ud574 nn \ud328\ud0a4\uc9c0\ub97c \uc7ac\uc124\uacc4\ud558\uc600\uc2b5\ub2c8\ub2e4.\n\uc774\uc81c \ubb34\uc5c7\uc774 \ubcc0\uacbd\ub418\uc5c8\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n**\ucee8\ud14c\uc774\ub108\ub97c autograd\ub85c \uad50\uccb4:**\n\n    \uc774\uc81c \ub354\uc774\uc0c1 ``ConcatTable`` \uac19\uc740 \ucee8\ud14c\uc774\ub108\ub098 ``CAddTable`` \uac19\uc740 \ubaa8\ub4c8, \ub610\ub294\n    nngraph\ub97c \uc774\uc6a9\ud558\uac70\ub098 \ub514\ubc84\uae45\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \ub300\uc2e0 autograd\ub97c \uc774\uc6a9\ud558\uc5ec \ub354 \uae54\ub054\ud558\uac8c\n    \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4,\n\n    * ``output = nn.CAddTable():forward({input1, input2})`` \ub300\uc2e0,\n      ``output = input1 + input2`` \ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n    * ``output = nn.MulConstant(0.5):forward(input)`` \ub300\uc2e0,\n      ``output = input * 0.5`` \ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n**\uc0c1\ud0dc(state)\ub294 \ubaa8\ub4c8 \ub0b4\uc5d0 \uc800\uc7a5\ub418\uc9c0 \uc54a\uace0, \uc2e0\uacbd\ub9dd \uadf8\ub798\ud504 \uc0c1\uc5d0 \uc874\uc7ac\ud569\ub2c8\ub2e4:**\n\n    \ub355\ubd84\uc5d0 \uc21c\ud658\uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc774 \ub354 \uac04\ub2e8\ud574\uc84c\uc2b5\ub2c8\ub2e4. \uc774\uc81c \uc21c\ud658\uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4e4\n    \ub54c, \ub354 \uc774\uc0c1 \uac00\uc911\uce58(weight)\ub97c \uacf5\uc720\ud558\ub294 \uac83\uc5d0 \ub300\ud574\uc11c\ub294 \uc0dd\uac01\ud560 \ud544\uc694\uc5c6\uc774 \ub3d9\uc77c\ud55c\n    Linear \uacc4\uce35\uc744 \uc5ec\ub7ec \ucc28\ub840 \ud638\ucd9c\ud558\uba74 \ub429\ub2c8\ub2e4.\n\n    .. figure:: /_static/img/torch-nn-vs-pytorch-nn.png\n       :alt: torch-nn-vs-pytorch-nn\n\n       torch-nn-vs-pytorch-nn\n\n**\uac04\uc18c\ud654\ub41c \ub514\ubc84\uae45:**\n\n    \ub514\ubc84\uae45\uc740 Python\uc758 pdb \ub514\ubc84\uac70\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc9c1\uad00\uc801\uc774\uba70,\n    **\ub514\ubc84\uac70\uc640 \uc2a4\ud0dd \ucd94\uc801(stack trace)\uc740 \uc5d0\ub7ec\uac00 \ubc1c\uc0dd\ud55c \uacf3\uc5d0\uc11c \uc815\ud655\ud788 \uba48\ucda5\ub2c8\ub2e4.**\n    \uc774\uc81c \ubcf4\uc774\ub294\ub300\ub85c \uc5bb\uc744 \uac83\uc785\ub2c8\ub2e4. (What you see is what you get.)\n\n\uc608\uc81c1: \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(ConvNet)\n-----------------------------\n\n\uc774\uc81c \uc5b4\ub5bb\uac8c \uc791\uc740 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4dc\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\ubaa8\ub4e0 \uc2e0\uacbd\ub9dd\uc740 \uae30\ubcf8 \ud074\ub798\uc2a4\uc778 ``nn.Module`` \ub85c\ubd80\ud130 \ud30c\uc0dd\ub429\ub2c8\ub2e4:\n\n-  \uc0dd\uc131\uc790\uc5d0\uc11c\ub294 \uc0ac\uc6a9\ud560 \ubaa8\ub4e0 \uacc4\uce35\uc744 \uc120\uc5b8\ud569\ub2c8\ub2e4.\n-  \uc21c\uc804\ud30c \ud568\uc218\uc5d0\uc11c\ub294 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc774 \uc785\ub825\uc5d0\uc11c \ucd9c\ub825\uae4c\uc9c0 \uc5b4\ub5bb\uac8c \uc2e4\ud589\ub418\ub294\uc9c0\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.\n\n"
+        "\nnn \ud328\ud0a4\uc9c0\n==========\n\nautograd\uc5d0 \uc644\ubcbd\ud788 \ud1b5\ud569\ub418\uac8c \ud558\uae30 \uc704\ud574 nn \ud328\ud0a4\uc9c0\ub97c \uc7ac\uc124\uacc4\ud558\uc600\uc2b5\ub2c8\ub2e4.\n\uc774\uc81c \ubb34\uc5c7\uc774 \ubcc0\uacbd\ub418\uc5c8\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n**\ucee8\ud14c\uc774\ub108\ub97c autograd\ub85c \uad50\uccb4:**\n\n    \uc774\uc81c \ub354\uc774\uc0c1 ``ConcatTable`` \uac19\uc740 \ucee8\ud14c\uc774\ub108\ub098 ``CAddTable`` \uac19\uc740 \ubaa8\ub4c8, \ub610\ub294\n    nngraph\ub97c \uc774\uc6a9\ud558\uac70\ub098 \ub514\ubc84\uae45\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4. \ub300\uc2e0 autograd\ub97c \uc774\uc6a9\ud558\uc5ec \ub354 \uae54\ub054\ud558\uac8c\n    \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4,\n\n    * ``output = nn.CAddTable():forward({input1, input2})`` \ub300\uc2e0,\n      ``output = input1 + input2`` \ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n    * ``output = nn.MulConstant(0.5):forward(input)`` \ub300\uc2e0,\n      ``output = input * 0.5`` \ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n**\uc0c1\ud0dc(state)\ub294 \ubaa8\ub4c8 \ub0b4\uc5d0 \uc800\uc7a5\ub418\uc9c0 \uc54a\uace0, \uc2e0\uacbd\ub9dd \uadf8\ub798\ud504 \uc0c1\uc5d0 \uc874\uc7ac\ud569\ub2c8\ub2e4:**\n\n    \ub355\ubd84\uc5d0 \uc21c\ud658\uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc774 \ub354 \uac04\ub2e8\ud574\uc84c\uc2b5\ub2c8\ub2e4. \uc774\uc81c \uc21c\ud658\uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4e4\n    \ub54c, \ub354 \uc774\uc0c1 \uac00\uc911\uce58(weight)\ub97c \uacf5\uc720\ud558\ub294 \uac83\uc5d0 \ub300\ud574\uc11c\ub294 \uc0dd\uac01\ud560 \ud544\uc694\uc5c6\uc774 \ub3d9\uc77c\ud55c\n    Linear \uacc4\uce35\uc744 \uc5ec\ub7ec \ucc28\ub840 \ud638\ucd9c\ud558\uba74 \ub429\ub2c8\ub2e4.\n\n    .. figure:: /_static/img/torch-nn-vs-pytorch-nn.png\n       :alt: torch-nn-vs-pytorch-nn\n\n       torch-nn-vs-pytorch-nn\n\n**\uac04\uc18c\ud654\ub41c \ub514\ubc84\uae45:**\n\n    \ub514\ubc84\uae45\uc740 Python\uc758 pdb \ub514\ubc84\uac70\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc9c1\uad00\uc801\uc774\uba70,\n    **\ub514\ubc84\uac70\uc640 \uc2a4\ud0dd \ucd94\uc801(stack trace)\uc740 \uc5d0\ub7ec\uac00 \ubc1c\uc0dd\ud55c \uacf3\uc5d0\uc11c \uc815\ud655\ud788 \uba48\ucda5\ub2c8\ub2e4.**\n    \uc774\uc81c \ubcf4\uc774\ub294\ub300\ub85c \uc5bb\uc744 \uac83\uc785\ub2c8\ub2e4. (What you see is what you get.)\n\n\uc608\uc81c1: \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd(ConvNet)\n-----------------------------\n\n\uc774\uc81c \uc5b4\ub5bb\uac8c \uc791\uc740 \ud569\uc131\uacf1 \uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4dc\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\ubaa8\ub4e0 \uc2e0\uacbd\ub9dd\uc740 \uae30\ubcf8 \ud074\ub798\uc2a4\uc778 ``nn.Module`` \ub85c\ubd80\ud130 \ud30c\uc0dd\ub429\ub2c8\ub2e4:\n\n-  \uc0dd\uc131\uc790\uc5d0\uc11c\ub294 \uc0ac\uc6a9\ud560 \ubaa8\ub4e0 \uacc4\uce35\uc744 \uc120\uc5b8\ud569\ub2c8\ub2e4.\n-  \uc21c\uc804\ud30c \ud568\uc218\uc5d0\uc11c\ub294 \uc2e0\uacbd\ub9dd \ubaa8\ub378\uc774 \uc785\ub825\uc5d0\uc11c \ucd9c\ub825\uae4c\uc9c0 \uc5b4\ub5bb\uac8c \uc2e4\ud589\ub418\ub294\uc9c0\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -201,7 +201,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/a1687058579b7f1d970855ee56beb272/autograd_tutorial_old.ipynb b/docs/_downloads/a1687058579b7f1d970855ee56beb272/autograd_tutorial_old.ipynb
index f6b6945d6..1083c2376 100644
--- a/docs/_downloads/a1687058579b7f1d970855ee56beb272/autograd_tutorial_old.ipynb
+++ b/docs/_downloads/a1687058579b7f1d970855ee56beb272/autograd_tutorial_old.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nAutograd\n========\n\nAutograd\ub294 \uc790\ub3d9 \ubbf8\ubd84\uc744 \uc218\ud589\ud558\ub294 torch\uc758 \ud575\uc2ec \ud328\ud0a4\uc9c0\ub85c, \uc790\ub3d9 \ubbf8\ubd84\uc744 \uc704\ud574\n\ud14c\uc78e(tape) \uae30\ubc18 \uc2dc\uc2a4\ud15c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n\uc21c\uc804\ud30c(forward) \ub2e8\uacc4\uc5d0\uc11c autograd \ud14c\uc78e\uc740 \uc218\ud589\ud558\ub294 \ubaa8\ub4e0 \uc5f0\uc0b0\uc744 \uae30\uc5b5\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0, \uc5ed\uc804\ud30c(backward) \ub2e8\uacc4\uc5d0\uc11c \uc5f0\uc0b0\ub4e4\uc744 \uc7ac\ud604(replay)\ud569\ub2c8\ub2e4.\n\n\uc5f0\uc0b0 \uae30\ub85d\uc744 \ucd94\uc801\ud558\ub294 Tensor\n----------------------------\n\nAutograd\uc5d0\uc11c ``requires_grad=True`` \ub85c \uc124\uc815\ub41c \uc785\ub825 ``Tensor`` \uc758 \uc5f0\uc0b0\uc740\n\uae30\ub85d\ub429\ub2c8\ub2e4. \uc5ed\uc804\ud30c \ub2e8\uacc4 \uc5f0\uc0b0 \ud6c4\uc5d0, \uc774 Tensor\uc5d0 \ub300\ud55c \ubcc0\ud654\ub3c4(grdient)\ub294 ``.grad``\n\uc18d\uc131\uc5d0 \ub204\uc801\ub429\ub2c8\ub2e4.\n\nAutograd \uad6c\ud604\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud55c \ud074\ub798\uc2a4\uac00 \ud558\ub098 \ub354 \uc788\ub294\ub370, \uc774\uac83\uc740 \ubc14\ub85c ``Function``\n\ud074\ub798\uc2a4\uc785\ub2c8\ub2e4. ``Tensor`` \uc640 ``Function`` \uc740 \uc11c\ub85c \uc5f0\uacb0\ub418\uc5b4 \uc788\uc73c\uba70, \ubaa8\ub4e0 \uc5f0\uc0b0 \uacfc\uc815\uc744\n\ubd80\ud638\ud654(encode)\ud558\uc5ec \uc21c\ud658\ud558\uc9c0 \uc54a\ub294 \uadf8\ub798\ud504(acyclic graph)\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uac01 \ubcc0\uc218\ub294\n``.grad_fn`` \uc18d\uc131\uc744 \uac16\uace0 \uc788\ub294\ub370, \uc774\ub294 ``Tensor`` \ub97c \uc0dd\uc131\ud55c ``Function`` \uc744\n\ucc38\uc870\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\ub2e8, \uc0ac\uc6a9\uc790\uac00 \ub9cc\ub4e0 Tensor\ub294 \uc608\uc678\ub85c, \uc774 \ub54c ``grad_fn`` \uc740\n``None`` \uc785\ub2c8\ub2e4.)\n\n\ub3c4\ud568\uc218\ub97c \uacc4\uc0b0\ud558\uae30 \uc704\ud574\uc11c\ub294 ``Tensor`` \uc758 ``.backward()`` \ub97c \ud638\ucd9c\ud558\uba74 \ub429\ub2c8\ub2e4.\n``Tensor`` \uac00 \uc2a4\uce7c\ub77c(scalar)\uc778 \uacbd\uc6b0(\uc608. \ud558\ub098\uc758 \uc694\uc18c\ub9cc \uac16\ub294 \ub4f1)\uc5d0\ub294, ``backward`` \uc5d0\n\uc778\uc790\ub97c \uc815\ud574\uc904 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc5ec\ub7ec \uac1c\uc758 \uc694\uc18c\ub97c \uac16\uace0 \uc788\uc744\n\ub54c\ub294 tensor\uc758 \ubaa8\uc591\uc744 ``gradient`` \uc758 \uc778\uc790\ub85c \uc9c0\uc815\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\nAutograd\n========\n\nAutograd\ub294 \uc790\ub3d9 \ubbf8\ubd84\uc744 \uc218\ud589\ud558\ub294 torch\uc758 \ud575\uc2ec \ud328\ud0a4\uc9c0\ub85c, \uc790\ub3d9 \ubbf8\ubd84\uc744 \uc704\ud574\n\ud14c\uc78e(tape) \uae30\ubc18 \uc2dc\uc2a4\ud15c\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n\uc21c\uc804\ud30c(forward) \ub2e8\uacc4\uc5d0\uc11c autograd \ud14c\uc78e\uc740 \uc218\ud589\ud558\ub294 \ubaa8\ub4e0 \uc5f0\uc0b0\uc744 \uae30\uc5b5\ud569\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0, \uc5ed\uc804\ud30c(backward) \ub2e8\uacc4\uc5d0\uc11c \uc5f0\uc0b0\ub4e4\uc744 \uc7ac\ud604(replay)\ud569\ub2c8\ub2e4.\n\n\uc5f0\uc0b0 \uae30\ub85d\uc744 \ucd94\uc801\ud558\ub294 Tensor\n----------------------------\n\nAutograd\uc5d0\uc11c ``requires_grad=True`` \ub85c \uc124\uc815\ub41c \uc785\ub825 ``Tensor`` \uc758 \uc5f0\uc0b0\uc740\n\uae30\ub85d\ub429\ub2c8\ub2e4. \uc5ed\uc804\ud30c \ub2e8\uacc4 \uc5f0\uc0b0 \ud6c4\uc5d0, \uc774 Tensor\uc5d0 \ub300\ud55c \ubcc0\ud654\ub3c4(grdient)\ub294 ``.grad``\n\uc18d\uc131\uc5d0 \ub204\uc801\ub429\ub2c8\ub2e4.\n\nAutograd \uad6c\ud604\uc5d0\uc11c \ub9e4\uc6b0 \uc911\uc694\ud55c \ud074\ub798\uc2a4\uac00 \ud558\ub098 \ub354 \uc788\ub294\ub370, \uc774\uac83\uc740 \ubc14\ub85c ``Function``\n\ud074\ub798\uc2a4\uc785\ub2c8\ub2e4. ``Tensor`` \uc640 ``Function`` \uc740 \uc11c\ub85c \uc5f0\uacb0\ub418\uc5b4 \uc788\uc73c\uba70, \ubaa8\ub4e0 \uc5f0\uc0b0 \uacfc\uc815\uc744\n\ubd80\ud638\ud654(encode)\ud558\uc5ec \uc21c\ud658\ud558\uc9c0 \uc54a\ub294 \uadf8\ub798\ud504(acyclic graph)\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4. \uac01 \ubcc0\uc218\ub294\n``.grad_fn`` \uc18d\uc131\uc744 \uac16\uace0 \uc788\ub294\ub370, \uc774\ub294 ``Tensor`` \ub97c \uc0dd\uc131\ud55c ``Function`` \uc744\n\ucc38\uc870\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. (\ub2e8, \uc0ac\uc6a9\uc790\uac00 \ub9cc\ub4e0 Tensor\ub294 \uc608\uc678\ub85c, \uc774 \ub54c ``grad_fn`` \uc740\n``None`` \uc785\ub2c8\ub2e4.)\n\n\ub3c4\ud568\uc218\ub97c \uacc4\uc0b0\ud558\uae30 \uc704\ud574\uc11c\ub294 ``Tensor`` \uc758 ``.backward()`` \ub97c \ud638\ucd9c\ud558\uba74 \ub429\ub2c8\ub2e4.\n``Tensor`` \uac00 \uc2a4\uce7c\ub77c(scalar)\uc778 \uacbd\uc6b0(\uc608. \ud558\ub098\uc758 \uc694\uc18c\ub9cc \uac16\ub294 \ub4f1)\uc5d0\ub294, ``backward`` \uc5d0\n\uc778\uc790\ub97c \uc815\ud574\uc904 \ud544\uc694\uac00 \uc5c6\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc5ec\ub7ec \uac1c\uc758 \uc694\uc18c\ub97c \uac16\uace0 \uc788\uc744\n\ub54c\ub294 tensor\uc758 \ubaa8\uc591\uc744 ``gradient`` \uc758 \uc778\uc790\ub85c \uc9c0\uc815\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -252,7 +252,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/a4f07fecba75b5e84fe9e56cac0c7b71/spatial_transformer_tutorial.py b/docs/_downloads/a4f07fecba75b5e84fe9e56cac0c7b71/spatial_transformer_tutorial.py
index 9fa3317aa..f17bd6b61 100644
--- a/docs/_downloads/a4f07fecba75b5e84fe9e56cac0c7b71/spatial_transformer_tutorial.py
+++ b/docs/_downloads/a4f07fecba75b5e84fe9e56cac0c7b71/spatial_transformer_tutorial.py
@@ -1,31 +1,27 @@
 # -*- coding: utf-8 -*-
 """
-Spatial Transformer Networks Tutorial
+공간 변형 네트워크(Spatial Transformer Networks) 튜토리얼
 =====================================
-**Author**: `Ghassen HAMROUNI <https://github.com/GHamrouni>`_
-
+**저자**: `Ghassen HAMROUNI <https://github.com/GHamrouni>`_  
+**번역**: `황성수 <https://github.com/adonisues>`_ , `정신유 <https://github.com/SSinyu>`_
 .. figure:: /_static/img/stn/FSeq.png
-
-In this tutorial, you will learn how to augment your network using
-a visual attention mechanism called spatial transformer
-networks. You can read more about the spatial transformer
-networks in the `DeepMind paper <https://arxiv.org/abs/1506.02025>`__
-
-Spatial transformer networks are a generalization of differentiable
-attention to any spatial transformation. Spatial transformer networks
-(STN for short) allow a neural network to learn how to perform spatial
-transformations on the input image in order to enhance the geometric
-invariance of the model.
-For example, it can crop a region of interest, scale and correct
-the orientation of an image. It can be a useful mechanism because CNNs
-are not invariant to rotation and scale and more general affine
-transformations.
-
-One of the best things about STN is the ability to simply plug it into
-any existing CNN with very little modification.
+이 튜토리얼에서는 공간 변형 네트워크(spatial transformer networks, 이하 STN)이라 
+불리는 비주얼 어텐션 메커니즘을 이용해 신경망을 증강(augment)시키는 방법에 대해 
+학습합니다. 이 방법에 대한 자세한 내용은 `DeepMind paper <https://arxiv.org/abs/1506.02025>`__ 에서
+확인할 수 있습니다.
+STN은 어떠한 공간적 변형(spatial transformation)에도 적용할 수 있는 미분 가능한 
+어텐션의 일반화입니다. 따라서 STN은 신경망의 기하학적 불변성(geometric invariance)을
+강화하기 위해 입력 이미지를 대상으로 어떠한 공간적 변형을 수행해야 하는지 학습하도록
+합니다.
+예를 들어 이미지의 관심 영역을 잘라내거나, 크기를 조정하거나, 방향(orientation)을 
+수정할 수 있습니다. CNN은 이러한 회전, 크기 조정 등의 일반적인 아핀(affine) 변환된 
+입력에 대해 결과의 변동이 크기 때문에 (민감하기 때문에), STN은 이를 극복하는데 매우
+유용한 메커니즘이 될 수 있습니다.
+STN이 가진 장점 중 하나는 아주 작은 수정만으로 기존에 사용하던 CNN에 간단하게 연결 시킬 
+수 있다는 것입니다.
 """
-# License: BSD
-# Author: Ghassen Hamrouni
+# 라이센스: BSD
+# 저자: Ghassen Hamrouni
 
 from __future__ import print_function
 import torch
@@ -37,26 +33,25 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-plt.ion()   # interactive mode
+plt.ion()   # 대화형 모드
 
 ######################################################################
-# Loading the data
+# 데이터 불러오기
 # ----------------
 #
-# In this post we experiment with the classic MNIST dataset. Using a
-# standard convolutional network augmented with a spatial transformer
-# network.
+# 이 튜토리얼에서는 MNIST 데이터셋을 이용해 실험합니다. 실험에는 STN으로 
+# 증강된 일반적인 CNN을 사용합니다.
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# Training dataset
+# 학습용 데이터셋
 train_loader = torch.utils.data.DataLoader(
     datasets.MNIST(root='.', train=True, download=True,
                    transform=transforms.Compose([
                        transforms.ToTensor(),
                        transforms.Normalize((0.1307,), (0.3081,))
                    ])), batch_size=64, shuffle=True, num_workers=4)
-# Test dataset
+# 테스트용 데이터셋
 test_loader = torch.utils.data.DataLoader(
     datasets.MNIST(root='.', train=False, transform=transforms.Compose([
         transforms.ToTensor(),
@@ -64,25 +59,22 @@
     ])), batch_size=64, shuffle=True, num_workers=4)
 
 ######################################################################
-# Depicting spatial transformer networks
+# Spatial Transformer Networks(STN) 구성하기
 # --------------------------------------
 #
-# Spatial transformer networks boils down to three main components :
+# STN은 다음의 세 가지 주요 구성 요소로 요약됩니다.
 #
-# -  The localization network is a regular CNN which regresses the
-#    transformation parameters. The transformation is never learned
-#    explicitly from this dataset, instead the network learns automatically
-#    the spatial transformations that enhances the global accuracy.
-# -  The grid generator generates a grid of coordinates in the input
-#    image corresponding to each pixel from the output image.
-# -  The sampler uses the parameters of the transformation and applies
-#    it to the input image.
+# -  위치 결정 네트워크(localization network)는 공간 변환 파라미터를 예측(regress)
+#    하는 일반적인 CNN 입니다. 공간 변환은 데이터 셋으로부터 명시적으로 학습되지 않고,
+#    신경망이 전체 정확도를 향상 시키도록 공간 변환을 자동으로 학습합니다.
+# -  그리드 생성기(grid generator)는 출력 이미지로부터 각 픽셀에 대응하는 입력
+#    이미지 내 좌표 그리드를 생성합니다.
+# -  샘플러(sampler)는 공간 변환 파라미터를 입력 이미지에 적용합니다.
 #
 # .. figure:: /_static/img/stn/stn-arch.png
 #
 # .. Note::
-#    We need the latest version of PyTorch that contains
-#    affine_grid and grid_sample modules.
+#    affine_grid 및 grid_sample 모듈이 포함된 최신 버전의 PyTorch가 필요합니다.
 #
 
 
@@ -95,7 +87,7 @@ def __init__(self):
         self.fc1 = nn.Linear(320, 50)
         self.fc2 = nn.Linear(50, 10)
 
-        # Spatial transformer localization-network
+        # 공간 변환을 위한 위치 결정 네트워크 (localization-network)
         self.localization = nn.Sequential(
             nn.Conv2d(1, 8, kernel_size=7),
             nn.MaxPool2d(2, stride=2),
@@ -105,18 +97,18 @@ def __init__(self):
             nn.ReLU(True)
         )
 
-        # Regressor for the 3 * 2 affine matrix
+        # [3 * 2] 크기의 아핀(affine) 행렬에 대해 예측
         self.fc_loc = nn.Sequential(
             nn.Linear(10 * 3 * 3, 32),
             nn.ReLU(True),
             nn.Linear(32, 3 * 2)
         )
 
-        # Initialize the weights/bias with identity transformation
+        # 항등 변환(identity transformation)으로 가중치/바이어스 초기화
         self.fc_loc[2].weight.data.zero_()
         self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
 
-    # Spatial transformer network forward function
+    # STN의 forward 함수
     def stn(self, x):
         xs = self.localization(x)
         xs = xs.view(-1, 10 * 3 * 3)
@@ -129,10 +121,10 @@ def stn(self, x):
         return x
 
     def forward(self, x):
-        # transform the input
+        # 입력을 변환
         x = self.stn(x)
 
-        # Perform the usual forward pass
+        # 일반적인 forward pass를 수행
         x = F.relu(F.max_pool2d(self.conv1(x), 2))
         x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
         x = x.view(-1, 320)
@@ -145,12 +137,12 @@ def forward(self, x):
 model = Net().to(device)
 
 ######################################################################
-# Training the model
+# 모델 학습하기
 # ------------------
 #
-# Now, let's use the SGD algorithm to train the model. The network is
-# learning the classification task in a supervised way. In the same time
-# the model is learning STN automatically in an end-to-end fashion.
+# 이제 SGD 알고리즘을 이용해 모델을 학습시켜 봅시다. 앞서 구성한 신경망은
+# 감독 학습 방식(supervised way)으로 분류 문제를 학습합니다. 또한 이 모델은
+# end-to-end 방식으로 STN을 자동으로 학습합니다. 
 
 
 optimizer = optim.SGD(model.parameters(), lr=0.01)
@@ -171,7 +163,7 @@ def train(epoch):
                 epoch, batch_idx * len(data), len(train_loader.dataset),
                 100. * batch_idx / len(train_loader), loss.item()))
 #
-# A simple test procedure to measure STN the performances on MNIST.
+# MNIST 데이터셋에서 STN의 성능을 측정하기 위한 간단한 테스트 절차
 #
 
 
@@ -184,9 +176,9 @@ def test():
             data, target = data.to(device), target.to(device)
             output = model(data)
 
-            # sum up batch loss
+            # 배치 손실 합하기
             test_loss += F.nll_loss(output, target, size_average=False).item()
-            # get the index of the max log-probability
+            # 로그-확률의 최대값에 해당하는 인덱스 가져오기
             pred = output.max(1, keepdim=True)[1]
             correct += pred.eq(target.view_as(pred)).sum().item()
 
@@ -196,14 +188,12 @@ def test():
                       100. * correct / len(test_loader.dataset)))
 
 ######################################################################
-# Visualizing the STN results
+# STN 결과 시각화하기
 # ---------------------------
 #
-# Now, we will inspect the results of our learned visual attention
-# mechanism.
+# 이제 학습한 비주얼 어텐션 메커니즘의 결과를 살펴보겠습니다.
 #
-# We define a small helper function in order to visualize the
-# transformations while training.
+# 학습하는 동안 변환된 결과를 시각화하기 위해 작은 도움(helper) 함수를 정의합니다.
 
 
 def convert_image_np(inp):
@@ -215,14 +205,13 @@ def convert_image_np(inp):
     inp = np.clip(inp, 0, 1)
     return inp
 
-# We want to visualize the output of the spatial transformers layer
-# after the training, we visualize a batch of input images and
-# the corresponding transformed batch using STN.
+# 학습 후 공간 변환 계층의 출력을 시각화하고, 입력 이미지 배치 데이터 및 
+# STN을 사용해 변환된 배치 데이터를 시각화 합니다.
 
 
 def visualize_stn():
     with torch.no_grad():
-        # Get a batch of training data
+        # 학습 데이터의 배치 가져오기
         data = next(iter(test_loader))[0].to(device)
 
         input_tensor = data.cpu()
@@ -234,7 +223,7 @@ def visualize_stn():
         out_grid = convert_image_np(
             torchvision.utils.make_grid(transformed_input_tensor))
 
-        # Plot the results side-by-side
+        # 결과를 나란히 표시하기
         f, axarr = plt.subplots(1, 2)
         axarr[0].imshow(in_grid)
         axarr[0].set_title('Dataset Images')
@@ -246,8 +235,8 @@ def visualize_stn():
     train(epoch)
     test()
 
-# Visualize the STN transformation on some input batch
+# 일부 입력 배치 데이터에서 STN 변환 결과를 시각화
 visualize_stn()
 
 plt.ioff()
-plt.show()
+plt.show()
\ No newline at end of file
diff --git a/docs/_downloads/a5513958454950ed22df8da4c47f6429/spatial_transformer_tutorial.ipynb b/docs/_downloads/a5513958454950ed22df8da4c47f6429/spatial_transformer_tutorial.ipynb
index 9a9b699cb..9ad8df15a 100644
--- a/docs/_downloads/a5513958454950ed22df8da4c47f6429/spatial_transformer_tutorial.ipynb
+++ b/docs/_downloads/a5513958454950ed22df8da4c47f6429/spatial_transformer_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nSpatial Transformer Networks Tutorial\n=====================================\n**Author**: `Ghassen HAMROUNI <https://github.com/GHamrouni>`_\n\n.. figure:: /_static/img/stn/FSeq.png\n\nIn this tutorial, you will learn how to augment your network using\na visual attention mechanism called spatial transformer\nnetworks. You can read more about the spatial transformer\nnetworks in the `DeepMind paper <https://arxiv.org/abs/1506.02025>`__\n\nSpatial transformer networks are a generalization of differentiable\nattention to any spatial transformation. Spatial transformer networks\n(STN for short) allow a neural network to learn how to perform spatial\ntransformations on the input image in order to enhance the geometric\ninvariance of the model.\nFor example, it can crop a region of interest, scale and correct\nthe orientation of an image. It can be a useful mechanism because CNNs\nare not invariant to rotation and scale and more general affine\ntransformations.\n\nOne of the best things about STN is the ability to simply plug it into\nany existing CNN with very little modification.\n\n"
+        "\n\uacf5\uac04 \ubcc0\ud615 \ub124\ud2b8\uc6cc\ud06c(Spatial Transformer Networks) \ud29c\ud1a0\ub9ac\uc5bc\n=====================================\n**\uc800\uc790**: `Ghassen HAMROUNI <https://github.com/GHamrouni>`_  \n**\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_ , `\uc815\uc2e0\uc720 <https://github.com/SSinyu>`_\n.. figure:: /_static/img/stn/FSeq.png\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uacf5\uac04 \ubcc0\ud615 \ub124\ud2b8\uc6cc\ud06c(spatial transformer networks, \uc774\ud558 STN)\uc774\ub77c \n\ubd88\ub9ac\ub294 \ube44\uc8fc\uc5bc \uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998\uc744 \uc774\uc6a9\ud574 \uc2e0\uacbd\ub9dd\uc744 \uc99d\uac15(augment)\uc2dc\ud0a4\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \n\ud559\uc2b5\ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc5d0 \ub300\ud55c \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 `DeepMind paper <https://arxiv.org/abs/1506.02025>`__ \uc5d0\uc11c\n\ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\nSTN\uc740 \uc5b4\ub5a0\ud55c \uacf5\uac04\uc801 \ubcc0\ud615(spatial transformation)\uc5d0\ub3c4 \uc801\uc6a9\ud560 \uc218 \uc788\ub294 \ubbf8\ubd84 \uac00\ub2a5\ud55c \n\uc5b4\ud150\uc158\uc758 \uc77c\ubc18\ud654\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c STN\uc740 \uc2e0\uacbd\ub9dd\uc758 \uae30\ud558\ud559\uc801 \ubd88\ubcc0\uc131(geometric invariance)\uc744\n\uac15\ud654\ud558\uae30 \uc704\ud574 \uc785\ub825 \uc774\ubbf8\uc9c0\ub97c \ub300\uc0c1\uc73c\ub85c \uc5b4\ub5a0\ud55c \uacf5\uac04\uc801 \ubcc0\ud615\uc744 \uc218\ud589\ud574\uc57c \ud558\ub294\uc9c0 \ud559\uc2b5\ud558\ub3c4\ub85d\n\ud569\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uc5b4 \uc774\ubbf8\uc9c0\uc758 \uad00\uc2ec \uc601\uc5ed\uc744 \uc798\ub77c\ub0b4\uac70\ub098, \ud06c\uae30\ub97c \uc870\uc815\ud558\uac70\ub098, \ubc29\ud5a5(orientation)\uc744 \n\uc218\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. CNN\uc740 \uc774\ub7ec\ud55c \ud68c\uc804, \ud06c\uae30 \uc870\uc815 \ub4f1\uc758 \uc77c\ubc18\uc801\uc778 \uc544\ud540(affine) \ubcc0\ud658\ub41c \n\uc785\ub825\uc5d0 \ub300\ud574 \uacb0\uacfc\uc758 \ubcc0\ub3d9\uc774 \ud06c\uae30 \ub54c\ubb38\uc5d0 (\ubbfc\uac10\ud558\uae30 \ub54c\ubb38\uc5d0), STN\uc740 \uc774\ub97c \uadf9\ubcf5\ud558\ub294\ub370 \ub9e4\uc6b0\n\uc720\uc6a9\ud55c \uba54\ucee4\ub2c8\uc998\uc774 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\nSTN\uc774 \uac00\uc9c4 \uc7a5\uc810 \uc911 \ud558\ub098\ub294 \uc544\uc8fc \uc791\uc740 \uc218\uc815\ub9cc\uc73c\ub85c \uae30\uc874\uc5d0 \uc0ac\uc6a9\ud558\ub358 CNN\uc5d0 \uac04\ub2e8\ud558\uac8c \uc5f0\uacb0 \uc2dc\ud0ac \n\uc218 \uc788\ub2e4\ub294 \uac83\uc785\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -26,14 +26,14 @@
       },
       "outputs": [],
       "source": [
-        "# License: BSD\n# Author: Ghassen Hamrouni\n\nfrom __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torchvision\nfrom torchvision import datasets, transforms\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nplt.ion()   # interactive mode"
+        "# \ub77c\uc774\uc13c\uc2a4: BSD\n# \uc800\uc790: Ghassen Hamrouni\n\nfrom __future__ import print_function\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torchvision\nfrom torchvision import datasets, transforms\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nplt.ion()   # \ub300\ud654\ud615 \ubaa8\ub4dc"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Loading the data\n----------------\n\nIn this post we experiment with the classic MNIST dataset. Using a\nstandard convolutional network augmented with a spatial transformer\nnetwork.\n\n"
+        "\ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\n----------------\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 MNIST \ub370\uc774\ud130\uc14b\uc744 \uc774\uc6a9\ud574 \uc2e4\ud5d8\ud569\ub2c8\ub2e4. \uc2e4\ud5d8\uc5d0\ub294 STN\uc73c\ub85c \n\uc99d\uac15\ub41c \uc77c\ubc18\uc801\uc778 CNN\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -44,14 +44,14 @@
       },
       "outputs": [],
       "source": [
-        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# Training dataset\ntrain_loader = torch.utils.data.DataLoader(\n    datasets.MNIST(root='.', train=True, download=True,\n                   transform=transforms.Compose([\n                       transforms.ToTensor(),\n                       transforms.Normalize((0.1307,), (0.3081,))\n                   ])), batch_size=64, shuffle=True, num_workers=4)\n# Test dataset\ntest_loader = torch.utils.data.DataLoader(\n    datasets.MNIST(root='.', train=False, transform=transforms.Compose([\n        transforms.ToTensor(),\n        transforms.Normalize((0.1307,), (0.3081,))\n    ])), batch_size=64, shuffle=True, num_workers=4)"
+        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# \ud559\uc2b5\uc6a9 \ub370\uc774\ud130\uc14b\ntrain_loader = torch.utils.data.DataLoader(\n    datasets.MNIST(root='.', train=True, download=True,\n                   transform=transforms.Compose([\n                       transforms.ToTensor(),\n                       transforms.Normalize((0.1307,), (0.3081,))\n                   ])), batch_size=64, shuffle=True, num_workers=4)\n# \ud14c\uc2a4\ud2b8\uc6a9 \ub370\uc774\ud130\uc14b\ntest_loader = torch.utils.data.DataLoader(\n    datasets.MNIST(root='.', train=False, transform=transforms.Compose([\n        transforms.ToTensor(),\n        transforms.Normalize((0.1307,), (0.3081,))\n    ])), batch_size=64, shuffle=True, num_workers=4)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Depicting spatial transformer networks\n--------------------------------------\n\nSpatial transformer networks boils down to three main components :\n\n-  The localization network is a regular CNN which regresses the\n   transformation parameters. The transformation is never learned\n   explicitly from this dataset, instead the network learns automatically\n   the spatial transformations that enhances the global accuracy.\n-  The grid generator generates a grid of coordinates in the input\n   image corresponding to each pixel from the output image.\n-  The sampler uses the parameters of the transformation and applies\n   it to the input image.\n\n.. figure:: /_static/img/stn/stn-arch.png\n\n.. Note::\n   We need the latest version of PyTorch that contains\n   affine_grid and grid_sample modules.\n\n\n"
+        "Spatial Transformer Networks(STN) \uad6c\uc131\ud558\uae30\n--------------------------------------\n\nSTN\uc740 \ub2e4\uc74c\uc758 \uc138 \uac00\uc9c0 \uc8fc\uc694 \uad6c\uc131 \uc694\uc18c\ub85c \uc694\uc57d\ub429\ub2c8\ub2e4.\n\n-  \uc704\uce58 \uacb0\uc815 \ub124\ud2b8\uc6cc\ud06c(localization network)\ub294 \uacf5\uac04 \ubcc0\ud658 \ud30c\ub77c\ubbf8\ud130\ub97c \uc608\uce21(regress)\n   \ud558\ub294 \uc77c\ubc18\uc801\uc778 CNN \uc785\ub2c8\ub2e4. \uacf5\uac04 \ubcc0\ud658\uc740 \ub370\uc774\ud130 \uc14b\uc73c\ub85c\ubd80\ud130 \uba85\uc2dc\uc801\uc73c\ub85c \ud559\uc2b5\ub418\uc9c0 \uc54a\uace0,\n   \uc2e0\uacbd\ub9dd\uc774 \uc804\uccb4 \uc815\ud655\ub3c4\ub97c \ud5a5\uc0c1 \uc2dc\ud0a4\ub3c4\ub85d \uacf5\uac04 \ubcc0\ud658\uc744 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4.\n-  \uadf8\ub9ac\ub4dc \uc0dd\uc131\uae30(grid generator)\ub294 \ucd9c\ub825 \uc774\ubbf8\uc9c0\ub85c\ubd80\ud130 \uac01 \ud53d\uc140\uc5d0 \ub300\uc751\ud558\ub294 \uc785\ub825\n   \uc774\ubbf8\uc9c0 \ub0b4 \uc88c\ud45c \uadf8\ub9ac\ub4dc\ub97c \uc0dd\uc131\ud569\ub2c8\ub2e4.\n-  \uc0d8\ud50c\ub7ec(sampler)\ub294 \uacf5\uac04 \ubcc0\ud658 \ud30c\ub77c\ubbf8\ud130\ub97c \uc785\ub825 \uc774\ubbf8\uc9c0\uc5d0 \uc801\uc6a9\ud569\ub2c8\ub2e4.\n\n.. figure:: /_static/img/stn/stn-arch.png\n\n.. Note::\n   affine_grid \ubc0f grid_sample \ubaa8\ub4c8\uc774 \ud3ec\ud568\ub41c \ucd5c\uc2e0 \ubc84\uc804\uc758 PyTorch\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -62,14 +62,14 @@
       },
       "outputs": [],
       "source": [
-        "class Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n        # Spatial transformer localization-network\n        self.localization = nn.Sequential(\n            nn.Conv2d(1, 8, kernel_size=7),\n            nn.MaxPool2d(2, stride=2),\n            nn.ReLU(True),\n            nn.Conv2d(8, 10, kernel_size=5),\n            nn.MaxPool2d(2, stride=2),\n            nn.ReLU(True)\n        )\n\n        # Regressor for the 3 * 2 affine matrix\n        self.fc_loc = nn.Sequential(\n            nn.Linear(10 * 3 * 3, 32),\n            nn.ReLU(True),\n            nn.Linear(32, 3 * 2)\n        )\n\n        # Initialize the weights/bias with identity transformation\n        self.fc_loc[2].weight.data.zero_()\n        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))\n\n    # Spatial transformer network forward function\n    def stn(self, x):\n        xs = self.localization(x)\n        xs = xs.view(-1, 10 * 3 * 3)\n        theta = self.fc_loc(xs)\n        theta = theta.view(-1, 2, 3)\n\n        grid = F.affine_grid(theta, x.size())\n        x = F.grid_sample(x, grid)\n\n        return x\n\n    def forward(self, x):\n        # transform the input\n        x = self.stn(x)\n\n        # Perform the usual forward pass\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return F.log_softmax(x, dim=1)\n\n\nmodel = Net().to(device)"
+        "class Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n        # \uacf5\uac04 \ubcc0\ud658\uc744 \uc704\ud55c \uc704\uce58 \uacb0\uc815 \ub124\ud2b8\uc6cc\ud06c (localization-network)\n        self.localization = nn.Sequential(\n            nn.Conv2d(1, 8, kernel_size=7),\n            nn.MaxPool2d(2, stride=2),\n            nn.ReLU(True),\n            nn.Conv2d(8, 10, kernel_size=5),\n            nn.MaxPool2d(2, stride=2),\n            nn.ReLU(True)\n        )\n\n        # [3 * 2] \ud06c\uae30\uc758 \uc544\ud540(affine) \ud589\ub82c\uc5d0 \ub300\ud574 \uc608\uce21\n        self.fc_loc = nn.Sequential(\n            nn.Linear(10 * 3 * 3, 32),\n            nn.ReLU(True),\n            nn.Linear(32, 3 * 2)\n        )\n\n        # \ud56d\ub4f1 \ubcc0\ud658(identity transformation)\uc73c\ub85c \uac00\uc911\uce58/\ubc14\uc774\uc5b4\uc2a4 \ucd08\uae30\ud654\n        self.fc_loc[2].weight.data.zero_()\n        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))\n\n    # STN\uc758 forward \ud568\uc218\n    def stn(self, x):\n        xs = self.localization(x)\n        xs = xs.view(-1, 10 * 3 * 3)\n        theta = self.fc_loc(xs)\n        theta = theta.view(-1, 2, 3)\n\n        grid = F.affine_grid(theta, x.size())\n        x = F.grid_sample(x, grid)\n\n        return x\n\n    def forward(self, x):\n        # \uc785\ub825\uc744 \ubcc0\ud658\n        x = self.stn(x)\n\n        # \uc77c\ubc18\uc801\uc778 forward pass\ub97c \uc218\ud589\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return F.log_softmax(x, dim=1)\n\n\nmodel = Net().to(device)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Training the model\n------------------\n\nNow, let's use the SGD algorithm to train the model. The network is\nlearning the classification task in a supervised way. In the same time\nthe model is learning STN automatically in an end-to-end fashion.\n\n"
+        "\ubaa8\ub378 \ud559\uc2b5\ud558\uae30\n------------------\n\n\uc774\uc81c SGD \uc54c\uace0\ub9ac\uc998\uc744 \uc774\uc6a9\ud574 \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ucf1c \ubd05\uc2dc\ub2e4. \uc55e\uc11c \uad6c\uc131\ud55c \uc2e0\uacbd\ub9dd\uc740\n\uac10\ub3c5 \ud559\uc2b5 \ubc29\uc2dd(supervised way)\uc73c\ub85c \ubd84\ub958 \ubb38\uc81c\ub97c \ud559\uc2b5\ud569\ub2c8\ub2e4. \ub610\ud55c \uc774 \ubaa8\ub378\uc740\nend-to-end \ubc29\uc2dd\uc73c\ub85c STN\uc744 \uc790\ub3d9\uc73c\ub85c \ud559\uc2b5\ud569\ub2c8\ub2e4. \n\n"
       ]
     },
     {
@@ -80,14 +80,14 @@
       },
       "outputs": [],
       "source": [
-        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n\n\ndef train(epoch):\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % 500 == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n#\n# A simple test procedure to measure STN the performances on MNIST.\n#\n\n\ndef test():\n    with torch.no_grad():\n        model.eval()\n        test_loss = 0\n        correct = 0\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n\n            # sum up batch loss\n            test_loss += F.nll_loss(output, target, size_average=False).item()\n            # get the index of the max log-probability\n            pred = output.max(1, keepdim=True)[1]\n            correct += pred.eq(target.view_as(pred)).sum().item()\n\n        test_loss /= len(test_loader.dataset)\n        print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'\n              .format(test_loss, correct, len(test_loader.dataset),\n                      100. * correct / len(test_loader.dataset)))"
+        "optimizer = optim.SGD(model.parameters(), lr=0.01)\n\n\ndef train(epoch):\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % 500 == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n#\n# MNIST \ub370\uc774\ud130\uc14b\uc5d0\uc11c STN\uc758 \uc131\ub2a5\uc744 \uce21\uc815\ud558\uae30 \uc704\ud55c \uac04\ub2e8\ud55c \ud14c\uc2a4\ud2b8 \uc808\ucc28\n#\n\n\ndef test():\n    with torch.no_grad():\n        model.eval()\n        test_loss = 0\n        correct = 0\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n\n            # \ubc30\uce58 \uc190\uc2e4 \ud569\ud558\uae30\n            test_loss += F.nll_loss(output, target, size_average=False).item()\n            # \ub85c\uadf8-\ud655\ub960\uc758 \ucd5c\ub300\uac12\uc5d0 \ud574\ub2f9\ud558\ub294 \uc778\ub371\uc2a4 \uac00\uc838\uc624\uae30\n            pred = output.max(1, keepdim=True)[1]\n            correct += pred.eq(target.view_as(pred)).sum().item()\n\n        test_loss /= len(test_loader.dataset)\n        print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'\n              .format(test_loss, correct, len(test_loader.dataset),\n                      100. * correct / len(test_loader.dataset)))"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Visualizing the STN results\n---------------------------\n\nNow, we will inspect the results of our learned visual attention\nmechanism.\n\nWe define a small helper function in order to visualize the\ntransformations while training.\n\n"
+        "STN \uacb0\uacfc \uc2dc\uac01\ud654\ud558\uae30\n---------------------------\n\n\uc774\uc81c \ud559\uc2b5\ud55c \ube44\uc8fc\uc5bc \uc5b4\ud150\uc158 \uba54\ucee4\ub2c8\uc998\uc758 \uacb0\uacfc\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\ud559\uc2b5\ud558\ub294 \ub3d9\uc548 \ubcc0\ud658\ub41c \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud558\uae30 \uc704\ud574 \uc791\uc740 \ub3c4\uc6c0(helper) \ud568\uc218\ub97c \uc815\uc758\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "def convert_image_np(inp):\n    \"\"\"Convert a Tensor to numpy image.\"\"\"\n    inp = inp.numpy().transpose((1, 2, 0))\n    mean = np.array([0.485, 0.456, 0.406])\n    std = np.array([0.229, 0.224, 0.225])\n    inp = std * inp + mean\n    inp = np.clip(inp, 0, 1)\n    return inp\n\n# We want to visualize the output of the spatial transformers layer\n# after the training, we visualize a batch of input images and\n# the corresponding transformed batch using STN.\n\n\ndef visualize_stn():\n    with torch.no_grad():\n        # Get a batch of training data\n        data = next(iter(test_loader))[0].to(device)\n\n        input_tensor = data.cpu()\n        transformed_input_tensor = model.stn(data).cpu()\n\n        in_grid = convert_image_np(\n            torchvision.utils.make_grid(input_tensor))\n\n        out_grid = convert_image_np(\n            torchvision.utils.make_grid(transformed_input_tensor))\n\n        # Plot the results side-by-side\n        f, axarr = plt.subplots(1, 2)\n        axarr[0].imshow(in_grid)\n        axarr[0].set_title('Dataset Images')\n\n        axarr[1].imshow(out_grid)\n        axarr[1].set_title('Transformed Images')\n\nfor epoch in range(1, 20 + 1):\n    train(epoch)\n    test()\n\n# Visualize the STN transformation on some input batch\nvisualize_stn()\n\nplt.ioff()\nplt.show()"
+        "def convert_image_np(inp):\n    \"\"\"Convert a Tensor to numpy image.\"\"\"\n    inp = inp.numpy().transpose((1, 2, 0))\n    mean = np.array([0.485, 0.456, 0.406])\n    std = np.array([0.229, 0.224, 0.225])\n    inp = std * inp + mean\n    inp = np.clip(inp, 0, 1)\n    return inp\n\n# \ud559\uc2b5 \ud6c4 \uacf5\uac04 \ubcc0\ud658 \uacc4\uce35\uc758 \ucd9c\ub825\uc744 \uc2dc\uac01\ud654\ud558\uace0, \uc785\ub825 \uc774\ubbf8\uc9c0 \ubc30\uce58 \ub370\uc774\ud130 \ubc0f \n# STN\uc744 \uc0ac\uc6a9\ud574 \ubcc0\ud658\ub41c \ubc30\uce58 \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654 \ud569\ub2c8\ub2e4.\n\n\ndef visualize_stn():\n    with torch.no_grad():\n        # \ud559\uc2b5 \ub370\uc774\ud130\uc758 \ubc30\uce58 \uac00\uc838\uc624\uae30\n        data = next(iter(test_loader))[0].to(device)\n\n        input_tensor = data.cpu()\n        transformed_input_tensor = model.stn(data).cpu()\n\n        in_grid = convert_image_np(\n            torchvision.utils.make_grid(input_tensor))\n\n        out_grid = convert_image_np(\n            torchvision.utils.make_grid(transformed_input_tensor))\n\n        # \uacb0\uacfc\ub97c \ub098\ub780\ud788 \ud45c\uc2dc\ud558\uae30\n        f, axarr = plt.subplots(1, 2)\n        axarr[0].imshow(in_grid)\n        axarr[0].set_title('Dataset Images')\n\n        axarr[1].imshow(out_grid)\n        axarr[1].set_title('Transformed Images')\n\nfor epoch in range(1, 20 + 1):\n    train(epoch)\n    test()\n\n# \uc77c\ubd80 \uc785\ub825 \ubc30\uce58 \ub370\uc774\ud130\uc5d0\uc11c STN \ubcc0\ud658 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\nvisualize_stn()\n\nplt.ioff()\nplt.show()"
       ]
     }
   ],
@@ -118,7 +118,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/a75cfadf4fa84dd594874d4c53b62820/char_rnn_generation_tutorial.ipynb b/docs/_downloads/a75cfadf4fa84dd594874d4c53b62820/char_rnn_generation_tutorial.ipynb
index 2f016f083..7f080987b 100644
--- a/docs/_downloads/a75cfadf4fa84dd594874d4c53b62820/char_rnn_generation_tutorial.ipynb
+++ b/docs/_downloads/a75cfadf4fa84dd594874d4c53b62820/char_rnn_generation_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP:  \ubb38\uc790-\ub2e8\uc704 RNN\uc73c\ub85c \uc774\ub984 \uc0dd\uc131\ud558\uae30\n********************************************************************************\n**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_\n  **\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 3\uac1c\ub85c \uc774\ub904\uc9c4 \"\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP\"\uc758 2\ubc88\uc9f8 \ud29c\ud1a0\ub9ac\uc5bc\uc785\ub2c8\ub2e4.\n`\uccab\ubc88\uc9f8 \ud29c\ud1a0\ub9ac\uc5bc </intermediate/char_rnn_classification_tutorial>`\n\uc5d0\uc11c\ub294 \uc774\ub984\uc758 \uc5b8\uc5b4\ub97c \ubd84\ub958\ud558\uae30 \uc704\ud574 RNN\uc744 \uc0ac\uc6a9\ud588\uc2b5\ub2c8\ub2e4.\n\uc774\ubc88\uc5d0\ub294 \ubc18\ub300\ub85c \uc5b8\uc5b4\ub85c \uc774\ub984\uc744 \uc0dd\uc131\ud560 \uc608\uc815\uc785\ub2c8\ub2e4.\n\n::\n\n    > python sample.py Russian RUS\n    Rovakov\n    Uantov\n    Shavakov\n\n    > python sample.py German GER\n    Gerren\n    Ereng\n    Rosher\n\n    > python sample.py Spanish SPA\n    Salla\n    Parer\n    Allan\n\n    > python sample.py Chinese CHI\n    Chan\n    Hang\n    Iun\n\n\uc6b0\ub9ac\ub294 \uba87 \uac1c\uc758 \uc120\ud615 \uacc4\uce35\uc73c\ub85c \uc791\uc740 RNN\uc744 \uc9c1\uc811 \ub9cc\ub4e4\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\uc804 \ud29c\ud1a0\ub9ac\uc5bc\uc778 \uc774\ub984\uc744 \uc77d\uc740 \ud6c4 \uadf8 \uc5b8\uc5b4\ub97c \uc608\uce21\ud558\ub294 \uac83\uacfc\uc758 \ud070 \ucc28\uc774\uc810\uc740\n\uc5b8\uc5b4\ub97c \uc785\ub825\ud558\uace0 \ud55c \ubc88\uc5d0 \ud55c \uae00\uc790\ub97c \uc0dd\uc131\ud558\uc5ec \ucd9c\ub825\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\uc5b8\uc5b4 \ud615\uc131(\ub2e8\uc5b4 \ub610\ub294 \ub2e4\ub978 \uace0\ucc28\uc6d0 \uad6c\uc870\ub85c\ub3c4 \uc218\ud589 \ub420 \uc218 \uc788\uc74c)\uc744 \uc704\ud574\n\ubb38\uc790\ub97c \ubc18\ubcf5\uc801\uc73c\ub85c \uc608\uce21\ud558\ub294 \uac83\uc744 \"\uc5b8\uc5b4 \ubaa8\ub378\" \uc774\ub77c\uace0 \ud569\ub2c8\ub2e4.\n\n**\ucd94\ucc9c \uc790\ub8cc:**\n\nPytorch\ub97c \uc124\uce58\ud588\uace0, Python\uc744 \uc54c\uace0, Tensor\ub97c \uc774\ud574\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4:\n\n-  https://pytorch.org/ \uc124\uce58 \uc548\ub0b4\n-  :doc:`/beginner/deep_learning_60min_blitz` PyTorch \uc2dc\uc791\ud558\uae30\n-  :doc:`/beginner/pytorch_with_examples` \ub113\uace0 \uae4a\uc740 \ud1b5\ucc30\uc744 \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/former_torchies_tutorial` \uc774\uc804 Lua Torch \uc0ac\uc6a9\uc790\ub97c \uc704\ud55c \uc790\ub8cc\n\nRNN\uacfc \uc791\ub3d9 \ubc29\uc2dd\uc744 \uc544\ub294 \uac83 \ub610\ud55c \uc720\uc6a9\ud569\ub2c8\ub2e4:\n\n-  `The Unreasonable Effectiveness of Recurrent Neural\n   Networks <https://karpathy.github.io/2015/05/21/rnn-effectiveness/>`__\n   \uc2e4\uc0dd\ud65c \uc608\uc81c\ub97c \ubcf4\uc5ec \uc90d\ub2c8\ub2e4.\n-  `Understanding LSTM\n   Networks <https://colah.github.io/posts/2015-08-Understanding-LSTMs/>`__\n   LSTM\uc5d0 \uad00\ud55c \uac83\uc774\uc9c0\ub9cc RNN\uc5d0 \uad00\ud574\uc11c\ub3c4 \uc720\uc775\ud569\ub2c8\ub2e4.\n\n\uc774\uc804 \ud29c\ud1a0\ub9ac\uc5bc\ub3c4 \ucd94\ucc9c\ud569\ub2c8\ub2e4. :doc:`/intermediate/char_rnn_classification_tutorial`\n\n\n\ub370\uc774\ud130 \uc900\ube44\n==================\n\n.. Note::\n   `\uc5ec\uae30 <https://download.pytorch.org/tutorial/data.zip>`_\n   \uc5d0\uc11c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4 \ubc1b\uace0, \ud604\uc7ac \ub514\ub809\ud1a0\ub9ac\uc5d0 \uc555\ucd95\uc744 \ud478\uc2ed\uc2dc\uc624.\n\n\uc774 \uacfc\uc815\uc758 \ub354 \uc790\uc138\ud55c \uc0ac\ud56d\uc740 \uc9c0\ub09c \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ubcf4\uc2ed\uc2dc\uc624.\n\uc694\uc57d\ud558\uba74, \uc904\ub9c8\ub2e4 \uc774\ub984\uc774 \uc801\ud78c \ud14d\uc2a4\ud2b8 \ud30c\uc77c ``data/names/[Language].txt`` \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\uac83\uc744 \uc5b4\ub808\uc774\ub85c \ubd84\ub9ac\ud558\uace0, Unicode\ub97c ASCII\ub85c \ubcc0\uacbd\ud558\uace0,\n\uc0ac\uc804 ``{language: [names ...]}`` \uc744 \ub9cc\ub4e4\uc5b4\uc11c \ub9c8\ubb34\ub9ac\ud569\ub2c8\ub2e4.\n\n\n"
+        "\n\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP:  \ubb38\uc790-\ub2e8\uc704 RNN\uc73c\ub85c \uc774\ub984 \uc0dd\uc131\ud558\uae30\n********************************************************************************\n**Author**: `Sean Robertson <https://github.com/spro/practical-pytorch>`_\n  **\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 3\uac1c\ub85c \uc774\ub904\uc9c4 \"\uae30\ucd08\ubd80\ud130 \uc2dc\uc791\ud558\ub294 NLP\"\uc758 2\ubc88\uc9f8 \ud29c\ud1a0\ub9ac\uc5bc\uc785\ub2c8\ub2e4.\n`\uccab\ubc88\uc9f8 \ud29c\ud1a0\ub9ac\uc5bc </intermediate/char_rnn_classification_tutorial>`\n\uc5d0\uc11c\ub294 \uc774\ub984\uc758 \uc5b8\uc5b4\ub97c \ubd84\ub958\ud558\uae30 \uc704\ud574 RNN\uc744 \uc0ac\uc6a9\ud588\uc2b5\ub2c8\ub2e4.\n\uc774\ubc88\uc5d0\ub294 \ubc18\ub300\ub85c \uc5b8\uc5b4\ub85c \uc774\ub984\uc744 \uc0dd\uc131\ud560 \uc608\uc815\uc785\ub2c8\ub2e4.\n\n::\n\n    > python sample.py Russian RUS\n    Rovakov\n    Uantov\n    Shavakov\n\n    > python sample.py German GER\n    Gerren\n    Ereng\n    Rosher\n\n    > python sample.py Spanish SPA\n    Salla\n    Parer\n    Allan\n\n    > python sample.py Chinese CHI\n    Chan\n    Hang\n    Iun\n\n\uc6b0\ub9ac\ub294 \uba87 \uac1c\uc758 \uc120\ud615 \uacc4\uce35\uc73c\ub85c \uc791\uc740 RNN\uc744 \uc9c1\uc811 \ub9cc\ub4e4\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\uc804 \ud29c\ud1a0\ub9ac\uc5bc\uc778 \uc774\ub984\uc744 \uc77d\uc740 \ud6c4 \uadf8 \uc5b8\uc5b4\ub97c \uc608\uce21\ud558\ub294 \uac83\uacfc\uc758 \ud070 \ucc28\uc774\uc810\uc740\n\uc5b8\uc5b4\ub97c \uc785\ub825\ud558\uace0 \ud55c \ubc88\uc5d0 \ud55c \uae00\uc790\ub97c \uc0dd\uc131\ud558\uc5ec \ucd9c\ub825\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.\n\uc5b8\uc5b4 \ud615\uc131(\ub2e8\uc5b4 \ub610\ub294 \ub2e4\ub978 \uace0\ucc28\uc6d0 \uad6c\uc870\ub85c\ub3c4 \uc218\ud589 \ub420 \uc218 \uc788\uc74c)\uc744 \uc704\ud574\n\ubb38\uc790\ub97c \ubc18\ubcf5\uc801\uc73c\ub85c \uc608\uce21\ud558\ub294 \uac83\uc744 \"\uc5b8\uc5b4 \ubaa8\ub378\" \uc774\ub77c\uace0 \ud569\ub2c8\ub2e4.\n\n**\ucd94\ucc9c \uc790\ub8cc:**\n\nPytorch\ub97c \uc124\uce58\ud588\uace0, Python\uc744 \uc54c\uace0, Tensor\ub97c \uc774\ud574\ud55c\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4:\n\n-  https://pytorch.org/ \uc124\uce58 \uc548\ub0b4\n-  :doc:`/beginner/deep_learning_60min_blitz` PyTorch \uc2dc\uc791\ud558\uae30\n-  :doc:`/beginner/pytorch_with_examples` \ub113\uace0 \uae4a\uc740 \ud1b5\ucc30\uc744 \uc704\ud55c \uc790\ub8cc\n-  :doc:`/beginner/former_torchies_tutorial` \uc774\uc804 Lua Torch \uc0ac\uc6a9\uc790\ub97c \uc704\ud55c \uc790\ub8cc\n\nRNN\uacfc \uc791\ub3d9 \ubc29\uc2dd\uc744 \uc544\ub294 \uac83 \ub610\ud55c \uc720\uc6a9\ud569\ub2c8\ub2e4:\n\n-  `The Unreasonable Effectiveness of Recurrent Neural\n   Networks <https://karpathy.github.io/2015/05/21/rnn-effectiveness/>`__\n   \uc2e4\uc0dd\ud65c \uc608\uc81c\ub97c \ubcf4\uc5ec \uc90d\ub2c8\ub2e4.\n-  `Understanding LSTM\n   Networks <https://colah.github.io/posts/2015-08-Understanding-LSTMs/>`__\n   LSTM\uc5d0 \uad00\ud55c \uac83\uc774\uc9c0\ub9cc RNN\uc5d0 \uad00\ud574\uc11c\ub3c4 \uc720\uc775\ud569\ub2c8\ub2e4.\n\n\uc774\uc804 \ud29c\ud1a0\ub9ac\uc5bc\ub3c4 \ucd94\ucc9c\ud569\ub2c8\ub2e4. :doc:`/intermediate/char_rnn_classification_tutorial`\n\n\n\ub370\uc774\ud130 \uc900\ube44\n==================\n\n.. Note::\n   `\uc5ec\uae30 <https://download.pytorch.org/tutorial/data.zip>`_\n   \uc5d0\uc11c \ub370\uc774\ud130\ub97c \ub2e4\uc6b4 \ubc1b\uace0, \ud604\uc7ac \ub514\ub809\ud1a0\ub9ac\uc5d0 \uc555\ucd95\uc744 \ud478\uc2ed\uc2dc\uc624.\n\n\uc774 \uacfc\uc815\uc758 \ub354 \uc790\uc138\ud55c \uc0ac\ud56d\uc740 \uc9c0\ub09c \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ubcf4\uc2ed\uc2dc\uc624.\n\uc694\uc57d\ud558\uba74, \uc904\ub9c8\ub2e4 \uc774\ub984\uc774 \uc801\ud78c \ud14d\uc2a4\ud2b8 \ud30c\uc77c ``data/names/[Language].txt`` \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\uac83\uc744 \uc5b4\ub808\uc774\ub85c \ubd84\ub9ac\ud558\uace0, Unicode\ub97c ASCII\ub85c \ubcc0\uacbd\ud558\uace0,\n\uc0ac\uc804 ``{language: [names ...]}`` \uc744 \ub9cc\ub4e4\uc5b4\uc11c \ub9c8\ubb34\ub9ac\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -215,7 +215,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/aa3898eb04d468790e00cb42405b1c23/transformer_tutorial.py b/docs/_downloads/aa3898eb04d468790e00cb42405b1c23/transformer_tutorial.py
index 7de36bbc0..8fbe8946d 100644
--- a/docs/_downloads/aa3898eb04d468790e00cb42405b1c23/transformer_tutorial.py
+++ b/docs/_downloads/aa3898eb04d468790e00cb42405b1c23/transformer_tutorial.py
@@ -1,6 +1,6 @@
 """
 nn.Transformer 와 TorchText 로 시퀀스-투-시퀀스(Sequence-to-Sequence) 모델링하기
-==========================================================================
+=================================================================================
 
 이 튜토리얼에서는
 `nn.Transformer <https://pytorch.org/docs/master/nn.html?highlight=nn%20transformer#torch.nn.Transformer>`__ 모듈을
@@ -114,7 +114,7 @@ def forward(self, x):
 
 ######################################################################
 # 데이터 로드하고 배치 만들기
-# -------------------
+# -----------------------------
 #
 
 
@@ -168,7 +168,7 @@ def batchify(data, bsz):
 
 ######################################################################
 # 입력(input) 과 타겟(target) 시퀀스를 생성하기 위한 함수들
-# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
 
 
@@ -194,7 +194,7 @@ def get_batch(source, i):
 
 ######################################################################
 # 인스턴스(instance) 초기화하기
-# --------------------
+# --------------------------------
 #
 
 
@@ -304,8 +304,8 @@ def evaluate(eval_model, data_source):
 
 
 ######################################################################
-# 평가 데이터셋(test dataset) 으로 모델을 평가하기
-# -------------------------------------
+# 평가 데이터셋(test dataset)으로 모델을 평가하기
+# -------------------------------------------------
 #
 # 평가 데이터셋에 대한 결과를 확인하기 위해서 최고의 모델을 적용합니다.
 
diff --git a/docs/_downloads/b45d95b1d38fe556c77d1ee548809d28/flask_rest_api_tutorial.py b/docs/_downloads/b45d95b1d38fe556c77d1ee548809d28/flask_rest_api_tutorial.py
index ce061fa7d..b21cdeeaf 100644
--- a/docs/_downloads/b45d95b1d38fe556c77d1ee548809d28/flask_rest_api_tutorial.py
+++ b/docs/_downloads/b45d95b1d38fe556c77d1ee548809d28/flask_rest_api_tutorial.py
@@ -47,7 +47,7 @@
 #
 # ::
 #
-#     $ pip install Flask==1.0.3 torchvision-0.3.0
+#     $ pip install Flask==1.0.3 torchvision==0.3.0
 
 
 ######################################################################
@@ -104,7 +104,7 @@ def predict():
 # 추론(Inference)
 # -----------------
 #
-# 다음 섹션에서는 추론 코드 작성에 집중하겠습니다. 먼저 이미지를 DenseNet에 공급(feed)할 수 
+# 다음 섹션에서는 추론 코드 작성에 집중하겠습니다. 먼저 이미지를 DenseNet에 공급(feed)할 수
 # 있도록 준비하는 방법을 살펴본 뒤, 모델로부터 예측 결과를 얻는 방법을 살펴보겠습니다.
 #
 # 이미지 준비하기
diff --git a/docs/_downloads/b5fa995b1432ebc93ea7bfe7ec9daed1/text_sentiment_ngrams_tutorial.ipynb b/docs/_downloads/b5fa995b1432ebc93ea7bfe7ec9daed1/text_sentiment_ngrams_tutorial.ipynb
index 7097f7e3a..6fe2d679e 100644
--- a/docs/_downloads/b5fa995b1432ebc93ea7bfe7ec9daed1/text_sentiment_ngrams_tutorial.ipynb
+++ b/docs/_downloads/b5fa995b1432ebc93ea7bfe7ec9daed1/text_sentiment_ngrams_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nTorchText\ub85c \ud14d\uc2a4\ud2b8 \ubd84\ub958\ud558\uae30\n==================================\n**\ubc88\uc5ed**: `\uae40\uac15\ubbfc <https://github.com/gangsss>`_ , `\uae40\uc9c4\ud604 <https://github.com/lewhe0>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ``torchtext`` \uc5d0 \ud3ec\ud568\ub418\uc5b4 \uc788\ub294 \ud14d\uc2a4\ud2b8 \ubd84\ub958\n\ub370\uc774\ud130\uc14b\uc758 \uc0ac\uc6a9 \ubc29\ubc95\uc744 \uc0b4\ud3b4 \ubd05\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc740 \ub2e4\uc74c\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.\n\n::\n\n   - AG_NEWS,\n   - SogouNews,\n   - DBpedia,\n   - YelpReviewPolarity,\n   - YelpReviewFull,\n   - YahooAnswers,\n   - AmazonReviewPolarity,\n   - AmazonReviewFull\n\n\uc774 \uc608\uc81c\uc5d0\uc11c\ub294 ``TextClassification`` \uc758 \ub370\uc774\ud130\uc14b\ub4e4 \uc911 \ud558\ub098\ub97c \uc774\uc6a9\ud574 \ubd84\ub958\ub97c \uc704\ud55c\n \uc9c0\ub3c4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc744 \ud6c8\ub828\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.\n\nngrams\ub97c \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\n-----------------------------------\n\nBag of ngrams \ud53c\uccd0\ub294 \uc9c0\uc5ed(local) \ub2e8\uc5b4 \uc21c\uc11c\uc5d0 \ub300\ud55c \ubd80\ubd84\uc801\uc778 \uc815\ubcf4\ub97c \ud3ec\ucc29\ud558\uae30 \uc704\ud574 \uc801\uc6a9\ud569\ub2c8\ub2e4.\n\uc2e4\uc81c \uc0c1\ud669\uc5d0\uc11c\ub294 bi-gram\uc774\ub098 tri-gram\uc740 \ub2e8 \ud558\ub098\uc758 \ub2e8\uc5b4\ub97c \uc774\uc6a9\ud558\ub294 \uac83\ubcf4\ub2e4 \ub354 \ub9ce\uc740 \uc774\uc775\uc744 \uc8fc\uae30 \ub54c\ubb38\uc5d0 \uc801\uc6a9\ub429\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.\n\n::\n\n   \"load data with ngrams\"\n   Bi-grams \uacb0\uacfc: \"load data\", \"data with\", \"with ngrams\"\n   Tri-grams \uacb0\uacfc: \"load data with\", \"data with ngrams\"\n\n``TextClassification`` \ub370\uc774\ud130\uc14b\uc740 ngrams method\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. ngrams\uc744 2\ub85c \uc124\uc815\ud558\uba74,\n\ub370\uc774\ud130\uc14b \uc548\uc758 \uc608\uc81c \ud14d\uc2a4\ud2b8\ub294 \uac01\uac01\uc758(single) \ub2e8\uc5b4\ub4e4\uc5d0 bi-grams \ubb38\uc790\uc5f4\uc774 \ub354\ud574\uc9c4 \ub9ac\uc2a4\ud2b8\uac00 \ub420 \uac83\uc785\ub2c8\ub2e4.\n\n\n"
+        "\nTorchText\ub85c \ud14d\uc2a4\ud2b8 \ubd84\ub958\ud558\uae30\n==================================\n**\ubc88\uc5ed**: `\uae40\uac15\ubbfc <https://github.com/gangsss>`_ , `\uae40\uc9c4\ud604 <https://github.com/lewhe0>`_\n\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 ``torchtext`` \uc5d0 \ud3ec\ud568\ub418\uc5b4 \uc788\ub294 \ud14d\uc2a4\ud2b8 \ubd84\ub958\n\ub370\uc774\ud130\uc14b\uc758 \uc0ac\uc6a9 \ubc29\ubc95\uc744 \uc0b4\ud3b4 \ubd05\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc740 \ub2e4\uc74c\uc744 \ud3ec\ud568\ud569\ub2c8\ub2e4.\n\n::\n\n   - AG_NEWS,\n   - SogouNews,\n   - DBpedia,\n   - YelpReviewPolarity,\n   - YelpReviewFull,\n   - YahooAnswers,\n   - AmazonReviewPolarity,\n   - AmazonReviewFull\n\n\uc774 \uc608\uc81c\uc5d0\uc11c\ub294 ``TextClassification`` \uc758 \ub370\uc774\ud130\uc14b\ub4e4 \uc911 \ud558\ub098\ub97c \uc774\uc6a9\ud574 \ubd84\ub958\ub97c \uc704\ud55c\n \uc9c0\ub3c4 \ud559\uc2b5 \uc54c\uace0\ub9ac\uc998\uc744 \ud6c8\ub828\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.\n\nngrams\ub97c \uc774\uc6a9\ud558\uc5ec \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\n-----------------------------------\n\nBag of ngrams \ud53c\uccd0\ub294 \uc9c0\uc5ed(local) \ub2e8\uc5b4 \uc21c\uc11c\uc5d0 \ub300\ud55c \ubd80\ubd84\uc801\uc778 \uc815\ubcf4\ub97c \ud3ec\ucc29\ud558\uae30 \uc704\ud574 \uc801\uc6a9\ud569\ub2c8\ub2e4.\n\uc2e4\uc81c \uc0c1\ud669\uc5d0\uc11c\ub294 bi-gram\uc774\ub098 tri-gram\uc740 \ub2e8 \ud558\ub098\uc758 \ub2e8\uc5b4\ub97c \uc774\uc6a9\ud558\ub294 \uac83\ubcf4\ub2e4 \ub354 \ub9ce\uc740 \uc774\uc775\uc744 \uc8fc\uae30 \ub54c\ubb38\uc5d0 \uc801\uc6a9\ub429\ub2c8\ub2e4.\n\uc608\ub97c \ub4e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.\n\n::\n\n   \"load data with ngrams\"\n   Bi-grams \uacb0\uacfc: \"load data\", \"data with\", \"with ngrams\"\n   Tri-grams \uacb0\uacfc: \"load data with\", \"data with ngrams\"\n\n``TextClassification`` \ub370\uc774\ud130\uc14b\uc740 ngrams method\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. ngrams\uc744 2\ub85c \uc124\uc815\ud558\uba74,\n\ub370\uc774\ud130\uc14b \uc548\uc758 \uc608\uc81c \ud14d\uc2a4\ud2b8\ub294 \uac01\uac01\uc758(single) \ub2e8\uc5b4\ub4e4\uc5d0 bi-grams \ubb38\uc790\uc5f4\uc774 \ub354\ud574\uc9c4 \ub9ac\uc2a4\ud2b8\uac00 \ub420 \uac83\uc785\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -225,7 +225,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/c029676472d90691aa145c6fb97a61c3/neural_networks_tutorial.ipynb b/docs/_downloads/c029676472d90691aa145c6fb97a61c3/neural_networks_tutorial.ipynb
index c3d25ac72..efb1eadbb 100644
--- a/docs/_downloads/c029676472d90691aa145c6fb97a61c3/neural_networks_tutorial.ipynb
+++ b/docs/_downloads/c029676472d90691aa145c6fb97a61c3/neural_networks_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uc2e0\uacbd\ub9dd(Neural Networks)\n=======================\n\n\uc2e0\uacbd\ub9dd\uc740 ``torch.nn`` \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc9c0\uae08\uae4c\uc9c0 ``autograd`` \ub97c \uc0b4\ud3b4\ubd24\ub294\ub370\uc694, ``nn`` \uc740 \ubaa8\ub378\uc744 \uc815\uc758\ud558\uace0 \ubbf8\ubd84\ud558\ub294\ub370\n``autograd`` \ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n``nn.Module`` \uc740 \uacc4\uce35(layer)\uacfc ``output`` \uc744 \ubc18\ud658\ud558\ub294 ``forward(input)``\n\uba54\uc11c\ub4dc\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \ubd84\ub958\ud558\ub294 \uc2e0\uacbd\ub9dd\uc744 \uc608\uc81c\ub85c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\n.. figure:: /_static/img/mnist.png\n   :alt: convnet\n\n   convnet\n\n\uc774\ub294 \uac04\ub2e8\ud55c \uc21c\uc804\ud30c \ub124\ud2b8\uc6cc\ud06c(Feed-forward network)\uc785\ub2c8\ub2e4. \uc785\ub825(input)\uc744 \ubc1b\uc544\n\uc5ec\ub7ec \uacc4\uce35\uc5d0 \ucc28\ub840\ub85c \uc804\ub2ec\ud55c \ud6c4, \ucd5c\uc885 \ucd9c\ub825(output)\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\n\uc2e0\uacbd\ub9dd\uc758 \uc77c\ubc18\uc801\uc778 \ud559\uc2b5 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n\n- \ud559\uc2b5 \uac00\ub2a5\ud55c \ub9e4\uac1c\ubcc0\uc218(\ub610\ub294 \uac00\uc911\uce58(weight))\ub97c \uac16\ub294 \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\n- \ub370\uc774\ud130\uc14b(dataset) \uc785\ub825\uc744 \ubc18\ubcf5\ud569\ub2c8\ub2e4.\n- \uc785\ub825\uc744 \uc2e0\uacbd\ub9dd\uc5d0\uc11c \uc804\ud30c(process)\ud569\ub2c8\ub2e4.\n- \uc190\uc2e4(loss; \ucd9c\ub825\uc774 \uc815\ub2f5\uc73c\ub85c\ubd80\ud130 \uc5bc\ub9c8\ub098 \ub5a8\uc5b4\uc838\uc788\ub294\uc9c0)\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.\n- \ubcc0\ud654\ub3c4(gradient)\ub97c \uc2e0\uacbd\ub9dd\uc758 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc5d0 \uc5ed\uc73c\ub85c \uc804\ud30c\ud569\ub2c8\ub2e4.\n- \uc2e0\uacbd\ub9dd\uc758 \uac00\uc911\uce58\ub97c \uac31\uc2e0\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uac04\ub2e8\ud55c \uaddc\uce59\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4:\n  ``\uc0c8\ub85c\uc6b4 \uac00\uc911\uce58(weight) = \uac00\uc911\uce58(weight) - \ud559\uc2b5\ub960(learning rate) * \ubcc0\ud654\ub3c4(gradient)``\n\n\uc2e0\uacbd\ub9dd \uc815\uc758\ud558\uae30\n------------------\n\n\uc774\uc81c \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\n"
+        "\n\uc2e0\uacbd\ub9dd(Neural Networks)\n=======================\n\n\uc2e0\uacbd\ub9dd\uc740 ``torch.nn`` \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc9c0\uae08\uae4c\uc9c0 ``autograd`` \ub97c \uc0b4\ud3b4\ubd24\ub294\ub370\uc694, ``nn`` \uc740 \ubaa8\ub378\uc744 \uc815\uc758\ud558\uace0 \ubbf8\ubd84\ud558\ub294\ub370\n``autograd`` \ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n``nn.Module`` \uc740 \uacc4\uce35(layer)\uacfc ``output`` \uc744 \ubc18\ud658\ud558\ub294 ``forward(input)``\n\uba54\uc11c\ub4dc\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc22b\uc790 \uc774\ubbf8\uc9c0\ub97c \ubd84\ub958\ud558\ub294 \uc2e0\uacbd\ub9dd\uc744 \uc608\uc81c\ub85c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\n.. figure:: /_static/img/mnist.png\n   :alt: convnet\n\n   convnet\n\n\uc774\ub294 \uac04\ub2e8\ud55c \uc21c\uc804\ud30c \ub124\ud2b8\uc6cc\ud06c(Feed-forward network)\uc785\ub2c8\ub2e4. \uc785\ub825(input)\uc744 \ubc1b\uc544\n\uc5ec\ub7ec \uacc4\uce35\uc5d0 \ucc28\ub840\ub85c \uc804\ub2ec\ud55c \ud6c4, \ucd5c\uc885 \ucd9c\ub825(output)\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\n\uc2e0\uacbd\ub9dd\uc758 \uc77c\ubc18\uc801\uc778 \ud559\uc2b5 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n\n- \ud559\uc2b5 \uac00\ub2a5\ud55c \ub9e4\uac1c\ubcc0\uc218(\ub610\ub294 \uac00\uc911\uce58(weight))\ub97c \uac16\ub294 \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\n- \ub370\uc774\ud130\uc14b(dataset) \uc785\ub825\uc744 \ubc18\ubcf5\ud569\ub2c8\ub2e4.\n- \uc785\ub825\uc744 \uc2e0\uacbd\ub9dd\uc5d0\uc11c \uc804\ud30c(process)\ud569\ub2c8\ub2e4.\n- \uc190\uc2e4(loss; \ucd9c\ub825\uc774 \uc815\ub2f5\uc73c\ub85c\ubd80\ud130 \uc5bc\ub9c8\ub098 \ub5a8\uc5b4\uc838\uc788\ub294\uc9c0)\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.\n- \ubcc0\ud654\ub3c4(gradient)\ub97c \uc2e0\uacbd\ub9dd\uc758 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc5d0 \uc5ed\uc73c\ub85c \uc804\ud30c\ud569\ub2c8\ub2e4.\n- \uc2e0\uacbd\ub9dd\uc758 \uac00\uc911\uce58\ub97c \uac31\uc2e0\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \ub2e4\uc74c\uacfc \uac19\uc740 \uac04\ub2e8\ud55c \uaddc\uce59\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4:\n  ``\uc0c8\ub85c\uc6b4 \uac00\uc911\uce58(weight) = \uac00\uc911\uce58(weight) - \ud559\uc2b5\ub960(learning rate) * \ubcc0\ud654\ub3c4(gradient)``\n\n\uc2e0\uacbd\ub9dd \uc815\uc758\ud558\uae30\n------------------\n\n\uc774\uc81c \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n"
       ]
     },
     {
@@ -179,7 +179,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb b/docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb
index f53e3b294..ca4f7dbd8 100644
--- a/docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb
+++ b/docs/_downloads/c4bf1a4ba1714ace73ad54fe5c6d9d00/pytorch_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nIntroduction to PyTorch\n***********************\n\nIntroduction to Torch's tensor library\n======================================\n\nAll of deep learning is computations on tensors, which are\ngeneralizations of a matrix that can be indexed in more than 2\ndimensions. We will see exactly what this means in-depth later. First,\nlets look what we can do with tensors.\n\n"
+        "\nIntroduction to PyTorch\n***********************\n\nIntroduction to Torch's tensor library\n======================================\n\nAll of deep learning is computations on tensors, which are\ngeneralizations of a matrix that can be indexed in more than 2\ndimensions. We will see exactly what this means in-depth later. First,\nlets look what we can do with tensors.\n"
       ]
     },
     {
@@ -258,7 +258,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/c531680c9d28951567d472785ed937cd/two_layer_net_optim.ipynb b/docs/_downloads/c531680c9d28951567d472785ed937cd/two_layer_net_optim.ipynb
index 56f84b05b..0b2cc624e 100644
--- a/docs/_downloads/c531680c9d28951567d472785ed937cd/two_layer_net_optim.ipynb
+++ b/docs/_downloads/c531680c9d28951567d472785ed937cd/two_layer_net_optim.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch: optim\n--------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88\uc5d0\ub294 PyTorch\uc758 nn \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc9c0\uae08\uae4c\uc9c0 \ud574\uc654\ub358 \uac83\ucc98\ub7fc \uc9c1\uc811 \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uac31\uc2e0\ud558\ub294 \ub300\uc2e0, optim \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec\n\uac00\uc911\uce58\ub97c \uac31\uc2e0\ud560 Optimizer\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. optim \ud328\ud0a4\uc9c0\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ub525\ub7ec\ub2dd\uc5d0 \uc0ac\uc6a9\ud558\ub294\nSGD+momentum, RMSProp, Adam \ub4f1\uacfc \uac19\uc740 \ub2e4\uc591\ud55c \ucd5c\uc801\ud654(Optimization) \uc54c\uace0\ub9ac\uc998\uc744\n\uc815\uc758\ud569\ub2c8\ub2e4.\n\n"
+        "\nPyTorch: optim\n--------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88\uc5d0\ub294 PyTorch\uc758 nn \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc9c0\uae08\uae4c\uc9c0 \ud574\uc654\ub358 \uac83\ucc98\ub7fc \uc9c1\uc811 \ubaa8\ub378\uc758 \uac00\uc911\uce58\ub97c \uac31\uc2e0\ud558\ub294 \ub300\uc2e0, optim \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec\n\uac00\uc911\uce58\ub97c \uac31\uc2e0\ud560 Optimizer\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. optim \ud328\ud0a4\uc9c0\ub294 \uc77c\ubc18\uc801\uc73c\ub85c \ub525\ub7ec\ub2dd\uc5d0 \uc0ac\uc6a9\ud558\ub294\nSGD+momentum, RMSProp, Adam \ub4f1\uacfc \uac19\uc740 \ub2e4\uc591\ud55c \ucd5c\uc801\ud654(Optimization) \uc54c\uace0\ub9ac\uc998\uc744\n\uc815\uc758\ud569\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/cad5020cab595c3bf83a518b7e4d4125/neural_style_tutorial.ipynb b/docs/_downloads/cad5020cab595c3bf83a518b7e4d4125/neural_style_tutorial.ipynb
index 858fc4f20..0e5a59512 100644
--- a/docs/_downloads/cad5020cab595c3bf83a518b7e4d4125/neural_style_tutorial.ipynb
+++ b/docs/_downloads/cad5020cab595c3bf83a518b7e4d4125/neural_style_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nNeural Transfer Using PyTorch\n=============================\n\n\n**Author**: `Alexis Jacq <https://alexis-jacq.github.io>`_\n\n**Edited by**: `Winston Herring <https://github.com/winston6>`_\n\nIntroduction\n------------\n\nThis tutorial explains how to implement the `Neural-Style algorithm <https://arxiv.org/abs/1508.06576>`__\ndeveloped by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.\nNeural-Style, or Neural-Transfer, allows you to take an image and\nreproduce it with a new artistic style. The algorithm takes three images,\nan input image, a content-image, and a style-image, and changes the input\nto resemble the content of the content-image and the artistic style of the style-image.\n\n\n.. figure:: /_static/img/neural-style/neuralstyle.png\n   :alt: content1\n\n"
+        "\nNeural Transfer Using PyTorch\n=============================\n\n\n**Author**: `Alexis Jacq <https://alexis-jacq.github.io>`_\n\n**Edited by**: `Winston Herring <https://github.com/winston6>`_\n\nIntroduction\n------------\n\nThis tutorial explains how to implement the `Neural-Style algorithm <https://arxiv.org/abs/1508.06576>`__\ndeveloped by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge.\nNeural-Style, or Neural-Transfer, allows you to take an image and\nreproduce it with a new artistic style. The algorithm takes three images,\nan input image, a content-image, and a style-image, and changes the input\nto resemble the content of the content-image and the artistic style of the style-image.\n\n\n.. figure:: /_static/img/neural-style/neuralstyle.png\n   :alt: content1\n"
       ]
     },
     {
@@ -294,7 +294,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/cd38eeeacb38da1eaaab2cc8c419d0e5/loading_data_recipe.ipynb b/docs/_downloads/cd38eeeacb38da1eaaab2cc8c419d0e5/loading_data_recipe.ipynb
index cd9d45cb7..6370465f0 100644
--- a/docs/_downloads/cd38eeeacb38da1eaaab2cc8c419d0e5/loading_data_recipe.ipynb
+++ b/docs/_downloads/cd38eeeacb38da1eaaab2cc8c419d0e5/loading_data_recipe.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\uc5d0\uc11c \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\n=======================\nPyTorch\ub294 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4dc\ub294\ub370 \ud544\uc694\ud55c \ub2e4\uc591\ud55c \uae30\ubcf8 \uc694\uc18c\ub97c \uac04\ub2e8\ud558\uace0 \uc9c1\uad00\uc801\uc774\uba70\n\uc548\uc815\uc801\uc778 API\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4. PyTorch\ub294 \uacf5\uc6a9 \ub370\uc774\ud130\uc14b\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d\n\ub3c4\uc640\uc8fc\ub294 \ud328\ud0a4\uc9c0\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\nPyTorch \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30 \uae30\ub2a5\uc758 \ud575\uc2ec\uc740\n`torch.utils.data.DataLoader <https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader>`__\n\ud074\ub798\uc2a4\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \ud30c\uc774\uc36c iterable\ub85c\uc368 \uc811\uadfc\ud560 \uc218 \uc788\uac8c \ud574\uc8fc\ub294 \ud074\ub798\uc2a4\uc785\ub2c8\ub2e4.\n\ub610\ud55c, `torch.utils.data.Dataset <https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset>`__\n\ud074\ub798\uc2a4\ub97c \ud1b5\ud574 PyTorch\uc5d0 \ub0b4\uc7a5\ub41c \ub2e4\uc591\ud55c \uace0\ud488\uc9c8 \ub370\uc774\ud130\uc14b\uc744 \uc774\uc6a9\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\n\uac1c\ubcc4 \ub370\uc774\ud130\uc14b\uc740 \uc544\ub798 \ud328\ud0a4\uc9c0\uc5d0\uc11c \ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc73c\uba70, \ub370\uc774\ud130\uc14b\uc740 \uacc4\uc18d\ud574\uc11c \ucd94\uac00\ub420 \uc608\uc815\uc785\ub2c8\ub2e4.\n\n* `torchvision <https://pytorch.org/docs/stable/torchvision/datasets.html>`__\n* `torchaudio <https://pytorch.org/audio/datasets.html>`__\n* `torchtext <https://pytorch.org/text/datasets.html>`__\n\n\n\uc774\ubc88 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 ``torchaudio.datasets.YESNO`` \ub370\uc774\ud130\uc14b\uc744 \uc0b4\ud3b4\ubcf4\uba74\uc11c,\nPyTorch ``Dataset`` \uc5d0\uc11c PyTorch ``DataLoader`` \ub85c \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc774\uace0 \ud6a8\uc728\uc801\uc73c\ub85c\n\ubd88\ub7ec\uc624\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\ucd08\uae30 \uc124\uc815\n-----\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0, \ub370\uc774\ud130\uc14b\uc774 \ud3ec\ud568\ub41c ``torchaudio`` \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.\n\n::\n\n   pip install torchaudio\n\n\n\n"
+        "\nPyTorch\uc5d0\uc11c \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\n=======================\nPyTorch\ub294 \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4dc\ub294\ub370 \ud544\uc694\ud55c \ub2e4\uc591\ud55c \uae30\ubcf8 \uc694\uc18c\ub97c \uac04\ub2e8\ud558\uace0 \uc9c1\uad00\uc801\uc774\uba70\n\uc548\uc815\uc801\uc778 API\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4. PyTorch\ub294 \uacf5\uc6a9 \ub370\uc774\ud130\uc14b\uc744 \uc27d\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub3c4\ub85d\n\ub3c4\uc640\uc8fc\ub294 \ud328\ud0a4\uc9c0\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\nPyTorch \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30 \uae30\ub2a5\uc758 \ud575\uc2ec\uc740\n`torch.utils.data.DataLoader <https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader>`__\n\ud074\ub798\uc2a4\uc785\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \ud30c\uc774\uc36c iterable\ub85c\uc368 \uc811\uadfc\ud560 \uc218 \uc788\uac8c \ud574\uc8fc\ub294 \ud074\ub798\uc2a4\uc785\ub2c8\ub2e4.\n\ub610\ud55c, `torch.utils.data.Dataset <https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset>`__\n\ud074\ub798\uc2a4\ub97c \ud1b5\ud574 PyTorch\uc5d0 \ub0b4\uc7a5\ub41c \ub2e4\uc591\ud55c \uace0\ud488\uc9c8 \ub370\uc774\ud130\uc14b\uc744 \uc774\uc6a9\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\n\uac1c\ubcc4 \ub370\uc774\ud130\uc14b\uc740 \uc544\ub798 \ud328\ud0a4\uc9c0\uc5d0\uc11c \ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc73c\uba70, \ub370\uc774\ud130\uc14b\uc740 \uacc4\uc18d\ud574\uc11c \ucd94\uac00\ub420 \uc608\uc815\uc785\ub2c8\ub2e4.\n\n* `torchvision <https://pytorch.org/docs/stable/torchvision/datasets.html>`__\n* `torchaudio <https://pytorch.org/audio/datasets.html>`__\n* `torchtext <https://pytorch.org/text/datasets.html>`__\n\n\n\uc774\ubc88 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 ``torchaudio.datasets.YESNO`` \ub370\uc774\ud130\uc14b\uc744 \uc0b4\ud3b4\ubcf4\uba74\uc11c,\nPyTorch ``Dataset`` \uc5d0\uc11c PyTorch ``DataLoader`` \ub85c \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc774\uace0 \ud6a8\uc728\uc801\uc73c\ub85c\n\ubd88\ub7ec\uc624\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\ucd08\uae30 \uc124\uc815\n-----\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0, \ub370\uc774\ud130\uc14b\uc774 \ud3ec\ud568\ub41c ``torchaudio`` \ud328\ud0a4\uc9c0\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.\n\n::\n\n   pip install torchaudio\n"
       ]
     },
     {
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/ce0d3d42f46408ae910553ca370054c0/graph_mode_static_quantization_tutorial.ipynb b/docs/_downloads/ce0d3d42f46408ae910553ca370054c0/graph_mode_static_quantization_tutorial.ipynb
index 79edcee36..f2f04e268 100644
--- a/docs/_downloads/ce0d3d42f46408ae910553ca370054c0/graph_mode_static_quantization_tutorial.ipynb
+++ b/docs/_downloads/ce0d3d42f46408ae910553ca370054c0/graph_mode_static_quantization_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n(prototype) Graph Mode Post Training Static Quantization in PyTorch\n=========================================================\n\n**Author**: `Jerry Zhang <https://github.com/jerryzh168>`_\n\nThis tutorial introduces the steps to do post training static quantization in graph mode. \nThe advantage of graph mode quantization is that as long as the model can be scripted or traced, \nwe can perform quantization fully automatically on the model. \nRight now we can do post training static and post training dynamic quantization \nand quantization aware training support will come later. \nWe have a separate tutorial for `Graph Mode Post Training Dynamic Quantization <https://pytorch.org/tutorials/prototype_source/graph_mode_dynamic_bert_tutorial.html>`_.\n\ntldr; The graph mode API looks like the following:\n\n.. code:: python\n\n    import torch\n    from torch.quantization import get_default_qconfig, quantize_jit\n    \n    ts_model = torch.jit.script(float_model.eval()) # or torch.jit.trace(float_model, input)\n    qconfig = get_default_qconfig('fbgemm')\n    def calibrate(model, data_loader):\n        model.eval()\n        with torch.no_grad():\n            for image, target in data_loader:\n                model(image)\n    quantized_model = quantize_jit(\n        ts_model, # TorchScript model\n        {'': qconfig}, # qconfig dict\n        calibrate, # calibration function\n        [data_loader_test]) # positional arguments to calibration function, typically some sample dataset\n\n\n"
+        "\n(prototype) Graph Mode Post Training Static Quantization in PyTorch\n=========================================================\n\n**Author**: `Jerry Zhang <https://github.com/jerryzh168>`_\n\nThis tutorial introduces the steps to do post training static quantization in graph mode. \nThe advantage of graph mode quantization is that as long as the model can be scripted or traced, \nwe can perform quantization fully automatically on the model. \nRight now we can do post training static and post training dynamic quantization \nand quantization aware training support will come later. \nWe have a separate tutorial for `Graph Mode Post Training Dynamic Quantization <https://pytorch.org/tutorials/prototype_source/graph_mode_dynamic_bert_tutorial.html>`_.\n\ntldr; The graph mode API looks like the following:\n\n.. code:: python\n\n    import torch\n    from torch.quantization import get_default_qconfig, quantize_jit\n    \n    ts_model = torch.jit.script(float_model.eval()) # or torch.jit.trace(float_model, input)\n    qconfig = get_default_qconfig('fbgemm')\n    def calibrate(model, data_loader):\n        model.eval()\n        with torch.no_grad():\n            for image, target in data_loader:\n                model(image)\n    quantized_model = quantize_jit(\n        ts_model, # TorchScript model\n        {'': qconfig}, # qconfig dict\n        calibrate, # calibration function\n        [data_loader_test]) # positional arguments to calibration function, typically some sample dataset\n"
       ]
     },
     {
@@ -258,7 +258,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/d493dae89f8804b07cdf678f7d0c2dc6/tensorboard_with_pytorch.ipynb b/docs/_downloads/d493dae89f8804b07cdf678f7d0c2dc6/tensorboard_with_pytorch.ipynb
index 55bbb9cd4..fa43e8748 100644
--- a/docs/_downloads/d493dae89f8804b07cdf678f7d0c2dc6/tensorboard_with_pytorch.ipynb
+++ b/docs/_downloads/d493dae89f8804b07cdf678f7d0c2dc6/tensorboard_with_pytorch.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\ub85c TensorBoard \uc0ac\uc6a9\ud558\uae30\n===================================\nTensorBoard\ub294 \uba38\uc2e0\ub7ec\ub2dd \uc2e4\ud5d8\uc744 \uc704\ud55c \uc2dc\uac01\ud654 \ud234\ud0b7(toolkit)\uc785\ub2c8\ub2e4.\nTensorBoard\ub97c \uc0ac\uc6a9\ud558\uba74 \uc190\uc2e4 \ubc0f \uc815\ud655\ub3c4\uc640 \uac19\uc740 \uce21\uc815 \ud56d\ubaa9\uc744 \ucd94\uc801 \ubc0f \uc2dc\uac01\ud654\ud558\ub294 \uac83,\n\ubaa8\ub378 \uadf8\ub798\ud504\ub97c \uc2dc\uac01\ud654\ud558\ub294 \uac83, \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \ubcf4\ub294 \uac83, \uc774\ubbf8\uc9c0\ub97c \ucd9c\ub825\ud558\ub294 \uac83 \ub4f1\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 TensorBoard \uc124\uce58, PyTorch\uc758 \uae30\ubcf8 \uc0ac\uc6a9\ubc95,\nTensorBoard UI\uc5d0 \uae30\ub85d\ud55c \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654 \ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.\n\n\uc124\uce58\ud558\uae30\n----------------------\n\ubaa8\ub378\uacfc \uce21\uc815 \ud56d\ubaa9\uc744 TensorBoard \ub85c\uadf8 \ub514\ub809\ud130\ub9ac\uc5d0 \uae30\ub85d\ud558\ub824\uba74 PyTorch\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\nAnaconda\ub97c \ud1b5\ud574 PyTorch 1.4 \uc774\uc0c1\uc744 \uc124\uce58\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.(\uad8c\uc7a5):\n::\n\n   $ conda install pytorch torchvision -c pytorch \n   \n\n\ub610\ub294 pip\ub97c \uc0ac\uc6a9\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\n::\n\n   $ pip install torch torchvision\n\n\n"
+        "\nPyTorch\ub85c TensorBoard \uc0ac\uc6a9\ud558\uae30\n===================================\nTensorBoard\ub294 \uba38\uc2e0\ub7ec\ub2dd \uc2e4\ud5d8\uc744 \uc704\ud55c \uc2dc\uac01\ud654 \ud234\ud0b7(toolkit)\uc785\ub2c8\ub2e4.\nTensorBoard\ub97c \uc0ac\uc6a9\ud558\uba74 \uc190\uc2e4 \ubc0f \uc815\ud655\ub3c4\uc640 \uac19\uc740 \uce21\uc815 \ud56d\ubaa9\uc744 \ucd94\uc801 \ubc0f \uc2dc\uac01\ud654\ud558\ub294 \uac83,\n\ubaa8\ub378 \uadf8\ub798\ud504\ub97c \uc2dc\uac01\ud654\ud558\ub294 \uac83, \ud788\uc2a4\ud1a0\uadf8\ub7a8\uc744 \ubcf4\ub294 \uac83, \uc774\ubbf8\uc9c0\ub97c \ucd9c\ub825\ud558\ub294 \uac83 \ub4f1\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 TensorBoard \uc124\uce58, PyTorch\uc758 \uae30\ubcf8 \uc0ac\uc6a9\ubc95,\nTensorBoard UI\uc5d0 \uae30\ub85d\ud55c \ub370\uc774\ud130\ub97c \uc2dc\uac01\ud654 \ud558\ub294 \ubc29\ubc95\uc744 \ub2e4\ub8f0 \uac83\uc785\ub2c8\ub2e4.\n\n\uc124\uce58\ud558\uae30\n----------------------\n\ubaa8\ub378\uacfc \uce21\uc815 \ud56d\ubaa9\uc744 TensorBoard \ub85c\uadf8 \ub514\ub809\ud130\ub9ac\uc5d0 \uae30\ub85d\ud558\ub824\uba74 PyTorch\ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\nAnaconda\ub97c \ud1b5\ud574 PyTorch 1.4 \uc774\uc0c1\uc744 \uc124\uce58\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.(\uad8c\uc7a5):\n::\n\n   $ conda install pytorch torchvision -c pytorch \n   \n\n\ub610\ub294 pip\ub97c \uc0ac\uc6a9\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\n::\n\n   $ pip install torch torchvision\n"
       ]
     },
     {
@@ -117,7 +117,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/d90127b0deeb355be3a350521d770206/chatbot_tutorial.py b/docs/_downloads/d90127b0deeb355be3a350521d770206/chatbot_tutorial.py
index 6db571cf6..bcce89918 100644
--- a/docs/_downloads/d90127b0deeb355be3a350521d770206/chatbot_tutorial.py
+++ b/docs/_downloads/d90127b0deeb355be3a350521d770206/chatbot_tutorial.py
@@ -1331,7 +1331,7 @@ def evaluateInput(encoder, decoder, searcher, voc):
     for k, v in state.items():
         if isinstance(v, torch.Tensor):
             state[k] = v.cuda()
-    
+
 # 학습 단계를 수행합니다
 print("Starting Training!")
 trainIters(model_name, voc, pairs, encoder, decoder, encoder_optimizer, decoder_optimizer,
diff --git a/docs/_downloads/d9398fce39ca80dc4bb8b8ea55b575a8/nn_tutorial.ipynb b/docs/_downloads/d9398fce39ca80dc4bb8b8ea55b575a8/nn_tutorial.ipynb
index 3e34c6157..90d5d838c 100644
--- a/docs/_downloads/d9398fce39ca80dc4bb8b8ea55b575a8/nn_tutorial.ipynb
+++ b/docs/_downloads/d9398fce39ca80dc4bb8b8ea55b575a8/nn_tutorial.ipynb
@@ -15,14 +15,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n`torch.nn` \uc774 *\uc2e4\uc81c\ub85c* \ubb34\uc5c7\uc778\uac00\uc694?\n===============================\n\uc800\uc790: Jeremy Howard, `fast.ai <https://www.fast.ai>`_.\n\n\ub3c4\uc6c0: Rachel Thomas, Francisco Ingham.\n\n\ubc88\uc5ed: `\ub0a8\uc0c1\ud638 <https://github.com/namdori61>`_\n\n"
+        "\n`torch.nn` \uc774 *\uc2e4\uc81c\ub85c* \ubb34\uc5c7\uc778\uac00\uc694?\n=====================================\n\uc800\uc790: Jeremy Howard, `fast.ai <https://www.fast.ai>`_.\n\n\ub3c4\uc6c0: Rachel Thomas, Francisco Ingham.\n\n\ubc88\uc5ed: `\ub0a8\uc0c1\ud638 <https://github.com/namdori61>`_\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc2a4\ud06c\ub9bd\ud2b8\uac00 \uc544\ub2cc \ub178\ud2b8\ubd81\uc73c\ub85c \uc2e4\ud589\ud558\uae30\ub97c \uad8c\uc7a5\ud569\ub2c8\ub2e4. \ub178\ud2b8\ubd81 (.ipynb) \ud30c\uc77c\uc744 \ub2e4\uc6b4 \ubc1b\uc73c\uc2dc\ub824\uba74,\n\ud398\uc774\uc9c0 \uc0c1\ub2e8\uc5d0 \uc788\ub294 \ub9c1\ud06c\ub97c \ud074\ub9ad\ud574\uc8fc\uc138\uc694.\n\nPyTorch \ub294 \uc5ec\ub7ec\ubd84\uc774 \uc2e0\uacbd\ub9dd(neural network)\ub97c \uc0dd\uc131\ud558\uace0 \ud559\uc2b5\uc2dc\ud0a4\ub294 \uac83\uc744 \ub3c4\uc640\uc8fc\uae30 \uc704\ud574\uc11c\n`torch.nn <https://pytorch.org/docs/stable/nn.html>`_ ,\n`torch.optim <https://pytorch.org/docs/stable/optim.html>`_ ,\n`Dataset <https://pytorch.org/docs/stable/data.html?highlight=dataset#torch.utils.data.Dataset>`_ ,\n\uadf8\ub9ac\uace0 `DataLoader <https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader>`_\n\uc640 \uac19\uc740 \uc798 \ub514\uc790\uc778\ub41c \ubaa8\ub4c8\uacfc \ud074\ub798\uc2a4\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774\ub4e4\uc758 \uc131\ub2a5\uc744 \ucd5c\ub300\ud55c \ud65c\uc6a9\ud558\uace0 \uc5ec\ub7ec\ubd84\uc758 \ubb38\uc81c\uc5d0 \ub9de\uac8c \ucee4\uc2a4\ud130\ub9c8\uc774\uc988\ud558\uae30 \uc704\ud574\uc11c,\n\uc815\ud655\ud788 \uc774\ub4e4\uc774 \uc5b4\ub5a4 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294\uc9c0 \uc774\ud574\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ud574\ub97c \uc99d\uc9c4\ud558\uae30 \uc704\ud574\uc11c, \uc6b0\ub9ac\ub294 \uba3c\uc800 \uc774\ub4e4 \ubaa8\ub378\ub4e4\ub85c \ubd80\ud130 \uc544\ubb34 \ud53c\uccd0\ub3c4 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\uace0\nMNIST \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574 \uae30\ucd08\uc801\uc778 \uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\uc2dc\ud0ac \uac83\uc785\ub2c8\ub2e4;\n\uc6b0\ub9ac\ub294 \ucc98\uc74c\uc5d0\ub294 \uac00\uc7a5 \uae30\ucd08\uc801\uc778 PyTorch \ud150\uc11c(tensor) \uae30\ub2a5\ub9cc\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0\ub098\uc11c \uc6b0\ub9ac\ub294 \uc810\ucc28\uc801\uc73c\ub85c ``torch.nn``, ``torch.optim``, ``Dataset``, \ub610\ub294\n``DataLoader`` \ub85c\ubd80\ud130 \ud55c\ubc88\uc5d0 \ud558\ub098\uc529 \ud53c\uccd0\ub97c \ucd94\uac00\ud558\uba74\uc11c, \uc815\ud655\ud788 \uac01 \ubd80\ubd84\uc774 \uc5b4\ub5a4 \uc77c\uc744 \ud558\ub294\uc9c0 \uadf8\ub9ac\uace0\n\uc774\uac83\uc774 \uc5b4\ub5bb\uac8c \ucf54\ub4dc\ub97c \ub354 \uc815\ud655\ud558\uace0 \uc720\uc5f0\ud558\uac8c \ub9cc\ub4dc\ub294\uc9c0 \ubcf4\uc5ec\uc904 \uac83\uc785\ub2c8\ub2e4.\n\n**\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 \uc5ec\ub7ec\ubd84\uc774 \uc774\ubbf8 PyTorch\ub97c \uc124\uce58\ud558\uc600\uace0, \uadf8\ub9ac\uace0 \ud150\uc11c \uc5f0\uc0b0\uc758 \uae30\ucd08\uc5d0 \ub300\ud574 \uc775\uc219\ud558\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.**\n(\ub9cc\uc57d \uc5ec\ub7ec\ubd84\uc774 Numpy \ubc30\uc5f4(array) \uc5f0\uc0b0\uc5d0 \uc775\uc219\ud558\ub2e4\uba74, \uc5ec\uae30\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 PyTorch \ud150\uc11c \uc5f0\uc0b0\ub3c4\n\uac70\uc758 \ub3d9\uc77c\ud558\ub2e4\ub294 \uac83\uc744 \uc54c\uac8c \ub420 \uac83\uc785\ub2c8\ub2e4).\n\nMNIST \ub370\uc774\ud130 \uc900\ube44\n----------------\n\n\uc6b0\ub9ac\ub294 \uc190\uc73c\ub85c \uc4f4 \uc22b\uc790(0\uc5d0\uc11c 9 \uc0ac\uc774)\uc758 \ud751\ubc31 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub41c \ud074\ub798\uc2dd\n`MNIST <http://deeplearning.net/data/mnist/>`_ \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83 \uc785\ub2c8\ub2e4.\n\n\uc6b0\ub9ac\ub294 \uacbd\ub85c \uc124\uc815\uc744 \ub2f4\ub2f9\ud558\ub294 (Python3 \ud45c\uc900 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 \uc77c\ubd80\uc778)\n`pathlib <https://docs.python.org/3/library/pathlib.html>`_ \uc744 \uc0ac\uc6a9\ud560 \uac83\uc774\uace0,\n`requests <http://docs.python-requests.org/en/master/>`_ \ub97c \uc774\uc6a9\ud558\uc5ec\n\ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc \ud560 \uac83\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \ubaa8\ub4c8\uc744 \uc0ac\uc6a9\ud560 \ub54c\ub9cc \uc784\ud3ec\ud2b8(import) \ud560 \uac83\uc774\ubbc0\ub85c,\n\uc5ec\ub7ec\ubd84\uc740 \ub9e4 \ud3ec\uc778\ud2b8\ub9c8\ub2e4 \uc815\ud655\ud788 \uc5b4\ub5a4 \uac83\uc774 \uc0ac\uc6a9\ub418\ub294\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc2a4\ud06c\ub9bd\ud2b8\uac00 \uc544\ub2cc \ub178\ud2b8\ubd81\uc73c\ub85c \uc2e4\ud589\ud558\uae30\ub97c \uad8c\uc7a5\ud569\ub2c8\ub2e4. \ub178\ud2b8\ubd81 (.ipynb) \ud30c\uc77c\uc744 \ub2e4\uc6b4 \ubc1b\uc73c\uc2dc\ub824\uba74,\n\ud398\uc774\uc9c0 \uc0c1\ub2e8\uc5d0 \uc788\ub294 \ub9c1\ud06c\ub97c \ud074\ub9ad\ud574\uc8fc\uc138\uc694.\n\nPyTorch \ub294 \uc5ec\ub7ec\ubd84\uc774 \uc2e0\uacbd\ub9dd(neural network)\ub97c \uc0dd\uc131\ud558\uace0 \ud559\uc2b5\uc2dc\ud0a4\ub294 \uac83\uc744 \ub3c4\uc640\uc8fc\uae30 \uc704\ud574\uc11c\n`torch.nn <https://pytorch.org/docs/stable/nn.html>`_ ,\n`torch.optim <https://pytorch.org/docs/stable/optim.html>`_ ,\n`Dataset <https://pytorch.org/docs/stable/data.html?highlight=dataset#torch.utils.data.Dataset>`_ ,\n\uadf8\ub9ac\uace0 `DataLoader <https://pytorch.org/docs/stable/data.html?highlight=dataloader#torch.utils.data.DataLoader>`_\n\uc640 \uac19\uc740 \uc798 \ub514\uc790\uc778\ub41c \ubaa8\ub4c8\uacfc \ud074\ub798\uc2a4\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774\ub4e4\uc758 \uc131\ub2a5\uc744 \ucd5c\ub300\ud55c \ud65c\uc6a9\ud558\uace0 \uc5ec\ub7ec\ubd84\uc758 \ubb38\uc81c\uc5d0 \ub9de\uac8c \ucee4\uc2a4\ud130\ub9c8\uc774\uc988\ud558\uae30 \uc704\ud574\uc11c,\n\uc815\ud655\ud788 \uc774\ub4e4\uc774 \uc5b4\ub5a4 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294\uc9c0 \uc774\ud574\ud560 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774\ud574\ub97c \uc99d\uc9c4\ud558\uae30 \uc704\ud574\uc11c, \uc6b0\ub9ac\ub294 \uba3c\uc800 \uc774\ub4e4 \ubaa8\ub378\ub4e4\ub85c \ubd80\ud130 \uc544\ubb34 \ud53c\uccd0\ub3c4 \uc0ac\uc6a9\ud558\uc9c0 \uc54a\uace0\nMNIST \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574 \uae30\ucd08\uc801\uc778 \uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\uc2dc\ud0ac \uac83\uc785\ub2c8\ub2e4;\n\uc6b0\ub9ac\ub294 \ucc98\uc74c\uc5d0\ub294 \uac00\uc7a5 \uae30\ucd08\uc801\uc778 PyTorch \ud150\uc11c(tensor) \uae30\ub2a5\ub9cc\uc744 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uadf8\ub9ac\uace0\ub098\uc11c \uc6b0\ub9ac\ub294 \uc810\ucc28\uc801\uc73c\ub85c ``torch.nn``, ``torch.optim``, ``Dataset``, \ub610\ub294\n``DataLoader`` \ub85c\ubd80\ud130 \ud55c\ubc88\uc5d0 \ud558\ub098\uc529 \ud53c\uccd0\ub97c \ucd94\uac00\ud558\uba74\uc11c, \uc815\ud655\ud788 \uac01 \ubd80\ubd84\uc774 \uc5b4\ub5a4 \uc77c\uc744 \ud558\ub294\uc9c0 \uadf8\ub9ac\uace0\n\uc774\uac83\uc774 \uc5b4\ub5bb\uac8c \ucf54\ub4dc\ub97c \ub354 \uc815\ud655\ud558\uace0 \uc720\uc5f0\ud558\uac8c \ub9cc\ub4dc\ub294\uc9c0 \ubcf4\uc5ec\uc904 \uac83\uc785\ub2c8\ub2e4.\n\n**\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc740 \uc5ec\ub7ec\ubd84\uc774 \uc774\ubbf8 PyTorch\ub97c \uc124\uce58\ud558\uc600\uace0, \uadf8\ub9ac\uace0 \ud150\uc11c \uc5f0\uc0b0\uc758 \uae30\ucd08\uc5d0 \ub300\ud574 \uc775\uc219\ud558\ub2e4\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.**\n(\ub9cc\uc57d \uc5ec\ub7ec\ubd84\uc774 Numpy \ubc30\uc5f4(array) \uc5f0\uc0b0\uc5d0 \uc775\uc219\ud558\ub2e4\uba74, \uc5ec\uae30\uc5d0\uc11c \uc0ac\uc6a9\ub418\ub294 PyTorch \ud150\uc11c \uc5f0\uc0b0\ub3c4\n\uac70\uc758 \ub3d9\uc77c\ud558\ub2e4\ub294 \uac83\uc744 \uc54c\uac8c \ub420 \uac83\uc785\ub2c8\ub2e4).\n\nMNIST \ub370\uc774\ud130 \uc900\ube44\n-------------------\n\n\uc6b0\ub9ac\ub294 \uc190\uc73c\ub85c \uc4f4 \uc22b\uc790(0\uc5d0\uc11c 9 \uc0ac\uc774)\uc758 \ud751\ubc31 \uc774\ubbf8\uc9c0\ub85c \uad6c\uc131\ub41c \ud074\ub798\uc2dd\n`MNIST <http://deeplearning.net/data/mnist/>`_ \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud560 \uac83 \uc785\ub2c8\ub2e4.\n\n\uc6b0\ub9ac\ub294 \uacbd\ub85c \uc124\uc815\uc744 \ub2f4\ub2f9\ud558\ub294 (Python3 \ud45c\uc900 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc758 \uc77c\ubd80\uc778)\n`pathlib <https://docs.python.org/3/library/pathlib.html>`_ \uc744 \uc0ac\uc6a9\ud560 \uac83\uc774\uace0,\n`requests <http://docs.python-requests.org/en/master/>`_ \ub97c \uc774\uc6a9\ud558\uc5ec\n\ub370\uc774\ud130\uc14b\uc744 \ub2e4\uc6b4\ub85c\ub4dc \ud560 \uac83\uc785\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \ubaa8\ub4c8\uc744 \uc0ac\uc6a9\ud560 \ub54c\ub9cc \uc784\ud3ec\ud2b8(import) \ud560 \uac83\uc774\ubbc0\ub85c,\n\uc5ec\ub7ec\ubd84\uc740 \ub9e4 \ud3ec\uc778\ud2b8\ub9c8\ub2e4 \uc815\ud655\ud788 \uc5b4\ub5a4 \uac83\uc774 \uc0ac\uc6a9\ub418\ub294\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "from pathlib import Path\nimport requests\n\nDATA_PATH = Path(\"data\")\nPATH = DATA_PATH / \"mnist\"\n\nPATH.mkdir(parents=True, exist_ok=True)\n\nURL = \"http://deeplearning.net/data/mnist/\"\nFILENAME = \"mnist.pkl.gz\"\n\nif not (PATH / FILENAME).exists():\n        content = requests.get(URL + FILENAME).content\n        (PATH / FILENAME).open(\"wb\").write(content)"
+        "from pathlib import Path\nimport requests\n\nDATA_PATH = Path(\"data\")\nPATH = DATA_PATH / \"mnist\"\n\nPATH.mkdir(parents=True, exist_ok=True)\n\nURL = \"https://github.com/pytorch/tutorials/raw/master/_static/\"\nFILENAME = \"mnist.pkl.gz\"\n\nif not (PATH / FILENAME).exists():\n        content = requests.get(URL + FILENAME).content\n        (PATH / FILENAME).open(\"wb\").write(content)"
       ]
     },
     {
@@ -292,7 +292,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "nn.Module \uc744 \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1 \ud558\uae30\n------------------------------\n\ub2e4\uc74c\uc73c\ub85c, \ub354 \uba85\ud655\ud558\uace0 \uac04\uacb0\ud55c \ud6c8\ub828 \ub8e8\ud504\ub97c \uc704\ud574 ``nn.Module`` \ubc0f ``nn.Parameter`` \ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\uc6b0\ub9ac\ub294 ``nn.Module`` (\uc790\uccb4\uac00 \ud074\ub798\uc2a4\uc774\uace0 \uc0c1\ud0dc\ub97c \ucd94\ucc99\ud560 \uc218 \uc788\ub294) \ud558\uc704 \ud074\ub798\uc2a4(subclass)\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.\n\uc774 \uacbd\uc6b0\uc5d0\ub294, \ud3ec\uc6cc\ub4dc(forward) \ub2e8\uacc4\uc5d0 \ub300\ud55c \uac00\uc911\uce58, \uc808\ud3b8, \uadf8\ub9ac\uace0 \uba54\uc18c\ub4dc(method) \ub4f1\uc744 \uc720\uc9c0\ud558\ub294\n\ud074\ub798\uc2a4\ub97c \ub9cc\ub4e4\uace0\uc790 \ud569\ub2c8\ub2e4.\n``nn.Module`` \uc740 \uc6b0\ub9ac\uac00 \uc0ac\uc6a9\ud560 \uba87 \uac00\uc9c0 \uc18d\uc131(attribute)\uacfc \uba54\uc18c\ub4dc\ub97c (``.parameters()`` \uc640\n``.zero_grad()`` \uac19\uc740) \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>``nn.Module`` (\ub300\ubb38\uc790 M) \uc740 PyTorch \uc758 \ud2b9\uc815 \uac1c\ub150\uc774\uace0, \uc6b0\ub9ac\ub294 \uc774 \ud074\ub798\uc2a4\ub97c\n   \ub9ce\uc774 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. ``nn.Module`` \ub97c Python \uc758 \ucf54\ub4dc\ub97c \uc784\ud3ec\ud2b8\ud558\uae30 \uc704\ud55c \ucf54\ub4dc \ud30c\uc77c\uc778\n   `module <https://docs.python.org/3/tutorial/modules.html>`_ (\uc18c\ubb38\uc790 ``m``)\n   \uc758 \uac1c\ub150\uacfc \ud5f7\uac08\ub9ac\uc9c0 \ub9d0\uc544\uc8fc\uc138\uc694.</p></div>\n\n"
+        "nn.Module \uc744 \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1 \ud558\uae30\n--------------------------------------\n\ub2e4\uc74c\uc73c\ub85c, \ub354 \uba85\ud655\ud558\uace0 \uac04\uacb0\ud55c \ud6c8\ub828 \ub8e8\ud504\ub97c \uc704\ud574 ``nn.Module`` \ubc0f ``nn.Parameter`` \ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n\uc6b0\ub9ac\ub294 ``nn.Module`` (\uc790\uccb4\uac00 \ud074\ub798\uc2a4\uc774\uace0 \uc0c1\ud0dc\ub97c \ucd94\ucc99\ud560 \uc218 \uc788\ub294) \ud558\uc704 \ud074\ub798\uc2a4(subclass)\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.\n\uc774 \uacbd\uc6b0\uc5d0\ub294, \ud3ec\uc6cc\ub4dc(forward) \ub2e8\uacc4\uc5d0 \ub300\ud55c \uac00\uc911\uce58, \uc808\ud3b8, \uadf8\ub9ac\uace0 \uba54\uc18c\ub4dc(method) \ub4f1\uc744 \uc720\uc9c0\ud558\ub294\n\ud074\ub798\uc2a4\ub97c \ub9cc\ub4e4\uace0\uc790 \ud569\ub2c8\ub2e4.\n``nn.Module`` \uc740 \uc6b0\ub9ac\uac00 \uc0ac\uc6a9\ud560 \uba87 \uac00\uc9c0 \uc18d\uc131(attribute)\uacfc \uba54\uc18c\ub4dc\ub97c (``.parameters()`` \uc640\n``.zero_grad()`` \uac19\uc740) \uac00\uc9c0\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>``nn.Module`` (\ub300\ubb38\uc790 M) \uc740 PyTorch \uc758 \ud2b9\uc815 \uac1c\ub150\uc774\uace0, \uc6b0\ub9ac\ub294 \uc774 \ud074\ub798\uc2a4\ub97c\n   \ub9ce\uc774 \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4. ``nn.Module`` \ub97c Python \uc758 \ucf54\ub4dc\ub97c \uc784\ud3ec\ud2b8\ud558\uae30 \uc704\ud55c \ucf54\ub4dc \ud30c\uc77c\uc778\n   `module <https://docs.python.org/3/tutorial/modules.html>`_ (\uc18c\ubb38\uc790 ``m``)\n   \uc758 \uac1c\ub150\uacfc \ud5f7\uac08\ub9ac\uc9c0 \ub9d0\uc544\uc8fc\uc138\uc694.</p></div>\n\n"
       ]
     },
     {
@@ -346,7 +346,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc774\uc804\uc5d0\ub294 \ud6c8\ub828 \ub8e8\ud504\ub97c \uc704\ud574 \uc774\ub984 \ubcc4\ub85c \uac01 \ub9e4\uac1c\ubcc0\uc218(parameter)\uc758 \uac12\uc744 \uc5c5\ub370\uc774\ud2b8\ud558\uace0 \ub2e4\uc74c\uacfc \uac19\uc774\n\uac01 \ub9e4\uac1c \ubcc0\uc218\uc5d0 \ub300\ud55c \uae30\uc6b8\uae30\ub4e4\uc744 \uac1c\ubcc4\uc801\uc73c\ub85c \uc218\ub3d9\uc73c\ub85c 0\uc73c\ub85c \uc81c\uac70\ud574\uc57c \ud588\uc2b5\ub2c8\ub2e4:\n::\n  with torch.no_grad():\n      weights -= weights.grad * lr\n      bias -= bias.grad * lr\n      weights.grad.zero_()\n      bias.grad.zero_()\n\n\n\uc774\uc81c \uc6b0\ub9ac\ub294 model.parameters() \ubc0f model.zero_grad() (\ubaa8\ub450\n``nn.Module`` \uc5d0 \ub300\ud574 PyTorch\uc5d0 \uc758\ud574 \uc815\uc758\ub428)\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ub7ec\ud55c \ub2e8\uacc4\ub97c \ub354 \uac04\uacb0\ud558\uac8c\n\ub9cc\ub4e4\uace0, \ud2b9\ud788 \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc5d0 \ub300\ud574\uc11c \uc77c\ubd80 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc78a\uc5b4 \ubc84\ub9ac\ub294 \uc624\ub958\ub97c \ub35c \ubc1c\uc0dd\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n::\n  with torch.no_grad():\n      for p in model.parameters(): p -= p.grad * lr\n      model.zero_grad()\n\n\n\uc774\uc81c \uc774\uac83\uc744 \ub098\uc911\uc5d0 \ub2e4\uc2dc \uc2e4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d ``fit`` \ud568\uc218\ub85c \uc791\uc740 \ud6c8\ub828 \ub8e8\ud504\ub97c \uac10\uc300 \uac83\uc785\ub2c8\ub2e4.\n\n"
+        "\uc774\uc804\uc5d0\ub294 \ud6c8\ub828 \ub8e8\ud504\ub97c \uc704\ud574 \uc774\ub984 \ubcc4\ub85c \uac01 \ub9e4\uac1c\ubcc0\uc218(parameter)\uc758 \uac12\uc744 \uc5c5\ub370\uc774\ud2b8\ud558\uace0 \ub2e4\uc74c\uacfc \uac19\uc774\n\uac01 \ub9e4\uac1c \ubcc0\uc218\uc5d0 \ub300\ud55c \uae30\uc6b8\uae30\ub4e4\uc744 \uac1c\ubcc4\uc801\uc73c\ub85c \uc218\ub3d9\uc73c\ub85c 0\uc73c\ub85c \uc81c\uac70\ud574\uc57c \ud588\uc2b5\ub2c8\ub2e4:\n\n::\n\n  with torch.no_grad():\n      weights -= weights.grad * lr\n      bias -= bias.grad * lr\n      weights.grad.zero_()\n      bias.grad.zero_()\n\n\n\uc774\uc81c \uc6b0\ub9ac\ub294 model.parameters() \ubc0f model.zero_grad() (\ubaa8\ub450\n``nn.Module`` \uc5d0 \ub300\ud574 PyTorch\uc5d0 \uc758\ud574 \uc815\uc758\ub428)\ub97c \ud65c\uc6a9\ud558\uc5ec \uc774\ub7ec\ud55c \ub2e8\uacc4\ub97c \ub354 \uac04\uacb0\ud558\uac8c\n\ub9cc\ub4e4\uace0, \ud2b9\ud788 \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc5d0 \ub300\ud574\uc11c \uc77c\ubd80 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc78a\uc5b4 \ubc84\ub9ac\ub294 \uc624\ub958\ub97c \ub35c \ubc1c\uc0dd\uc2dc\ud0ac \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n\n::\n\n  with torch.no_grad():\n      for p in model.parameters(): p -= p.grad * lr\n      model.zero_grad()\n\n\n\uc774\uc81c \uc774\uac83\uc744 \ub098\uc911\uc5d0 \ub2e4\uc2dc \uc2e4\ud589\ud560 \uc218 \uc788\ub3c4\ub85d ``fit`` \ud568\uc218\ub85c \uc791\uc740 \ud6c8\ub828 \ub8e8\ud504\ub97c \uac10\uc300 \uac83\uc785\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -382,7 +382,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "nn.Linear \ub97c \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1 \ud558\uae30\n------------------------------\n\n\uacc4\uc18d\ud574\uc11c \ucf54\ub4dc\ub97c \ub9ac\ud329\ud1a0\ub9c1 \ud569\ub2c8\ub2e4. ``self.weights`` \ubc0f ``self.bias`` \ub97c \uc218\ub3d9\uc73c\ub85c \uc815\uc758 \ubc0f\n\ucd08\uae30\ud654\ud558\uace0, ``xb  @ self.weights + self.bias`` \ub97c \uacc4\uc0b0\ud558\ub294 \ub300\uc2e0\uc5d0,\n\uc704\uc758 \ubaa8\ub4e0 \uac83\uc744 \ud574\uc904 Pytorch \ud074\ub798\uc2a4\uc778\n`nn.Linear <https://pytorch.org/docs/stable/nn.html#linear-layers>`_ \ub97c \uc120\ud615\n\ub808\uc774\uc5b4\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\nPytorch \uc5d0\ub294 \ub2e4\uc591\ud55c \uc720\ud615\uc758 \ucf54\ub4dc\ub97c \ud06c\uac8c \ub2e8\uc21c\ud654 \ud560 \uc218 \uc788\ub294 \ubbf8\ub9ac \uc815\uc758\ub41c \ub808\uc774\uc5b4\uac00 \uc788\uace0 \uc774\ub294 \ub610\ud55c\n\uc885\uc885 \uae30\uc874 \ucf54\ub4dc\ubcf4\ub2e4 \uc18d\ub3c4\ub97c \ube60\ub974\uac8c \ud569\ub2c8\ub2e4.\n\n"
+        "nn.Linear \ub97c \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1 \ud558\uae30\n------------------------------------\n\n\uacc4\uc18d\ud574\uc11c \ucf54\ub4dc\ub97c \ub9ac\ud329\ud1a0\ub9c1 \ud569\ub2c8\ub2e4. ``self.weights`` \ubc0f ``self.bias`` \ub97c \uc218\ub3d9\uc73c\ub85c \uc815\uc758 \ubc0f\n\ucd08\uae30\ud654\ud558\uace0, ``xb  @ self.weights + self.bias`` \ub97c \uacc4\uc0b0\ud558\ub294 \ub300\uc2e0\uc5d0,\n\uc704\uc758 \ubaa8\ub4e0 \uac83\uc744 \ud574\uc904 Pytorch \ud074\ub798\uc2a4\uc778\n`nn.Linear <https://pytorch.org/docs/stable/nn.html#linear-layers>`_ \ub97c \uc120\ud615\n\ub808\uc774\uc5b4\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\nPytorch \uc5d0\ub294 \ub2e4\uc591\ud55c \uc720\ud615\uc758 \ucf54\ub4dc\ub97c \ud06c\uac8c \ub2e8\uc21c\ud654 \ud560 \uc218 \uc788\ub294 \ubbf8\ub9ac \uc815\uc758\ub41c \ub808\uc774\uc5b4\uac00 \uc788\uace0 \uc774\ub294 \ub610\ud55c\n\uc885\uc885 \uae30\uc874 \ucf54\ub4dc\ubcf4\ub2e4 \uc18d\ub3c4\ub97c \ube60\ub974\uac8c \ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -436,7 +436,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "optim \uc744 \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1 \ud558\uae30\n------------------------------\n\nPytorch\uc5d0\ub294 \ub2e4\uc591\ud55c \ucd5c\uc801\ud654(optimization) \uc54c\uace0\ub9ac\uc998\uc744 \uac00\uc9c4 \ud328\ud0a4\uc9c0\uc778 ``torch.optim`` \ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\uac01 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc218\ub3d9\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8 \ud558\ub294 \ub300\uc2e0, \uc635\ud2f0\ub9c8\uc774\uc800(optimizer)\uc758 ``step`` \uba54\uc18c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec\n\uc5c5\ub370\uc774\ud2b8\ub97c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774\ub807\uac8c \ud558\uba74 \uc774\uc804\uc5d0 \uc218\ub3d9\uc73c\ub85c \ucf54\ub529\ud55c \ucd5c\uc801\ud654 \ub2e8\uacc4\ub97c \ub300\uccb4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n::\n  with torch.no_grad():\n      for p in model.parameters(): p -= p.grad * lr\n      model.zero_grad()\n\n\ub300\uc2e0\uc5d0 \uc774\ub807\uac8c \ub9d0\uc774\uc8e0:\n::\n  opt.step()\n  opt.zero_grad()\n\n(``optim.zero_grad()`` \ub294 \uae30\uc6b8\uae30\ub97c 0\uc73c\ub85c \uc7ac\uc124\uc815 \ud574\uc90d\ub2c8\ub2e4. \ub2e4\uc74c \ubbf8\ub2c8 \ubc30\uce58\uc5d0 \ub300\ud55c\n\uae30\uc6b8\uae30\ub97c \uacc4\uc0b0\ud558\uae30 \uc804\uc5d0 \ud638\ucd9c\ud574\uc57c \ud569\ub2c8\ub2e4.)\n\n"
+        "optim \uc744 \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1 \ud558\uae30\n---------------------------------\n\nPytorch\uc5d0\ub294 \ub2e4\uc591\ud55c \ucd5c\uc801\ud654(optimization) \uc54c\uace0\ub9ac\uc998\uc744 \uac00\uc9c4 \ud328\ud0a4\uc9c0\uc778 ``torch.optim`` \ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\uac01 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc218\ub3d9\uc73c\ub85c \uc5c5\ub370\uc774\ud2b8 \ud558\ub294 \ub300\uc2e0, \uc635\ud2f0\ub9c8\uc774\uc800(optimizer)\uc758 ``step`` \uba54\uc18c\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec\n\uc5c5\ub370\uc774\ud2b8\ub97c \uc9c4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774\ub807\uac8c \ud558\uba74 \uc774\uc804\uc5d0 \uc218\ub3d9\uc73c\ub85c \ucf54\ub529\ud55c \ucd5c\uc801\ud654 \ub2e8\uacc4\ub97c \ub300\uccb4\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n\n::\n\n  with torch.no_grad():\n      for p in model.parameters(): p -= p.grad * lr\n      model.zero_grad()\n\n\ub300\uc2e0\uc5d0 \uc774\ub807\uac8c \ub9d0\uc774\uc8e0:\n\n::\n\n  opt.step()\n  opt.zero_grad()\n\n(``optim.zero_grad()`` \ub294 \uae30\uc6b8\uae30\ub97c 0\uc73c\ub85c \uc7ac\uc124\uc815 \ud574\uc90d\ub2c8\ub2e4. \ub2e4\uc74c \ubbf8\ub2c8 \ubc30\uce58\uc5d0 \ub300\ud55c\n\uae30\uc6b8\uae30\ub97c \uacc4\uc0b0\ud558\uae30 \uc804\uc5d0 \ud638\ucd9c\ud574\uc57c \ud569\ub2c8\ub2e4.)\n\n"
       ]
     },
     {
@@ -472,7 +472,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Dataset \uc744 \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1\ud558\uae30\n------------------------------\n\nPyTorch \uc5d0\ub294 \ucd94\uc0c1 Dataset \ud074\ub798\uc2a4\uac00 \uc788\uc2b5\ub2c8\ub2e4. Dataset \uc740\n``__len__`` \ud568\uc218 (Python\uc758 \ud45c\uc900 ``len`` \ud568\uc218\uc5d0 \uc758\ud574 \ud638\ucd9c\ub428) \ubc0f\n``__getitem__`` \ud568\uc218\ub97c \uac00\uc9c4 \uc5b4\ub5a4 \uac83\uc774\ub77c\ub3c4 \ub420 \uc218 \uc788\uc73c\uba70, \uc774 \ud568\uc218\ub4e4\uc744 \uc778\ub371\uc2f1(indexing)\ud558\uae30\n\uc704\ud55c \ubc29\ubc95\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n`\uc774 \ud29c\ud1a0\ub9ac\uc5bc <https://pytorch.org/tutorials/beginner/data_loading_tutorial.html>`_\n\uc740 ``Dataset`` \uc758 \ud558\uc704 \ud074\ub798\uc2a4\ub85c\uc368, \uc0ac\uc6a9\uc790 \uc9c0\uc815 ``FacialLandmarkDataset`` \ud074\ub798\uc2a4\ub97c \ub9cc\ub4dc\ub294\n\uc88b\uc740 \uc608\ub97c \uc81c\uc2dc\ud569\ub2c8\ub2e4.\n\nPyTorch \uc758 `TensorDataset <https://pytorch.org/docs/stable/_modules/torch/utils/data/dataset.html#TensorDataset>`_\n\uc740 \ud150\uc11c\ub97c \uac10\uc2f8\ub294(wrapping) Dataset \uc785\ub2c8\ub2e4.\n\uae38\uc774\uc640 \uc778\ub371\uc2f1 \ubc29\uc2dd\uc744 \uc815\uc758\ud568\uc73c\ub85c\uc368 \ud150\uc11c\uc758 \uccab \ubc88\uc9f8 \ucc28\uc6d0\uc744 \ub530\ub77c \ubc18\ubcf5, \uc778\ub371\uc2f1 \ubc0f \uc2ac\ub77c\uc774\uc2a4(slice)\ud558\ub294 \ubc29\ubc95\ub3c4 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774\ub807\uac8c\ud558\uba74 \ud6c8\ub828 \ud560 \ub54c \ub3d9\uc77c\ud55c \ub77c\uc778\uc5d0\uc11c \ub3c5\ub9bd(independent) \ubcc0\uc218\uc640 \uc885\uc18d(dependent) \ubcc0\uc218\uc5d0 \uc27d\uac8c \uc561\uc138\uc2a4 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "Dataset \uc744 \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1\ud558\uae30\n----------------------------------\n\nPyTorch \uc5d0\ub294 \ucd94\uc0c1 Dataset \ud074\ub798\uc2a4\uac00 \uc788\uc2b5\ub2c8\ub2e4. Dataset \uc740\n``__len__`` \ud568\uc218 (Python\uc758 \ud45c\uc900 ``len`` \ud568\uc218\uc5d0 \uc758\ud574 \ud638\ucd9c\ub428) \ubc0f\n``__getitem__`` \ud568\uc218\ub97c \uac00\uc9c4 \uc5b4\ub5a4 \uac83\uc774\ub77c\ub3c4 \ub420 \uc218 \uc788\uc73c\uba70, \uc774 \ud568\uc218\ub4e4\uc744 \uc778\ub371\uc2f1(indexing)\ud558\uae30\n\uc704\ud55c \ubc29\ubc95\uc73c\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n`\uc774 \ud29c\ud1a0\ub9ac\uc5bc <https://pytorch.org/tutorials/beginner/data_loading_tutorial.html>`_\n\uc740 ``Dataset`` \uc758 \ud558\uc704 \ud074\ub798\uc2a4\ub85c\uc368, \uc0ac\uc6a9\uc790 \uc9c0\uc815 ``FacialLandmarkDataset`` \ud074\ub798\uc2a4\ub97c \ub9cc\ub4dc\ub294\n\uc88b\uc740 \uc608\ub97c \uc81c\uc2dc\ud569\ub2c8\ub2e4.\n\nPyTorch \uc758 `TensorDataset <https://pytorch.org/docs/stable/_modules/torch/utils/data/dataset.html#TensorDataset>`_\n\uc740 \ud150\uc11c\ub97c \uac10\uc2f8\ub294(wrapping) Dataset \uc785\ub2c8\ub2e4.\n\uae38\uc774\uc640 \uc778\ub371\uc2f1 \ubc29\uc2dd\uc744 \uc815\uc758\ud568\uc73c\ub85c\uc368 \ud150\uc11c\uc758 \uccab \ubc88\uc9f8 \ucc28\uc6d0\uc744 \ub530\ub77c \ubc18\ubcf5, \uc778\ub371\uc2f1 \ubc0f \uc2ac\ub77c\uc774\uc2a4(slice)\ud558\ub294 \ubc29\ubc95\ub3c4 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774\ub807\uac8c\ud558\uba74 \ud6c8\ub828 \ud560 \ub54c \ub3d9\uc77c\ud55c \ub77c\uc778\uc5d0\uc11c \ub3c5\ub9bd(independent) \ubcc0\uc218\uc640 \uc885\uc18d(dependent) \ubcc0\uc218\uc5d0 \uc27d\uac8c \uc561\uc138\uc2a4 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -508,7 +508,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc774\uc804\uc5d0\ub294 x \ubc0f y \uac12\uc758 \ubbf8\ub2c8 \ubc30\uce58\ub97c \ubcc4\ub3c4\ub85c \ubc18\ubcf5\ud574\uc57c\ud588\uc2b5\ub2c8\ub2e4:\n::\n    xb = x_train[start_i:end_i]\n    yb = y_train[start_i:end_i]\n\n\n\uc774\uc81c \uc774 \ub450 \ub2e8\uacc4\ub97c \ud568\uaed8 \uc218\ud589 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n::\n    xb,yb = train_ds[i*bs : i*bs+bs]\n\n\n"
+        "\uc774\uc804\uc5d0\ub294 x \ubc0f y \uac12\uc758 \ubbf8\ub2c8 \ubc30\uce58\ub97c \ubcc4\ub3c4\ub85c \ubc18\ubcf5\ud574\uc57c\ud588\uc2b5\ub2c8\ub2e4:\n\n::\n\n    xb = x_train[start_i:end_i]\n    yb = y_train[start_i:end_i]\n\n\n\uc774\uc81c \uc774 \ub450 \ub2e8\uacc4\ub97c \ud568\uaed8 \uc218\ud589 \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n\n::\n\n    xb,yb = train_ds[i*bs : i*bs+bs]\n\n\n"
       ]
     },
     {
@@ -526,7 +526,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "DataLoader \ub97c \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1\ud558\uae30\n-------------------------------\n\nPytorch \uc758 ``DataLoader`` \ub294 \ubc30\uce58 \uad00\ub9ac\ub97c \ub2f4\ub2f9\ud569\ub2c8\ub2e4.\n\uc5ec\ub7ec\ubd84\ub4e4\uc740 \ubaa8\ub4e0 ``Dataset`` \uc73c\ub85c\ubd80\ud130 ``DataLoader`` \ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n``DataLoader`` \ub294 \ubc30\uce58\ub4e4\uc5d0 \ub300\ud574\uc11c \ubc18\ubcf5\ud558\uae30 \uc27d\uac8c \ub9cc\ub4e4\uc5b4\uc90d\ub2c8\ub2e4.\n``train_ds[i*bs : i*bs+bs]`` \ub97c \uc0ac\uc6a9\ud558\ub294 \ub300\uc2e0,\nDataLoader \ub294 \ub9e4 \ubbf8\ub2c8\ubc30\uce58\ub97c \uc790\ub3d9\uc801\uc73c\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\n"
+        "DataLoader \ub97c \uc774\uc6a9\ud558\uc5ec \ub9ac\ud329\ud1a0\ub9c1\ud558\uae30\n-----------------------------------\n\nPytorch \uc758 ``DataLoader`` \ub294 \ubc30\uce58 \uad00\ub9ac\ub97c \ub2f4\ub2f9\ud569\ub2c8\ub2e4.\n\uc5ec\ub7ec\ubd84\ub4e4\uc740 \ubaa8\ub4e0 ``Dataset`` \uc73c\ub85c\ubd80\ud130 ``DataLoader`` \ub97c \uc0dd\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n``DataLoader`` \ub294 \ubc30\uce58\ub4e4\uc5d0 \ub300\ud574\uc11c \ubc18\ubcf5\ud558\uae30 \uc27d\uac8c \ub9cc\ub4e4\uc5b4\uc90d\ub2c8\ub2e4.\n``train_ds[i*bs : i*bs+bs]`` \ub97c \uc0ac\uc6a9\ud558\ub294 \ub300\uc2e0,\nDataLoader \ub294 \ub9e4 \ubbf8\ub2c8\ubc30\uce58\ub97c \uc790\ub3d9\uc801\uc73c\ub85c \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -544,7 +544,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc774\uc804\uc5d0\ub294 \ub8e8\ud504\uac00 \ub2e4\uc74c\uacfc \uac19\uc774 \ubc30\uce58 (xb, yb)\ub97c \ubc18\ubcf5\ud588\uc2b5\ub2c8\ub2e4:\n::\n      for i in range((n-1)//bs + 1):\n          xb,yb = train_ds[i*bs : i*bs+bs]\n          pred = model(xb)\n\n\uc774\uc81c (xb, yb)\uac00 DataLoader \uc5d0\uc11c \uc790\ub3d9\uc73c\ub85c \ub85c\ub4dc\ub418\ubbc0\ub85c \ub8e8\ud504\uac00 \ud6e8\uc52c \uae68\ub057\ud574\uc84c\uc2b5\ub2c8\ub2e4:\n::\n      for xb,yb in train_dl:\n          pred = model(xb)\n\n"
+        "\uc774\uc804\uc5d0\ub294 \ub8e8\ud504\uac00 \ub2e4\uc74c\uacfc \uac19\uc774 \ubc30\uce58 (xb, yb)\ub97c \ubc18\ubcf5\ud588\uc2b5\ub2c8\ub2e4:\n\n::\n\n      for i in range((n-1)//bs + 1):\n          xb,yb = train_ds[i*bs : i*bs+bs]\n          pred = model(xb)\n\n\uc774\uc81c (xb, yb)\uac00 DataLoader \uc5d0\uc11c \uc790\ub3d9\uc73c\ub85c \ub85c\ub4dc\ub418\ubbc0\ub85c \ub8e8\ud504\uac00 \ud6e8\uc52c \uae68\ub057\ud574\uc84c\uc2b5\ub2c8\ub2e4:\n\n::\n\n      for xb,yb in train_dl:\n          pred = model(xb)\n\n"
       ]
     },
     {
@@ -562,7 +562,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Pytorch\uc758 nn.Module, nn.Parameter, Dataset \ubc0f DataLoader \ub355\ubd84\uc5d0 \uc774\uc81c \ud6c8\ub828 \ub8e8\ud504\uac00\n\ud6e8\uc52c \ub354 \uc791\uc544\uc9c0\uace0 \uc774\ud574\ud558\uae30 \uc26c\uc6cc\uc84c\uc2b5\ub2c8\ub2e4.\n\uc774\uc81c \uc2e4\uc81c\ub85c \ud6a8\uacfc\uc801\uc778 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \ub370 \ud544\uc694\ud55c \uae30\ubcf8 \uae30\ub2a5\uc744 \ucd94\uac00\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uac80\uc99d(validation) \ucd94\uac00\ud558\uae30\n-----------------------\n\n\uc139\uc158 1\uc5d0\uc11c, \uc6b0\ub9ac\ub294 \ud6c8\ub828 \ub370\uc774\ud130\uc5d0 \uc0ac\uc6a9\ud558\uae30 \uc704\ud574 \ud569\ub9ac\uc801\uc778 \ud6c8\ub828 \ub8e8\ud504\ub97c \uc124\uc815\ud558\ub824\uace0\ud588\uc2b5\ub2c8\ub2e4.\n\uc2e4\uc804\uc5d0\uc11c, \uc5ec\ub7ec\ubd84\ub4e4\uc740 \uacfc\uc801\ud569(overfitting)\uc744 \ud655\uc778\ud558\uae30 \uc704\ud574\uc11c **\ud56d\uc0c1**\n`\uac80\uc99d \ub370\uc774\ud130\uc14b(validation set) <https://www.fast.ai/2017/11/13/validation-sets/>`_ \uc774\n\uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\n\ud6c8\ub828 \ub370\uc774\ud130\ub97c \uc11e\ub294(shuffling) \uac83\uc740 \ubc30\uce58\uc640 \uacfc\uc801\ud569 \uc0ac\uc774\uc758 \uc0c1\uad00\uad00\uacc4\ub97c \ubc29\uc9c0\ud558\uae30 \uc704\ud574\n`\uc911\uc694\ud569\ub2c8\ub2e4. <https://www.quora.com/Does-the-order-of-training-data-matter-when-training-neural-networks>`_\n\ubc18\uba74\uc5d0, \uac80\uc99d \uc190\uc2e4(validation loss)\uc740 \uac80\uc99d \ub370\uc774\ud130\uc14b\uc744 \uc11e\ub4e0 \uc548\uc11e\ub4e0 \ub3d9\uc77c\ud569\ub2c8\ub2e4.\n\ub370\uc774\ud130\ub97c \uc11e\ub294 \uac83\uc740 \ucd94\uac00 \uc2dc\uac04\uc774 \uac78\ub9ac\ubbc0\ub85c, \uac80\uc99d \ub370\uc774\ud130\ub97c \uc11e\ub294 \uac83\uc740 \uc758\ubbf8\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n\uac80\uc99d \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \ubc30\uce58 \uc0ac\uc774\uc988\ub294 \ud559\uc2b5 \ub370\uc774\ud130\uc14b \ubc30\uce58 \ud06c\uae30\uc758 2\ubc30\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774\ub294 \uac80\uc99d \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574\uc11c\ub294 \uc5ed\uc804\ud30c(backpropagation)\uac00 \ud544\uc694\ud558\uc9c0 \uc54a\uc73c\ubbc0\ub85c \uba54\ubaa8\ub9ac\ub97c\n\ub35c \uc0ac\uc6a9\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4 (\uae30\uc6b8\uae30\ub97c \uc800\uc7a5\ud560 \ud544\uc694\uac00 \uc5c6\uc74c).\n\ub354 \ud070 \ubc30\uce58 \ud06c\uae30\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc190\uc2e4\uc744 \ub354 \ube68\ub9ac \uacc4\uc0b0\ud558\uae30 \uc704\ud574 \uc774\ub807\uac8c \ud569\ub2c8\ub2e4.\n\n"
+        "Pytorch\uc758 nn.Module, nn.Parameter, Dataset \ubc0f DataLoader \ub355\ubd84\uc5d0 \uc774\uc81c \ud6c8\ub828 \ub8e8\ud504\uac00\n\ud6e8\uc52c \ub354 \uc791\uc544\uc9c0\uace0 \uc774\ud574\ud558\uae30 \uc26c\uc6cc\uc84c\uc2b5\ub2c8\ub2e4.\n\uc774\uc81c \uc2e4\uc81c\ub85c \ud6a8\uacfc\uc801\uc778 \ubaa8\ub378\uc744 \ub9cc\ub4dc\ub294 \ub370 \ud544\uc694\ud55c \uae30\ubcf8 \uae30\ub2a5\uc744 \ucd94\uac00\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uac80\uc99d(validation) \ucd94\uac00\ud558\uae30\n---------------------------\n\n\uc139\uc158 1\uc5d0\uc11c, \uc6b0\ub9ac\ub294 \ud6c8\ub828 \ub370\uc774\ud130\uc5d0 \uc0ac\uc6a9\ud558\uae30 \uc704\ud574 \ud569\ub9ac\uc801\uc778 \ud6c8\ub828 \ub8e8\ud504\ub97c \uc124\uc815\ud558\ub824\uace0\ud588\uc2b5\ub2c8\ub2e4.\n\uc2e4\uc804\uc5d0\uc11c, \uc5ec\ub7ec\ubd84\ub4e4\uc740 \uacfc\uc801\ud569(overfitting)\uc744 \ud655\uc778\ud558\uae30 \uc704\ud574\uc11c **\ud56d\uc0c1**\n`\uac80\uc99d \ub370\uc774\ud130\uc14b(validation set) <https://www.fast.ai/2017/11/13/validation-sets/>`_ \uc774\n\uc788\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\n\ud6c8\ub828 \ub370\uc774\ud130\ub97c \uc11e\ub294(shuffling) \uac83\uc740 \ubc30\uce58\uc640 \uacfc\uc801\ud569 \uc0ac\uc774\uc758 \uc0c1\uad00\uad00\uacc4\ub97c \ubc29\uc9c0\ud558\uae30 \uc704\ud574\n`\uc911\uc694\ud569\ub2c8\ub2e4. <https://www.quora.com/Does-the-order-of-training-data-matter-when-training-neural-networks>`_\n\ubc18\uba74\uc5d0, \uac80\uc99d \uc190\uc2e4(validation loss)\uc740 \uac80\uc99d \ub370\uc774\ud130\uc14b\uc744 \uc11e\ub4e0 \uc548\uc11e\ub4e0 \ub3d9\uc77c\ud569\ub2c8\ub2e4.\n\ub370\uc774\ud130\ub97c \uc11e\ub294 \uac83\uc740 \ucd94\uac00 \uc2dc\uac04\uc774 \uac78\ub9ac\ubbc0\ub85c, \uac80\uc99d \ub370\uc774\ud130\ub97c \uc11e\ub294 \uac83\uc740 \uc758\ubbf8\uac00 \uc5c6\uc2b5\ub2c8\ub2e4.\n\n\uac80\uc99d \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud55c \ubc30\uce58 \uc0ac\uc774\uc988\ub294 \ud559\uc2b5 \ub370\uc774\ud130\uc14b \ubc30\uce58 \ud06c\uae30\uc758 2\ubc30\ub97c \uc0ac\uc6a9\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774\ub294 \uac80\uc99d \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud574\uc11c\ub294 \uc5ed\uc804\ud30c(backpropagation)\uac00 \ud544\uc694\ud558\uc9c0 \uc54a\uc73c\ubbc0\ub85c \uba54\ubaa8\ub9ac\ub97c\n\ub35c \uc0ac\uc6a9\ud558\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4 (\uae30\uc6b8\uae30\ub97c \uc800\uc7a5\ud560 \ud544\uc694\uac00 \uc5c6\uc74c).\n\ub354 \ud070 \ubc30\uce58 \ud06c\uae30\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc190\uc2e4\uc744 \ub354 \ube68\ub9ac \uacc4\uc0b0\ud558\uae30 \uc704\ud574 \uc774\ub807\uac8c \ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -670,7 +670,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc774\ub7ec\ud55c \uae30\ubcf8 3\uc904\uc758 \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\ucee8\ubcfc\ub8e8\uc158 \uc2e0\uacbd\ub9dd(CNN)\uc744 \ud6c8\ub828\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0 \uc0b4\ud3b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4!\n\nCNN \uc73c\ub85c \ub118\uc5b4\uac00\uae30\n---------------\n\n\uc774\uc81c 3\uac1c\uc758 \ucee8\ubcfc\ub8e8\uc158 \ub808\uc774\uc5b4\ub85c \uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774\uc804 \uc139\uc158\uc758 \uc5b4\ub5a4 \ud568\uc218\ub3c4 \ubaa8\ub378\uc758 \ud615\uc2dd\uc5d0 \ub300\ud574 \uac00\uc815\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0,\n\ubcc4\ub3c4\uc758 \uc218\uc815\uc5c6\uc774 CNN\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\nPytorch \uc758 \uc0ac\uc804\uc815\uc758\ub41c\n`Conv2d <https://pytorch.org/docs/stable/nn.html#torch.nn.Conv2d>`_ \ud074\ub798\uc2a4\ub97c\n\ucee8\ubcfc\ub8e8\uc158 \ub808\uc774\uc5b4\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. 3\uac1c\uc758 \ucee8\ubcfc\ub8e8\uc158 \ub808\uc774\uc5b4\ub85c CNN\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\n\uac01 \ucee8\ubcfc\ub8e8\uc158 \ub4a4\uc5d0\ub294 ReLU\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ub9c8\uc9c0\ub9c9\uc73c\ub85c \ud3c9\uade0 \ud480\ub9c1(average pooling)\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n(``view`` \ub294 PyTorch\uc758 numpy ``reshape`` \ubc84\uc804\uc785\ub2c8\ub2e4.)\n\n"
+        "\uc774\ub7ec\ud55c \uae30\ubcf8 3\uc904\uc758 \ucf54\ub4dc\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \ubaa8\ub378\uc744 \ud6c8\ub828\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\ucee8\ubcfc\ub8e8\uc158 \uc2e0\uacbd\ub9dd(CNN)\uc744 \ud6c8\ub828\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0 \uc0b4\ud3b4 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4!\n\nCNN \uc73c\ub85c \ub118\uc5b4\uac00\uae30\n--------------------\n\n\uc774\uc81c 3\uac1c\uc758 \ucee8\ubcfc\ub8e8\uc158 \ub808\uc774\uc5b4\ub85c \uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud560 \uac83\uc785\ub2c8\ub2e4.\n\uc774\uc804 \uc139\uc158\uc758 \uc5b4\ub5a4 \ud568\uc218\ub3c4 \ubaa8\ub378\uc758 \ud615\uc2dd\uc5d0 \ub300\ud574 \uac00\uc815\ud558\uc9c0 \uc54a\uae30 \ub54c\ubb38\uc5d0,\n\ubcc4\ub3c4\uc758 \uc218\uc815\uc5c6\uc774 CNN\uc744 \ud559\uc2b5\ud558\ub294 \ub370 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\nPytorch \uc758 \uc0ac\uc804\uc815\uc758\ub41c\n`Conv2d <https://pytorch.org/docs/stable/nn.html#torch.nn.Conv2d>`_ \ud074\ub798\uc2a4\ub97c\n\ucee8\ubcfc\ub8e8\uc158 \ub808\uc774\uc5b4\ub85c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. 3\uac1c\uc758 \ucee8\ubcfc\ub8e8\uc158 \ub808\uc774\uc5b4\ub85c CNN\uc744 \uc815\uc758\ud569\ub2c8\ub2e4.\n\uac01 \ucee8\ubcfc\ub8e8\uc158 \ub4a4\uc5d0\ub294 ReLU\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ub9c8\uc9c0\ub9c9\uc73c\ub85c \ud3c9\uade0 \ud480\ub9c1(average pooling)\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.\n(``view`` \ub294 PyTorch\uc758 numpy ``reshape`` \ubc84\uc804\uc785\ub2c8\ub2e4.)\n\n"
       ]
     },
     {
@@ -906,7 +906,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/dbcf5e6a5e95bf9f7a0e49e123c13b60/saving_and_loading_a_general_checkpoint.ipynb b/docs/_downloads/dbcf5e6a5e95bf9f7a0e49e123c13b60/saving_and_loading_a_general_checkpoint.ipynb
index cd1c072c7..892f41e34 100644
--- a/docs/_downloads/dbcf5e6a5e95bf9f7a0e49e123c13b60/saving_and_loading_a_general_checkpoint.ipynb
+++ b/docs/_downloads/dbcf5e6a5e95bf9f7a0e49e123c13b60/saving_and_loading_a_general_checkpoint.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\uc5d0\uc11c \uc77c\ubc18\uc801\uc778 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n===================================================================\n\ucd94\ub860(inference) \ub610\ub294 \ud559\uc2b5(training)\uc758 \uc7ac\uac1c\ub97c \uc704\ud574 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \ubaa8\ub378\uc744\n\uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \uac83\uc740 \ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc911\ub2e8\ud588\ub358 \ubd80\ubd84\uc744 \uc120\ud0dd\ud558\ub294\ub370 \ub3c4\uc6c0\uc744 \uc904 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uccb4\ud06c\ud3ec\uc778\ud2b8\ub97c \uc800\uc7a5\ud560 \ub54c\ub294 \ub2e8\uc21c\ud788 \ubaa8\ub378\uc758 state_dict \uc774\uc0c1\uc758 \uac83\uc744 \uc800\uc7a5\ud574\uc57c \ud569\ub2c8\ub2e4.\n\ubaa8\ub378 \ud559\uc2b5 \uc911\uc5d0 \uac31\uc2e0\ub418\ub294 \ud37c\ubc84\uc640 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc744 \ud3ec\ud568\ud558\ub294 \uc635\ud2f0\ub9c8\uc774\uc800(Optimizer)\uc758\nstate_dict\ub97c \ud568\uaed8 \uc800\uc7a5\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc774 \uc678\uc5d0\ub3c4 \uc911\ub2e8 \uc2dc\uc810\uc758 \uc5d0\ud3ec\ud06c(epoch),\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \uae30\ub85d\ub41c \ud559\uc2b5 \uc624\ucc28(training loss), \uc678\ubd80 ``torch.nn.Embedding`` \uacc4\uce35 \ub4f1,\n\uc54c\uace0\ub9ac\uc998\uc5d0 \ub530\ub77c \uc800\uc7a5\ud558\uace0 \uc2f6\uc740 \ud56d\ubaa9\ub4e4\uc774 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\n\uc5ec\ub7ec \uccb4\ud06c\ud3ec\uc778\ud2b8\ub4e4\uc744 \uc800\uc7a5\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc0ac\uc804(dictionary)\uc5d0 \uccb4\ud06c\ud3ec\uc778\ud2b8\ub4e4\uc744 \uad6c\uc131\ud558\uace0\n``torch.save()`` \ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\uc744 \uc9c1\ub82c\ud654(serialize)\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778\nPyTorch\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uc5ec\ub7ec \uccb4\ud06c\ud3ec\uc778\ud2b8\ub4e4\uc744 \uc800\uc7a5\ud560 \ub54c ``.tar`` \ud655\uc7a5\uc790\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc774\n\uc77c\ubc18\uc801\uc778 \uaddc\uce59\uc785\ub2c8\ub2e4. \ud56d\ubaa9\ub4e4\uc744 \ubd88\ub7ec\uc62c \ub54c\uc5d0\ub294, \uba3c\uc800 \ubaa8\ub378\uacfc \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \ucd08\uae30\ud654\ud558\uace0,\ntorch.load()\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4. \uc774\ud6c4 \uc6d0\ud558\ub294\ub300\ub85c \uc800\uc7a5\ud55c \ud56d\ubaa9\ub4e4\uc744 \uc0ac\uc804\uc5d0\n\uc870\ud68c\ud558\uc5ec \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uccb4\ud06c\ud3ec\uc778\ud2b8\ub4e4\uc744 \uc5b4\ub5bb\uac8c \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815\n-----\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0 ``torch`` \uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n\n::\n\n   pip install torch\n\n\n\n"
+        "\nPyTorch\uc5d0\uc11c \uc77c\ubc18\uc801\uc778 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n===================================================================\n\ucd94\ub860(inference) \ub610\ub294 \ud559\uc2b5(training)\uc758 \uc7ac\uac1c\ub97c \uc704\ud574 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \ubaa8\ub378\uc744\n\uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \uac83\uc740 \ub9c8\uc9c0\ub9c9\uc73c\ub85c \uc911\ub2e8\ud588\ub358 \ubd80\ubd84\uc744 \uc120\ud0dd\ud558\ub294\ub370 \ub3c4\uc6c0\uc744 \uc904 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uccb4\ud06c\ud3ec\uc778\ud2b8\ub97c \uc800\uc7a5\ud560 \ub54c\ub294 \ub2e8\uc21c\ud788 \ubaa8\ub378\uc758 state_dict \uc774\uc0c1\uc758 \uac83\uc744 \uc800\uc7a5\ud574\uc57c \ud569\ub2c8\ub2e4.\n\ubaa8\ub378 \ud559\uc2b5 \uc911\uc5d0 \uac31\uc2e0\ub418\ub294 \ud37c\ubc84\uc640 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc744 \ud3ec\ud568\ud558\ub294 \uc635\ud2f0\ub9c8\uc774\uc800(Optimizer)\uc758\nstate_dict\ub97c \ud568\uaed8 \uc800\uc7a5\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc774 \uc678\uc5d0\ub3c4 \uc911\ub2e8 \uc2dc\uc810\uc758 \uc5d0\ud3ec\ud06c(epoch),\n\ub9c8\uc9c0\ub9c9\uc73c\ub85c \uae30\ub85d\ub41c \ud559\uc2b5 \uc624\ucc28(training loss), \uc678\ubd80 ``torch.nn.Embedding`` \uacc4\uce35 \ub4f1,\n\uc54c\uace0\ub9ac\uc998\uc5d0 \ub530\ub77c \uc800\uc7a5\ud558\uace0 \uc2f6\uc740 \ud56d\ubaa9\ub4e4\uc774 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\n\uc5ec\ub7ec \uccb4\ud06c\ud3ec\uc778\ud2b8\ub4e4\uc744 \uc800\uc7a5\ud558\uae30 \uc704\ud574\uc11c\ub294 \uc0ac\uc804(dictionary)\uc5d0 \uccb4\ud06c\ud3ec\uc778\ud2b8\ub4e4\uc744 \uad6c\uc131\ud558\uace0\n``torch.save()`` \ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\uc744 \uc9c1\ub82c\ud654(serialize)\ud574\uc57c \ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778\nPyTorch\uc5d0\uc11c\ub294 \uc774\ub7ec\ud55c \uc5ec\ub7ec \uccb4\ud06c\ud3ec\uc778\ud2b8\ub4e4\uc744 \uc800\uc7a5\ud560 \ub54c ``.tar`` \ud655\uc7a5\uc790\ub97c \uc0ac\uc6a9\ud558\ub294 \uac83\uc774\n\uc77c\ubc18\uc801\uc778 \uaddc\uce59\uc785\ub2c8\ub2e4. \ud56d\ubaa9\ub4e4\uc744 \ubd88\ub7ec\uc62c \ub54c\uc5d0\ub294, \uba3c\uc800 \ubaa8\ub378\uacfc \uc635\ud2f0\ub9c8\uc774\uc800\ub97c \ucd08\uae30\ud654\ud558\uace0,\ntorch.load()\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc0ac\uc804\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4. \uc774\ud6c4 \uc6d0\ud558\ub294\ub300\ub85c \uc800\uc7a5\ud55c \ud56d\ubaa9\ub4e4\uc744 \uc0ac\uc804\uc5d0\n\uc870\ud68c\ud558\uc5ec \uc811\uadfc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 \uc5ec\ub7ec \uccb4\ud06c\ud3ec\uc778\ud2b8\ub4e4\uc744 \uc5b4\ub5bb\uac8c \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815\n-----\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0 ``torch`` \uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n\n::\n\n   pip install torch\n"
       ]
     },
     {
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/dd1c511de656ab48216de2866264b28f/deep_learning_tutorial.ipynb b/docs/_downloads/dd1c511de656ab48216de2866264b28f/deep_learning_tutorial.ipynb
index 148227faf..ca01a55fd 100644
--- a/docs/_downloads/dd1c511de656ab48216de2866264b28f/deep_learning_tutorial.ipynb
+++ b/docs/_downloads/dd1c511de656ab48216de2866264b28f/deep_learning_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\ub97c \uc774\uc6a9\ud55c \ub525\ub7ec\ub2dd\n**************************\n**\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\ub525\ub7ec\ub2dd \ube14\ub85d \uad6c\ucd95 : \uc544\ud540 \ub9f5(affine maps), \ube44\uc120\ud615\uc131, \uac1d\uccb4\n==========================================================================\n\n\ub525\ub7ec\ub2dd\uc740 \uc601\ub9ac\ud55c \ubc29\ubc95\uc73c\ub85c \ube44\uc120\ud615\uc131\uc744 \uac00\uc9c4 \uc120\ud615\uc131\uc744 \uad6c\uc131\ud558\ub294 \uac83\uc73c\ub85c\n\uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4. \ube44\uc120\ud615\uc131\uc758 \ub3c4\uc785\uc740 \uac15\ub825\ud55c \ubaa8\ub378\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.\n\uc774 \uc139\uc158\uc5d0\uc11c \uc774 \ud575\uc2ec \uad6c\uc131 \uc694\uc18c\ub97c \ub2e4\ub8e8\uace0, \uac1d\uccb4 \ud568\uc218\ub97c \ub9cc\ub4e4\uace0, \uc5b4\ub5bb\uac8c\n\ubaa8\ub378\uc774 \ud559\uc2b5\ub418\uc9c0\ub294 \uc0b4\ud3b4\ubd05\uc2dc\ub2e4.\n\n\n\uc544\ud540 \ub9f5\n~~~~~~~~~~~\n\n\ub525\ub7ec\ub2dd\uc758 \ud575\uc2ec \uc791\uc5c5\uc790 \uc911 \ud558\ub098\ub294 \uc544\ud540 \ub9f5 \uc785\ub2c8\ub2e4.\n\uc774 \ud568\uc218 $f(x)$ \ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.\n\n\\begin{align}f(x) = Ax + b\\end{align}\n\n\uc5ec\uae30\uc11c $A$ \ub294 \ud589\ub82c, $x, b$ \ub294 \ubca1\ud130 \uc785\ub2c8\ub2e4.\n\uc5ec\uae30\uc11c \ud559\uc2b5\ub418\ub294 \ubcc0\uc218\ub294 $A$ \uc640 $b$ \uc785\ub2c8\ub2e4.\n\uc885\uc885 $b$ \ub294 *\ud3b8\ud5a5(Bias)* \uc774\ub77c \ubd88\ub9bd\ub2c8\ub2e4.\n\n\nPyTorch \uc640 \ub300\ubd80\ubd84\uc758 \ub2e4\ub978 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub4e4\uc740 \uace0\uc804\uc801\uc778 \uc120\ud615 \ub300\uc218\ud559\uc640\n\uc870\uae08 \ub2e4\ub974\uac8c \ub3d9\uc791\ud569\ub2c8\ub2e4. \uc785\ub825\uc758 \uc5f4 \ub300\uc2e0\uc5d0 \ud589\uc73c\ub85c \ub9e4\ud551\ud569\ub2c8\ub2e4.\n\uc989 \uc8fc\uc5b4\uc9c4 $A$ \uc5d0\uc11c \ucd9c\ub825\uc758 $i$ \ubc88\uc9f8 \ud589\uc740\n\uc785\ub825\uc758 $i$ \ubc88\uc9f8 \ud589\uc5d0 \ub9e4\ud551\ub418\uace0 \ud3b8\ud5a5(Bias)\uc744 \ub354\ud569\ub2c8\ub2e4.\n\uc544\ub798 \uc608\uc2dc\ub97c \uc0b4\ud3b4\ubcf4\uc2ed\uc2dc\uc624.\n\n\n"
+        "\nPyTorch\ub97c \uc774\uc6a9\ud55c \ub525\ub7ec\ub2dd\n**************************\n**\ubc88\uc5ed**: `\ud669\uc131\uc218 <https://github.com/adonisues>`_\n\n\ub525\ub7ec\ub2dd \ube14\ub85d \uad6c\ucd95 : \uc544\ud540 \ub9f5(affine maps), \ube44\uc120\ud615\uc131, \uac1d\uccb4\n==========================================================================\n\n\ub525\ub7ec\ub2dd\uc740 \uc601\ub9ac\ud55c \ubc29\ubc95\uc73c\ub85c \ube44\uc120\ud615\uc131\uc744 \uac00\uc9c4 \uc120\ud615\uc131\uc744 \uad6c\uc131\ud558\ub294 \uac83\uc73c\ub85c\n\uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4. \ube44\uc120\ud615\uc131\uc758 \ub3c4\uc785\uc740 \uac15\ub825\ud55c \ubaa8\ub378\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.\n\uc774 \uc139\uc158\uc5d0\uc11c \uc774 \ud575\uc2ec \uad6c\uc131 \uc694\uc18c\ub97c \ub2e4\ub8e8\uace0, \uac1d\uccb4 \ud568\uc218\ub97c \ub9cc\ub4e4\uace0, \uc5b4\ub5bb\uac8c\n\ubaa8\ub378\uc774 \ud559\uc2b5\ub418\uc9c0\ub294 \uc0b4\ud3b4\ubd05\uc2dc\ub2e4.\n\n\n\uc544\ud540 \ub9f5\n~~~~~~~~~~~\n\n\ub525\ub7ec\ub2dd\uc758 \ud575\uc2ec \uc791\uc5c5\uc790 \uc911 \ud558\ub098\ub294 \uc544\ud540 \ub9f5 \uc785\ub2c8\ub2e4.\n\uc774 \ud568\uc218 $f(x)$ \ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.\n\n\\begin{align}f(x) = Ax + b\\end{align}\n\n\uc5ec\uae30\uc11c $A$ \ub294 \ud589\ub82c, $x, b$ \ub294 \ubca1\ud130 \uc785\ub2c8\ub2e4.\n\uc5ec\uae30\uc11c \ud559\uc2b5\ub418\ub294 \ubcc0\uc218\ub294 $A$ \uc640 $b$ \uc785\ub2c8\ub2e4.\n\uc885\uc885 $b$ \ub294 *\ud3b8\ud5a5(Bias)* \uc774\ub77c \ubd88\ub9bd\ub2c8\ub2e4.\n\n\nPyTorch \uc640 \ub300\ubd80\ubd84\uc758 \ub2e4\ub978 \ub525\ub7ec\ub2dd \ud504\ub808\uc784\uc6cc\ud06c\ub4e4\uc740 \uace0\uc804\uc801\uc778 \uc120\ud615 \ub300\uc218\ud559\uc640\n\uc870\uae08 \ub2e4\ub974\uac8c \ub3d9\uc791\ud569\ub2c8\ub2e4. \uc785\ub825\uc758 \uc5f4 \ub300\uc2e0\uc5d0 \ud589\uc73c\ub85c \ub9e4\ud551\ud569\ub2c8\ub2e4.\n\uc989 \uc8fc\uc5b4\uc9c4 $A$ \uc5d0\uc11c \ucd9c\ub825\uc758 $i$ \ubc88\uc9f8 \ud589\uc740\n\uc785\ub825\uc758 $i$ \ubc88\uc9f8 \ud589\uc5d0 \ub9e4\ud551\ub418\uace0 \ud3b8\ud5a5(Bias)\uc744 \ub354\ud569\ub2c8\ub2e4.\n\uc544\ub798 \uc608\uc2dc\ub97c \uc0b4\ud3b4\ubcf4\uc2ed\uc2dc\uc624.\n"
       ]
     },
     {
@@ -175,7 +175,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/dd583ee0bd16160dc842469ec717b9d3/audio_preprocessing_tutorial.ipynb b/docs/_downloads/dd583ee0bd16160dc842469ec717b9d3/audio_preprocessing_tutorial.ipynb
index f41b66feb..8eb398339 100644
--- a/docs/_downloads/dd583ee0bd16160dc842469ec717b9d3/audio_preprocessing_tutorial.ipynb
+++ b/docs/_downloads/dd583ee0bd16160dc842469ec717b9d3/audio_preprocessing_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\ntorchaudio Tutorial\n===================\n\nPyTorch is an open source deep learning platform that provides a\nseamless path from research prototyping to production deployment with\nGPU support.\n\nSignificant effort in solving machine learning problems goes into data\npreparation. ``torchaudio`` leverages PyTorch\u2019s GPU support, and provides\nmany tools to make data loading easy and more readable. In this\ntutorial, we will see how to load and preprocess data from a simple\ndataset.\n\nFor this tutorial, please make sure the ``matplotlib`` package is\ninstalled for easier visualization.\n\n\n"
+        "\ntorchaudio Tutorial\n===================\n\nPyTorch is an open source deep learning platform that provides a\nseamless path from research prototyping to production deployment with\nGPU support.\n\nSignificant effort in solving machine learning problems goes into data\npreparation. ``torchaudio`` leverages PyTorch\u2019s GPU support, and provides\nmany tools to make data loading easy and more readable. In this\ntutorial, we will see how to load and preprocess data from a simple\ndataset.\n\nFor this tutorial, please make sure the ``matplotlib`` package is\ninstalled for easier visualization.\n"
       ]
     },
     {
@@ -398,7 +398,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/e139fbbcadcc4d83aab8995db4b9147c/saving_loading_models.ipynb b/docs/_downloads/e139fbbcadcc4d83aab8995db4b9147c/saving_loading_models.ipynb
index 6f3e42358..3c8645884 100644
--- a/docs/_downloads/e139fbbcadcc4d83aab8995db4b9147c/saving_loading_models.ipynb
+++ b/docs/_downloads/e139fbbcadcc4d83aab8995db4b9147c/saving_loading_models.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n=========================\n**Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_\n  **\ubc88\uc5ed**: `\ubc15\uc815\ud658 <http://github.com/9bow>`_\n\n\uc774 \ubb38\uc11c\uc5d0\uc11c\ub294 PyTorch \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ub2e4\uc591\ud55c \ubc29\ubc95\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774 \ubb38\uc11c \uc804\uccb4\ub97c \ub2e4 \uc77d\ub294 \uac83\ub3c4 \uc88b\uc740 \ubc29\ubc95\uc774\uc9c0\ub9cc, \ud544\uc694\ud55c \uc0ac\uc6a9 \uc608\uc758 \ucf54\ub4dc\ub9cc \ucc38\uace0\ud558\ub294\n\uac83\ub3c4 \uace0\ub824\ud574\ubcf4\uc138\uc694.\n\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc62c \ub54c\ub294 3\uac00\uc9c0\uc758 \ud575\uc2ec \ud568\uc218\uc640 \uc775\uc219\ud574\uc9c8 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4:\n\n1) `torch.save <https://pytorch.org/docs/stable/torch.html?highlight=save#torch.save>`__:\n   \uc9c1\ub82c\ud654\ub41c \uac1d\uccb4\ub97c \ub514\uc2a4\ud06c\uc5d0 \uc800\uc7a5\ud569\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 Python\uc758\n   `pickle <https://docs.python.org/3/library/pickle.html>`__ \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc9c1\ub82c\ud654\ud569\ub2c8\ub2e4\n   \uc774 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub4e0 \uc885\ub958\uc758 \uac1d\uccb4\uc758 \ubaa8\ub378, Tensor \ubc0f \uc0ac\uc804\uc744 \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n2) `torch.load <https://pytorch.org/docs/stable/torch.html?highlight=torch%20load#torch.load>`__:\n   `pickle <https://docs.python.org/3/library/pickle.html>`__\\ \uc744 \uc0ac\uc6a9\ud558\uc5ec\n   \uc800\uc7a5\ub41c \uac1d\uccb4 \ud30c\uc77c\ub4e4\uc744 \uc5ed\uc9c1\ub82c\ud654\ud558\uc5ec \uba54\ubaa8\ub9ac\uc5d0 \uc62c\ub9bd\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \ub370\uc774\ud130\ub97c \uc7a5\uce58\uc5d0\n   \ubd88\ub7ec\uc62c \ub54c\ub3c4 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n   (`\uc7a5\uce58\uac04 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#device>`__ \ucc38\uace0)\n\n3) `torch.nn.Module.load_state_dict <https://pytorch.org/docs/stable/nn.html?highlight=load_state_dict#torch.nn.Module.load_state_dict>`__:\n   \uc5ed\uc9c1\ub82c\ud654\ub41c *state_dict* \ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.\n   *state_dict* \uc5d0 \ub300\ud55c \ub354 \uc790\uc138\ud55c \uc815\ubcf4\ub294 `state_dict\uac00 \ubb34\uc5c7\uc778\uac00\uc694?\n   <#state-dict>`__ \ub97c \ucc38\uace0\ud558\uc138\uc694.\n\n\n\n**\ubaa9\ucc28:**\n\n-  `state_dict\uac00 \ubb34\uc5c7\uc778\uac00\uc694? <#state-dict>`__\n-  `\ucd94\ub860(inference)\ub97c \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#inference>`__\n-  `\uc77c\ubc18 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#checkpoint>`__\n-  `\uc5ec\ub7ec\uac1c(multiple)\uc758 \ubaa8\ub378\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud558\uae30 <#multiple>`__\n-  `\ub2e4\ub978 \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ube60\ub974\uac8c \ubaa8\ub378 \uc2dc\uc791\ud558\uae30(warmstart) <#warmstart>`__\n-  `\uc7a5\uce58(device)\uac04 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#device>`__\n\n\n"
+        "\n\ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30\n=========================\n**Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_\n  **\ubc88\uc5ed**: `\ubc15\uc815\ud658 <http://github.com/9bow>`_\n\n\uc774 \ubb38\uc11c\uc5d0\uc11c\ub294 PyTorch \ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uace0 \ubd88\ub7ec\uc624\ub294 \ub2e4\uc591\ud55c \ubc29\ubc95\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\uc774 \ubb38\uc11c \uc804\uccb4\ub97c \ub2e4 \uc77d\ub294 \uac83\ub3c4 \uc88b\uc740 \ubc29\ubc95\uc774\uc9c0\ub9cc, \ud544\uc694\ud55c \uc0ac\uc6a9 \uc608\uc758 \ucf54\ub4dc\ub9cc \ucc38\uace0\ud558\ub294\n\uac83\ub3c4 \uace0\ub824\ud574\ubcf4\uc138\uc694.\n\n\ubaa8\ub378\uc744 \uc800\uc7a5\ud558\uac70\ub098 \ubd88\ub7ec\uc62c \ub54c\ub294 3\uac00\uc9c0\uc758 \ud575\uc2ec \ud568\uc218\uc640 \uc775\uc219\ud574\uc9c8 \ud544\uc694\uac00 \uc788\uc2b5\ub2c8\ub2e4:\n\n1) `torch.save <https://pytorch.org/docs/stable/torch.html?highlight=save#torch.save>`__:\n   \uc9c1\ub82c\ud654\ub41c \uac1d\uccb4\ub97c \ub514\uc2a4\ud06c\uc5d0 \uc800\uc7a5\ud569\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 Python\uc758\n   `pickle <https://docs.python.org/3/library/pickle.html>`__ \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc9c1\ub82c\ud654\ud569\ub2c8\ub2e4\n   \uc774 \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub4e0 \uc885\ub958\uc758 \uac1d\uccb4\uc758 \ubaa8\ub378, Tensor \ubc0f \uc0ac\uc804\uc744 \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n2) `torch.load <https://pytorch.org/docs/stable/torch.html?highlight=torch%20load#torch.load>`__:\n   `pickle <https://docs.python.org/3/library/pickle.html>`__\\ \uc744 \uc0ac\uc6a9\ud558\uc5ec\n   \uc800\uc7a5\ub41c \uac1d\uccb4 \ud30c\uc77c\ub4e4\uc744 \uc5ed\uc9c1\ub82c\ud654\ud558\uc5ec \uba54\ubaa8\ub9ac\uc5d0 \uc62c\ub9bd\ub2c8\ub2e4. \uc774 \ud568\uc218\ub294 \ub370\uc774\ud130\ub97c \uc7a5\uce58\uc5d0\n   \ubd88\ub7ec\uc62c \ub54c\ub3c4 \uc0ac\uc6a9\ud569\ub2c8\ub2e4.\n   (`\uc7a5\uce58\uac04 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#device>`__ \ucc38\uace0)\n\n3) `torch.nn.Module.load_state_dict <https://pytorch.org/docs/stable/nn.html?highlight=load_state_dict#torch.nn.Module.load_state_dict>`__:\n   \uc5ed\uc9c1\ub82c\ud654\ub41c *state_dict* \ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub4e4\uc744 \ubd88\ub7ec\uc635\ub2c8\ub2e4.\n   *state_dict* \uc5d0 \ub300\ud55c \ub354 \uc790\uc138\ud55c \uc815\ubcf4\ub294 `state_dict\uac00 \ubb34\uc5c7\uc778\uac00\uc694?\n   <#state-dict>`__ \ub97c \ucc38\uace0\ud558\uc138\uc694.\n\n\n\n**\ubaa9\ucc28:**\n\n-  `state_dict\uac00 \ubb34\uc5c7\uc778\uac00\uc694? <#state-dict>`__\n-  `\ucd94\ub860(inference)\ub97c \uc704\ud574 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#inference>`__\n-  `\uc77c\ubc18 \uccb4\ud06c\ud3ec\uc778\ud2b8(checkpoint) \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#checkpoint>`__\n-  `\uc5ec\ub7ec\uac1c(multiple)\uc758 \ubaa8\ub378\uc744 \ud558\ub098\uc758 \ud30c\uc77c\uc5d0 \uc800\uc7a5\ud558\uae30 <#multiple>`__\n-  `\ub2e4\ub978 \ubaa8\ub378\uc758 \ub9e4\uac1c\ubcc0\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ube60\ub974\uac8c \ubaa8\ub378 \uc2dc\uc791\ud558\uae30(warmstart) <#warmstart>`__\n-  `\uc7a5\uce58(device)\uac04 \ubaa8\ub378 \uc800\uc7a5\ud558\uae30 & \ubd88\ub7ec\uc624\uae30 <#device>`__\n"
       ]
     },
     {
@@ -77,7 +77,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/e3e6e72b3ed19d4dd77f128e92beda52/profiler.ipynb b/docs/_downloads/e3e6e72b3ed19d4dd77f128e92beda52/profiler.ipynb
index 61049d068..7ebb86eba 100644
--- a/docs/_downloads/e3e6e72b3ed19d4dd77f128e92beda52/profiler.ipynb
+++ b/docs/_downloads/e3e6e72b3ed19d4dd77f128e92beda52/profiler.ipynb
@@ -15,14 +15,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch Profiler\n====================================\nThis recipe explains how to use PyTorch profiler and measure the time and\nmemory consumption of the model's operators.\n\nIntroduction\n------------\nPyTorch includes a simple profiler API that is useful when user needs\nto determine the most expensive operators in the model.\n\nIn this recipe, we will use a simple Resnet model to demonstrate how to\nuse profiler to analyze model performance.\n\nSetup\n-----\nTo install ``torch`` and ``torchvision`` use the following command:\n\n::\n\n   pip install torch torchvision\n\n\n\n"
+        "\nPyTorch \ud504\ub85c\ud30c\uc77c\ub7ec(Profiler)\n====================================\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 \uc5b4\ub5bb\uac8c PyTorch \ud504\ub85c\ud30c\uc77c\ub7ec\ub97c \uc0ac\uc6a9\ud558\ub294\uc9c0, \uadf8\ub9ac\uace0 \ubaa8\ub378\uc758 \uc5f0\uc0b0\uc790\ub4e4\uc774 \uc18c\ube44\ud558\ub294 \uba54\ubaa8\ub9ac\uc640 \uc2dc\uac04\uc744 \uce21\uc815\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uac1c\uc694\n------------\nPyTorch\ub294 \uc0ac\uc6a9\uc790\uac00 \ubaa8\ub378 \ub0b4\uc758 \uc5f0\uc0b0 \ube44\uc6a9\uc774 \ud070(expensive) \uc5f0\uc0b0\uc790\ub4e4\uc774 \ubb34\uc5c7\uc778\uc9c0 \uc54c\uace0\uc2f6\uc744 \ub54c \uc720\uc6a9\ud558\uac8c \uc0ac\uc6a9\ud560 \uc218 \uc788\ub294 \uac04\ub2e8\ud55c \ud504\ub85c\ud30c\uc77c\ub7ec API\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4.\n\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 \ubaa8\ub378\uc758 \uc131\ub2a5(performance)\uc744 \ubd84\uc11d\ud558\ub824\uace0 \ud560 \ub54c \uc5b4\ub5bb\uac8c \ud504\ub85c\ud30c\uc77c\ub7ec\ub97c \uc0ac\uc6a9\ud574\uc57c \ud558\ub294\uc9c0\ub97c \ubcf4\uc5ec\uc8fc\uae30 \uc704\ud574 \uac04\ub2e8\ud55c ResNet \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc124\uc815(Setup)\n-------------\n``torch`` \uc640 ``torchvision`` \uc744 \uc124\uce58\ud558\uae30 \uc704\ud574\uc11c \uc544\ub798\uc758 \ucee4\ub9e8\ub4dc\ub97c \uc785\ub825\ud569\ub2c8\ub2e4:\n\n\n::\n\n   pip install torch torchvision\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Steps\n-----\n\n1. Import all necessary libraries\n2. Instantiate a simple Resnet model\n3. Use profiler to analyze execution time\n4. Use profiler to analyze memory consumption\n5. Using tracing functionality\n\n1. Import all necessary libraries\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIn this recipe we will use ``torch``, ``torchvision.models``\nand ``profiler`` modules:\n\n\n"
+        "\ub2e8\uacc4(Steps)\n-------------\n\n1. \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub4e4 \ubd88\ub7ec\uc624\uae30\n2. \uac04\ub2e8\ud55c ResNet \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4\ud654 \ud558\uae30\n3. \ud504\ub85c\ud30c\uc77c\ub7ec\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e4\ud589\uc2dc\uac04 \ubd84\uc11d\ud558\uae30\n4. \ud504\ub85c\ud30c\uc77c\ub7ec\ub97c \uc0ac\uc6a9\ud558\uc5ec \uba54\ubaa8\ub9ac \uc18c\ube44 \ubd84\uc11d\ud558\uae30\n5. \ucd94\uc801\uae30\ub2a5 \uc0ac\uc6a9\ud558\uae30\n\n1. \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub4e4 \ubd88\ub7ec\uc624\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 ``torch`` \uc640 ``torchvision.models``,\n\uadf8\ub9ac\uace0 ``profiler`` \ubaa8\ub4c8\uc744 \uc0ac\uc6a9\ud569\ub2c8\ub2e4:\n\n\n"
       ]
     },
     {
@@ -40,7 +40,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "2. Instantiate a simple Resnet model\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nLet's create an instance of a Resnet model and prepare an input\nfor it:\n\n\n"
+        "2. \uac04\ub2e8\ud55c ResNet \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4\ud654 \ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nResNet \ubaa8\ub378 \uc778\uc2a4\ud134\uc2a4\ub97c \ub9cc\ub4e4\uace0 \uc785\ub825\uac12\uc744\n\uc900\ube44\ud569\ub2c8\ub2e4 :\n\n\n"
       ]
     },
     {
@@ -58,7 +58,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "3. Use profiler to analyze execution time\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPyTorch profiler is enabled through the context manager and accepts\na number of parameters, some of the most useful are:\n\n- ``record_shapes`` - whether to record shapes of the operator inputs;\n- ``profile_memory`` - whether to report amount of memory consumed by\n  model's Tensors;\n- ``use_cuda`` - whether to measure execution time of CUDA kernels.\n\nLet's see how we can use profiler to analyze the execution time:\n\n"
+        "3. \ud504\ub85c\ud30c\uc77c\ub7ec\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2e4\ud589\uc2dc\uac04 \ubd84\uc11d\ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPyTorch \ud504\ub85c\ud30c\uc77c\ub7ec\ub294 \ucee8\ud14d\uc2a4\ud2b8 \uba54\ub2c8\uc800(context manager)\ub97c \ud1b5\ud574 \ud65c\uc131\ud654\ub418\uace0,\n\uc5ec\ub7ec \ub9e4\uac1c\ubcc0\uc218\ub97c \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc720\uc6a9\ud55c \uba87 \uac00\uc9c0 \ub9e4\uac1c\ubcc0\uc218\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n\n- ``record_shapes`` - \uc5f0\uc0ac\uc790 \uc785\ub825(input)\uc758 shape\uc744 \uae30\ub85d\ud560\uc9c0 \uc5ec\ubd80;\n- ``profile_memory`` - \ubaa8\ub378\uc758 \ud150\uc11c(Tensor)\ub4e4\uc774 \uc18c\ube44\ud558\ub294 \uba54\ubaa8\ub9ac \uc591\uc744 \ubcf4\uace0(report)\ud560\uc9c0 \uc5ec\ubd80;\n- ``use_cuda`` - CUDA \ucee4\ub110\uc758 \uc2e4\ud589\uc2dc\uac04\uc744 \uce21\uc815\ud560\uc9c0 \uc5ec\ubd80;\n\n\ud504\ub85c\ud30c\uc77c\ub7ec\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc5b4\ub5bb\uac8c \uc2e4\ud589\uc2dc\uac04\uc744 \ubd84\uc11d\ud558\ub294\uc9c0 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\n"
       ]
     },
     {
@@ -76,7 +76,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Note that we can use ``record_function`` context manager to label\narbitrary code ranges with user provided names\n(``model_inference`` is used as a label in the example above).\nProfiler allows one to check which operators were called during the\nexecution of a code range wrapped with a profiler context manager.\nIf multiple profiler ranges are active at the same time (e.g. in\nparallel PyTorch threads), each profiling context manager tracks only\nthe operators of its corresponding range.\nProfiler also automatically profiles the async tasks launched\nwith ``torch.jit._fork`` and (in case of a backward pass)\nthe backward pass operators launched with ``backward()`` call.\n\nLet's print out the stats for the execution above:\n\n"
+        "``record_function`` \ucee8\ud14d\uc2a4\ud2b8 \uad00\ub9ac\uc790\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc784\uc758\uc758 \ucf54\ub4dc \ubc94\uc704\uc5d0\n\uc0ac\uc6a9\uc790\uac00 \uc9c0\uc815\ud55c \uc774\ub984\uc73c\ub85c \ub808\uc774\ube14(label)\uc744 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n(\uc704 \uc608\uc81c\uc5d0\uc11c\ub294 ``model_inference`` \ub97c \ub808\uc774\ube14\ub85c \uc0ac\uc6a9\ud588\uc2b5\ub2c8\ub2e4.)\n\ud504\ub85c\ud30c\uc77c\ub7ec\ub97c \uc0ac\uc6a9\ud558\uba74 \ud504\ub85c\ud30c\uc77c\ub7ec \ucee8\ud14d\uc2a4\ud2b8 \uad00\ub9ac\uc790\ub85c \uac10\uc2f8\uc9c4(wrap) \ucf54\ub4dc \ubc94\uc704\ub97c\n\uc2e4\ud589\ud558\ub294 \ub3d9\uc548 \uc5b4\ub5a4 \uc5f0\uc0b0\uc790\ub4e4\uc774 \ud638\ucd9c\ub418\uc5c8\ub294\uc9c0 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\ub9cc\uc57d \uc5ec\ub7ec \ud504\ub85c\ud30c\uc77c\ub7ec\uc758 \ubc94\uc704\uac00 \ub3d9\uc2dc\uc5d0 \ud65c\uc131\ud654\ub41c \uacbd\uc6b0(\uc608. PyTorch \uc4f0\ub808\ub4dc\uac00 \ubcd1\ub82c\ub85c\n\uc2e4\ud589 \uc911\uc778 \uacbd\uc6b0), \uac01 \ud504\ub85c\ud30c\uc77c\ub9c1 \ucee8\ud14d\uc2a4\ud2b8 \uad00\ub9ac\uc790\ub294 \uac01\uac01\uc758 \ubc94\uc704 \ub0b4\uc758 \uc5f0\uc0b0\uc790\ub4e4\ub9cc\n\ucd94\uc801(track)\ud569\ub2c8\ub2e4.\n\ud504\ub85c\ud30c\uc77c\ub7ec\ub294 \ub610\ud55c ``torch.jit._fork`` \ub85c \uc2e4\ud589\ub41c \ube44\ub3d9\uae30 \uc791\uc5c5\uacfc\n(\uc5ed\uc804\ud30c \ub2e8\uacc4\uc758 \uacbd\uc6b0) ``backward()`` \uc758 \ud638\ucd9c\ub85c \uc2e4\ud589\ub41c \uc5ed\uc804\ud30c \uc5f0\uc0b0\uc790\ub4e4\ub3c4\n\uc790\ub3d9\uc73c\ub85c \ud504\ub85c\ud30c\uc77c\ub9c1\ud569\ub2c8\ub2e4.\n\n\uc704 \ucf54\ub4dc\ub97c \uc2e4\ud589\ud55c \ud1b5\uacc4\ub97c \ucd9c\ub825\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4:\n\n"
       ]
     },
     {
@@ -94,7 +94,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The output will look like (omitting some columns):\n\n"
+        "(\uba87\uba87 \uc5f4\uc744 \uc81c\uc678\ud558\uace0) \ucd9c\ub825\uac12\uc774 \uc774\ub807\uac8c \ubcf4\uc77c \uac83\uc785\ub2c8\ub2e4:\n\n"
       ]
     },
     {
@@ -112,7 +112,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Here we see that, as expected, most of the time is spent in convolution (and specifically in ``mkldnn_convolution``\nfor PyTorch compiled with MKL-DNN support).\nNote the difference between self cpu time and cpu time - operators can call other operators, self cpu time exludes time\nspent in children operator calls, while total cpu time includes it.\n\nTo get a finer granularity of results and include operator input shapes, pass ``group_by_input_shape=True``:\n\n"
+        "\uc608\uc0c1\ud588\ub358 \ub300\ub85c, \ub300\ubd80\ubd84\uc758 \uc2dc\uac04\uc774 \ud569\uc131\uacf1(convolution) \uc5f0\uc0b0(\ud2b9\ud788 MKL-DNN\uc744 \uc9c0\uc6d0\ud558\ub3c4\ub85d\n\ucef4\ud30c\uc77c\ub41c PyTorch\uc758 \uacbd\uc6b0\uc5d0\ub294 ``mkldnn_convolution`` )\uc5d0\uc11c \uc18c\uc694\ub418\ub294 \uac83\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n(\uacb0\uacfc \uc5f4\ub4e4 \uc911) Self CPU time\uacfc CPU time\uc758 \ucc28\uc774\uc5d0 \uc720\uc758\ud574\uc57c \ud569\ub2c8\ub2e4 -\n\uc5f0\uc0b0\uc790\ub294 \ub2e4\ub978 \uc5f0\uc0b0\uc790\ub4e4\uc744 \ud638\ucd9c\ud560 \uc218 \uc788\uc73c\uba70, Self CPU time\uc5d0\ub294 \ud558\uc704(child) \uc5f0\uc0b0\uc790 \ud638\ucd9c\uc5d0\uc11c \ubc1c\uc0dd\ud55c\n\uc2dc\uac04\uc744 \uc81c\uc678\ud574\uc11c, Totacl CPU time\uc5d0\ub294 \ud3ec\ud568\ud574\uc11c \ud45c\uc2dc\ud569\ub2c8\ub2e4.\n\n\ubcf4\ub2e4 \uc138\ubd80\uc801\uc778 \uacb0\uacfc \uc815\ubcf4 \ubc0f \uc5f0\uc0b0\uc790\uc758 \uc785\ub825 shape\uc744 \ud568\uaed8 \ubcf4\ub824\uba74 ``group_by_input_shape=True`` \ub97c\n\uc778\uc790\ub85c \uc804\ub2ec\ud558\uba74 \ub429\ub2c8\ub2e4:\n\n"
       ]
     },
     {
@@ -123,14 +123,14 @@
       },
       "outputs": [],
       "source": [
-        "print(prof.key_averages(group_by_input_shape=True).table(sort_by=\"cpu_time_total\", row_limit=10))\n\n# (omitting some columns)\n# -------------------------  -----------  --------  -------------------------------------\n# Name                       CPU total    # Calls         Input Shapes\n# -------------------------  -----------  --------  -------------------------------------\n# model_inference            69.571ms     1         []\n# conv2d                     9.019ms      4         [[5, 64, 56, 56], [64, 64, 3, 3], []]\n# convolution                9.006ms      4         [[5, 64, 56, 56], [64, 64, 3, 3], []]\n# _convolution               8.982ms      4         [[5, 64, 56, 56], [64, 64, 3, 3], []]\n# mkldnn_convolution         8.894ms      4         [[5, 64, 56, 56], [64, 64, 3, 3], []]\n# max_pool2d                 7.200ms      1         [[5, 64, 112, 112]]\n# conv2d                     7.189ms      3         [[5, 512, 7, 7], [512, 512, 3, 3], []]\n# convolution                7.180ms      3         [[5, 512, 7, 7], [512, 512, 3, 3], []]\n# _convolution               7.171ms      3         [[5, 512, 7, 7], [512, 512, 3, 3], []]\n# max_pool2d_with_indices    7.170ms      1         [[5, 64, 112, 112]]\n# -------------------------  -----------  --------  --------------------------------------"
+        "print(prof.key_averages(group_by_input_shape=True).table(sort_by=\"cpu_time_total\", row_limit=10))\n\n# (\uba87\uba87 \uc5f4\uc740 \uc81c\uc678\ud558\uc600\uc2b5\ub2c8\ub2e4)\n# -------------------------  -----------  --------  -------------------------------------\n# Name                       CPU total    # Calls         Input Shapes\n# -------------------------  -----------  --------  -------------------------------------\n# model_inference            69.571ms     1         []\n# conv2d                     9.019ms      4         [[5, 64, 56, 56], [64, 64, 3, 3], []]\n# convolution                9.006ms      4         [[5, 64, 56, 56], [64, 64, 3, 3], []]\n# _convolution               8.982ms      4         [[5, 64, 56, 56], [64, 64, 3, 3], []]\n# mkldnn_convolution         8.894ms      4         [[5, 64, 56, 56], [64, 64, 3, 3], []]\n# max_pool2d                 7.200ms      1         [[5, 64, 112, 112]]\n# conv2d                     7.189ms      3         [[5, 512, 7, 7], [512, 512, 3, 3], []]\n# convolution                7.180ms      3         [[5, 512, 7, 7], [512, 512, 3, 3], []]\n# _convolution               7.171ms      3         [[5, 512, 7, 7], [512, 512, 3, 3], []]\n# max_pool2d_with_indices    7.170ms      1         [[5, 64, 112, 112]]\n# -------------------------  -----------  --------  --------------------------------------"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "4. Use profiler to analyze memory consumption\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPyTorch profiler can also show the amount of memory (used by the model's tensors)\nthat was allocated (or released) during the execution of the model's operators.\nIn the output below, 'self' memory corresponds to the memory allocated (released)\nby the operator, excluding the children calls to the other operators.\nTo enable memory profiling functionality pass ``profile_memory=True``.\n\n"
+        "4. \ud504\ub85c\ud30c\uc77c\ub7ec\ub97c \uc0ac\uc6a9\ud558\uc5ec \uba54\ubaa8\ub9ac \uc18c\ube44 \ubd84\uc11d\ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nPyTorch \ud504\ub85c\ud30c\uc77c\ub7ec\ub294 \ubaa8\ub378\uc758 \uc5f0\uc0b0\uc790\ub4e4\uc744 \uc2e4\ud589\ud558\uba70 (\ubaa8\ub378\uc758 \ud150\uc11c\ub4e4\uc774 \uc0ac\uc6a9\ud558\uba70) \ud560\ub2f9(\ub610\ub294 \ud574\uc81c)\ud55c\n\uba54\ubaa8\ub9ac\uc758 \uc591\ub3c4 \ud45c\uc2dc\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc544\ub798 \ucd9c\ub825 \uacb0\uacfc\uc5d0\uc11c 'Self' memory\ub294 \ud574\ub2f9 \uc5f0\uc0b0\uc790\uc5d0 \uc758\ud574 \ud638\ucd9c\ub41c \ud558\uc704(child) \uc5f0\uc0b0\uc790\ub4e4\uc744 \uc81c\uc678\ud55c,\n\uc5f0\uc0b0\uc790 \uc790\uccb4\uc5d0 \ud560\ub2f9(\ud574\uc81c)\ub41c \uba54\ubaa8\ub9ac\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4.\n\uba54\ubaa8\ub9ac \ud504\ub85c\ud30c\uc77c\ub9c1 \uae30\ub2a5\uc744 \ud65c\uc131\ud654\ud558\ub824\uba74 ``profile_memory=True`` \ub97c \uc778\uc790\ub85c \uc804\ub2ec\ud558\uba74 \ub429\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -141,14 +141,14 @@
       },
       "outputs": [],
       "source": [
-        "with profiler.profile(profile_memory=True, record_shapes=True) as prof:\n    model(inputs)\n\nprint(prof.key_averages().table(sort_by=\"self_cpu_memory_usage\", row_limit=10))\n\n# (omitting some columns)\n# ---------------------------  ---------------  ---------------  ---------------\n# Name                         CPU Mem          Self CPU Mem     Number of Calls\n# ---------------------------  ---------------  ---------------  ---------------\n# empty                        94.79 Mb         94.79 Mb         123\n# resize_                      11.48 Mb         11.48 Mb         2\n# addmm                        19.53 Kb         19.53 Kb         1\n# empty_strided                4 b              4 b              1\n# conv2d                       47.37 Mb         0 b              20\n# ---------------------------  ---------------  ---------------  ---------------\n\nprint(prof.key_averages().table(sort_by=\"cpu_memory_usage\", row_limit=10))\n\n# (omitting some columns)\n# ---------------------------  ---------------  ---------------  ---------------\n# Name                         CPU Mem          Self CPU Mem     Number of Calls\n# ---------------------------  ---------------  ---------------  ---------------\n# empty                        94.79 Mb         94.79 Mb         123\n# batch_norm                   47.41 Mb         0 b              20\n# _batch_norm_impl_index       47.41 Mb         0 b              20\n# native_batch_norm            47.41 Mb         0 b              20\n# conv2d                       47.37 Mb         0 b              20\n# convolution                  47.37 Mb         0 b              20\n# _convolution                 47.37 Mb         0 b              20\n# mkldnn_convolution           47.37 Mb         0 b              20\n# empty_like                   47.37 Mb         0 b              20\n# max_pool2d                   11.48 Mb         0 b              1\n# max_pool2d_with_indices      11.48 Mb         0 b              1\n# resize_                      11.48 Mb         11.48 Mb         2\n# addmm                        19.53 Kb         19.53 Kb         1\n# adaptive_avg_pool2d          10.00 Kb         0 b              1\n# mean                         10.00 Kb         0 b              1\n# ---------------------------  ---------------  ---------------  ---------------"
+        "with profiler.profile(profile_memory=True, record_shapes=True) as prof:\n    model(inputs)\n\nprint(prof.key_averages().table(sort_by=\"self_cpu_memory_usage\", row_limit=10))\n\n# (\uba87\uba87 \uc5f4\uc740 \uc81c\uc678\ud558\uc600\uc2b5\ub2c8\ub2e4)\n# ---------------------------  ---------------  ---------------  ---------------\n# Name                         CPU Mem          Self CPU Mem     Number of Calls\n# ---------------------------  ---------------  ---------------  ---------------\n# empty                        94.79 Mb         94.79 Mb         123\n# resize_                      11.48 Mb         11.48 Mb         2\n# addmm                        19.53 Kb         19.53 Kb         1\n# empty_strided                4 b              4 b              1\n# conv2d                       47.37 Mb         0 b              20\n# ---------------------------  ---------------  ---------------  ---------------\n\nprint(prof.key_averages().table(sort_by=\"cpu_memory_usage\", row_limit=10))\n\n# (\uba87\uba87 \uc5f4\uc740 \uc81c\uc678\ud558\uc600\uc2b5\ub2c8\ub2e4)\n# ---------------------------  ---------------  ---------------  ---------------\n# Name                         CPU Mem          Self CPU Mem     Number of Calls\n# ---------------------------  ---------------  ---------------  ---------------\n# empty                        94.79 Mb         94.79 Mb         123\n# batch_norm                   47.41 Mb         0 b              20\n# _batch_norm_impl_index       47.41 Mb         0 b              20\n# native_batch_norm            47.41 Mb         0 b              20\n# conv2d                       47.37 Mb         0 b              20\n# convolution                  47.37 Mb         0 b              20\n# _convolution                 47.37 Mb         0 b              20\n# mkldnn_convolution           47.37 Mb         0 b              20\n# empty_like                   47.37 Mb         0 b              20\n# max_pool2d                   11.48 Mb         0 b              1\n# max_pool2d_with_indices      11.48 Mb         0 b              1\n# resize_                      11.48 Mb         11.48 Mb         2\n# addmm                        19.53 Kb         19.53 Kb         1\n# adaptive_avg_pool2d          10.00 Kb         0 b              1\n# mean                         10.00 Kb         0 b              1\n# ---------------------------  ---------------  ---------------  ---------------"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "5. Using tracing functionality\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nProfiling results can be outputted as a .json trace file:\n\n"
+        "5. \ucd94\uc801\uae30\ub2a5 \uc0ac\uc6a9\ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\ud504\ub85c\ud30c\uc77c\ub9c1 \uacb0\uacfc\ub294 .json \ud615\ud0dc\uc758 \ucd94\uc801 \ud30c\uc77c(trace file)\ub85c \ucd9c\ub825\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n\n"
       ]
     },
     {
@@ -166,14 +166,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "User can examine the sequence of profiled operators after loading the trace file\nin Chrome (``chrome://tracing``):\n\n![](../../_static/img/trace_img.png)\n\n   :scale: 25 %\n\n"
+        "\uc0ac\uc6a9\uc790\ub294 Chrome \ube0c\ub77c\uc6b0\uc800( ``chrome://tracing`` )\uc5d0\uc11c \ucd94\uc801 \ud30c\uc77c\uc744 \ubd88\ub7ec\uc640\n\ud504\ub85c\ud30c\uc77c\ub41c \uc77c\ub828\uc758 \uc5f0\uc0b0\uc790\ub4e4\uc744 \uac80\ud1a0\ud574\ubcfc \uc218 \uc788\uc2b5\ub2c8\ub2e4:\n\n![](../../_static/img/trace_img.png)\n\n   :scale: 25 %\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Learn More\n----------\n\nTake a look at the following tutorial to learn how to visualize your model with TensorBoard:\n\n-  `Visualizing models, data, and training with TensorBoard <https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html>`_ tutorial\n\n\n"
+        "\ub354 \uc54c\uc544\ubcf4\uae30\n-------------\n\n\ub2e4\uc74c\uc758 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \ud1b5\ud574 Tensorboard\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubaa8\ub378 \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uc138\uc694:\n\n- :doc:`/intermediate/tensorboard_tutorial`\n\n\n"
       ]
     }
   ],
@@ -193,7 +193,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/e3f9fff2f30691a0831549aa6b6712f5/two_layer_net_module.ipynb b/docs/_downloads/e3f9fff2f30691a0831549aa6b6712f5/two_layer_net_module.ipynb
index c3a50bfe5..e5843b806 100644
--- a/docs/_downloads/e3f9fff2f30691a0831549aa6b6712f5/two_layer_net_module.ipynb
+++ b/docs/_downloads/e3f9fff2f30691a0831549aa6b6712f5/two_layer_net_module.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch: \uc0ac\uc6a9\uc790 \uc815\uc758 nn Module\n-------------------------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88\uc5d0\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 Module\uc758 \uc11c\ube0c\ud074\ub798\uc2a4\ub85c \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uae30\uc874 Module\uc758 \uac04\ub2e8\ud55c\n\uad6c\uc131\ubcf4\ub2e4 \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uc6d0\ud55c\ub2e4\uba74, \uc774 \ubc29\ubc95\uc73c\ub85c \ubaa8\ub378\uc744 \uc815\uc758\ud558\uba74 \ub429\ub2c8\ub2e4.\n\n"
+        "\nPyTorch: \uc0ac\uc6a9\uc790 \uc815\uc758 nn Module\n-------------------------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88\uc5d0\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 Module\uc758 \uc11c\ube0c\ud074\ub798\uc2a4\ub85c \ubaa8\ub378\uc744 \uc815\uc758\ud569\ub2c8\ub2e4. \uae30\uc874 Module\uc758 \uac04\ub2e8\ud55c\n\uad6c\uc131\ubcf4\ub2e4 \ub354 \ubcf5\uc7a1\ud55c \ubaa8\ub378\uc744 \uc6d0\ud55c\ub2e4\uba74, \uc774 \ubc29\ubc95\uc73c\ub85c \ubaa8\ub378\uc744 \uc815\uc758\ud558\uba74 \ub429\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb b/docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb
index 0c830156a..912624eb0 100644
--- a/docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb
+++ b/docs/_downloads/e72ffa33b709a3d3f5b3f4c195c07637/aws_distributed_training_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n(advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS\n=============================================================\n\n**Author**: `Nathan Inkawhich <https://github.com/inkawhich>`_\n\n**Edited by**: `Teng Li <https://github.com/teng-li>`_\n\n\n"
+        "\n(advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS\n=============================================================\n\n**Author**: `Nathan Inkawhich <https://github.com/inkawhich>`_\n\n**Edited by**: `Teng Li <https://github.com/teng-li>`_\n"
       ]
     },
     {
@@ -232,7 +232,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/e733d8cec5d7c07a409a12a4273a4a28/torchtext_translation_tutorial.py b/docs/_downloads/e733d8cec5d7c07a409a12a4273a4a28/torchtext_translation_tutorial.py
index 61f2a0a59..68b64682e 100644
--- a/docs/_downloads/e733d8cec5d7c07a409a12a4273a4a28/torchtext_translation_tutorial.py
+++ b/docs/_downloads/e733d8cec5d7c07a409a12a4273a4a28/torchtext_translation_tutorial.py
@@ -5,7 +5,7 @@
 이 튜토리얼에서는 ``torchtext`` 의 유용한 여러 클래스들과 시퀀스 투 시퀀스(sequence-to-sequence, seq2seq)모델을 통해
 영어와 독일어 문장들이 포함된 유명한 데이터 셋을 이용해서 독일어 문장을 영어로 번역해 볼 것입니다.
 
-이 튜토리얼은 
+이 튜토리얼은
 PyTorch 커뮤니티 멤버인 `Ben Trevett <https://github.com/bentrevett>`__ 이 작성한
 `튜토리얼 <https://github.com/bentrevett/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb>`__ 에 기초하고 있으며
 `Seth Weidman <https://github.com/SethHWeidman/>`__ 이 Ben의 허락을 받고 만들었습니다.
@@ -16,14 +16,16 @@
     - `TranslationDataset <https://torchtext.readthedocs.io/en/latest/datasets.html#torchtext.datasets.TranslationDataset>`__
     - `Field <https://torchtext.readthedocs.io/en/latest/data.html#torchtext.data.Field>`__
     - `BucketIterator <https://torchtext.readthedocs.io/en/latest/data.html#torchtext.data.BucketIterator>`__
+
 """
 
 ######################################################################
 # `Field` 와 `TranslationDataset`
-# ----------------
+# --------------------------------
+#
 # ``torchtext`` 에는 언어 변환 모델을 만들때 쉽게 사용할 수 있는 데이터셋을 만들기 적합한 다양한 도구가 있습니다.
 # 그 중에서도 중요한 클래스 중 하나인 `Field <https://github.com/pytorch/text/blob/master/torchtext/data/field.py#L64>`__ 는
-# 각 문장이 어떻게 전처리되어야 하는지 지정하며, 또 다른 중요한 클래스로는 `TranslationDataset` 이 있습니다. 
+# 각 문장이 어떻게 전처리되어야 하는지 지정하며, 또 다른 중요한 클래스로는 `TranslationDataset` 이 있습니다.
 # ``torchtext`` 에는 이 외에도 비슷한 데이터셋들이 있는데, 이번 튜토리얼에서는 `Multi30k dataset <https://github.com/multi30k/dataset>`__ 을 사용할 것입니다.
 # 이 데이터 셋은 평균 약 13개의 단어로 구성된 약 삼만 개의 문장을 영어와 독일어 두 언어로 포함하고 있습니다.
 #
@@ -43,6 +45,7 @@
 #
 # Spacy가 설치되어 있다면, 다음 코드는 ``TranslationDataset`` 에 있는 각 문장을 ``Field`` 에 정의된
 # 내용을 기반으로 토큰화할 것입니다.
+
 from torchtext.datasets import Multi30k
 from torchtext.data import Field, BucketIterator
 
@@ -75,7 +78,7 @@
 
 ######################################################################
 # ``BucketIterator``
-# ----------------
+# --------------------
 # 마지막으로 사용해 볼 ``torchtext`` 에 특화된 기능은 바로 ``BucketIterator`` 입니다.
 # 첫 번째 인자로 ``TranslationDataset`` 을 전달받기 때문에 사용하기가 쉽습니다. 문서에서도 볼 수 있듯
 # 이 기능은 비슷한 길이의 예제들을 묶어주는 반복자(iterator)를 정의합니다. 각각의 새로운 에포크(epoch)마다
@@ -93,7 +96,7 @@
     device = device)
 
 ######################################################################
-# 이 반복자들은 ``DataLoader`` 와 마찬가지로 호출할 수 있습니다. 아래 ``train`` 과 
+# 이 반복자들은 ``DataLoader`` 와 마찬가지로 호출할 수 있습니다. 아래 ``train`` 과
 # ``evaluation`` 함수에서 보면, 다음과 같이 간단히 호출할 수 있음을 알 수 있습니다 :
 # ::
 #
@@ -108,15 +111,15 @@
 
 ######################################################################
 # ``nn.Module`` 과 ``Optimizer`` 정의하기
-# ----------------
+# ------------------------------------------
 # 대부분은 ``torchtext`` 가 알아서 해줍니다 : 데이터셋이 만들어지고 반복자가 정의되면, 이 튜토리얼에서
 # 우리가 해야 할 일이라고는 그저 ``nn.Module`` 와 ``Optimizer`` 를 모델로서 정의하고 훈련시키는 것이 전부입니다.
-# 
+#
 #
 # 이 튜토리얼에서 사용할 모델은 `이곳 <https://arxiv.org/abs/1409.0473>`__ 에서 설명하고 있는 구조를 따르고 있으며,
-# 더 자세한 내용은 `여기 <https://github.com/SethHWeidman/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb>`__ 
+# 더 자세한 내용은 `여기 <https://github.com/SethHWeidman/pytorch-seq2seq/blob/master/3%20-%20Neural%20Machine%20Translation%20by%20Jointly%20Learning%20to%20Align%20and%20Translate.ipynb>`__
 # 를 참고하시기 바랍니다.
-# 
+#
 # 참고 : 이 튜토리얼에서 사용하는 모델은 언어 번역을 위해 사용할 예시 모델입니다. 이 모델을 사용하는 것은
 # 이 작업에 적당한 표준 모델이기 때문이지, 번역에 적합한 모델이기 때문은 아닙니다. 여러분이 최신 기술 트렌드를
 # 잘 따라가고 있다면 잘 아시겠지만, 현재 번역에서 가장 뛰어난 모델은 Transformers입니다. PyTorch가
diff --git a/docs/_downloads/e85264945029fe236addcb864bf5f13f/data_parallel_tutorial.py b/docs/_downloads/e85264945029fe236addcb864bf5f13f/data_parallel_tutorial.py
index ae3bbe337..82b4fc991 100644
--- a/docs/_downloads/e85264945029fe236addcb864bf5f13f/data_parallel_tutorial.py
+++ b/docs/_downloads/e85264945029fe236addcb864bf5f13f/data_parallel_tutorial.py
@@ -1,6 +1,7 @@
 """
 선택 사항: 데이터 병렬 처리 (Data Parallelism)
-==========================
+====================================================
+
 **글쓴이**: `Sung Kim <https://github.com/hunkim>`_ and `Jenny Kang <https://github.com/jennykang>`_
 **번역**: '정아진 <https://github.com/ajin-jng>'
 
@@ -19,7 +20,7 @@
 
     mytensor = my_tensor.to(device)
 
-''my_tensor.to(device)'' 를 호출 시 에는 ''my_tensor'' 를 다시쓰는 대신 ''my_tensor'' 의 또다른 복사본이 생긴다는 사실을 기억하십시오. 
+''my_tensor.to(device)'' 를 호출 시 에는 ''my_tensor'' 를 다시쓰는 대신 ''my_tensor'' 의 또다른 복사본이 생긴다는 사실을 기억하십시오.
 당신은 그것을 새로운 tensor 에 소속시키고 GPU에 그 tensor를 써야합니다.
 
 여러 GPU를 통해 앞과 뒤의 전파를 실행하는 것은 당연한 일 입니다.
@@ -59,7 +60,7 @@
 
 ######################################################################
 # 더미(Dummy) 데이터셋
-# -------------
+# -----------------------
 #
 # 더미(ramdom) 데이터셋을 만들어 봅시다. Getitem 만 구현하면 됩니다.
 #
@@ -86,7 +87,7 @@ def __len__(self):
 #
 # 데모를 위해 모델은 입력을 받고 선형 연산을 수행하며 출력을 제공합니다. 그러나 ``DataParallel`` 의 어떤 모델 (CNN, RNN, Capsule Net 등) 에서든 사용할 수 있습니다.
 #
-#우리는 input과 output의 크기를 모니터링하기 위해 모델안에 print 문을 넣었습니다.
+# 우리는 input과 output의 크기를 모니터링하기 위해 모델안에 print 문을 넣었습니다.
 # 무엇이 배치 순위 (batch rank) 0 에 프린트 되는지 주의 깊게 봐주시길 바랍니다.
 #
 
@@ -110,7 +111,7 @@ def forward(self, input):
 # -----------------------------
 #
 # 이것은 이 튜토리얼의 핵심 부분입니다. 먼저, model instance 를 만들고 가지고 있는 GPU가 여러개인지 확인해야합니다.
-# 만약 다수의 GPU를 보유중이라면, ``nn.DataParallel`` 을 사용하여 모델을 래핑 (wrapping) 할 수 있습니다. 
+# 만약 다수의 GPU를 보유중이라면, ``nn.DataParallel`` 을 사용하여 모델을 래핑 (wrapping) 할 수 있습니다.
 # 그런 다음 ``model.to(device)`` 를 통하여 모델을 GPU에 넣을 수 있습니다.
 #
 
diff --git a/docs/_downloads/edd578af5440b92a4d66dcd1d4aa8601/zeroing_out_gradients.ipynb b/docs/_downloads/edd578af5440b92a4d66dcd1d4aa8601/zeroing_out_gradients.ipynb
index 14b7509e2..e56dfa1c8 100644
--- a/docs/_downloads/edd578af5440b92a4d66dcd1d4aa8601/zeroing_out_gradients.ipynb
+++ b/docs/_downloads/edd578af5440b92a4d66dcd1d4aa8601/zeroing_out_gradients.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch\uc5d0\uc11c \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4e4\uae30\n================================\n\uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud560 \ub54c\ub294 \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \uc8fc\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c \n``.backward()`` \ub97c \ud638\ucd9c\ud560 \ub54c\ub9c8\ub2e4 \ubcc0\ud654\ub3c4\uac00 \ubc84\ud37c\uc5d0 \uc313\uc774\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. (\ub36e\uc5b4\uc4f0\uc9c0 \uc54a\ub294\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4.)\n\n\uac1c\uc694\n------------\n\uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\uc2dc\ud0ac \ub54c, \uacbd\uc0ac \ud558\uac15\ubc95\uc744 \uac70\uccd0 \ubaa8\ub378 \uc815\ud655\ub3c4\ub97c \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uacbd\uc0ac \ud558\uac15\ubc95\uc740 \uac04\ub2e8\ud788 \n\uc124\uba85\ud574 \ubaa8\ub378\uc758 \uac00\uc911\uce58\uc640 \ud3b8\ud5a5\uc744 \uc57d\uac04\uc529 \uc218\uc815\ud558\uba74\uc11c \uc190\uc2e4(\ub610\ub294 \uc624\ub958)\ub97c \ucd5c\uc18c\ud654\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \n\n``torch.Tensor`` \ub294 PyTorch \uc758 \ud575\uc2ec\uc774 \ub418\ub294 \ud074\ub798\uc2a4 \uc785\ub2c8\ub2e4. \ud150\uc11c\ub97c \uc0dd\uc131\ud560 \ub54c \n``.requires_grad`` \uc18d\uc131\uc744 ``True`` \ub85c \uc124\uc815\ud558\uba74, \ud150\uc11c\uc5d0 \uac00\ud574\uc9c4 \ubaa8\ub4e0 \uc5f0\uc0b0\uc744 \ucd94\uc801\ud569\ub2c8\ub2e4. \n\ub4a4\ub530\ub974\ub294 \ubaa8\ub4e0 \uc5ed\uc804\ud30c \ub2e8\uacc4\uc5d0\uc11c\ub3c4 \ub9c8\ucc2c\uac00\uc9c0\ub85c, \uc774 \ud150\uc11c\uc758 \ubcc0\ud654\ub3c4\ub294 ``.grad`` \uc18d\uc131\uc5d0 \ub204\uc801\ub420 \uac83\uc785\ub2c8\ub2e4.\n\ubaa8\ub4e0 \ubcc0\ud654\ub3c4\uc758 \ucd95\uc801 \ub610\ub294 \ud569\uc740 \uc190\uc2e4 \ud150\uc11c\uc5d0\uc11c ``.backward()`` \ub97c \ud638\ucd9c\ud560 \ub54c \uacc4\uc0b0\ub429\ub2c8\ub2e4.\n\n\ud150\uc11c\uc758 \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \uc8fc\uc5b4\uc57c \ud558\ub294 \uacbd\uc6b0\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \ud559\uc2b5 \uacfc\uc815 \ubc18\ubcf5\ubb38\uc744 \n\uc2dc\uc791\ud560 \ub54c, \ub204\uc801\ub418\ub294 \ubcc0\ud654\ub3c4\ub97c \uc815\ud655\ud558\uac8c \ucd94\uc801\ud558\uae30 \uc704\ud574\uc11c\ub294 \ubcc0\ud654\ub3c4\ub97c \uc6b0\uc120 0\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4. \n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 PyTorch \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubd05\ub2c8\ub2e4.\nPyTorch\uc5d0 \ub0b4\uc7a5\ub41c ``CIFAR10`` \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uacfc\uc815\uc744 \ud1b5\ud574 \uc54c\uc544\ubd05\uc2dc\ub2e4.\n\n\uc124\uc815\n-----\n\uc774 \ub808\uc2dc\ud53c\uc5d0\ub294 \ub370\uc774\ud130\ub97c \ud559\uc2b5\uc2dc\ud0a4\ub294 \ub0b4\uc6a9\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uae30 \ub54c\ubb38\uc5d0, \uc2e4\ud589 \uac00\ub2a5\ud55c \ub178\ud2b8\ubd81 \ud30c\uc77c\uc774 \uc788\ub2e4\uba74\n\ub7f0\ud0c0\uc784\uc744 GPU \ub610\ub294 TPU\ub85c \uc804\ud658\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4. \uc2dc\uc791\ud558\uae30\uc5d0 \uc55e\uc11c, ``torch`` \uc640 \n``torchvision`` \ud328\ud0a4\uc9c0\uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud569\ub2c8\ub2e4.\n\n::\n\n   pip install torchvision\n\n\n\n"
+        "\nPyTorch\uc5d0\uc11c \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4e4\uae30\n================================\n\uc2e0\uacbd\ub9dd\uc744 \uad6c\ucd95\ud560 \ub54c\ub294 \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \uc8fc\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4. \uae30\ubcf8\uc801\uc73c\ub85c \n``.backward()`` \ub97c \ud638\ucd9c\ud560 \ub54c\ub9c8\ub2e4 \ubcc0\ud654\ub3c4\uac00 \ubc84\ud37c\uc5d0 \uc313\uc774\uae30 \ub54c\ubb38\uc785\ub2c8\ub2e4. (\ub36e\uc5b4\uc4f0\uc9c0 \uc54a\ub294\ub2e4\ub294 \uc758\ubbf8\uc785\ub2c8\ub2e4.)\n\n\uac1c\uc694\n------------\n\uc2e0\uacbd\ub9dd\uc744 \ud559\uc2b5\uc2dc\ud0ac \ub54c, \uacbd\uc0ac \ud558\uac15\ubc95\uc744 \uac70\uccd0 \ubaa8\ub378 \uc815\ud655\ub3c4\ub97c \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uacbd\uc0ac \ud558\uac15\ubc95\uc740 \uac04\ub2e8\ud788 \n\uc124\uba85\ud574 \ubaa8\ub378\uc758 \uac00\uc911\uce58\uc640 \ud3b8\ud5a5\uc744 \uc57d\uac04\uc529 \uc218\uc815\ud558\uba74\uc11c \uc190\uc2e4(\ub610\ub294 \uc624\ub958)\ub97c \ucd5c\uc18c\ud654\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. \n\n``torch.Tensor`` \ub294 PyTorch \uc758 \ud575\uc2ec\uc774 \ub418\ub294 \ud074\ub798\uc2a4 \uc785\ub2c8\ub2e4. \ud150\uc11c\ub97c \uc0dd\uc131\ud560 \ub54c \n``.requires_grad`` \uc18d\uc131\uc744 ``True`` \ub85c \uc124\uc815\ud558\uba74, \ud150\uc11c\uc5d0 \uac00\ud574\uc9c4 \ubaa8\ub4e0 \uc5f0\uc0b0\uc744 \ucd94\uc801\ud569\ub2c8\ub2e4. \n\ub4a4\ub530\ub974\ub294 \ubaa8\ub4e0 \uc5ed\uc804\ud30c \ub2e8\uacc4\uc5d0\uc11c\ub3c4 \ub9c8\ucc2c\uac00\uc9c0\ub85c, \uc774 \ud150\uc11c\uc758 \ubcc0\ud654\ub3c4\ub294 ``.grad`` \uc18d\uc131\uc5d0 \ub204\uc801\ub420 \uac83\uc785\ub2c8\ub2e4.\n\ubaa8\ub4e0 \ubcc0\ud654\ub3c4\uc758 \ucd95\uc801 \ub610\ub294 \ud569\uc740 \uc190\uc2e4 \ud150\uc11c\uc5d0\uc11c ``.backward()`` \ub97c \ud638\ucd9c\ud560 \ub54c \uacc4\uc0b0\ub429\ub2c8\ub2e4.\n\n\ud150\uc11c\uc758 \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \uc8fc\uc5b4\uc57c \ud558\ub294 \uacbd\uc6b0\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \ud559\uc2b5 \uacfc\uc815 \ubc18\ubcf5\ubb38\uc744 \n\uc2dc\uc791\ud560 \ub54c, \ub204\uc801\ub418\ub294 \ubcc0\ud654\ub3c4\ub97c \uc815\ud655\ud558\uac8c \ucd94\uc801\ud558\uae30 \uc704\ud574\uc11c\ub294 \ubcc0\ud654\ub3c4\ub97c \uc6b0\uc120 0\uc73c\ub85c \ub9cc\ub4e4\uc5b4 \uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4. \n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c\ub294 PyTorch \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubcc0\ud654\ub3c4\ub97c 0\uc73c\ub85c \ub9cc\ub4dc\ub294 \ubc29\ubc95\uc744 \ubc30\uc6cc\ubd05\ub2c8\ub2e4.\nPyTorch\uc5d0 \ub0b4\uc7a5\ub41c ``CIFAR10`` \ub370\uc774\ud130\uc14b\uc5d0 \ub300\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uacfc\uc815\uc744 \ud1b5\ud574 \uc54c\uc544\ubd05\uc2dc\ub2e4.\n\n\uc124\uc815\n-----\n\uc774 \ub808\uc2dc\ud53c\uc5d0\ub294 \ub370\uc774\ud130\ub97c \ud559\uc2b5\uc2dc\ud0a4\ub294 \ub0b4\uc6a9\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uae30 \ub54c\ubb38\uc5d0, \uc2e4\ud589 \uac00\ub2a5\ud55c \ub178\ud2b8\ubd81 \ud30c\uc77c\uc774 \uc788\ub2e4\uba74\n\ub7f0\ud0c0\uc784\uc744 GPU \ub610\ub294 TPU\ub85c \uc804\ud658\ud558\ub294 \uac83\uc774 \uc88b\uc2b5\ub2c8\ub2e4. \uc2dc\uc791\ud558\uae30\uc5d0 \uc55e\uc11c, ``torch`` \uc640 \n``torchvision`` \ud328\ud0a4\uc9c0\uac00 \uc5c6\ub2e4\uba74 \uc124\uce58\ud569\ub2c8\ub2e4.\n\n::\n\n   pip install torchvision\n"
       ]
     },
     {
@@ -132,7 +132,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/ee55f2537c08fa13e041e479675a6c2c/numpy_extensions_tutorial.py b/docs/_downloads/ee55f2537c08fa13e041e479675a6c2c/numpy_extensions_tutorial.py
index afc9a118c..9475ff084 100644
--- a/docs/_downloads/ee55f2537c08fa13e041e479675a6c2c/numpy_extensions_tutorial.py
+++ b/docs/_downloads/ee55f2537c08fa13e041e479675a6c2c/numpy_extensions_tutorial.py
@@ -1,35 +1,34 @@
 # -*- coding: utf-8 -*-
 """
-Creating Extensions Using numpy and scipy
-=========================================
+numpy 와 scipy 를 이용한 확장(Extensions) 만들기
+=====================================================
 **Author**: `Adam Paszke <https://github.com/apaszke>`_
 
 **Updated by**: `Adam Dziedzic <https://github.com/adam-dziedzic>`_
 
-In this tutorial, we shall go through two tasks:
+**번역**: `Ajin Jeong <https://github.com/ajin-jng>`_
 
-1. Create a neural network layer with no parameters.
+이번 튜토리얼에서는 두 가지 작업을 수행할 것입니다:
 
-    -  This calls into **numpy** as part of its implementation
+1. 매개 변수가 없는 신경망 계층(layer) 만들기
+    - 이는 구현의 일부로 **numpy** 를 호출합니다.
 
-2. Create a neural network layer that has learnable weights
-
-    -  This calls into **SciPy** as part of its implementation
+2. 학습 가능한 가중치가 있는 신경망 계층(layer) 만들기
+    - 이는 구현의 일부로 **Scipy** 를 호출합니다.
 """
 
 import torch
 from torch.autograd import Function
 
 ###############################################################
-# Parameter-less example
-# ----------------------
+# 매개 변수가 없는(Parameter-less) 예시
+# ----------------------------------------
 #
-# This layer doesn’t particularly do anything useful or mathematically
-# correct.
+# 이 계층(layer)은 특별히 유용하거나 수학적으로 올바른 작업을 수행하지 않습니다.
 #
-# It is aptly named BadFFTFunction
+# 이름은 대충 BadFFTFunction으로 지었습니다.
 #
-# **Layer Implementation**
+# **계층(layer) 구현**
 
 from numpy.fft import rfft2, irfft2
 
@@ -47,15 +46,14 @@ def backward(ctx, grad_output):
         result = irfft2(numpy_go)
         return grad_output.new(result)
 
-# since this layer does not have any parameters, we can
-# simply declare this as a function, rather than as an nn.Module class
+# 이 계층에는 매개 변수가 없으므로 nn.Module 클래스가 아닌 함수로 간단히 선언할 수 있습니다.
 
 
 def incorrect_fft(input):
     return BadFFTFunction.apply(input)
 
 ###############################################################
-# **Example usage of the created layer:**
+# **생성된 계층(layer)의 사용 예시:**
 
 input = torch.randn(8, 8, requires_grad=True)
 result = incorrect_fft(input)
@@ -64,18 +62,16 @@ def incorrect_fft(input):
 print(input)
 
 ###############################################################
-# Parametrized example
-# --------------------
+# 매개 변수가 있는(Parameterized) 예시
+# ----------------------------------------
 #
-# In deep learning literature, this layer is confusingly referred
-# to as convolution while the actual operation is cross-correlation
-# (the only difference is that filter is flipped for convolution,
-# which is not the case for cross-correlation).
+# 딥러닝 문헌에서 이 계층(layer)의 실제 연산은 상호 상관(cross-correlation)이지만
+# 합성곱(convolution)이라고 헷갈리게 부르고 있습니다.
+# (합성곱은 필터를 뒤집어서 연산을 하는 반면, 상호 상관은 그렇지 않은 차이가 있습니다)
 #
-# Implementation of a layer with learnable weights, where cross-correlation
-# has a filter (kernel) that represents weights.
+# 학습 가능한 가중치를 가는 필터(커널)를 갖는 상호 상관 계층을 구현해보겠습니다.
 #
-# The backward pass computes the gradient wrt the input and the gradient wrt the filter.
+# 역전파 단계(backward pass)에서는 입력에 대한 기울기(gradient)와 필터에 대한 기울기를 계산합니다.
 
 from numpy import flip
 import numpy as np
@@ -87,7 +83,7 @@ def incorrect_fft(input):
 class ScipyConv2dFunction(Function):
     @staticmethod
     def forward(ctx, input, filter, bias):
-        # detach so we can cast to NumPy
+        # 분리(detach)하여 NumPy로 변환(cast)할 수 있습니다.
         input, filter, bias = input.detach(), filter.detach(), bias.detach()
         result = correlate2d(input.numpy(), filter.numpy(), mode='valid')
         result += bias.numpy()
@@ -101,7 +97,7 @@ def backward(ctx, grad_output):
         grad_output = grad_output.numpy()
         grad_bias = np.sum(grad_output, keepdims=True)
         grad_input = convolve2d(grad_output, filter.numpy(), mode='full')
-        # the previous line can be expressed equivalently as:
+        # 윗줄은 다음과 같이 표현할 수도 있습니다:
         # grad_input = correlate2d(grad_output, flip(flip(filter.numpy(), axis=0), axis=1), mode='full')
         grad_filter = correlate2d(input.numpy(), grad_output, mode='valid')
         return torch.from_numpy(grad_input), torch.from_numpy(grad_filter).to(torch.float), torch.from_numpy(grad_bias).to(torch.float)
@@ -118,7 +114,7 @@ def forward(self, input):
 
 
 ###############################################################
-# **Example usage:**
+# **사용 예시:**
 
 module = ScipyConv2d(3, 3)
 print("Filter and bias: ", list(module.parameters()))
@@ -129,7 +125,7 @@ def forward(self, input):
 print("Gradient for the input map: ", input.grad)
 
 ###############################################################
-# **Check the gradients:**
+# **기울기(gradient) 확인:**
 
 from torch.autograd.gradcheck import gradcheck
 
diff --git a/docs/_downloads/ef3541eb2ef78e22efa65b3d6f4ba737/pruning_tutorial.py b/docs/_downloads/ef3541eb2ef78e22efa65b3d6f4ba737/pruning_tutorial.py
index 30c392c8c..192e06c26 100644
--- a/docs/_downloads/ef3541eb2ef78e22efa65b3d6f4ba737/pruning_tutorial.py
+++ b/docs/_downloads/ef3541eb2ef78e22efa65b3d6f4ba737/pruning_tutorial.py
@@ -7,15 +7,15 @@
 
 최첨단 딥러닝 모델들은 굉장히 많은 수의 파라미터값들로 구성되기 때문에, 쉽게 배포되기 어렵습니다.
 이와 반대로, 생물학적 신경망들은 효율적으로 희소하게 연결된 것으로 알려져 있습니다.
-모델의 정확도가 손상되지 않는 범위에서 메모리, 배터리, 하드웨어 소비량을 줄이고, 
-기기에 경량화된 모델을 배치하며, 개인이 이용하고 있는 기기에서 프라이버시가 보장되기 위해서는 
+모델의 정확도가 손상되지 않는 범위에서 메모리, 배터리, 하드웨어 소비량을 줄이고,
+기기에 경량화된 모델을 배치하며, 개인이 이용하고 있는 기기에서 프라이버시가 보장되기 위해서는
 모델에 포함된 파라미터 수를 줄여 압축하는 최적의 기법을 파악하는 것이 중요합니다.
-연구 측면에서는, 가지치기 기법은 굉장히 많은 수의 파라미터값들로 구성된 모델과 
+연구 측면에서는, 가지치기 기법은 굉장히 많은 수의 파라미터값들로 구성된 모델과
 굉장히 적은 수의 파라미터값들로 구성된 모델 간 학습 역학 차이를 조사하는데 주로 이용되기도 하며,
-하위 신경망 모델과 파라미터값들의 초기화가 운이 좋게 잘 된 케이스를 바탕으로 
+하위 신경망 모델과 파라미터값들의 초기화가 운이 좋게 잘 된 케이스를 바탕으로
 ("`lottery tickets <https://arxiv.org/abs/1803.03635>`_") 신경망 구조를 찾는 기술들에 대해 반대 의견을 제시하기도 합니다.
 
-이번 튜토리얼에서는, ``torch.nn.utils.prune`` 을 이용하여 여러분이 설계한 딥러닝 모델에 대해 가지치기 기법을 적용해보는 것을 배워보고, 
+이번 튜토리얼에서는, ``torch.nn.utils.prune`` 을 이용하여 여러분이 설계한 딥러닝 모델에 대해 가지치기 기법을 적용해보는 것을 배워보고,
 심화적으로 여러분의 맞춤형 가지치기 기법을 구현하는 방법에 대해 배워보도록 하겠습니다.
 
 요구사항
@@ -31,7 +31,7 @@
 ######################################################################
 # 딥러닝 모델 생성
 # -----------------------
-# 이번 튜토리얼에서는, 얀 르쿤 교수님의 연구진들이 1998년도에 발표한 ``LeNet 
+# 이번 튜토리얼에서는, 얀 르쿤 교수님의 연구진들이 1998년도에 발표한 ``LeNet
 # <http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf>`` 의 모델 구조를 이용합니다.
 
 
@@ -63,9 +63,10 @@ def forward(self, x):
 ######################################################################
 # 모듈 점검
 # -----------------
-# 
-# 가지치기 기법이 적용되지 않은 LeNet 모델의 ``conv1`` 층을 점검해봅시다. 
-# 여기에는 2개의 파라미터값들인 ``가중치``값과 ``편향``값을이 포함될 것이며, 버퍼는 존재하지 않을 것입니다.
+#
+# 가지치기 기법이 적용되지 않은 LeNet 모델의 ``conv1`` 층을 점검해봅시다.
+# 여기에는 2개의 파라미터값들인 ``가중치`` 값과 ``편향`` 값이 포함될 것이며, 버퍼는 존재하지 않을 것입니다.
+#
 
 module = model.conv1
 print(list(module.named_parameters()))
@@ -76,51 +77,51 @@ def forward(self, x):
 ######################################################################
 # 모듈 가지치기 기법 적용 예제
 # -----------------------------------
-# 
+#
 # 모듈에 대해 가지치기 기법을 적용하기 위해 (이번 예제에서는, LeNet 모델의 ``conv1`` 층)
-# 첫 번째로는, ``torch.nn.utils.prune`` (또는 ``BasePruningMethod`` 의 서브 클래스로 직접 `구현
-# <torch-nn-utils-prune>`_ )
+# 첫 번째로는, ``torch.nn.utils.prune`` (또는 ``BasePruningMethod`` 의 서브 클래스로 직접
+# `구현 <torch-nn-utils-prune>`_ )
 # 내 존재하는 가지치기 기법을 선택합니다.
 # 그 후, 해당 모듈 내에서 가지치기 기법을 적용하고자 하는 모듈과 파라미터를 지정합니다.
 # 마지막으로, 가지치기 기법에 적당한 키워드 인자값을 이용하여 가지치기 매개변수를 지정합니다.
 # 이번 예제에서는, ``conv1`` 층의 가중치의 30%값들을 랜덤으로 가지치기 기법을 적용해보겠습니다.
 # 모듈은 함수에 대한 첫 번째 인자값으로 전달되며, ``name`` 은 문자열 식별자를 이용하여 해당 모듈 내 매개변수를 구분합니다.
-# 그리고, ``amount`` 는 가지치기 기법을 적용하기 위한 대상 가중치값들의 백분율 (0과 1사이의 실수값), 
+# 그리고, ``amount`` 는 가지치기 기법을 적용하기 위한 대상 가중치값들의 백분율 (0과 1사이의 실수값),
 # 혹은 가중치값의 연결의 개수 (음수가 아닌 정수) 를 지정합니다.
 
-prune.random_unstructured(module, name="weight", amount=0.3) 
+prune.random_unstructured(module, name="weight", amount=0.3)
 
 ######################################################################
-# 가지치기 기법은 가중치값들을 파라미터값들로부터 제거하고 ``weight_orig`` (즉, 초기 가중치 이름에 "_orig"을 붙인) 이라는 
+# 가지치기 기법은 가중치값들을 파라미터값들로부터 제거하고 ``weight_orig`` (즉, 초기 가중치 이름에 "_orig"을 붙인) 이라는
 # 새로운 파라미터값으로 대체하는 것으로 실행됩니다.
-# ``weight_orig`` 은 텐서값에 가지치기 기법이 적용되지 않은 상태를 저장합니다. 
+# ``weight_orig`` 은 텐서값에 가지치기 기법이 적용되지 않은 상태를 저장합니다.
 # ``bias`` 은 가지치기 기법이 적용되지 않았기 때문에 그대로 남아 있습니다.
 print(list(module.named_parameters()))
 
 ######################################################################
-# 위에서 선택한 가지치기 기법에 의해 생성되는 가지치기 마스크는 초기 파라미터  ``name`` 에 ``weight_mask`` 
+# 위에서 선택한 가지치기 기법에 의해 생성되는 가지치기 마스크는 초기 파라미터  ``name`` 에 ``weight_mask``
 # (즉, 초기 가중치 이름에 "_mask"를 붙인) 이름의 모듈 버퍼로 저장됩니다.
 print(list(module.named_buffers()))
 
 ######################################################################
-# 수정이 되지 않은 상태에서 순전파를 진행하기 위해서는 ``가중치``값 속성이 존재해야 합니다.
-# ``torch.nn.utils.prune``  내 구현된 가지치기 기법은 가지치기 기법이 적용된 가중치값들을 이용하여 
+# 수정이 되지 않은 상태에서 순전파를 진행하기 위해서는 ``가중치`` 값 속성이 존재해야 합니다.
+# ``torch.nn.utils.prune`` 내 구현된 가지치기 기법은 가지치기 기법이 적용된 가중치값들을 이용하여
 # (기존의 가중치값에 가지치기 기법이 적용된) 순전파를 진행하고, ``weight`` 속성값에 가지치기 기법이 적용된 가중치값들을 저장합니다.
 # 이제 가중치값들은 ``module`` 의 매개변수가 아니라 하나의 속성값으로 취급되는 점을 주의하세요.
 print(module.weight)
 
 ######################################################################
 # 최종적으로, 가지치기 기법은 파이토치의 ``forward_pre_hooks`` 를 이용하여 각 순전파가 진행되기 전에 가지치기 기법이 적용됩니다.
-# 구체적으로, 지금까지 진행한 것 처럼, 모듈이 가지치기 기법이 적용되었을 때, 
+# 구체적으로, 지금까지 진행한 것 처럼, 모듈이 가지치기 기법이 적용되었을 때,
 # 가지치기 기법이 적용된 각 파라미터값들이 ``forward_pre_hook`` 를 얻게됩니다.
-# 이러한 경우, ``weight`` 이름인 기존 파라미터값에 대해서만 가지치기 기법을 적용하였기 때문에, 
+# 이러한 경우, ``weight`` 이름인 기존 파라미터값에 대해서만 가지치기 기법을 적용하였기 때문에,
 # 훅은 오직 1개만 존재할 것입니다.
 print(module._forward_pre_hooks)
 
 ######################################################################
-# 완결성을 위해, 편향값에 대해서도 가지치기 기법을 적용할 수 있으며, 
+# 완결성을 위해, 편향값에 대해서도 가지치기 기법을 적용할 수 있으며,
 # 모듈의 파라미터, 버퍼, 훅, 속성값들이 어떻게 변경되는지 확인할 수 있습니다.
-# 또 다른 가지치기 기법을 적용해보기 위해, ``l1_unstructured`` 가지치기 함수에서 구현된 내용과 같이, 
+# 또 다른 가지치기 기법을 적용해보기 위해, ``l1_unstructured`` 가지치기 함수에서 구현된 내용과 같이,
 # L1 Norm 값이 가장 작은 편향값 3개를 가지치기를 시도해봅시다.
 prune.l1_unstructured(module, name="bias", amount=3)
 
@@ -142,7 +143,7 @@ def forward(self, x):
 ######################################################################
 # 가지치기 기법 반복 적용
 # ------------------------------------
-# 
+#
 # 모듈 내 같은 파라미터값에 대해 가지치기 기법이 여러번 적용될 수 있으며, 다양한 가지치기 기법의 조합이 적용된 것과 동일하게 적용될 수 있습니다.
 # 새로운 마스크와 이전의 마스크의 결합은 ``PruningContainer`` 의 ``compute_mask`` 메소드를 통해 처리할 수 있습니다.
 #
@@ -166,7 +167,7 @@ def forward(self, x):
 ######################################################################
 # 가지치기 기법이 적용된 모델의 직렬화
 # ---------------------------------------------
-# 마스크 버퍼들과 가지치기 기법이 적용된 텐서 계산에 사용된 기존의 파라미터를 포함하여 관련된 모든 텐서값들은 
+# 마스크 버퍼들과 가지치기 기법이 적용된 텐서 계산에 사용된 기존의 파라미터를 포함하여 관련된 모든 텐서값들은
 # 필요한 경우 모델의 ``state_dict`` 에 저장되기 떄문에, 쉽게 직렬화하여 저장할 수 있습니다.
 print(model.state_dict().keys())
 
@@ -175,14 +176,14 @@ def forward(self, x):
 # 가지치기 기법의 재-파라미터화 제거
 # -----------------------------------------
 #
-# 가지치기 기법이 적용된 것을 영구적으로 만들기 위해서, 재-파라미터화 관점의 
+# 가지치기 기법이 적용된 것을 영구적으로 만들기 위해서, 재-파라미터화 관점의
 # ``weight_orig`` 와 ``weight_mask`` 값을 제거하고, ``forward_pre_hook`` 값을 제거합니다.
 # 제거하기 위해 ``torch.nn.utils.prune`` 내 ``remove`` 함수를 이용할 수 있습니다.
 # 가지치기 기법이 적용되지 않은 것처럼 실행되는 것이 아닌 점을 주의하세요.
 # 이는 단지 가지치기 기법이 적용된 상태에서 가중치 파라미터값을 모델 파라미터값으로 재할당하는 것을 통해 영구적으로 만드는 것일 뿐입니다.
 
 ######################################################################
-# 재-파라미터화를 제거하기 전 상태 
+# 재-파라미터화를 제거하기 전 상태
 print(list(module.named_parameters()))
 ######################################################################
 print(list(module.named_buffers()))
@@ -190,7 +191,7 @@ def forward(self, x):
 print(module.weight)
 
 ######################################################################
-# 재-파라미터를 제거한 후 상태 
+# 재-파라미터를 제거한 후 상태
 prune.remove(module, 'weight')
 print(list(module.named_parameters()))
 ######################################################################
@@ -198,9 +199,9 @@ def forward(self, x):
 
 ######################################################################
 # 모델 내 여러 파라미터값들에 대하여 가지치기 기법 적용
-# --------------------------------------
+# ----------------------------------------------------------
 #
-# 가지치기 기법을 적용하고 싶은 파라미터값들을 지정함으로써, 이번 예제에서 볼 수 있는 것 처럼, 
+# 가지치기 기법을 적용하고 싶은 파라미터값들을 지정함으로써, 이번 예제에서 볼 수 있는 것 처럼,
 # 신경망 모델 내 여러 텐서값들에 대해서 쉽게 가지치기 기법을 적용할 수 있습니다.
 
 new_model = LeNet()
@@ -296,22 +297,22 @@ def forward(self, x):
 ######################################################################
 # ``torch.nn.utils.prune`` 에서 확장된 맞춤형 가지치기 기법
 # ------------------------------------------------------------------
-# 맞춤형 가지치기 기법은, 다른 가지치기 기법을 적용하는 것과 같은 방식으로, 
+# 맞춤형 가지치기 기법은, 다른 가지치기 기법을 적용하는 것과 같은 방식으로,
 # ``BasePruningMethod`` 의 기본 클래스인 ``nn.utils.prune`` 모듈을 활용하여 구현할 수 있습니다.
 # 기본 클래스는 ``__call__``, ``apply_mask``, ``apply``, ``prune``, ``remove`` 메소드들을 내포하고 있습니다.
 # 특별한 케이스가 아닌 경우, 기본적으로 구성된 메소드들을 재구성할 필요가 없습니다.
-# 그러나, ``__init__`` (구성요소), ``compute_mask`` 
+# 그러나, ``__init__`` (구성요소), ``compute_mask``
 # (가지치기 기법의 논리에 따라 주어진 텐서값에 마스크를 적용하는 방법) 을 고려하여 구성해야 합니다.
-# 게다가, 가지치기 기법을 어떠한 방식으로 적용하는지 명확하게 구성해야 합니다. 
+# 게다가, 가지치기 기법을 어떠한 방식으로 적용하는지 명확하게 구성해야 합니다.
 # (지원되는 옵션은 ``global``, ``structured``, ``unstructured`` 입니다.)
 # 이러한 방식은, 가지치기 기법을 반복적으로 적용해야 하는 경우 마스크를 결합하는 방법을 결정하기 위해 필요합니다.
-# 즉, 이미 가지치기 기법이 적용된 모델에 대해서 가지치기 기법을 적용할 때, 
+# 즉, 이미 가지치기 기법이 적용된 모델에 대해서 가지치기 기법을 적용할 때,
 # 기존의 가지치기 기법이 적용되지 않은 파라미터 값에 대해 가지치기 기법이 영향을 미칠 것으로 예상됩니다.
-# ``PRUNING_TYPE``을 지정한다면, 가지치기 기법을 적용하기 위해 파라미터 값을 올바르게 제거하는 
+# ``PRUNING_TYPE`` 을 지정한다면, 가지치기 기법을 적용하기 위해 파라미터 값을 올바르게 제거하는
 # ``PruningContainer`` (마스크 가지치기 기법을 반복적으로 적용하는 것을 처리하는)를 가능하게 합니다.
 # 예를 들어, 다른 모든 항목이 존재하는 텐서를 가지치기 기법을 구현하고 싶을 때,
 # (또는, 텐서가 이전에 가지치기 기법에 의해 제거되었거나 남아있는 텐서에 대해)
-# 한 층의 개별 연결에 작용하며 전체 유닛/채널 (``'structured'``), 또는 다른 파라미터 간 
+# 한 층의 개별 연결에 작용하며 전체 유닛/채널 (``'structured'``), 또는 다른 파라미터 간
 # (``'global'``) 연결에는 작용하지 않기 때문에 ``PRUNING_TYPE='unstructured'`` 방식으로 진행됩니다.
 
 class FooBarPruningMethod(prune.BasePruningMethod):
@@ -322,7 +323,7 @@ class FooBarPruningMethod(prune.BasePruningMethod):
 
     def compute_mask(self, t, default_mask):
         mask = default_mask.clone()
-        mask.view(-1)[::2] = 0 
+        mask.view(-1)[::2] = 0
         return mask
 
 ######################################################################
@@ -332,7 +333,7 @@ def foobar_unstructured(module, name):
     텐서 내 다른 모든 항목들을 제거하여 `module` 에서 `name` 이라는 파라미터에 대해 가자치기 기법을 적용
     다음 내용에 따라 모듈을 수정 (또는 수정된 모듈을 반환):
         1) 가지치기 기법에 의해 매개변수 `name` 에 적용된 이진 마스크에 해당하는 명명된 버퍼 `name+'_mask'` 를 추가합니다.
-        `name` 파라미터는 가지치기 기법이 적용된 것으로 대체되며, 가지치기 기법이 적용되지 않은 
+        `name` 파라미터는 가지치기 기법이 적용된 것으로 대체되며, 가지치기 기법이 적용되지 않은
         기존의 파라미터는 `name+'_orig'` 라는 이름의 새로운 매개변수에 저장됩니다.
 
     인자값:
@@ -340,8 +341,8 @@ def foobar_unstructured(module, name):
         name (string): 모듈 내 가지치기 기법이 적용될 파라미터의 이름
 
     반환값:
-        module (nn.Module): 입력 모듈에 대해서 가지치기 기법이 적용된 모듈 
-    
+        module (nn.Module): 입력 모듈에 대해서 가지치기 기법이 적용된 모듈
+
     예시:
         >>> m = nn.Linear(3, 4)
         >>> foobar_unstructured(m, name='bias')
diff --git a/docs/_downloads/f11c58c36c9b8a5daf09d3f9a792ef84/memory_format_tutorial.ipynb b/docs/_downloads/f11c58c36c9b8a5daf09d3f9a792ef84/memory_format_tutorial.ipynb
index c13b1af35..bd2000abc 100644
--- a/docs/_downloads/f11c58c36c9b8a5daf09d3f9a792ef84/memory_format_tutorial.ipynb
+++ b/docs/_downloads/f11c58c36c9b8a5daf09d3f9a792ef84/memory_format_tutorial.ipynb
@@ -15,14 +15,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n(beta) Channels Last Memory Format in PyTorch\n*******************************************************\n**Author**: `Vitaly Fedyunin <https://github.com/VitalyFedyunin>`_\n\nWhat is Channels Last\n---------------------\n\nChannels Last memory format is an alternative way of ordering NCHW tensors in memory preserving dimensions ordering. Channels Last tensors ordered in such a way that channels become the densest dimension (aka storing images pixel-per-pixel).\n\nFor example, classic (contiguous) storage of NCHW tensor (in our case it is two 2x2 images with 3 color channels) look like this:\n\n.. figure:: /_static/img/classic_memory_format.png\n   :alt: classic_memory_format\n\nChannels Last memory format orders data differently:\n\n.. figure:: /_static/img/channels_last_memory_format.png\n   :alt: channels_last_memory_format\n\nPytorch supports memory formats (and provides back compatibility with existing models including eager, JIT, and TorchScript) by utilizing  existing strides structure.\nFor example, 10x3x16x16 batch in Channels Last format will have strides equal to (768, 1, 48, 3).\n\n"
+        "\n(\uc2e4\ud5d8\uc6a9) PyTorch\ub97c \uc0ac\uc6a9\ud55c Channels Last Memory Format\n*******************************************************\n\n**Author**: `Vitaly Fedyunin <https://github.com/VitalyFedyunin>`_\n\n**\ubc88\uc5ed**: `Choi Yoonjeong <https://github.com/potatochips178>`_\n\nChannels Last\uac00 \ubb34\uc5c7\uc778\uac00\uc694\n----------------------------\nChannels Last \uba54\ubaa8\ub9ac \ud615\uc2dd(memory format)\uc740 \ucc28\uc6d0 \uc21c\uc11c\ub97c \uc720\uc9c0\ud558\uba74\uc11c \uba54\ubaa8\ub9ac \uc0c1\uc758 NCHW \ud150\uc11c(tensor)\ub97c \uc815\ub82c\ud558\ub294 \ub610 \ub2e4\ub978 \ubc29\uc2dd\uc785\ub2c8\ub2e4.\nChannels Last \ud150\uc11c\ub294 \ucc44\ub110(Channel)\uc774 \uac00\uc7a5 \ubc00\ub3c4\uac00 \ub192\uc740(densest) \ucc28\uc6d0\uc73c\ub85c \uc815\ub82c(\uc608. \uc774\ubbf8\uc9c0\ub97c \ud53d\uc140x\ud53d\uc140\ub85c \uc800\uc7a5)\ub429\ub2c8\ub2e4.\n\n\uc608\ub97c \ub4e4\uc5b4, (2\uac1c\uc758 2 x 2 \uc774\ubbf8\uc9c0\uc5d0 3\uac1c\uc758 \ucc44\ub110\uc774 \uc874\uc7ac\ud558\ub294 \uacbd\uc6b0) \uc804\ud615\uc801\uc778(\uc5f0\uc18d\uc801\uc778) NCHW \ud150\uc11c\uc758 \uc800\uc7a5 \ubc29\uc2dd\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:\n\n.. figure:: /_static/img/classic_memory_format.png\n   :alt: classic_memory_format\n\nChannels Last \uba54\ubaa8\ub9ac \ud615\uc2dd\uc740 \ub370\uc774\ud130\ub97c \ub2e4\ub974\uac8c \uc815\ub82c\ud569\ub2c8\ub2e4:\n\n.. figure:: /_static/img/channels_last_memory_format.png\n   :alt: channels_last_memory_format\n\nPyTorch\ub294 \uae30\uc874\uc758 \uc2a4\ud2b8\ub77c\uc774\ub4dc(strides) \uad6c\uc870\ub97c \uc0ac\uc6a9\ud568\uc73c\ub85c\uc368 \uba54\ubaa8\ub9ac \ud615\uc2dd\uc744 \uc9c0\uc6d0(\ud558\uba70, eager, JIT \ubc0f TorchScript\ub97c \ud3ec\ud568\ud55c\n\uae30\uc874\uc758 \ubaa8\ub378\ub4e4\uacfc \ud558\uc704 \ud638\ud658\uc131\uc744 \uc81c\uacf5)\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, Channels Last \ud615\uc2dd\uc5d0\uc11c 10x3x16x16 \ubc30\uce58(batch)\ub294 (768, 1, 48, 3)\uc640\n\uac19\uc740 \ud3ed(strides)\uc744 \uac00\uc9c0\uace0 \uc788\uac8c \ub429\ub2c8\ub2e4.\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Channels Last memory format is implemented for 4D NCWH Tensors only.\n\n\n"
+        "Channels Last \uba54\ubaa8\ub9ac \ud615\uc2dd\uc740 \uc624\uc9c1 4D NCWH Tensors\uc5d0\uc11c\ub9cc \uc2e4\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -40,14 +40,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Memory Format API\n-----------------------\n\nHere is how to convert tensors between contiguous and channels\nlast memory formats.\n\n"
+        "\uba54\ubaa8\ub9ac \ud615\uc2dd(Memory Format) API\n---------------------------------\n\n\uc5f0\uc18d \uba54\ubaa8\ub9ac \ud615\uc2dd\uacfc channels last \uba54\ubaa8\ub9ac \ud615\uc2dd \uac04\uc5d0 \ud150\uc11c\ub97c \ubcc0\ud658\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Classic PyTorch contiguous tensor\n\n"
+        "\uc804\ud615\uc801\uc778 PyTorch\uc758 \uc5f0\uc18d\uc801\uc778 \ud150\uc11c(tensor)\n\n"
       ]
     },
     {
@@ -58,14 +58,14 @@
       },
       "outputs": [],
       "source": [
-        "x = torch.empty(N, C, H, W)\nprint(x.stride()) # Ouputs: (3072, 1024, 32, 1)"
+        "x = torch.empty(N, C, H, W)\nprint(x.stride()) # \uacb0\uacfc: (3072, 1024, 32, 1)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Conversion operator\n\n"
+        "\ubcc0\ud658 \uc5f0\uc0b0\uc790\n\n"
       ]
     },
     {
@@ -76,14 +76,14 @@
       },
       "outputs": [],
       "source": [
-        "x = x.contiguous(memory_format=torch.channels_last)\nprint(x.shape) # Outputs: (10, 3, 32, 32) as dimensions order preserved\nprint(x.stride()) # Outputs: (3072, 1, 96, 3)"
+        "x = x.contiguous(memory_format=torch.channels_last)\nprint(x.shape) # \uacb0\uacfc: (10, 3, 32, 32) \ucc28\uc6d0 \uc21c\uc11c\ub294 \ubcf4\uc874\ud568\nprint(x.stride()) # \uacb0\uacfc: (3072, 1, 96, 3)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Back to contiguous\n\n"
+        "\uc5f0\uc18d\uc801\uc778 \ud615\uc2dd\uc73c\ub85c \ub418\ub3cc\ub9ac\uae30\n\n"
       ]
     },
     {
@@ -94,14 +94,14 @@
       },
       "outputs": [],
       "source": [
-        "x = x.contiguous(memory_format=torch.contiguous_format)\nprint(x.stride()) # Outputs: (3072, 1024, 32, 1)"
+        "x = x.contiguous(memory_format=torch.contiguous_format)\nprint(x.stride()) # \uacb0\uacfc: (3072, 1024, 32, 1)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Alternative option\n\n"
+        "\ub2e4\ub978 \ubc29\uc2dd\n\n"
       ]
     },
     {
@@ -112,14 +112,14 @@
       },
       "outputs": [],
       "source": [
-        "x = x.to(memory_format=torch.channels_last)\nprint(x.stride()) # Ouputs: (3072, 1, 96, 3)"
+        "x = x.to(memory_format=torch.channels_last)\nprint(x.stride()) # \uacb0\uacfc: (3072, 1, 96, 3)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Format checks\n\n"
+        "\ud615\uc2dd(format) \ud655\uc778\n\n"
       ]
     },
     {
@@ -130,14 +130,14 @@
       },
       "outputs": [],
       "source": [
-        "print(x.is_contiguous(memory_format=torch.channels_last)) # Ouputs: True"
+        "print(x.is_contiguous(memory_format=torch.channels_last)) # \uacb0\uacfc: True"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Create as Channels Last\n\n"
+        "Channels Last \ubc29\uc2dd\uc73c\ub85c \uc0dd\uc131\ud558\uae30\n\n"
       ]
     },
     {
@@ -148,14 +148,14 @@
       },
       "outputs": [],
       "source": [
-        "x = torch.empty(N, C, H, W, memory_format=torch.channels_last)\nprint(x.stride()) # Ouputs: (3072, 1, 96, 3)"
+        "x = torch.empty(N, C, H, W, memory_format=torch.channels_last)\nprint(x.stride()) # \uacb0\uacfc: (3072, 1, 96, 3)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "``clone`` preserves memory format\n\n"
+        "``clone`` \uc740 \uba54\ubaa8\ub9ac \ud615\uc2dd\uc744 \ubcf4\uc874\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -166,14 +166,14 @@
       },
       "outputs": [],
       "source": [
-        "y = x.clone()\nprint(y.stride()) # Ouputs: (3072, 1, 96, 3)"
+        "y = x.clone()\nprint(y.stride()) # \uacb0\uacfc: (3072, 1, 96, 3)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "``to``, ``cuda``, ``float`` ... preserves memory format\n\n"
+        "``to``, ``cuda``, ``float`` ... \ub4f1\ub3c4 \uba54\ubaa8\ub9ac \ud615\uc2dd\uc744 \ubcf4\uc874\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -184,14 +184,14 @@
       },
       "outputs": [],
       "source": [
-        "if torch.cuda.is_available():\n    y = x.cuda()\n    print(y.stride()) # Ouputs: (3072, 1, 96, 3)"
+        "if torch.cuda.is_available():\n    y = x.cuda()\n    print(y.stride()) # \uacb0\uacfc: (3072, 1, 96, 3)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "``empty_like``, ``*_like`` operators preserves memory format\n\n"
+        "``empty_like``, ``*_like`` \uc5f0\uc0b0\uc790\ub3c4 \uba54\ubaa8\ub9ac \ud615\uc2dd\uc744 \ubcf4\uc874\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -202,14 +202,14 @@
       },
       "outputs": [],
       "source": [
-        "y = torch.empty_like(x)\nprint(y.stride()) # Ouputs: (3072, 1, 96, 3)"
+        "y = torch.empty_like(x)\nprint(y.stride()) # \uacb0\uacfc: (3072, 1, 96, 3)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Pointwise operators preserves memory format\n\n"
+        "Pointwise \uc5f0\uc0b0\uc790\ub3c4 \uba54\ubaa8\ub9ac \ud615\uc2dd\uc744 \ubcf4\uc874\ud569\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -220,14 +220,14 @@
       },
       "outputs": [],
       "source": [
-        "z = x + y\nprint(z.stride()) # Ouputs: (3072, 1, 96, 3)"
+        "z = x + y\nprint(z.stride()) # \uacb0\uacfc: (3072, 1, 96, 3)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Conv, Batchnorm modules support Channels Last\n(only works for CudNN >= 7.6)\n\n"
+        "Conv, Batchnorm \ubaa8\ub4c8\uc740 Channels Last\ub97c \uc9c0\uc6d0\ud569\ub2c8\ub2e4. (\ub2e8, CudNN >=7.6 \uc5d0\uc11c\ub9cc \ub3d9\uc791)\n\n"
       ]
     },
     {
@@ -238,14 +238,14 @@
       },
       "outputs": [],
       "source": [
-        "if torch.backends.cudnn.version() >= 7603:\n    input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float32, device=\"cuda\", requires_grad=True)\n    model = torch.nn.Conv2d(8, 4, 3).cuda().float()\n\n    input = input.contiguous(memory_format=torch.channels_last)\n    model = model.to(memory_format=torch.channels_last) # Module parameters need to be Channels Last\n\n    out = model(input)\n    print(out.is_contiguous(memory_format=torch.channels_last)) # Ouputs: True"
+        "if torch.backends.cudnn.version() >= 7603:\n    input = torch.randint(1, 10, (2, 8, 4, 4), dtype=torch.float32, device=\"cuda\", requires_grad=True)\n    model = torch.nn.Conv2d(8, 4, 3).cuda().float()\n\n    input = input.contiguous(memory_format=torch.channels_last)\n    model = model.to(memory_format=torch.channels_last) # \ubaa8\ub4c8 \uc778\uc790\ub4e4\uc740 Channels Last\ub85c \ubcc0\ud658\uc774 \ud544\uc694\ud569\ub2c8\ub2e4\n\n    out = model(input)\n    print(out.is_contiguous(memory_format=torch.channels_last)) # \uacb0\uacfc: True"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Performance Gains\n-------------------------------------------------------------------------------------------\nThe most significant performance gains are observed on Nvidia's hardware with\nTensor Cores support. We were able to archive over 22%  perf gains while running '\nAMP (Automated Mixed Precision) training scripts supplied by Nvidia https://github.com/NVIDIA/apex.\n\n``python main_amp.py -a resnet50 --b 200 --workers 16 --opt-level O2  ./data``\n\n"
+        "\uc131\ub2a5 \ud5a5\uc0c1\n-------------------------------------------------------------------------------------------\nTensor Cores\ub97c \uc9c0\uc6d0\ud558\ub294 Nvidia\uc758 \ud558\ub4dc\uc6e8\uc5b4\uc5d0\uc11c \uac00\uc7a5 \uc758\ubbf8\uc2ec\uc7a5\ud55c \uc131\ub2a5 \ud5a5\uc0c1\uc744 \ubcf4\uc600\uc2b5\ub2c8\ub2e4.\nNvidia\uac00 \uc81c\uacf5\ud558\ub294 AMP(Automated Mixed Precision) \ud559\uc2b5 \uc2a4\ud06c\ub9bd\ud2b8\ub85c 22% \uc774\uc0c1\uc758 \uc131\ub2a5 \ud5a5\uc0c1\uc744 \ud655\uc778\ud560 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4.\nhttps://github.com/NVIDIA/apex\n\n``python main_amp.py -a resnet50 --b 200 --workers 16 --opt-level O2  ./data``\n\n"
       ]
     },
     {
@@ -263,7 +263,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Passing ``--channels-last true`` allows running a model in Channels Last format with observed 22% perf gain.\n\n``python main_amp.py -a resnet50 --b 200 --workers 16 --opt-level O2 --channels-last true ./data``\n\n"
+        "``--channels-last true`` \uc778\uc790\ub97c \uc804\ub2ec\ud558\uc5ec Channels Last \ud615\uc2dd\uc73c\ub85c \ubaa8\ub378\uc744 \uc2e4\ud589\ud558\uba74 22%\uc758 \uc131\ub2a5 \ud5a5\uc0c1\uc744 \ubcf4\uc785\ub2c8\ub2e4.\n\n``python main_amp.py -a resnet50 --b 200 --workers 16 --opt-level O2 --channels-last true ./data``\n\n"
       ]
     },
     {
@@ -281,14 +281,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "The following list of models has the full support of Channels Last and showing 8%-35% perf gains on Volta devices:\n``alexnet``, ``mnasnet0_5``, ``mnasnet0_75``, ``mnasnet1_0``, ``mnasnet1_3``, ``mobilenet_v2``, ``resnet101``, ``resnet152``, ``resnet18``, ``resnet34``, ``resnet50``, ``resnext50_32x4d``, ``shufflenet_v2_x0_5``, ``shufflenet_v2_x1_0``, ``shufflenet_v2_x1_5``, ``shufflenet_v2_x2_0``, ``squeezenet1_0``, ``squeezenet1_1``, ``vgg11``, ``vgg11_bn``, ``vgg13``, ``vgg13_bn``, ``vgg16``, ``vgg16_bn``, ``vgg19``, ``vgg19_bn``, ``wide_resnet101_2``, ``wide_resnet50_2``\n\n\n"
+        "\uc544\ub798 \ubaa9\ub85d\uc758 \ubaa8\ub378\ub4e4\uc740 Channels Last \ud615\uc2dd\uc744 \uc804\uc801\uc73c\ub85c \uc9c0\uc6d0(full support)\ud558\uba70 Volta \uc7a5\ube44\uc5d0\uc11c 8%-35%\uc758 \uc131\ub2a5 \ud5a5\uc0c1\uc744 \ubcf4\uc785\ub2c8\ub2e4:\n``alexnet``, ``mnasnet0_5``, ``mnasnet0_75``, ``mnasnet1_0``, ``mnasnet1_3``, ``mobilenet_v2``, ``resnet101``, ``resnet152``, ``resnet18``, ``resnet34``, ``resnet50``, ``resnext50_32x4d``, ``shufflenet_v2_x0_5``, ``shufflenet_v2_x1_0``, ``shufflenet_v2_x1_5``, ``shufflenet_v2_x2_0``, ``squeezenet1_0``, ``squeezenet1_1``, ``vgg11``, ``vgg11_bn``, ``vgg13``, ``vgg13_bn``, ``vgg16``, ``vgg16_bn``, ``vgg19``, ``vgg19_bn``, ``wide_resnet101_2``, ``wide_resnet50_2``\n\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Converting existing models\n--------------------------\n\nChannels Last support not limited by existing models, as any model can be converted to Channels Last and propagate format through the graph as soon as input formatted correctly.\n\n\n"
+        "\uae30\uc874 \ubaa8\ub378\ub4e4 \ubcc0\ud658\ud558\uae30\n--------------------------\n\nChannels Last \uc9c0\uc6d0\uc740 \uae30\uc874 \ubaa8\ub378\uc774 \ubb34\uc5c7\uc774\ub0d0\uc5d0 \ub530\ub77c \uc81c\ud55c\ub418\uc9c0 \uc54a\uc73c\uba70, \uc5b4\ub5a0\ud55c \ubaa8\ub378\ub3c4 Channels Last\ub85c \ubcc0\ud658\ud560 \uc218 \uc788\uc73c\uba70\n\uc785\ub825(input)\uc758 \ud615\uc2dd\ub9cc \ub9de\ucdb0\uc8fc\uba74 (\uc2e0\uacbd\ub9dd) \uadf8\ub798\ud504\ub97c \ud1b5\ud574 \ubc14\ub85c \uc804\ud30c(propagate)\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n\n"
       ]
     },
     {
@@ -299,14 +299,14 @@
       },
       "outputs": [],
       "source": [
-        "# Need to be done once, after model initialization (or load)\nmodel = model.to(memory_format=torch.channels_last) # Replace with your model\n\n# Need to be done for every input\ninput = input.to(memory_format=torch.channels_last) # Replace with your input\noutput = model(input)"
+        "# \ubaa8\ub378\uc744 \ucd08\uae30\ud654\ud55c(\ub610\ub294 \ubd88\ub7ec\uc628) \uc774\ud6c4, \ud55c \ubc88 \uc2e4\ud589\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\nmodel = model.to(memory_format=torch.channels_last) # \uc6d0\ud558\ub294 \ubaa8\ub378\ub85c \uad50\uccb4\ud558\uae30\n\n# \ubaa8\ub4e0 \uc785\ub825\uc5d0 \ub300\ud574\uc11c \uc2e4\ud589\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\ninput = input.to(memory_format=torch.channels_last) # \uc6d0\ud558\ub294 \uc785\ub825\uc73c\ub85c \uad50\uccb4\ud558\uae30\noutput = model(input)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "However, not all operators fully converted to support Channels Last (usually returning\ncontiguous output instead). That means you need to verify the list of used operators\nagainst supported operators list https://github.com/pytorch/pytorch/wiki/Operators-with-Channels-Last-support,\nor introduce memory format checks into eager execution mode and run your model.\n\nAfter running the code below, operators will raise an exception if the output of the\noperator doesn't match the memory format of the input.\n\n\n\n"
+        "\uadf8\ub7ec\ub098, \ubaa8\ub4e0 \uc5f0\uc0b0\uc790\ub4e4\uc774 Channels Last\ub97c \uc9c0\uc6d0\ud558\ub3c4\ub85d \uc644\uc804\ud788 \ubc14\ub010 \uac83\uc740 \uc544\ub2d9\ub2c8\ub2e4(\uc77c\ubc18\uc801\uc73c\ub85c\ub294 \uc5f0\uc18d\uc801\uc778 \ucd9c\ub825\uc744 \ub300\uc2e0 \ubc18\ud658\ud569\ub2c8\ub2e4).\n\uc989, Channel Last \uc9c0\uc6d0 \uc5f0\uc0b0\uc790 \ubaa9\ub85d https://github.com/pytorch/pytorch/wiki/Operators-with-Channels-Last-support \uc5d0\uc11c \uc0ac\uc6a9\ud55c \uc5f0\uc0b0\uc790\ub4e4\uc774 \uc874\uc7ac\ud558\ub294\uc9c0 \ud655\uc778\ud558\uac70\ub098,\neager \uc2e4\ud589 \ubaa8\ub4dc\uc5d0\uc11c \uba54\ubaa8\ub9ac \ud615\uc2dd \uac80\uc0ac\ub97c \ub3c4\uc785\ud558\uace0 \ubaa8\ub378\uc744 \uc2e4\ud589\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n\uc544\ub798 \ucf54\ub4dc\uc5d0\uc11c, \uc5f0\uc0b0\uc790\ub4e4\uc758 \ucd9c\ub825\uc774 \uc785\ub825\uc758 \uba54\ubaa8\ub9ac \ud615\uc2dd\uacfc \uc77c\uce58\ud558\uc9c0 \uc54a\uc73c\uba74 \uc608\uc678(exception)\ub97c \ubc1c\uc0dd\uc2dc\ud0b5\ub2c8\ub2e4.\n\n\n\n"
       ]
     },
     {
@@ -324,14 +324,14 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "If you found an operator that doesn't support Channels Last tensors\nand you want to contribute, feel free to use following developers\nguide https://github.com/pytorch/pytorch/wiki/Writing-memory-format-aware-operators.\n\n\n"
+        "\ub9cc\uc57d Channels Last \ud150\uc11c\ub97c \uc9c0\uc6d0\ud558\uc9c0 \uc54a\ub294 \uc5f0\uc0b0\uc790\ub97c \ubc1c\uacac\ud558\uc600\uace0, \uae30\uc5ec\ud558\uae30\ub97c \uc6d0\ud55c\ub2e4\uba74\n\ub2e4\uc74c \uac1c\ubc1c \ubb38\uc11c\ub97c \ucc38\uace0\ud574\uc8fc\uc138\uc694.\nhttps://github.com/pytorch/pytorch/wiki/Writing-memory-format-aware-operators\n\n\n"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Work to do\n----------\nThere are still many things to do, such as:\n\n- Resolving ambiguity of N1HW and NC11 Tensors;\n- Testing of Distributed Training support;\n- Improving operators coverage.\n\nIf you have feedback and/or suggestions for improvement, please let us\nknow by creating `an issue <https://github.com/pytorch/pytorch/issues>`_.\n\n"
+        "\ud574\uc57c\ud560 \uc77c\n----------\n\ub2e4\uc74c\uacfc \uac19\uc774 \uc5ec\uc804\ud788 \ud574\uc57c \ud560 \uc77c\uc774 \ub9ce\uc774 \ub0a8\uc544\uc788\uc2b5\ub2c8\ub2e4:\n\n- N1HW\uc640 NC11 Tensors\uc758 \ubaa8\ud638\uc131 \ud574\uacb0\ud558\uae30;\n- \ubd84\uc0b0 \ud559\uc2b5\uc744 \uc9c0\uc6d0\ud558\ub294\uc9c0 \ud655\uc778\ud558\uae30;\n- \uc5f0\uc0b0\uc790 \ubc94\uc704(operators coverage) \uac1c\uc120(improve)\ud558\uae30\n\n\uac1c\uc120\ud560 \ubd80\ubd84\uc5d0 \ub300\ud55c \ud53c\ub4dc\ubc31 \ub610\ub294 \uc81c\uc548\uc774 \uc788\ub2e4\uba74 `\uc774\uc288\ub97c \ub9cc\ub4e4\uc5b4 <https://github.com/pytorch/pytorch/issues>`_ \uc54c\ub824\uc8fc\uc138\uc694.\n\n\n"
       ]
     }
   ],
@@ -351,7 +351,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/f16255c783f9e487235b8eff6c8792b9/nn_tutorial.py b/docs/_downloads/f16255c783f9e487235b8eff6c8792b9/nn_tutorial.py
index bbce4b7bf..6606bf3cb 100644
--- a/docs/_downloads/f16255c783f9e487235b8eff6c8792b9/nn_tutorial.py
+++ b/docs/_downloads/f16255c783f9e487235b8eff6c8792b9/nn_tutorial.py
@@ -1,13 +1,15 @@
 # -*- coding: utf-8 -*-
 """
 `torch.nn` 이 *실제로* 무엇인가요?
-===============================
+=====================================
 저자: Jeremy Howard, `fast.ai <https://www.fast.ai>`_.
 
 도움: Rachel Thomas, Francisco Ingham.
 
 번역: `남상호 <https://github.com/namdori61>`_
+
 """
+
 ###############################################################################
 # 이 튜토리얼을 스크립트가 아닌 노트북으로 실행하기를 권장합니다. 노트북 (.ipynb) 파일을 다운 받으시려면,
 # 페이지 상단에 있는 링크를 클릭해주세요.
@@ -32,7 +34,7 @@
 # 거의 동일하다는 것을 알게 될 것입니다).
 #
 # MNIST 데이터 준비
-# ----------------
+# -------------------
 #
 # 우리는 손으로 쓴 숫자(0에서 9 사이)의 흑백 이미지로 구성된 클래식
 # `MNIST <http://deeplearning.net/data/mnist/>`_ 데이터셋을 사용할 것 입니다.
@@ -51,7 +53,7 @@
 
 PATH.mkdir(parents=True, exist_ok=True)
 
-URL = "http://deeplearning.net/data/mnist/"
+URL = "https://github.com/pytorch/tutorials/raw/master/_static/"
 FILENAME = "mnist.pkl.gz"
 
 if not (PATH / FILENAME).exists():
@@ -277,7 +279,7 @@ def model(xb):
 
 ###############################################################################
 # nn.Module 을 이용하여 리팩토링 하기
-# ------------------------------
+# --------------------------------------
 # 다음으로, 더 명확하고 간결한 훈련 루프를 위해 ``nn.Module`` 및 ``nn.Parameter`` 를 사용합니다.
 # 우리는 ``nn.Module`` (자체가 클래스이고 상태를 추척할 수 있는) 하위 클래스(subclass)를 만듭니다.
 # 이 경우에는, 포워드(forward) 단계에 대한 가중치, 절편, 그리고 메소드(method) 등을 유지하는
@@ -317,7 +319,9 @@ def forward(self, xb):
 ###############################################################################
 # 이전에는 훈련 루프를 위해 이름 별로 각 매개변수(parameter)의 값을 업데이트하고 다음과 같이
 # 각 매개 변수에 대한 기울기들을 개별적으로 수동으로 0으로 제거해야 했습니다:
+#
 # ::
+#
 #   with torch.no_grad():
 #       weights -= weights.grad * lr
 #       bias -= bias.grad * lr
@@ -328,7 +332,9 @@ def forward(self, xb):
 # 이제 우리는 model.parameters() 및 model.zero_grad() (모두
 # ``nn.Module`` 에 대해 PyTorch에 의해 정의됨)를 활용하여 이러한 단계를 더 간결하게
 # 만들고, 특히 더 복잡한 모델에 대해서 일부 매개변수를 잊어 버리는 오류를 덜 발생시킬 수 있습니다:
+#
 # ::
+#
 #   with torch.no_grad():
 #       for p in model.parameters(): p -= p.grad * lr
 #       model.zero_grad()
@@ -361,7 +367,7 @@ def fit():
 
 ###############################################################################
 # nn.Linear 를 이용하여 리팩토링 하기
-# ------------------------------
+# ------------------------------------
 #
 # 계속해서 코드를 리팩토링 합니다. ``self.weights`` 및 ``self.bias`` 를 수동으로 정의 및
 # 초기화하고, ``xb  @ self.weights + self.bias`` 를 계산하는 대신에,
@@ -394,20 +400,24 @@ def forward(self, xb):
 
 ###############################################################################
 # optim 을 이용하여 리팩토링 하기
-# ------------------------------
+# ---------------------------------
 #
 # Pytorch에는 다양한 최적화(optimization) 알고리즘을 가진 패키지인 ``torch.optim`` 도 있습니다.
 # 각 매개변수를 수동으로 업데이트 하는 대신, 옵티마이저(optimizer)의 ``step`` 메소드를 사용하여
 # 업데이트를 진행할 수 있습니다.
 #
 # 이렇게 하면 이전에 수동으로 코딩한 최적화 단계를 대체할 수 있습니다:
+#
 # ::
+#
 #   with torch.no_grad():
 #       for p in model.parameters(): p -= p.grad * lr
 #       model.zero_grad()
 #
 # 대신에 이렇게 말이죠:
+#
 # ::
+#
 #   opt.step()
 #   opt.zero_grad()
 #
@@ -443,7 +453,7 @@ def get_model():
 
 ###############################################################################
 # Dataset 을 이용하여 리팩토링하기
-# ------------------------------
+# ----------------------------------
 #
 # PyTorch 에는 추상 Dataset 클래스가 있습니다. Dataset 은
 # ``__len__`` 함수 (Python의 표준 ``len`` 함수에 의해 호출됨) 및
@@ -468,13 +478,17 @@ def get_model():
 
 ###############################################################################
 # 이전에는 x 및 y 값의 미니 배치를 별도로 반복해야했습니다:
+#
 # ::
+#
 #     xb = x_train[start_i:end_i]
 #     yb = y_train[start_i:end_i]
 #
 #
 # 이제 이 두 단계를 함께 수행 할 수 있습니다:
+#
 # ::
+#
 #     xb,yb = train_ds[i*bs : i*bs+bs]
 #
 
@@ -494,7 +508,7 @@ def get_model():
 
 ###############################################################################
 # DataLoader 를 이용하여 리팩토링하기
-# -------------------------------
+# -----------------------------------
 #
 # Pytorch 의 ``DataLoader`` 는 배치 관리를 담당합니다.
 # 여러분들은 모든 ``Dataset`` 으로부터 ``DataLoader`` 를 생성할 수 있습니다.
@@ -509,13 +523,17 @@ def get_model():
 
 ###############################################################################
 # 이전에는 루프가 다음과 같이 배치 (xb, yb)를 반복했습니다:
+#
 # ::
+#
 #       for i in range((n-1)//bs + 1):
 #           xb,yb = train_ds[i*bs : i*bs+bs]
 #           pred = model(xb)
 #
 # 이제 (xb, yb)가 DataLoader 에서 자동으로 로드되므로 루프가 훨씬 깨끗해졌습니다:
+#
 # ::
+#
 #       for xb,yb in train_dl:
 #           pred = model(xb)
 
@@ -538,7 +556,7 @@ def get_model():
 # 이제 실제로 효과적인 모델을 만드는 데 필요한 기본 기능을 추가해 보겠습니다.
 #
 # 검증(validation) 추가하기
-# -----------------------
+# ---------------------------
 #
 # 섹션 1에서, 우리는 훈련 데이터에 사용하기 위해 합리적인 훈련 루프를 설정하려고했습니다.
 # 실전에서, 여러분들은 과적합(overfitting)을 확인하기 위해서 **항상**
@@ -650,7 +668,7 @@ def get_data(train_ds, valid_ds, bs):
 # 컨볼루션 신경망(CNN)을 훈련하는 데 사용할 수 있는지 살펴 보겠습니다!
 #
 # CNN 으로 넘어가기
-# ---------------
+# --------------------
 #
 # 이제 3개의 컨볼루션 레이어로 신경망을 구축할 것입니다.
 # 이전 섹션의 어떤 함수도 모델의 형식에 대해 가정하지 않기 때문에,
diff --git a/docs/_downloads/f1992c37a58ca1039b273eaac839f845/custom_dataset_transforms_loader.py b/docs/_downloads/f1992c37a58ca1039b273eaac839f845/custom_dataset_transforms_loader.py
index b5e0b93bd..e73039647 100644
--- a/docs/_downloads/f1992c37a58ca1039b273eaac839f845/custom_dataset_transforms_loader.py
+++ b/docs/_downloads/f1992c37a58ca1039b273eaac839f845/custom_dataset_transforms_loader.py
@@ -222,7 +222,7 @@ def __getitem__(self, idx):
 # 변형들은 훈련시에 사용이 되며 주로 데이터 증강으로 참조되며 최근의 모델 개발에선 흔히 사용됩니다.
 #
 # 데이터셋을 다룰때 자주 일어나는 문제중 하나는 모든 샘플들이 같은 크기를 가지고 있지 않을 경우입니다.
-# 대부분의 신경망들은 미리 정해진 크기의 이미지들을 받아드립니다.
+# 대부분의 신경망들은 미리 정해진 크기의 이미지들을 받아들입니다.
 # 그렇기 때문에 우리는 전처리 코드를 작성해야할 필요가 있습니다.
 # 이제 세개의 변형을 만들어 봅시다.
 #
diff --git a/docs/_downloads/f41f6eb36b8dff464b9cd21b8ea30765/super_resolution_with_onnxruntime.ipynb b/docs/_downloads/f41f6eb36b8dff464b9cd21b8ea30765/super_resolution_with_onnxruntime.ipynb
index edbf49ad3..a3010faf3 100644
--- a/docs/_downloads/f41f6eb36b8dff464b9cd21b8ea30765/super_resolution_with_onnxruntime.ipynb
+++ b/docs/_downloads/f41f6eb36b8dff464b9cd21b8ea30765/super_resolution_with_onnxruntime.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n(\uc120\ud0dd) PyTorch \ubaa8\ub378\uc744 ONNX\uc73c\ub85c \ubcc0\ud658\ud558\uace0 ONNX \ub7f0\ud0c0\uc784\uc5d0\uc11c \uc2e4\ud589\ud558\uae30\n========================================================================\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc5b4\ub5bb\uac8c PyTorch\uc5d0\uc11c \uc815\uc758\ub41c \ubaa8\ub378\uc744 ONNX \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\uace0 \ub610 \uc5b4\ub5bb\uac8c \uadf8 \ubcc0\ud658\ub41c \ubaa8\ub378\uc744\nONNX \ub7f0\ud0c0\uc784\uc5d0\uc11c \uc2e4\ud589\ud560 \uc218 \uc788\ub294\uc9c0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\nONNX \ub7f0\ud0c0\uc784\uc740 ONNX \ubaa8\ub378\uc744 \uc704\ud55c \uc5d4\uc9c4\uc73c\ub85c\uc11c \uc131\ub2a5\uc5d0 \ucd08\uc810\uc744 \ub9de\ucd94\uace0 \uc788\uace0 \uc5ec\ub7ec \ub2e4\uc591\ud55c \ud50c\ub7ab\ud3fc\uacfc \ud558\ub4dc\uc6e8\uc5b4(\uc708\ub3c4\uc6b0,\n\ub9ac\ub205\uc2a4, \ub9e5\uc744 \ube44\ub86f\ud55c \ud50c\ub7ab\ud3fc \ubfd0\ub9cc \uc544\ub2c8\ub77c CPU, GPU \ub4f1\uc758 \ud558\ub4dc\uc6e8\uc5b4)\uc5d0\uc11c \ud6a8\uc728\uc801\uc778 \ucd94\ub860\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.\nONNX \ub7f0\ud0c0\uc784\uc740 `\uc5ec\uae30\n<https://cloudblogs.microsoft.com/opensource/2019/05/22/onnx-runtime-machine-learning-inferencing-0-4-release>`__ \uc5d0\uc11c\n\uc124\uba85\ub41c \uac83\uacfc \uac19\uc774 \uc5ec\ub7ec \ubaa8\ub378\ub4e4\uc758 \uc131\ub2a5\uc744 \uc0c1\ub2f9\ud788 \ub192\uc77c \uc218 \uc788\ub2e4\ub294 \uc810\uc774 \uc99d\uba85\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc9c4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 `ONNX <https://github.com/onnx/onnx>`__\n\uc640 `ONNX Runtime <https://github.com/microsoft/onnxruntime>`__ \uc124\uce58\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.\nONNX\uc640 ONNX \ub7f0\ud0c0\uc784\uc758 \ubc14\uc774\ub108\ub9ac \ube4c\ub4dc\ub97c ``pip install onnx onnxruntime`` \ub97c \ud1b5\ud574 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\nONNX \ub7f0\ud0c0\uc784\uc740 \ubc84\uc804 3.5\uc5d0\uc11c 3.7\uae4c\uc9c0\uc758 Python\uacfc \ud638\ud658\ub429\ub2c8\ub2e4.\n``\ucc38\uace0``: \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc740 PyTorch\uc758 master \ube0c\ub79c\uce58\ub97c \ud544\uc694\ub85c\ud558\uba70 `\ub9c1\ud06c <https://github.com/pytorch/pytorch#from-source>`__ \uc5d0\uc11c\n\uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n"
+        "\n(\uc120\ud0dd) PyTorch \ubaa8\ub378\uc744 ONNX\uc73c\ub85c \ubcc0\ud658\ud558\uace0 ONNX \ub7f0\ud0c0\uc784\uc5d0\uc11c \uc2e4\ud589\ud558\uae30\n========================================================================\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 \uc5b4\ub5bb\uac8c PyTorch\uc5d0\uc11c \uc815\uc758\ub41c \ubaa8\ub378\uc744 ONNX \ud615\uc2dd\uc73c\ub85c \ubcc0\ud658\ud558\uace0 \ub610 \uc5b4\ub5bb\uac8c \uadf8 \ubcc0\ud658\ub41c \ubaa8\ub378\uc744\nONNX \ub7f0\ud0c0\uc784\uc5d0\uc11c \uc2e4\ud589\ud560 \uc218 \uc788\ub294\uc9c0\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\nONNX \ub7f0\ud0c0\uc784\uc740 ONNX \ubaa8\ub378\uc744 \uc704\ud55c \uc5d4\uc9c4\uc73c\ub85c\uc11c \uc131\ub2a5\uc5d0 \ucd08\uc810\uc744 \ub9de\ucd94\uace0 \uc788\uace0 \uc5ec\ub7ec \ub2e4\uc591\ud55c \ud50c\ub7ab\ud3fc\uacfc \ud558\ub4dc\uc6e8\uc5b4(\uc708\ub3c4\uc6b0,\n\ub9ac\ub205\uc2a4, \ub9e5\uc744 \ube44\ub86f\ud55c \ud50c\ub7ab\ud3fc \ubfd0\ub9cc \uc544\ub2c8\ub77c CPU, GPU \ub4f1\uc758 \ud558\ub4dc\uc6e8\uc5b4)\uc5d0\uc11c \ud6a8\uc728\uc801\uc778 \ucd94\ub860\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.\nONNX \ub7f0\ud0c0\uc784\uc740 `\uc5ec\uae30\n<https://cloudblogs.microsoft.com/opensource/2019/05/22/onnx-runtime-machine-learning-inferencing-0-4-release>`__ \uc5d0\uc11c\n\uc124\uba85\ub41c \uac83\uacfc \uac19\uc774 \uc5ec\ub7ec \ubaa8\ub378\ub4e4\uc758 \uc131\ub2a5\uc744 \uc0c1\ub2f9\ud788 \ub192\uc77c \uc218 \uc788\ub2e4\ub294 \uc810\uc774 \uc99d\uba85\ub418\uc5c8\uc2b5\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc9c4\ud589\ud558\uae30 \uc704\ud574\uc11c\ub294 `ONNX <https://github.com/onnx/onnx>`__\n\uc640 `ONNX Runtime <https://github.com/microsoft/onnxruntime>`__ \uc124\uce58\uac00 \ud544\uc694\ud569\ub2c8\ub2e4.\nONNX\uc640 ONNX \ub7f0\ud0c0\uc784\uc758 \ubc14\uc774\ub108\ub9ac \ube4c\ub4dc\ub97c ``pip install onnx onnxruntime`` \ub97c \ud1b5\ud574 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\nONNX \ub7f0\ud0c0\uc784\uc740 \ubc84\uc804 3.5\uc5d0\uc11c 3.7\uae4c\uc9c0\uc758 Python\uacfc \ud638\ud658\ub429\ub2c8\ub2e4.\n``\ucc38\uace0``: \ubcf8 \ud29c\ud1a0\ub9ac\uc5bc\uc740 PyTorch\uc758 master \ube0c\ub79c\uce58\ub97c \ud544\uc694\ub85c\ud558\uba70 `\ub9c1\ud06c <https://github.com/pytorch/pytorch#from-source>`__ \uc5d0\uc11c\n\uc124\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -69,7 +69,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\uc774\uc81c Tracing\uc774\ub098 \uc2a4\ud06c\ub9bd\ud305\uc744 \ud1b5\ud574\uc11c PyTorch \ubaa8\ub378\uc744 \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 tracing\uc744 \ud1b5\ud574 \ubcc0\ud658\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\ubaa8\ub378\uc744 \ubcc0\ud658\ud558\uae30 \uc704\ud574\uc11c\ub294 ``torch.onnx.export()`` \ud568\uc218\ub97c \ud638\ucd9c\ud569\ub2c8\ub2e4.\n\uc774 \ud568\uc218\ub294 \ubaa8\ub378\uc744 \uc2e4\ud589\ud558\uc5ec \uc5b4\ub5a4 \uc5f0\uc0b0\uc790\ub4e4\uc774 \ucd9c\ub825\uac12\uc744 \uacc4\uc0b0\ud558\ub294\ub370 \uc0ac\uc6a9\ub418\uc5c8\ub294\uc9c0\ub97c \uae30\ub85d\ud569\ub2c8\ub2e4.\n``export`` \ud568\uc218\uac00 \ubaa8\ub378\uc744 \uc2e4\ud589\ud558\uae30 \ub54c\ubb38\uc5d0, \uc6b0\ub9ac\uac00 \uc9c1\uc811 \ud150\uc11c\ub97c \uc785\ub825\uac12\uc73c\ub85c \ub118\uaca8\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\uc774 \ud150\uc11c\uc758 \uac12\uc740 \uc54c\ub9de\uc740 \uc790\ub8cc\ud615\uacfc \ubaa8\uc591\uc774\ub77c\uba74 \ub79c\ub364\ud558\uac8c \uacb0\uc815\ub418\uc5b4\ub3c4 \ubb34\ubc29\ud569\ub2c8\ub2e4.\n\ud2b9\uc815 \ucc28\uc6d0\uc744 \ub3d9\uc801\uc778 \ucc28\uc6d0\uc73c\ub85c \uc9c0\uc815\ud558\uc9c0 \uc54a\ub294 \uc774\uc0c1, ONNX\ub85c \ubcc0\ud658\ub41c \uadf8\ub798\ud504\uc758 \uacbd\uc6b0 \uc785\ub825\uac12\uc758 \uc0ac\uc774\uc988\ub294 \ubaa8\ub4e0 \ucc28\uc6d0\uc5d0 \ub300\ud574 \uace0\uc815\ub429\ub2c8\ub2e4.  \n\uc608\uc2dc\uc5d0\uc11c\ub294 \ubaa8\ub378\uc774 \ud56d\uc0c1 \ubc30\uce58 \uc0ac\uc774\uc988 1\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ubcc0\ud658\ud558\uc600\uc9c0\ub9cc, \uccab\ubc88\uc9f8 \ucc28\uc6d0\uc744 ``torch.onnx.export()`` \uc758\n``dynamic_axes`` \uc778\uc790\uc5d0 \ub3d9\uc801\uc778 \ucc28\uc6d0\uc73c\ub85c \uc9c0\uc815\ud574\uc8fc\uba74 \ub429\ub2c8\ub2e4.\n\nPyTorch\uc758 \ubcc0\ud658 \uc778\ud130\ud398\uc774\uc2a4\uc5d0 \ub300\ud574 \ub354 \uc790\uc138\ud788 \uc54c\uace0 \uc2f6\ub2e4\uba74\n`torch.onnx \ubb38\uc11c <https://pytorch.org/docs/master/onnx.html>`__ \ub97c \ucc38\uace0\ud574\uc8fc\uc138\uc694.\n\n\n"
+        "\uc774\uc81c Tracing\uc774\ub098 \uc2a4\ud06c\ub9bd\ud305\uc744 \ud1b5\ud574\uc11c PyTorch \ubaa8\ub378\uc744 \ubcc0\ud658\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 tracing\uc744 \ud1b5\ud574 \ubcc0\ud658\ub41c \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\ubaa8\ub378\uc744 \ubcc0\ud658\ud558\uae30 \uc704\ud574\uc11c\ub294 ``torch.onnx.export()`` \ud568\uc218\ub97c \ud638\ucd9c\ud569\ub2c8\ub2e4.\n\uc774 \ud568\uc218\ub294 \ubaa8\ub378\uc744 \uc2e4\ud589\ud558\uc5ec \uc5b4\ub5a4 \uc5f0\uc0b0\uc790\ub4e4\uc774 \ucd9c\ub825\uac12\uc744 \uacc4\uc0b0\ud558\ub294\ub370 \uc0ac\uc6a9\ub418\uc5c8\ub294\uc9c0\ub97c \uae30\ub85d\ud569\ub2c8\ub2e4.\n``export`` \ud568\uc218\uac00 \ubaa8\ub378\uc744 \uc2e4\ud589\ud558\uae30 \ub54c\ubb38\uc5d0, \uc6b0\ub9ac\uac00 \uc9c1\uc811 \ud150\uc11c\ub97c \uc785\ub825\uac12\uc73c\ub85c \ub118\uaca8\uc8fc\uc5b4\uc57c \ud569\ub2c8\ub2e4.\n\uc774 \ud150\uc11c\uc758 \uac12\uc740 \uc54c\ub9de\uc740 \uc790\ub8cc\ud615\uacfc \ubaa8\uc591\uc774\ub77c\uba74 \ub79c\ub364\ud558\uac8c \uacb0\uc815\ub418\uc5b4\ub3c4 \ubb34\ubc29\ud569\ub2c8\ub2e4.\n\ud2b9\uc815 \ucc28\uc6d0\uc744 \ub3d9\uc801\uc778 \ucc28\uc6d0\uc73c\ub85c \uc9c0\uc815\ud558\uc9c0 \uc54a\ub294 \uc774\uc0c1, ONNX\ub85c \ubcc0\ud658\ub41c \uadf8\ub798\ud504\uc758 \uacbd\uc6b0 \uc785\ub825\uac12\uc758 \uc0ac\uc774\uc988\ub294 \ubaa8\ub4e0 \ucc28\uc6d0\uc5d0 \ub300\ud574 \uace0\uc815\ub429\ub2c8\ub2e4.\n\uc608\uc2dc\uc5d0\uc11c\ub294 \ubaa8\ub378\uc774 \ud56d\uc0c1 \ubc30\uce58 \uc0ac\uc774\uc988 1\uc744 \uc0ac\uc6a9\ud558\ub3c4\ub85d \ubcc0\ud658\ud558\uc600\uc9c0\ub9cc, \uccab\ubc88\uc9f8 \ucc28\uc6d0\uc744 ``torch.onnx.export()`` \uc758\n``dynamic_axes`` \uc778\uc790\uc5d0 \ub3d9\uc801\uc778 \ucc28\uc6d0\uc73c\ub85c \uc9c0\uc815\ud574\uc8fc\uba74 \ub429\ub2c8\ub2e4.\n\nPyTorch\uc758 \ubcc0\ud658 \uc778\ud130\ud398\uc774\uc2a4\uc5d0 \ub300\ud574 \ub354 \uc790\uc138\ud788 \uc54c\uace0 \uc2f6\ub2e4\uba74\n`torch.onnx \ubb38\uc11c <https://pytorch.org/docs/master/onnx.html>`__ \ub97c \ucc38\uace0\ud574\uc8fc\uc138\uc694.\n\n\n"
       ]
     },
     {
@@ -130,7 +130,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "ONNX \ub7f0\ud0c0\uc784\uc5d0\uc11c \uc774\ubbf8\uc9c0\ub97c \uc785\ub825\uac12\uc73c\ub85c \ubaa8\ub378\uc744 \uc2e4\ud589\ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\n"
+        "ONNX \ub7f0\ud0c0\uc784\uc5d0\uc11c \uc774\ubbf8\uc9c0\ub97c \uc785\ub825\uac12\uc73c\ub85c \ubaa8\ub378\uc744 \uc2e4\ud589\ud558\uae30\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\n"
       ]
     },
     {
@@ -205,7 +205,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        ".. figure:: /_static/img/cat_superres_with_ort.jpg\n   :alt: output\\_cat\n\n\nONNX \ub7f0\ud0c0\uc784\uc740 \ud06c\ub85c\uc2a4\ud50c\ub7ab\ud3fc \uc5d4\uc9c4\uc73c\ub85c\uc11c CPU\uc640 GPU \ubfd0\ub9cc \uc544\ub2c8\ub77c \uc5ec\ub7ec \ud50c\ub7ab\ud3fc\uc5d0\uc11c \uc2e4\ud589\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\nONNX \ub7f0\ud0c0\uc784\uc740 Azure Machine Learning Services\uc640 \uac19\uc740 \ud074\ub77c\uc6b0\ub4dc\uc5d0 \ubc30\ud3ec\ub418\uc5b4 \ubaa8\ub378 \ucd94\ub860\uc744 \ud558\ub294\ub370\n\uc0ac\uc6a9\ub420 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 `\ub9c1\ud06c <https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-onnx>`__ \ub97c\n\ucc38\uc870\ud574\uc8fc\uc2ed\uc2dc\uc624.\n\nONNX \ub7f0\ud0c0\uc784\uc758 \uc131\ub2a5\uc5d0 \uad00\ud55c \uac83\uc740 `\uc5ec\uae30 <https://github.com/microsoft/onnxruntime#high-performance>`__ \uc5d0\uc11c \n\ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\nONNX \ub7f0\ud0c0\uc784\uc5d0 \uad00\ud55c \ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 `\uc774 \uacf3 <https://github.com/microsoft/onnxruntime>`__ \uc744 \ucc38\uc870\ud574 \uc8fc\uc138\uc694.\n\n"
+        ".. figure:: /_static/img/cat_superres_with_ort.jpg\n   :alt: output\\_cat\n\n\nONNX \ub7f0\ud0c0\uc784\uc740 \ud06c\ub85c\uc2a4\ud50c\ub7ab\ud3fc \uc5d4\uc9c4\uc73c\ub85c\uc11c CPU\uc640 GPU \ubfd0\ub9cc \uc544\ub2c8\ub77c \uc5ec\ub7ec \ud50c\ub7ab\ud3fc\uc5d0\uc11c \uc2e4\ud589\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\nONNX \ub7f0\ud0c0\uc784\uc740 Azure Machine Learning Services\uc640 \uac19\uc740 \ud074\ub77c\uc6b0\ub4dc\uc5d0 \ubc30\ud3ec\ub418\uc5b4 \ubaa8\ub378 \ucd94\ub860\uc744 \ud558\ub294\ub370\n\uc0ac\uc6a9\ub420 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4.\n\ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 `\ub9c1\ud06c <https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-onnx>`__ \ub97c\n\ucc38\uc870\ud574\uc8fc\uc2ed\uc2dc\uc624.\n\nONNX \ub7f0\ud0c0\uc784\uc758 \uc131\ub2a5\uc5d0 \uad00\ud55c \uac83\uc740 `\uc5ec\uae30 <https://github.com/microsoft/onnxruntime#high-performance>`__ \uc5d0\uc11c\n\ud655\uc778\ud558\uc2e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\nONNX \ub7f0\ud0c0\uc784\uc5d0 \uad00\ud55c \ub354 \uc790\uc138\ud55c \ub0b4\uc6a9\uc740 `\uc774 \uacf3 <https://github.com/microsoft/onnxruntime>`__ \uc744 \ucc38\uc870\ud574 \uc8fc\uc138\uc694.\n\n"
       ]
     },
     {
@@ -236,7 +236,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/f43ed4eaa18b84dc854a0e4799225d7c/static_quantization_tutorial.ipynb b/docs/_downloads/f43ed4eaa18b84dc854a0e4799225d7c/static_quantization_tutorial.ipynb
index f3dd35c49..167c42562 100644
--- a/docs/_downloads/f43ed4eaa18b84dc854a0e4799225d7c/static_quantization_tutorial.ipynb
+++ b/docs/_downloads/f43ed4eaa18b84dc854a0e4799225d7c/static_quantization_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n(beta) Static Quantization with Eager Mode in PyTorch\n=========================================================\n\n**Author**: `Raghuraman Krishnamoorthi <https://github.com/raghuramank100>`_\n\n**Edited by**: `Seth Weidman <https://github.com/SethHWeidman/>`_\n\nThis tutorial shows how to do post-training static quantization, as well as illustrating\ntwo more advanced techniques - per-channel quantization and quantization-aware training -\nto further improve the model's accuracy. Note that quantization is currently only supported\nfor CPUs, so we will not be utilizing GPUs / CUDA in this tutorial.\n\nBy the end of this tutorial, you will see how quantization in PyTorch can result in\nsignificant decreases in model size while increasing speed. Furthermore, you'll see how\nto easily apply some advanced quantization techniques shown\n`here <https://arxiv.org/abs/1806.08342>`_ so that your quantized models take much less\nof an accuracy hit than they would otherwise.\n\nWarning: we use a lot of boilerplate code from other PyTorch repos to, for example,\ndefine the ``MobileNetV2`` model archtecture, define data loaders, and so on. We of course\nencourage you to read it; but if you want to get to the quantization features, feel free\nto skip to the \"4. Post-training static quantization\" section.\n\nWe'll start by doing the necessary imports:\n\n"
+        "\n(beta) Static Quantization with Eager Mode in PyTorch\n=========================================================\n\n**Author**: `Raghuraman Krishnamoorthi <https://github.com/raghuramank100>`_\n\n**Edited by**: `Seth Weidman <https://github.com/SethHWeidman/>`_\n\nThis tutorial shows how to do post-training static quantization, as well as illustrating\ntwo more advanced techniques - per-channel quantization and quantization-aware training -\nto further improve the model's accuracy. Note that quantization is currently only supported\nfor CPUs, so we will not be utilizing GPUs / CUDA in this tutorial.\n\nBy the end of this tutorial, you will see how quantization in PyTorch can result in\nsignificant decreases in model size while increasing speed. Furthermore, you'll see how\nto easily apply some advanced quantization techniques shown\n`here <https://arxiv.org/abs/1806.08342>`_ so that your quantized models take much less\nof an accuracy hit than they would otherwise.\n\nWarning: we use a lot of boilerplate code from other PyTorch repos to, for example,\ndefine the ``MobileNetV2`` model archtecture, define data loaders, and so on. We of course\nencourage you to read it; but if you want to get to the quantization features, feel free\nto skip to the \"4. Post-training static quantization\" section.\n\nWe'll start by doing the necessary imports:\n"
       ]
     },
     {
@@ -287,7 +287,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/f498e3bcd9b6159ecfb1a07d6551287d/data_loading_tutorial.ipynb b/docs/_downloads/f498e3bcd9b6159ecfb1a07d6551287d/data_loading_tutorial.ipynb
index 12e0e87cf..5ac0c71ff 100644
--- a/docs/_downloads/f498e3bcd9b6159ecfb1a07d6551287d/data_loading_tutorial.ipynb
+++ b/docs/_downloads/f498e3bcd9b6159ecfb1a07d6551287d/data_loading_tutorial.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n\uc0ac\uc6a9\uc790 \uc815\uc758 Dataset, Dataloader, Transforms \uc791\uc131\ud558\uae30\n==========================================================\n\n**\uc800\uc790** : Sasank Chilamkurthy <https://chsasank.github.io>\n**\ubc88\uc5ed** : \uc815\uc724\uc131 <https://github.com/Yunseong-Jeong>\n\n\uba38\uc2e0\ub7ec\ub2dd \ubb38\uc81c\ub97c \ud478\ub294 \uacfc\uc815\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\ub294\ub370 \ub9ce\uc740 \ub178\ub825\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\nPyTorch\ub294 \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc624\ub294 \uacfc\uc815\uc744 \uc27d\uac8c\ud574\uc8fc\uace0, \ub610 \uc798 \uc0ac\uc6a9\ud55c\ub2e4\uba74 \ucf54\ub4dc\uc758 \uac00\ub3c5\uc131\ub3c4 \ubcf4\ub2e4 \ub192\uc5ec\uc904 \uc218 \uc788\ub294 \ub3c4\uad6c\ub4e4\uc744\n\uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc77c\ubc18\uc801\uc774\uc9c0 \uc54a\uc740 \ub370\uc774\ud130\uc14b\uc73c\ub85c\ubd80\ud130 \ub370\uc774\ud130\ub97c \uc77d\uc5b4\uc624\uace0\n\uc804\ucc98\ub9ac\ud558\uace0 \uc99d\uac00\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc9c4\ud589\ud558\uae30 \uc704\ud574 \uc544\ub798 \ud328\ud0a4\uc9c0\ub4e4\uc744 \uc124\uce58\ud574\uc8fc\uc138\uc694.\n\n-  ``scikit-image``: \uc774\ubbf8\uc9c0 I/O \uc640 \ubcc0\ud615\uc744 \uc704\ud574 \ud544\uc694\ud569\ub2c8\ub2e4.\n-  ``pandas``: CSV \ud30c\uc77c \ud30c\uc2f1\uc744 \ubcf4\ub2e4 \uc27d\uac8c \ud574\uc90d\ub2c8\ub2e4.\n\n\n\n"
+        "\n\uc0ac\uc6a9\uc790 \uc815\uc758 Dataset, Dataloader, Transforms \uc791\uc131\ud558\uae30\n==========================================================\n\n**\uc800\uc790** : Sasank Chilamkurthy <https://chsasank.github.io>\n**\ubc88\uc5ed** : \uc815\uc724\uc131 <https://github.com/Yunseong-Jeong>\n\n\uba38\uc2e0\ub7ec\ub2dd \ubb38\uc81c\ub97c \ud478\ub294 \uacfc\uc815\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc900\ube44\ud558\ub294\ub370 \ub9ce\uc740 \ub178\ub825\uc774 \ud544\uc694\ud569\ub2c8\ub2e4.\nPyTorch\ub294 \ub370\uc774\ud130\ub97c \ubd88\ub7ec\uc624\ub294 \uacfc\uc815\uc744 \uc27d\uac8c\ud574\uc8fc\uace0, \ub610 \uc798 \uc0ac\uc6a9\ud55c\ub2e4\uba74 \ucf54\ub4dc\uc758 \uac00\ub3c5\uc131\ub3c4 \ubcf4\ub2e4 \ub192\uc5ec\uc904 \uc218 \uc788\ub294 \ub3c4\uad6c\ub4e4\uc744\n\uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c \uc77c\ubc18\uc801\uc774\uc9c0 \uc54a\uc740 \ub370\uc774\ud130\uc14b\uc73c\ub85c\ubd80\ud130 \ub370\uc774\ud130\ub97c \uc77d\uc5b4\uc624\uace0\n\uc804\ucc98\ub9ac\ud558\uace0 \uc99d\uac00\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc774\ubc88 \ud29c\ud1a0\ub9ac\uc5bc\uc744 \uc9c4\ud589\ud558\uae30 \uc704\ud574 \uc544\ub798 \ud328\ud0a4\uc9c0\ub4e4\uc744 \uc124\uce58\ud574\uc8fc\uc138\uc694.\n\n-  ``scikit-image``: \uc774\ubbf8\uc9c0 I/O \uc640 \ubcc0\ud615\uc744 \uc704\ud574 \ud544\uc694\ud569\ub2c8\ub2e4.\n-  ``pandas``: CSV \ud30c\uc77c \ud30c\uc2f1\uc744 \ubcf4\ub2e4 \uc27d\uac8c \ud574\uc90d\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -105,7 +105,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Transforms\n----------\n\n\uc704\uc5d0\uc11c \ubcfc \uc218 \uc788\uc5c8\ub358 \ud55c\uac00\uc9c0 \ubb38\uc81c\uc810\uc740 \uc0d8\ud50c\ub4e4\uc774 \ub2e4 \uac19\uc740 \uc0ac\uc774\uc988\uac00 \uc544\ub2c8\ub77c\ub294 \uac83\uc785\ub2c8\ub2e4.\n\ub300\ubd80\ubd84\uc758 \uc2e0\uacbd\ub9dd(neural networks)\uc740 \uace0\uc815\ub41c \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\ub77c\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.\n\uadf8\ub7ec\ubbc0\ub85c \uc6b0\ub9ac\ub294 \uc2e0\uacbd\ub9dd\uc5d0 \uc8fc\uae30 \uc804\uc5d0 \ucc98\ub9ac\ud560 \uacfc\uc815\uc744 \uc791\uc131\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n3\uac00\uc9c0\uc758 transforms \uc744 \ub9cc\ub4e4\uc5b4 \ubd05\uc2dc\ub2e4:\n-  ``Rescale``: \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\ub97c \uc870\uc808\ud569\ub2c8\ub2e4.\n-  ``RandomCrop``: \uc774\ubbf8\uc9c0\ub97c \ubb34\uc791\uc704\ub85c \uc790\ub985\ub2c8\ub2e4.\n                   \uc774\uac83\uc744 data augmentation\uc774\ub77c \ud569\ub2c8\ub2e4.\n-  ``ToTensor``: numpy \uc774\ubbf8\uc9c0\uc5d0\uc11c torch \uc774\ubbf8\uc9c0\ub85c \ubcc0\uacbd\ud569\ub2c8\ub2e4.\n                 (\ucd95\ubcc0\ud658\uc774 \ud544\uc694\ud569\ub2c8\ub2e4)\n\n\uac04\ub2e8\ud55c \ud568\uc218\ub300\uc2e0\uc5d0 \ud638\ucd9c \ud560 \uc218 \uc788\ub294 \ud074\ub798\uc2a4\ub85c \uc791\uc131 \ud569\ub2c8\ub2e4.\n\uc774\ub807\uac8c \ud55c\ub2e4\uba74, \ud074\ub798\uc2a4\uac00 \ud638\ucd9c \ub420 \ub54c\ub9c8\ub2e4 \uc804\uc774(Transform)\uc758 \ub9e4\uac1c\ubcc0\uc218\uac00 \uc804\ub2ec \ub418\uc9c0 \uc54a\uc544\ub3c4 \ub429\ub2c8\ub2e4.\n\uc774\uc640 \uac19\uc774, ``__call__`` \ud568\uc218\ub97c \uad6c\ud604\ud574\uc57c \ud569\ub2c8\ub2e4.\n\ud544\uc694\ud558\ub2e4\uba74, ``__init__`` \ud568\uc218\ub3c4 \uad6c\ud604\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc774 \uc804\uc774(transform)\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n::\n\n    tsfm = Transform(params)\n    transformed_sample = tsfm(sample)\n\n\uc544\ub798\uc5d0\uc11c\ub294 \uc774\ubbf8\uc9c0\uc640 \ub79c\ub4dc\ub9c8\ud06c(landmark)\ub4e4\uc744 \uc5b4\ub5bb\uac8c \uc801\uc6a9\ud558\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n"
+        "Transforms\n----------\n\n\uc704\uc5d0\uc11c \ubcfc \uc218 \uc788\uc5c8\ub358 \ud55c\uac00\uc9c0 \ubb38\uc81c\uc810\uc740 \uc0d8\ud50c\ub4e4\uc774 \ub2e4 \uac19\uc740 \uc0ac\uc774\uc988\uac00 \uc544\ub2c8\ub77c\ub294 \uac83\uc785\ub2c8\ub2e4.\n\ub300\ubd80\ubd84\uc758 \uc2e0\uacbd\ub9dd(neural networks)\uc740 \uace0\uc815\ub41c \ud06c\uae30\uc758 \uc774\ubbf8\uc9c0\ub77c\uace0 \uac00\uc815\ud569\ub2c8\ub2e4.\n\uadf8\ub7ec\ubbc0\ub85c \uc6b0\ub9ac\ub294 \uc2e0\uacbd\ub9dd\uc5d0 \uc8fc\uae30 \uc804\uc5d0 \ucc98\ub9ac\ud560 \uacfc\uc815\uc744 \uc791\uc131\ud574\uc57c \ud569\ub2c8\ub2e4.\n\n3\uac00\uc9c0\uc758 transforms \uc744 \ub9cc\ub4e4\uc5b4 \ubd05\uc2dc\ub2e4:\n\n- ``Rescale``: \uc774\ubbf8\uc9c0\uc758 \ud06c\uae30\ub97c \uc870\uc808\ud569\ub2c8\ub2e4.\n- ``RandomCrop``: \uc774\ubbf8\uc9c0\ub97c \ubb34\uc791\uc704\ub85c \uc790\ub985\ub2c8\ub2e4.\n  \uc774\uac83\uc744 data augmentation\uc774\ub77c \ud569\ub2c8\ub2e4.\n- ``ToTensor``: numpy \uc774\ubbf8\uc9c0\uc5d0\uc11c torch \uc774\ubbf8\uc9c0\ub85c \ubcc0\uacbd\ud569\ub2c8\ub2e4.\n  (\ucd95\ubcc0\ud658\uc774 \ud544\uc694\ud569\ub2c8\ub2e4)\n\n\uac04\ub2e8\ud55c \ud568\uc218\ub300\uc2e0\uc5d0 \ud638\ucd9c \ud560 \uc218 \uc788\ub294 \ud074\ub798\uc2a4\ub85c \uc791\uc131 \ud569\ub2c8\ub2e4.\n\uc774\ub807\uac8c \ud55c\ub2e4\uba74, \ud074\ub798\uc2a4\uac00 \ud638\ucd9c \ub420 \ub54c\ub9c8\ub2e4 \uc804\uc774(Transform)\uc758 \ub9e4\uac1c\ubcc0\uc218\uac00 \uc804\ub2ec \ub418\uc9c0 \uc54a\uc544\ub3c4 \ub429\ub2c8\ub2e4.\n\uc774\uc640 \uac19\uc774, ``__call__`` \ud568\uc218\ub97c \uad6c\ud604\ud574\uc57c \ud569\ub2c8\ub2e4.\n\ud544\uc694\ud558\ub2e4\uba74, ``__init__`` \ud568\uc218\ub3c4 \uad6c\ud604\ud574\uc57c \ud569\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc774 \uc804\uc774(transform)\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n::\n\n    tsfm = Transform(params)\n    transformed_sample = tsfm(sample)\n\n\uc544\ub798\uc5d0\uc11c\ub294 \uc774\ubbf8\uc9c0\uc640 \ub79c\ub4dc\ub9c8\ud06c(landmark)\ub4e4\uc744 \uc5b4\ub5bb\uac8c \uc801\uc6a9\ud558\ub294\uc9c0 \uc0b4\ud3b4\ubcf4\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n"
       ]
     },
     {
@@ -197,7 +197,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/f720861441352889ad66f9e2b85807f7/two_layer_net_custom_function.ipynb b/docs/_downloads/f720861441352889ad66f9e2b85807f7/two_layer_net_custom_function.ipynb
index d4f9990ba..76201e5c0 100644
--- a/docs/_downloads/f720861441352889ad66f9e2b85807f7/two_layer_net_custom_function.ipynb
+++ b/docs/_downloads/f720861441352889ad66f9e2b85807f7/two_layer_net_custom_function.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPyTorch: \uc0c8 autograd Function \uc815\uc758\ud558\uae30\n----------------------------------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch Variable \uc5f0\uc0b0\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc21c\uc804\ud30c\ub97c \uacc4\uc0b0\ud558\uace0, PyTorch autograd\ub97c \uc0ac\uc6a9\ud558\uc5ec\n\ubcc0\ud654\ub3c4(gradient)\ub97c \uacc4\uc0b0\ud558\ub294 \uac83\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc5ec\uae30\uc11c\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 autograd \ud568\uc218\ub97c \uad6c\ud604\ud558\uc5ec ReLU \uae30\ub2a5\uc744 \uc218\ud589\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n"
+        "\nPyTorch: \uc0c8 autograd Function \uc815\uc758\ud558\uae30\n----------------------------------------\n\n\ud558\ub098\uc758 \uc740\ub2c9\uce35(hidden layer)\uacfc \ud3b8\ud5a5(bias)\uc774 \uc5c6\ub294 \uc644\uc804\ud788 \uc5f0\uacb0\ub41c ReLU \uc2e0\uacbd\ub9dd\uc744,\n\uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance) \uc81c\uacf1\uc744 \ucd5c\uc18c\ud654\ud558\ub294 \uc2dd\uc73c\ub85c x\ub85c\ubd80\ud130 y\ub97c \uc608\uce21\ud558\ub3c4\ub85d\n\ud559\uc2b5\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\nPyTorch Variable \uc5f0\uc0b0\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc21c\uc804\ud30c\ub97c \uacc4\uc0b0\ud558\uace0, PyTorch autograd\ub97c \uc0ac\uc6a9\ud558\uc5ec\n\ubcc0\ud654\ub3c4(gradient)\ub97c \uacc4\uc0b0\ud558\ub294 \uac83\uc744 \uad6c\ud604\ud558\uaca0\uc2b5\ub2c8\ub2e4.\n\n\uc5ec\uae30\uc11c\ub294 \uc0ac\uc6a9\uc790 \uc815\uc758 autograd \ud568\uc218\ub97c \uad6c\ud604\ud558\uc5ec ReLU \uae30\ub2a5\uc744 \uc218\ud589\ud558\ub3c4\ub85d \ud558\uaca0\uc2b5\ub2c8\ub2e4.\n"
       ]
     },
     {
@@ -46,7 +46,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/f84ef04333132b77a0663044e78e7cbb/dynamic_quantization.ipynb b/docs/_downloads/f84ef04333132b77a0663044e78e7cbb/dynamic_quantization.ipynb
index f99f5f2f1..18ae5c908 100644
--- a/docs/_downloads/f84ef04333132b77a0663044e78e7cbb/dynamic_quantization.ipynb
+++ b/docs/_downloads/f84ef04333132b77a0663044e78e7cbb/dynamic_quantization.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nDynamic Quantization\n====================\n\nIn this recipe you will see how to take advantage of Dynamic\nQuantization to accelerate inference on an LSTM-style recurrent neural\nnetwork. This reduces the size of the model weights and speeds up model\nexecution.\n\nIntroduction\n-------------\n\nThere are a number of trade-offs that can be made when designing neural\nnetworks. During model developmenet and training you can alter the\nnumber of layers and number of parameters in a recurrent neural network\nand trade-off accuracy against model size and/or model latency or\nthroughput. Such changes can take lot of time and compute resources\nbecause you are iterating over the model training. Quantization gives\nyou a way to make a similar trade off between performance and model\naccuracy with a known model after training is completed.\n\nYou can give it a try in a single session and you will certainly reduce\nyour model size significantly and may get a significant latency\nreduction without losing a lot of accuracy.\n\nWhat is dynamic quantization?\n-------------\n\nQuantizing a network means converting it to use a reduced precision\ninteger representation for the weights and/or activations. This saves on\nmodel size and allows the use of higher throughput math operations on\nyour CPU or GPU.\n\nWhen converting from floating point to integer values you are\nessentially multiplying the floating point value by some scale factor\nand rounding the result to a whole number. The various quantization\napproaches differ in the way they approach determining that scale\nfactor.\n\nThe key idea with dynamic quantization as described here is that we are\ngoing to determine the scale factor for activations dynamically based on\nthe data range observed at runtime. This ensures that the scale factor\nis \"tuned\" so that as much signal as possible about each observed\ndataset is preserved.\n\nThe model parameters on the other hand are known during model conversion\nand they are converted ahead of time and stored in INT8 form.\n\nArithmetic in the quantized model is done using vectorized INT8\ninstructions. Accumulation is typically done with INT16 or INT32 to\navoid overflow. This higher precision value is scaled back to INT8 if\nthe next layer is quantized or converted to FP32 for output.\n\nDynamic quantization is relatively free of tuning parameters which makes\nit well suited to be added into production pipelines as a standard part\nof converting LSTM models to deployment.\n\n\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Limitations on the approach taken here\n\n\n   This recipe provides a quick introduction to the dynamic quantization\n   features in PyTorch and the workflow for using it. Our focus is on\n   explaining the specific functions used to convert the model. We will\n   make a number of significant simplifications in the interest of brevity\n   and clarity</p></div>\n\n\n1. You will start with a minimal LSTM network\n2. You are simply going to initialize the network with a random hidden\n   state\n3. You are going to test the network with random inputs\n4. You are not going to train the network in this tutorial\n5. You will see that the quantized form of this network is smaller and\n   runs faster than the floating point network we started with\n6. You will see that the output values are generally in the same\n   ballpark as the output of the FP32 network, but we are not\n   demonstrating here the expected accuracy loss on a real trained\n   network\n\nYou will see how dynamic quantization is done and be able to see\nsuggestive reductions in memory use and latency times. Providing a\ndemonstration that the technique can preserve high levels of model\naccuracy on a trained LSTM is left to a more advanced tutorial. If you\nwant to move right away to that more rigorous treatment please proceed\nto the `advanced dynamic quantization\ntutorial <https://pytorch.org/tutorials/advanced/dynamic_quantization_tutorial.html>`__.\n\nSteps\n-------------\n\nThis recipe has 5 steps.\n\n1. Set Up - Here you define a very simple LSTM, import modules, and establish\n   some random input tensors.\n\n2. Do the Quantization - Here you instantiate a floating point model and then create quantized\n   version of it.\n\n3. Look at Model Size - Here you show that the model size gets smaller.\n\n4. Look at Latency - Here you run the two models and compare model runtime (latency).\n\n5. Look at Accuracy - Here you run the two models and compare outputs.\n\n\n1: Set Up\n~~~~~~~~~~~~~~~\nThis is a straightfoward bit of code to set up for the rest of the\nrecipe.\n\nThe unique module we are importing here is torch.quantization which\nincludes PyTorch's quantized operators and conversion functions. We also\ndefine a very simple LSTM model and set up some inputs.\n\n\n"
+        "\nDynamic Quantization\n====================\n\nIn this recipe you will see how to take advantage of Dynamic\nQuantization to accelerate inference on an LSTM-style recurrent neural\nnetwork. This reduces the size of the model weights and speeds up model\nexecution.\n\nIntroduction\n-------------\n\nThere are a number of trade-offs that can be made when designing neural\nnetworks. During model developmenet and training you can alter the\nnumber of layers and number of parameters in a recurrent neural network\nand trade-off accuracy against model size and/or model latency or\nthroughput. Such changes can take lot of time and compute resources\nbecause you are iterating over the model training. Quantization gives\nyou a way to make a similar trade off between performance and model\naccuracy with a known model after training is completed.\n\nYou can give it a try in a single session and you will certainly reduce\nyour model size significantly and may get a significant latency\nreduction without losing a lot of accuracy.\n\nWhat is dynamic quantization?\n-------------\n\nQuantizing a network means converting it to use a reduced precision\ninteger representation for the weights and/or activations. This saves on\nmodel size and allows the use of higher throughput math operations on\nyour CPU or GPU.\n\nWhen converting from floating point to integer values you are\nessentially multiplying the floating point value by some scale factor\nand rounding the result to a whole number. The various quantization\napproaches differ in the way they approach determining that scale\nfactor.\n\nThe key idea with dynamic quantization as described here is that we are\ngoing to determine the scale factor for activations dynamically based on\nthe data range observed at runtime. This ensures that the scale factor\nis \"tuned\" so that as much signal as possible about each observed\ndataset is preserved.\n\nThe model parameters on the other hand are known during model conversion\nand they are converted ahead of time and stored in INT8 form.\n\nArithmetic in the quantized model is done using vectorized INT8\ninstructions. Accumulation is typically done with INT16 or INT32 to\navoid overflow. This higher precision value is scaled back to INT8 if\nthe next layer is quantized or converted to FP32 for output.\n\nDynamic quantization is relatively free of tuning parameters which makes\nit well suited to be added into production pipelines as a standard part\nof converting LSTM models to deployment.\n\n\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Limitations on the approach taken here\n\n\n   This recipe provides a quick introduction to the dynamic quantization\n   features in PyTorch and the workflow for using it. Our focus is on\n   explaining the specific functions used to convert the model. We will\n   make a number of significant simplifications in the interest of brevity\n   and clarity</p></div>\n\n\n1. You will start with a minimal LSTM network\n2. You are simply going to initialize the network with a random hidden\n   state\n3. You are going to test the network with random inputs\n4. You are not going to train the network in this tutorial\n5. You will see that the quantized form of this network is smaller and\n   runs faster than the floating point network we started with\n6. You will see that the output values are generally in the same\n   ballpark as the output of the FP32 network, but we are not\n   demonstrating here the expected accuracy loss on a real trained\n   network\n\nYou will see how dynamic quantization is done and be able to see\nsuggestive reductions in memory use and latency times. Providing a\ndemonstration that the technique can preserve high levels of model\naccuracy on a trained LSTM is left to a more advanced tutorial. If you\nwant to move right away to that more rigorous treatment please proceed\nto the `advanced dynamic quantization\ntutorial <https://pytorch.org/tutorials/advanced/dynamic_quantization_tutorial.html>`__.\n\nSteps\n-------------\n\nThis recipe has 5 steps.\n\n1. Set Up - Here you define a very simple LSTM, import modules, and establish\n   some random input tensors.\n\n2. Do the Quantization - Here you instantiate a floating point model and then create quantized\n   version of it.\n\n3. Look at Model Size - Here you show that the model size gets smaller.\n\n4. Look at Latency - Here you run the two models and compare model runtime (latency).\n\n5. Look at Accuracy - Here you run the two models and compare outputs.\n\n\n1: Set Up\n~~~~~~~~~~~~~~~\nThis is a straightfoward bit of code to set up for the rest of the\nrecipe.\n\nThe unique module we are importing here is torch.quantization which\nincludes PyTorch's quantized operators and conversion functions. We also\ndefine a very simple LSTM model and set up some inputs.\n"
       ]
     },
     {
@@ -125,7 +125,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
diff --git a/docs/_downloads/f8cb9bf90ebfb30e3e9f9cedbd7ca1d2/defining_a_neural_network.ipynb b/docs/_downloads/f8cb9bf90ebfb30e3e9f9cedbd7ca1d2/defining_a_neural_network.ipynb
index 7251e590b..2efae4eb8 100644
--- a/docs/_downloads/f8cb9bf90ebfb30e3e9f9cedbd7ca1d2/defining_a_neural_network.ipynb
+++ b/docs/_downloads/f8cb9bf90ebfb30e3e9f9cedbd7ca1d2/defining_a_neural_network.ipynb
@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\nPytorch\ub97c \uc0ac\uc6a9\ud574 \uc2e0\uacbd\ub9dd \uc815\uc758\ud558\uae30\n====================================\n\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd(models)\uc744 \uc0ac\uc6a9\ud558\uba70 \uc774\uac83\uc740 \uc0c1\ud638\uc5f0\uacb0\ub41c \uc9d1\ub2e8\uc758 \ub9ce\uc740 \uacc4\uce35\uc73c\ub85c \uad6c\uc131\ub41c \uacc4\uc0b0 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \n\ub370\uc774\ud130\uac00 \uc774 \uc0c1\ud638\uc5f0\uacb0\ub41c \uc9d1\ub2e8\uc744 \ud1b5\uacfc\ud558\uba74\uc11c, \uc2e0\uacbd\ub9dd\uc740 \uc785\ub825\uc744 \ucd9c\ub825\uc73c\ub85c \ubc14\uafb8\uae30 \uc704\ud574 \uc694\uad6c\ub41c \uacc4\uc0b0 \ubc29\ubc95\uc5d0 \uc5b4\ub5bb\uac8c \uadfc\uc811\ud558\ub294 \uc9c0\ub97c \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.  \nPyTorch\uc5d0\uc11c, \uc2e0\uacbd\ub9dd\uc740 ``torch.nn`` \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud574 \uad6c\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \n\n\uc18c\uac1c\n-----\nPyTorch\ub294 ``torch.nn`` \uc744 \ud3ec\ud568\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4e4\uace0 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uac83\uc744 \ub3c4\uc6b8 \uc218 \uc788\ub3c4\ub85d \uc12c\uc138\ud558\uac8c \ub9cc\ub4e4\uc5b4\uc9c4 \ubaa8\ub4c8\uacfc \ud074\ub798\uc2a4\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \n``nn.Moduel`` \uc740 \uacc4\uce35, \uadf8\ub9ac\uace0 ``output`` \uc744 \ubc18\ud658\ud558\ub294 ``forward(input)`` \uba54\uc18c\ub4dc\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \n\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c, `MNIST dataset <https://pytorch.org/docs/stable/torchvision/datasets.html#mnist>`__ \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\uae30 \uc704\ud574 ``torch.nn`` \uc744 \uc0ac\uc6a9\ud560 \uc608\uc815\uc785\ub2c8\ub2e4.\n\n\uc124\uce58\n-----\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0, \uc900\ube44\uac00 \ub418\uc5b4\uc788\uc9c0 \uc54a\ub2e4\uba74 ``torch`` \ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \n\n::\n\n   pip install torchaudio\n\n\n\n"
+        "\nPytorch\ub97c \uc0ac\uc6a9\ud574 \uc2e0\uacbd\ub9dd \uc815\uc758\ud558\uae30\n====================================\n\ub525\ub7ec\ub2dd\uc740 \uc778\uacf5\uc2e0\uacbd\ub9dd(models)\uc744 \uc0ac\uc6a9\ud558\uba70 \uc774\uac83\uc740 \uc0c1\ud638\uc5f0\uacb0\ub41c \uc9d1\ub2e8\uc758 \ub9ce\uc740 \uacc4\uce35\uc73c\ub85c \uad6c\uc131\ub41c \uacc4\uc0b0 \uc2dc\uc2a4\ud15c\uc785\ub2c8\ub2e4. \n\ub370\uc774\ud130\uac00 \uc774 \uc0c1\ud638\uc5f0\uacb0\ub41c \uc9d1\ub2e8\uc744 \ud1b5\uacfc\ud558\uba74\uc11c, \uc2e0\uacbd\ub9dd\uc740 \uc785\ub825\uc744 \ucd9c\ub825\uc73c\ub85c \ubc14\uafb8\uae30 \uc704\ud574 \uc694\uad6c\ub41c \uacc4\uc0b0 \ubc29\ubc95\uc5d0 \uc5b4\ub5bb\uac8c \uadfc\uc811\ud558\ub294 \uc9c0\ub97c \ubc30\uc6b8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.  \nPyTorch\uc5d0\uc11c, \uc2e0\uacbd\ub9dd\uc740 ``torch.nn`` \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud574 \uad6c\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \n\n\uc18c\uac1c\n-----\nPyTorch\ub294 ``torch.nn`` \uc744 \ud3ec\ud568\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \ub9cc\ub4e4\uace0 \ud6c8\ub828\uc2dc\ud0a4\ub294 \uac83\uc744 \ub3c4\uc6b8 \uc218 \uc788\ub3c4\ub85d \uc12c\uc138\ud558\uac8c \ub9cc\ub4e4\uc5b4\uc9c4 \ubaa8\ub4c8\uacfc \ud074\ub798\uc2a4\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \n``nn.Moduel`` \uc740 \uacc4\uce35, \uadf8\ub9ac\uace0 ``output`` \uc744 \ubc18\ud658\ud558\ub294 ``forward(input)`` \uba54\uc18c\ub4dc\ub97c \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \n\n\uc774 \ub808\uc2dc\ud53c\uc5d0\uc11c, `MNIST dataset <https://pytorch.org/docs/stable/torchvision/datasets.html#mnist>`__ \uc744 \uc0ac\uc6a9\ud558\uc5ec \uc2e0\uacbd\ub9dd\uc744 \uc815\uc758\ud558\uae30 \uc704\ud574 ``torch.nn`` \uc744 \uc0ac\uc6a9\ud560 \uc608\uc815\uc785\ub2c8\ub2e4.\n\n\uc124\uce58\n-----\n\uc2dc\uc791\ud558\uae30 \uc804\uc5d0, \uc900\ube44\uac00 \ub418\uc5b4\uc788\uc9c0 \uc54a\ub2e4\uba74 ``torch`` \ub97c \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4. \n\n::\n\n   pip install torchaudio\n"
       ]
     },
     {
@@ -114,7 +114,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.7.9"
     }
   },
   "nbformat": 4,
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diff --git a/docs/_images/sphx_glr_spatial_transformer_tutorial_001.png b/docs/_images/sphx_glr_spatial_transformer_tutorial_001.png
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diff --git a/docs/_images/sphx_glr_transfer_learning_tutorial_001.png b/docs/_images/sphx_glr_transfer_learning_tutorial_001.png
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index 2fd2d77cf..674edf752 100644
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diff --git a/docs/_sources/index.rst.txt b/docs/_sources/index.rst.txt
index a1fa70ccd..3cf443f62 100644
--- a/docs/_sources/index.rst.txt
+++ b/docs/_sources/index.rst.txt
@@ -128,8 +128,7 @@
 
 .. customcarditem::
    :header: 기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기
-   :card_description:
-   torchtext를 사용하지 않고 기본적인 문자-단위 RNN을 사용하여 단어를 분류하는 모델을 기초부터 만들고 학습합니다. 총 3개로 이뤄진 튜토리얼 시리즈의 첫번째 편입니다.
+   :card_description: torchtext를 사용하지 않고 기본적인 문자-단위 RNN을 사용하여 단어를 분류하는 모델을 기초부터 만들고 학습합니다. 총 3개로 이뤄진 튜토리얼 시리즈의 첫번째 편입니다.
    :image: _static/img/thumbnails/cropped/NLP-From-Scratch-Classifying-Names-with-a-Character-Level-RNN.png
    :link: intermediate/char_rnn_classification_tutorial
    :tags: Text
diff --git a/docs/_static/jquery-3.5.1.js b/docs/_static/jquery-3.5.1.js
new file mode 100644
index 000000000..50937333b
--- /dev/null
+++ b/docs/_static/jquery-3.5.1.js
@@ -0,0 +1,10872 @@
+/*!
+ * jQuery JavaScript Library v3.5.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2020-05-04T22:49Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.5.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( _i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.5
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2020-03-14
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px";
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	} );
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/_static/language_data.js b/docs/_static/language_data.js
index 5266fb19e..863704b31 100644
--- a/docs/_static/language_data.js
+++ b/docs/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2021 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -13,7 +13,8 @@
 var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
 
 
-/* Non-minified version JS is _stemmer.js if file is provided */ 
+/* Non-minified version is copied as a separate JS file, is available */
+
 /**
  * Porter Stemmer
  */
@@ -199,7 +200,6 @@ var Stemmer = function() {
 
 
 
-
 var splitChars = (function() {
     var result = {};
     var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
diff --git a/docs/_static/underscore-1.12.0.js b/docs/_static/underscore-1.12.0.js
new file mode 100644
index 000000000..3af6352e6
--- /dev/null
+++ b/docs/_static/underscore-1.12.0.js
@@ -0,0 +1,2027 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.12.0
+  //     https://underscorejs.org
+  //     (c) 2009-2020 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.12.0';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key]; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _(obj) {
+    if (obj instanceof _) return obj;
+    if (!(this instanceof _)) return new _(obj);
+    this._wrapped = obj;
+  }
+
+  _.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _.prototype.valueOf = _.prototype.toJSON = _.prototype.value;
+
+  _.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _) a = a._wrapped;
+    if (b instanceof _) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath(path) {
+    return isArray(path) ? path : [path];
+  }
+  _.toPath = toPath;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath$1(path) {
+    return _.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath$1(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has$1(obj, path) {
+    path = toPath$1(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath$1(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_.iteratee !== iteratee) return _.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    // If a variable is not specified, place data values in local scope.
+    if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n';
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(settings.variable || 'obj', '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    var argument = settings.variable || 'obj';
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath$1(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath$1(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten$1(array, depth) {
+    return flatten(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _[name] = obj[name];
+      _.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_, args));
+      };
+    });
+    return _;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has$1,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten$1,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _$1 = mixin(allExports);
+  // Legacy Node.js API.
+  _$1._ = _$1;
+
+  return _$1;
+
+})));
+//# sourceMappingURL=underscore.js.map
diff --git a/docs/advanced/ONNXLive.html b/docs/advanced/ONNXLive.html
index 01740b972..6cdad5597 100644
--- a/docs/advanced/ONNXLive.html
+++ b/docs/advanced/ONNXLive.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -500,11 +500,9 @@ <h2>결론<a class="headerlink" href="#id3" title="Permalink to this headline">
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/cpp_autograd.html b/docs/advanced/cpp_autograd.html
index 861cd19e3..6748714cf 100644
--- a/docs/advanced/cpp_autograd.html
+++ b/docs/advanced/cpp_autograd.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -758,11 +758,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/cpp_export.html b/docs/advanced/cpp_export.html
index cd633ecf9..19b9494c2 100644
--- a/docs/advanced/cpp_export.html
+++ b/docs/advanced/cpp_export.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -362,7 +362,7 @@ <h3>어노테이션(annotation)을 통해 TorchScript로 변환하기<a class="h
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 
 <span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">))</span>
 
@@ -379,7 +379,7 @@ <h3>어노테이션(annotation)을 통해 TorchScript로 변환하기<a class="h
 모듈을 <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code> 로 변환하기 위해서는, 아래와 같이 <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> 함수를 사용해
 모듈을 컴파일해야 합니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">weight</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">))</span>
 
@@ -422,7 +422,7 @@ <h2>단계 3. C++에서 Script 모듈 로딩하기<a class="headerlink" href="#c
 <div class="section" id="c">
 <h3>간단한 C++ 어플리케이션<a class="headerlink" href="#c" title="Permalink to this headline">¶</a></h3>
 <p>우선 모듈을 로드하는 코드에 대해 살펴보도록 하겠습니다. 아래의 간단한 코드로 모듈을 쉽게 읽어올 수 있습니다:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf">&lt;torch/script.h&gt; // 필요한 단 하나의 헤더파일.</span><span class="cp"></span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf">&lt;torch/script.h&gt;</span><span class="c1"> // 필요한 단 하나의 헤더파일.</span><span class="cp"></span>
 
 <span class="cp">#include</span> <span class="cpf">&lt;iostream&gt;</span><span class="cp"></span>
 <span class="cp">#include</span> <span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
@@ -430,18 +430,18 @@ <h3>간단한 C++ 어플리케이션<a class="headerlink" href="#c" title="Perma
 <span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
   <span class="k">if</span> <span class="p">(</span><span class="n">argc</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">)</span> <span class="p">{</span>
     <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;usage: example-app &lt;path-to-exported-script-module&gt;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-    <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+    <span class="k">return</span> <span class="mi">-1</span><span class="p">;</span>
   <span class="p">}</span>
 
 
-  <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">script</span><span class="o">::</span><span class="n">Module</span> <span class="n">module</span><span class="p">;</span>
+  <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">script</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
   <span class="k">try</span> <span class="p">{</span>
     <span class="c1">// torch::jit::load()을 사용해 ScriptModule을 파일로부터 역직렬화</span>
-    <span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+    <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
   <span class="p">}</span>
   <span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
     <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;error loading the model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-    <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+    <span class="k">return</span> <span class="mi">-1</span><span class="p">;</span>
   <span class="p">}</span>
 
   <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;ok</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
@@ -560,7 +560,7 @@ <h2>단계 4. Script 모듈을 C++에서 실행하기<a class="headerlink" href=
 <span class="n">inputs</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">ones</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">}));</span>
 
 <span class="c1">// 모델을 실행한 뒤 리턴값을 텐서로 변환합니다.</span>
-<span class="n">at</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">output</span> <span class="o">=</span> <span class="n">module</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">).</span><span class="n">toTensor</span><span class="p">();</span>
+<span class="n">at</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">output</span> <span class="o">=</span> <span class="k">module</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">).</span><span class="n">toTensor</span><span class="p">();</span>
 <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="n">output</span><span class="p">.</span><span class="n">slice</span><span class="p">(</span><span class="cm">/*dim=*/</span><span class="mi">1</span><span class="p">,</span> <span class="cm">/*start=*/</span><span class="mi">0</span><span class="p">,</span> <span class="cm">/*end=*/</span><span class="mi">5</span><span class="p">)</span> <span class="o">&lt;&lt;</span> <span class="sc">&#39;\n&#39;</span><span class="p">;</span>
 </pre></div>
 </div>
@@ -609,7 +609,7 @@ <h2>단계 5. API 더 알아보기<a class="headerlink" href="#api" title="Perma
 <p>물론 이 튜토리얼에서 다루지못한 개념들도 많습니다. 예를 들어 여러분의 <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code> 이 C++나 CUDA로
 정의된 커스텀 연산자를 사용할 수 있게하는 방법 또는 이러한 커스텀 연산자를 C++ 상용 환경의 <code class="docutils literal notranslate"><span class="pre">ScriptModule</span></code> 에서
 사용할 수 있게하는 방법에 대해서는 본 튜토리얼에서 다루지 않았습니다. 좋은 소식은 이러한 것들이 가능하다는 것이고 지원되고
-있다는 점입니다! 저희가 곧 이것에 관한 튜토리얼을 업로드할 때까지 <a href="#id1"><span class="problematic" id="id2">`이 폴더&lt;https://github.com/pytorch/pytorch/tree/master/test/custom_operator&gt;`_</span></a>
+있다는 점입니다! 저희가 곧 이것에 관한 튜토리얼을 업로드할 때까지 <a class="reference external" href="https://github.com/pytorch/pytorch/tree/master/test/custom_operator">이 폴더</a>
 를 예시로 삼아 참고하시면 되겠습니다. 또 아래 링크들이 도움이 될 것입니다:</p>
 <ul class="simple">
 <li><p>The Torch Script reference: <a class="reference external" href="https://pytorch.org/docs/master/jit.html">https://pytorch.org/docs/master/jit.html</a></p></li>
@@ -702,11 +702,9 @@ <h2>단계 5. API 더 알아보기<a class="headerlink" href="#api" title="Perma
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/cpp_extension.html b/docs/advanced/cpp_extension.html
index d11812be9..e68a117f2 100644
--- a/docs/advanced/cpp_extension.html
+++ b/docs/advanced/cpp_extension.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -343,7 +343,7 @@ <h2>Motivation and Example<a class="headerlink" href="#motivation-and-example" t
 <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> and implement the forward pass of the LLTM. This would
 look something like this:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LLTM</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">LLTM</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">input_features</span> <span class="o">=</span> <span class="n">input_features</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">state_size</span> <span class="o">=</span> <span class="n">state_size</span>
@@ -427,8 +427,8 @@ <h3>Building with <code class="xref py py-mod docutils literal notranslate"><spa
 <p>For the “ahead of time” flavor, we build our C++ extension by writing a
 <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> script that uses setuptools to compile our C++ code. For the LLTM, it
 looks as simple as this:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">setuptools</span> <span class="k">import</span> <span class="n">setup</span><span class="p">,</span> <span class="n">Extension</span>
-<span class="kn">from</span> <span class="nn">torch.utils</span> <span class="k">import</span> <span class="n">cpp_extension</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">setuptools</span> <span class="kn">import</span> <span class="n">setup</span><span class="p">,</span> <span class="n">Extension</span>
+<span class="kn">from</span> <span class="nn">torch.utils</span> <span class="kn">import</span> <span class="n">cpp_extension</span>
 
 <span class="n">setup</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;lltm_cpp&#39;</span><span class="p">,</span>
       <span class="n">ext_modules</span><span class="o">=</span><span class="p">[</span><span class="n">cpp_extension</span><span class="o">.</span><span class="n">CppExtension</span><span class="p">(</span><span class="s1">&#39;lltm_cpp&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;lltm.cpp&#39;</span><span class="p">])],</span>
@@ -710,7 +710,7 @@ <h3>Using Your Extension<a class="headerlink" href="#using-your-extension" title
 
 
 <span class="k">class</span> <span class="nc">LLTM</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_features</span><span class="p">,</span> <span class="n">state_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">LLTM</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">input_features</span> <span class="o">=</span> <span class="n">input_features</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">state_size</span> <span class="o">=</span> <span class="n">state_size</span>
@@ -845,7 +845,7 @@ <h3>JIT Compiling Extensions<a class="headerlink" href="#jit-compiling-extension
 of compiling and loading your extensions on the fly by calling a simple
 function in PyTorch’s API called <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.utils.cpp_extension.load()</span></code>. For
 the LLTM, this would look as simple as this:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="k">import</span> <span class="n">load</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="kn">import</span> <span class="n">load</span>
 
 <span class="n">lltm_cpp</span> <span class="o">=</span> <span class="n">load</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;lltm_cpp&quot;</span><span class="p">,</span> <span class="n">sources</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;lltm.cpp&quot;</span><span class="p">])</span>
 </pre></div>
@@ -996,7 +996,7 @@ <h2>Writing a Mixed C++/CUDA extension<a class="headerlink" href="#writing-a-mix
 
 <span class="cp">#include</span> <span class="cpf">&lt;vector&gt;</span><span class="cp"></span>
 
-<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">scalar_t</span><span class="o">&gt;</span>
+<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">scalar_t</span><span class="o">&gt;</span>
 <span class="n">__device__</span> <span class="n">__forceinline__</span> <span class="n">scalar_t</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">scalar_t</span> <span class="n">z</span><span class="p">)</span> <span class="p">{</span>
   <span class="k">return</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">+</span> <span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">z</span><span class="p">));</span>
 <span class="p">}</span>
@@ -1006,24 +1006,24 @@ <h2>Writing a Mixed C++/CUDA extension<a class="headerlink" href="#writing-a-mix
 CUDA-specific declarations like <code class="docutils literal notranslate"><span class="pre">__device__</span></code> and <code class="docutils literal notranslate"><span class="pre">__forceinline__</span></code> and
 functions like <code class="docutils literal notranslate"><span class="pre">exp</span></code>. Let’s continue with a few more helper functions that
 we’ll need:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">scalar_t</span><span class="o">&gt;</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">scalar_t</span><span class="o">&gt;</span>
 <span class="n">__device__</span> <span class="n">__forceinline__</span> <span class="n">scalar_t</span> <span class="n">d_sigmoid</span><span class="p">(</span><span class="n">scalar_t</span> <span class="n">z</span><span class="p">)</span> <span class="p">{</span>
   <span class="k">const</span> <span class="k">auto</span> <span class="n">s</span> <span class="o">=</span> <span class="n">sigmoid</span><span class="p">(</span><span class="n">z</span><span class="p">);</span>
   <span class="k">return</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">s</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span><span class="p">;</span>
 <span class="p">}</span>
 
-<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">scalar_t</span><span class="o">&gt;</span>
+<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">scalar_t</span><span class="o">&gt;</span>
 <span class="n">__device__</span> <span class="n">__forceinline__</span> <span class="n">scalar_t</span> <span class="n">d_tanh</span><span class="p">(</span><span class="n">scalar_t</span> <span class="n">z</span><span class="p">)</span> <span class="p">{</span>
   <span class="k">const</span> <span class="k">auto</span> <span class="n">t</span> <span class="o">=</span> <span class="n">tanh</span><span class="p">(</span><span class="n">z</span><span class="p">);</span>
   <span class="k">return</span> <span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">t</span> <span class="o">*</span> <span class="n">t</span><span class="p">);</span>
 <span class="p">}</span>
 
-<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">scalar_t</span><span class="o">&gt;</span>
+<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">scalar_t</span><span class="o">&gt;</span>
 <span class="n">__device__</span> <span class="n">__forceinline__</span> <span class="n">scalar_t</span> <span class="n">elu</span><span class="p">(</span><span class="n">scalar_t</span> <span class="n">z</span><span class="p">,</span> <span class="n">scalar_t</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">)</span> <span class="p">{</span>
-  <span class="k">return</span> <span class="n">fmax</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span> <span class="o">+</span> <span class="n">fmin</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">*</span> <span class="p">(</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">));</span>
+  <span class="k">return</span> <span class="nf">fmax</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">z</span><span class="p">)</span> <span class="o">+</span> <span class="n">fmin</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">*</span> <span class="p">(</span><span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">));</span>
 <span class="p">}</span>
 
-<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">scalar_t</span><span class="o">&gt;</span>
+<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">scalar_t</span><span class="o">&gt;</span>
 <span class="n">__device__</span> <span class="n">__forceinline__</span> <span class="n">scalar_t</span> <span class="n">d_elu</span><span class="p">(</span><span class="n">scalar_t</span> <span class="n">z</span><span class="p">,</span> <span class="n">scalar_t</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">)</span> <span class="p">{</span>
   <span class="k">const</span> <span class="k">auto</span> <span class="n">e</span> <span class="o">=</span> <span class="n">exp</span><span class="p">(</span><span class="n">z</span><span class="p">);</span>
   <span class="k">const</span> <span class="k">auto</span> <span class="n">d_relu</span> <span class="o">=</span> <span class="n">z</span> <span class="o">&lt;</span> <span class="mf">0.0</span> <span class="o">?</span> <span class="mf">0.0</span> <span class="o">:</span> <span class="mf">1.0</span><span class="p">;</span>
@@ -1054,7 +1054,7 @@ <h2>Writing a Mixed C++/CUDA extension<a class="headerlink" href="#writing-a-mix
   <span class="k">auto</span> <span class="n">candidate_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">old_cell</span><span class="p">);</span>
 
   <span class="k">const</span> <span class="kt">int</span> <span class="n">threads</span> <span class="o">=</span> <span class="mi">1024</span><span class="p">;</span>
-  <span class="k">const</span> <span class="n">dim3</span> <span class="nf">blocks</span><span class="p">((</span><span class="n">state_size</span> <span class="o">+</span> <span class="n">threads</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">threads</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">);</span>
+  <span class="k">const</span> <span class="n">dim3</span> <span class="n">blocks</span><span class="p">((</span><span class="n">state_size</span> <span class="o">+</span> <span class="n">threads</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">threads</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">);</span>
 
   <span class="n">AT_DISPATCH_FLOATING_TYPES</span><span class="p">(</span><span class="n">gates</span><span class="p">.</span><span class="n">type</span><span class="p">(),</span> <span class="s">&quot;lltm_forward_cuda&quot;</span><span class="p">,</span> <span class="p">([</span><span class="o">&amp;</span><span class="p">]</span> <span class="p">{</span>
     <span class="n">lltm_cuda_forward_kernel</span><span class="o">&lt;</span><span class="n">scalar_t</span><span class="o">&gt;&lt;&lt;&lt;</span><span class="n">blocks</span><span class="p">,</span> <span class="n">threads</span><span class="o">&gt;&gt;&gt;</span><span class="p">(</span>
@@ -1112,7 +1112,7 @@ <h2>Writing a Mixed C++/CUDA extension<a class="headerlink" href="#writing-a-mix
 about CUDA</a> may
 help.</p>
 <p>The actual CUDA kernel is fairly simple (if you’ve ever programmed GPUs before):</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">scalar_t</span><span class="o">&gt;</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">scalar_t</span><span class="o">&gt;</span>
 <span class="n">__global__</span> <span class="kt">void</span> <span class="n">lltm_cuda_forward_kernel</span><span class="p">(</span>
     <span class="k">const</span> <span class="n">scalar_t</span><span class="o">*</span> <span class="n">__restrict__</span> <span class="n">gates</span><span class="p">,</span>
     <span class="k">const</span> <span class="n">scalar_t</span><span class="o">*</span> <span class="n">__restrict__</span> <span class="n">old_cell</span><span class="p">,</span>
@@ -1205,7 +1205,7 @@ <h3>Using accessors<a class="headerlink" href="#using-accessors" title="Permalin
 sizes and strides in an <code class="docutils literal notranslate"><span class="pre">int32_t</span></code>. This is important as using the 64-bit
 variant (<code class="docutils literal notranslate"><span class="pre">PackedAccessor64</span></code>) can make the kernel slower.</p>
 <p>The function declaration becomes</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">scalar_t</span><span class="o">&gt;</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">scalar_t</span><span class="o">&gt;</span>
 <span class="n">__global__</span> <span class="kt">void</span> <span class="n">lltm_cuda_forward_kernel</span><span class="p">(</span>
     <span class="k">const</span> <span class="n">torch</span><span class="o">::</span><span class="n">PackedTensorAccessor32</span><span class="o">&lt;</span><span class="n">scalar_t</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">torch</span><span class="o">::</span><span class="n">RestrictPtrTraits</span><span class="o">&gt;</span> <span class="n">gates</span><span class="p">,</span>
     <span class="k">const</span> <span class="n">torch</span><span class="o">::</span><span class="n">PackedTensorAccessor32</span><span class="o">&lt;</span><span class="n">scalar_t</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">torch</span><span class="o">::</span><span class="n">RestrictPtrTraits</span><span class="o">&gt;</span> <span class="n">old_cell</span><span class="p">,</span>
@@ -1252,7 +1252,7 @@ <h3>Using accessors<a class="headerlink" href="#using-accessors" title="Permalin
   <span class="k">auto</span> <span class="n">candidate_cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">old_cell</span><span class="p">);</span>
 
   <span class="k">const</span> <span class="kt">int</span> <span class="n">threads</span> <span class="o">=</span> <span class="mi">1024</span><span class="p">;</span>
-  <span class="k">const</span> <span class="n">dim3</span> <span class="nf">blocks</span><span class="p">((</span><span class="n">state_size</span> <span class="o">+</span> <span class="n">threads</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">threads</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">);</span>
+  <span class="k">const</span> <span class="n">dim3</span> <span class="n">blocks</span><span class="p">((</span><span class="n">state_size</span> <span class="o">+</span> <span class="n">threads</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">threads</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">);</span>
 
   <span class="n">AT_DISPATCH_FLOATING_TYPES</span><span class="p">(</span><span class="n">gates</span><span class="p">.</span><span class="n">type</span><span class="p">(),</span> <span class="s">&quot;lltm_forward_cuda&quot;</span><span class="p">,</span> <span class="p">([</span><span class="o">&amp;</span><span class="p">]</span> <span class="p">{</span>
     <span class="n">lltm_cuda_forward_kernel</span><span class="o">&lt;</span><span class="n">scalar_t</span><span class="o">&gt;&lt;&lt;&lt;</span><span class="n">blocks</span><span class="p">,</span> <span class="n">threads</span><span class="o">&gt;&gt;&gt;</span><span class="p">(</span>
@@ -1271,7 +1271,7 @@ <h3>Using accessors<a class="headerlink" href="#using-accessors" title="Permalin
 </div>
 <p>The backwards pass follows much the same pattern and I won’t elaborate further
 on it:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">scalar_t</span><span class="o">&gt;</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">scalar_t</span><span class="o">&gt;</span>
 <span class="n">__global__</span> <span class="kt">void</span> <span class="n">lltm_cuda_backward_kernel</span><span class="p">(</span>
     <span class="n">torch</span><span class="o">::</span><span class="n">PackedTensorAccessor32</span><span class="o">&lt;</span><span class="n">scalar_t</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">torch</span><span class="o">::</span><span class="n">RestrictPtrTraits</span><span class="o">&gt;</span> <span class="n">d_old_cell</span><span class="p">,</span>
     <span class="n">torch</span><span class="o">::</span><span class="n">PackedTensorAccessor32</span><span class="o">&lt;</span><span class="n">scalar_t</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">torch</span><span class="o">::</span><span class="n">RestrictPtrTraits</span><span class="o">&gt;</span> <span class="n">d_gates</span><span class="p">,</span>
@@ -1323,7 +1323,7 @@ <h3>Using accessors<a class="headerlink" href="#using-accessors" title="Permalin
   <span class="k">const</span> <span class="k">auto</span> <span class="n">state_size</span> <span class="o">=</span> <span class="n">new_cell</span><span class="p">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">);</span>
 
   <span class="k">const</span> <span class="kt">int</span> <span class="n">threads</span> <span class="o">=</span> <span class="mi">1024</span><span class="p">;</span>
-  <span class="k">const</span> <span class="n">dim3</span> <span class="nf">blocks</span><span class="p">((</span><span class="n">state_size</span> <span class="o">+</span> <span class="n">threads</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">threads</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">);</span>
+  <span class="k">const</span> <span class="n">dim3</span> <span class="n">blocks</span><span class="p">((</span><span class="n">state_size</span> <span class="o">+</span> <span class="n">threads</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">threads</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">);</span>
 
   <span class="n">AT_DISPATCH_FLOATING_TYPES</span><span class="p">(</span><span class="n">X</span><span class="p">.</span><span class="n">type</span><span class="p">(),</span> <span class="s">&quot;lltm_forward_cuda&quot;</span><span class="p">,</span> <span class="p">([</span><span class="o">&amp;</span><span class="p">]</span> <span class="p">{</span>
     <span class="n">lltm_cuda_backward_kernel</span><span class="o">&lt;</span><span class="n">scalar_t</span><span class="o">&gt;&lt;&lt;&lt;</span><span class="n">blocks</span><span class="p">,</span> <span class="n">threads</span><span class="o">&gt;&gt;&gt;</span><span class="p">(</span>
@@ -1355,8 +1355,8 @@ <h3>Using accessors<a class="headerlink" href="#using-accessors" title="Permalin
 <h3>Integrating a C++/CUDA Operation with PyTorch<a class="headerlink" href="#integrating-a-c-cuda-operation-with-pytorch" title="Permalink to this headline">¶</a></h3>
 <p>Integration of our CUDA-enabled op with PyTorch is again very straightforward.
 If you want to write a <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> script, it could look like this:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">setuptools</span> <span class="k">import</span> <span class="n">setup</span>
-<span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="k">import</span> <span class="n">BuildExtension</span><span class="p">,</span> <span class="n">CUDAExtension</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">setuptools</span> <span class="kn">import</span> <span class="n">setup</span>
+<span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="kn">import</span> <span class="n">BuildExtension</span><span class="p">,</span> <span class="n">CUDAExtension</span>
 
 <span class="n">setup</span><span class="p">(</span>
     <span class="n">name</span><span class="o">=</span><span class="s1">&#39;lltm&#39;</span><span class="p">,</span>
@@ -1375,7 +1375,7 @@ <h3>Integrating a C++/CUDA Operation with PyTorch<a class="headerlink" href="#in
 specify the <code class="docutils literal notranslate"><span class="pre">.cu</span></code> file along with the <code class="docutils literal notranslate"><span class="pre">.cpp</span></code> files – the library takes
 care of all the hassle this entails for you. The JIT mechanism is even
 simpler:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="k">import</span> <span class="n">load</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.cpp_extension</span> <span class="kn">import</span> <span class="n">load</span>
 
 <span class="n">lltm</span> <span class="o">=</span> <span class="n">load</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">&#39;lltm&#39;</span><span class="p">,</span> <span class="n">sources</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;lltm_cuda.cpp&#39;</span><span class="p">,</span> <span class="s1">&#39;lltm_cuda_kernel.cu&#39;</span><span class="p">])</span>
 </pre></div>
@@ -1500,11 +1500,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/cpp_frontend.html b/docs/advanced/cpp_frontend.html
index 785f654b3..3f3625fa8 100644
--- a/docs/advanced/cpp_frontend.html
+++ b/docs/advanced/cpp_frontend.html
@@ -31,7 +31,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="Custom C++ and CUDA Extensions" href="cpp_extension.html" />
-    <link rel="prev" title="(beta) Channels Last Memory Format in PyTorch" href="../intermediate/memory_format_tutorial.html" /> 
+    <link rel="prev" title="(실험용) PyTorch를 사용한 Channels Last Memory Format" href="../intermediate/memory_format_tutorial.html" /> 
 
   
   <script src="../_static/js/modernizr.min.js"></script>
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -551,7 +551,7 @@ <h4>Defining a Module and Registering Parameters<a class="headerlink" href="#def
 <p>In C++, it would look like this:</p>
 <div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf">&lt;torch/torch.h&gt;</span><span class="cp"></span>
 
-<span class="k">struct</span> <span class="nl">Net</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
+<span class="k">struct</span> <span class="nc">Net</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
   <span class="n">Net</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">N</span><span class="p">,</span> <span class="kt">int64_t</span> <span class="n">M</span><span class="p">)</span> <span class="p">{</span>
     <span class="n">W</span> <span class="o">=</span> <span class="n">register_parameter</span><span class="p">(</span><span class="s">&quot;W&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">}));</span>
     <span class="n">b</span> <span class="o">=</span> <span class="n">register_parameter</span><span class="p">(</span><span class="s">&quot;b&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">(</span><span class="n">M</span><span class="p">));</span>
@@ -593,7 +593,7 @@ <h4>Registering Submodules and Traversing the Module Hierarchy<a class="headerli
 <p>This allows, for example, to use the <code class="docutils literal notranslate"><span class="pre">parameters()</span></code> method to recursively
 access all parameters in our module hierarchy:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">Net</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="k">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">parameters</span><span class="p">()))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">parameters</span><span class="p">()))</span>
 <span class="go">[Parameter containing:</span>
 <span class="go">tensor([0.0808, 0.8613, 0.2017, 0.5206, 0.5353], requires_grad=True), Parameter containing:</span>
 <span class="go">tensor([[-0.3740, -0.0976, -0.4786, -0.4928],</span>
@@ -606,13 +606,13 @@ <h4>Registering Submodules and Traversing the Module Hierarchy<a class="headerli
 </div>
 <p>To register submodules in C++, use the aptly named <code class="docutils literal notranslate"><span class="pre">register_module()</span></code> method
 to register a module like <code class="docutils literal notranslate"><span class="pre">torch::nn::Linear</span></code>:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">Net</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">Net</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
   <span class="n">Net</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">N</span><span class="p">,</span> <span class="kt">int64_t</span> <span class="n">M</span><span class="p">)</span>
       <span class="o">:</span> <span class="n">linear</span><span class="p">(</span><span class="n">register_module</span><span class="p">(</span><span class="s">&quot;linear&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Linear</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">)))</span> <span class="p">{</span>
     <span class="n">another_bias</span> <span class="o">=</span> <span class="n">register_parameter</span><span class="p">(</span><span class="s">&quot;b&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">(</span><span class="n">M</span><span class="p">));</span>
   <span class="p">}</span>
   <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">forward</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">input</span><span class="p">)</span> <span class="p">{</span>
-    <span class="k">return</span> <span class="n">linear</span><span class="p">(</span><span class="n">input</span><span class="p">)</span> <span class="o">+</span> <span class="n">another_bias</span><span class="p">;</span>
+    <span class="k">return</span> <span class="nf">linear</span><span class="p">(</span><span class="n">input</span><span class="p">)</span> <span class="o">+</span> <span class="n">another_bias</span><span class="p">;</span>
   <span class="p">}</span>
   <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Linear</span> <span class="n">linear</span><span class="p">;</span>
   <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">another_bias</span><span class="p">;</span>
@@ -747,7 +747,7 @@ <h4>Module Ownership<a class="headerlink" href="#module-ownership" title="Permal
 simplest and was shown in the examples thus far: module objects are allocated on
 the stack and when passed to a function, can be either copied, moved (with
 <code class="docutils literal notranslate"><span class="pre">std::move</span></code>) or taken by reference or by pointer:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">Net</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span> <span class="p">};</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">Net</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span> <span class="p">};</span>
 
 <span class="kt">void</span> <span class="nf">a</span><span class="p">(</span><span class="n">Net</span> <span class="n">net</span><span class="p">)</span> <span class="p">{</span> <span class="p">}</span>
 <span class="kt">void</span> <span class="nf">b</span><span class="p">(</span><span class="n">Net</span><span class="o">&amp;</span> <span class="n">net</span><span class="p">)</span> <span class="p">{</span> <span class="p">}</span>
@@ -766,7 +766,7 @@ <h4>Module Ownership<a class="headerlink" href="#module-ownership" title="Permal
 The advantage of reference semantics is that, like in Python, it reduces the
 cognitive overhead of thinking about how modules must be passed to functions and
 how arguments must be declared (assuming you use <code class="docutils literal notranslate"><span class="pre">shared_ptr</span></code> everywhere).</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">Net</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{};</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">Net</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{};</span>
 
 <span class="kt">void</span> <span class="nf">a</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">shared_ptr</span><span class="o">&lt;</span><span class="n">Net</span><span class="o">&gt;</span> <span class="n">net</span><span class="p">)</span> <span class="p">{</span> <span class="p">}</span>
 
@@ -782,7 +782,7 @@ <h4>Module Ownership<a class="headerlink" href="#module-ownership" title="Permal
 design, in order to stay close to the ergonomics of the Python API, relies on
 shared ownership. For example, take our earlier (here shortened) definition of
 <code class="docutils literal notranslate"><span class="pre">Net</span></code>:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">Net</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">Net</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
   <span class="n">Net</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">N</span><span class="p">,</span> <span class="kt">int64_t</span> <span class="n">M</span><span class="p">)</span>
     <span class="o">:</span> <span class="n">linear</span><span class="p">(</span><span class="n">register_module</span><span class="p">(</span><span class="s">&quot;linear&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Linear</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">)))</span>
   <span class="p">{</span> <span class="p">}</span>
@@ -804,7 +804,7 @@ <h4>Module Ownership<a class="headerlink" href="#module-ownership" title="Permal
 while retaining reference semantics. To understand how this works, we can take a
 look at a simplified definition of the <code class="docutils literal notranslate"><span class="pre">torch::nn::Linear</span></code> module in the core
 library (the full definition is <a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/api/include/torch/nn/modules/linear.h">here</a>):</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">LinearImpl</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">LinearImpl</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
   <span class="n">LinearImpl</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">in</span><span class="p">,</span> <span class="kt">int64_t</span> <span class="n">out</span><span class="p">);</span>
 
   <span class="n">Tensor</span> <span class="nf">forward</span><span class="p">(</span><span class="k">const</span> <span class="n">Tensor</span><span class="o">&amp;</span> <span class="n">input</span><span class="p">);</span>
@@ -827,7 +827,7 @@ <h4>Module Ownership<a class="headerlink" href="#module-ownership" title="Permal
 closely. Reference semantics become the default, but without the extra typing of
 <code class="docutils literal notranslate"><span class="pre">std::shared_ptr</span></code> or <code class="docutils literal notranslate"><span class="pre">std::make_shared</span></code>. For our <code class="docutils literal notranslate"><span class="pre">Net</span></code>, using the module
 holder API looks like this:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">NetImpl</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{};</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">NetImpl</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{};</span>
 <span class="n">TORCH_MODULE</span><span class="p">(</span><span class="n">Net</span><span class="p">);</span>
 
 <span class="kt">void</span> <span class="nf">a</span><span class="p">(</span><span class="n">Net</span> <span class="n">net</span><span class="p">)</span> <span class="p">{</span> <span class="p">}</span>
@@ -853,7 +853,7 @@ <h4>Module Ownership<a class="headerlink" href="#module-ownership" title="Permal
 constructor of the holder.</p>
 <p>In practice, this means you can use submodules either like shown earlier, where
 the module is registered and constructed in the <em>initializer list</em>:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">Net</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">Net</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
   <span class="n">Net</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">N</span><span class="p">,</span> <span class="kt">int64_t</span> <span class="n">M</span><span class="p">)</span>
     <span class="o">:</span> <span class="n">linear</span><span class="p">(</span><span class="n">register_module</span><span class="p">(</span><span class="s">&quot;linear&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Linear</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">)))</span>
   <span class="p">{</span> <span class="p">}</span>
@@ -863,7 +863,7 @@ <h4>Module Ownership<a class="headerlink" href="#module-ownership" title="Permal
 </div>
 <p>or you can first construct the holder with a null pointer and then assign to it
 in the constructor (more familiar for Pythonistas):</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">Net</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">Net</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
   <span class="n">Net</span><span class="p">(</span><span class="kt">int64_t</span> <span class="n">N</span><span class="p">,</span> <span class="kt">int64_t</span> <span class="n">M</span><span class="p">)</span> <span class="p">{</span>
     <span class="n">linear</span> <span class="o">=</span> <span class="n">register_module</span><span class="p">(</span><span class="s">&quot;linear&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">Linear</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">M</span><span class="p">));</span>
   <span class="p">}</span>
@@ -922,7 +922,7 @@ <h4>The Generator Module<a class="headerlink" href="#the-generator-module" title
 transposed 2D convolutions, batch normalizations and ReLU activation units.
 We explicitly pass inputs (in a functional way) between modules in the
 <code class="docutils literal notranslate"><span class="pre">forward()</span></code> method of a module we define ourselves:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nl">DCGANGeneratorImpl</span> <span class="p">:</span> <span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">struct</span> <span class="nc">DCGANGeneratorImpl</span> <span class="o">:</span> <span class="n">nn</span><span class="o">::</span><span class="n">Module</span> <span class="p">{</span>
   <span class="n">DCGANGeneratorImpl</span><span class="p">(</span><span class="kt">int</span> <span class="n">kNoiseSize</span><span class="p">)</span>
       <span class="o">:</span> <span class="n">conv1</span><span class="p">(</span><span class="n">nn</span><span class="o">::</span><span class="n">ConvTranspose2dOptions</span><span class="p">(</span><span class="n">kNoiseSize</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
                   <span class="p">.</span><span class="n">bias</span><span class="p">(</span><span class="nb">false</span><span class="p">)),</span>
@@ -1382,12 +1382,12 @@ <h2>Inspecting Generated Images<a class="headerlink" href="#inspecting-generated
 
 <span class="kn">import</span> <span class="nn">argparse</span>
 
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 
 
 <span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">()</span>
-<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;-i&quot;</span><span class="p">,</span> <span class="s2">&quot;--sample-file&quot;</span><span class="p">,</span> <span class="n">required</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;-i&quot;</span><span class="p">,</span> <span class="s2">&quot;--sample-file&quot;</span><span class="p">,</span> <span class="n">required</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;-o&quot;</span><span class="p">,</span> <span class="s2">&quot;--out-file&quot;</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="s2">&quot;out.png&quot;</span><span class="p">)</span>
 <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s2">&quot;-d&quot;</span><span class="p">,</span> <span class="s2">&quot;--dimension&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
 <span class="n">options</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
@@ -1400,11 +1400,11 @@ <h2>Inspecting Generated Images<a class="headerlink" href="#inspecting-generated
   <span class="n">array</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
   <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="n">options</span><span class="o">.</span><span class="n">dimension</span><span class="p">,</span> <span class="n">options</span><span class="o">.</span><span class="n">dimension</span><span class="p">,</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">index</span><span class="p">)</span>
   <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
-  <span class="n">axis</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span>
-  <span class="n">axis</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span>
+  <span class="n">axis</span><span class="o">.</span><span class="n">get_xaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+  <span class="n">axis</span><span class="o">.</span><span class="n">get_yaxis</span><span class="p">()</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">options</span><span class="o">.</span><span class="n">out_file</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Saved &quot;</span><span class="p">,</span> <span class="n">options</span><span class="o">.</span><span class="n">out_file</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Saved &quot;</span><span class="p">,</span> <span class="n">options</span><span class="o">.</span><span class="n">out_file</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Let’s now train our model for around 30 epochs:</p>
@@ -1474,7 +1474,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
         <a href="cpp_extension.html" class="btn btn-neutral float-right" title="Custom C++ and CUDA Extensions" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
-        <a href="../intermediate/memory_format_tutorial.html" class="btn btn-neutral" title="(beta) Channels Last Memory Format in PyTorch" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
+        <a href="../intermediate/memory_format_tutorial.html" class="btn btn-neutral" title="(실험용) PyTorch를 사용한 Channels Last Memory Format" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
       
     </div>
   
@@ -1555,11 +1555,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/dispatcher.html b/docs/advanced/dispatcher.html
index 25f73ee7f..462b4ba3e 100644
--- a/docs/advanced/dispatcher.html
+++ b/docs/advanced/dispatcher.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -501,9 +501,9 @@ <h2>Going beyond autograd<a class="headerlink" href="#going-beyond-autograd" tit
     <span class="n">AutogradContext</span> <span class="o">*</span><span class="n">ctx</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">self</span><span class="p">,</span> <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">other</span><span class="p">)</span> <span class="p">{</span>
 
     <span class="k">if</span> <span class="p">(</span><span class="n">self</span><span class="p">.</span><span class="n">device</span><span class="p">().</span><span class="n">type</span><span class="p">()</span> <span class="o">==</span> <span class="n">DeviceType</span><span class="o">::</span><span class="n">CPU</span><span class="p">)</span> <span class="p">{</span>
-      <span class="k">return</span> <span class="n">add_cpu</span><span class="p">(</span><span class="n">self</span><span class="p">,</span> <span class="n">other</span><span class="p">);</span>
+      <span class="k">return</span> <span class="nf">add_cpu</span><span class="p">(</span><span class="n">self</span><span class="p">,</span> <span class="n">other</span><span class="p">);</span>
     <span class="p">}</span> <span class="k">else</span> <span class="k">if</span> <span class="p">(</span><span class="n">self</span><span class="p">.</span><span class="n">device</span><span class="p">().</span><span class="n">type</span><span class="p">()</span> <span class="o">==</span> <span class="n">DeviceType</span><span class="o">::</span><span class="n">CUDA</span><span class="p">)</span> <span class="p">{</span>
-      <span class="k">return</span> <span class="n">add_cuda</span><span class="p">(</span><span class="n">self</span><span class="p">,</span> <span class="n">other</span><span class="p">);</span>
+      <span class="k">return</span> <span class="nf">add_cuda</span><span class="p">(</span><span class="n">self</span><span class="p">,</span> <span class="n">other</span><span class="p">);</span>
     <span class="p">}</span> <span class="k">else</span> <span class="p">{</span>
       <span class="n">TORCH_CHECK</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s">&quot;Unsupported device &quot;</span><span class="p">,</span> <span class="n">self</span><span class="p">.</span><span class="n">device</span><span class="p">().</span><span class="n">type</span><span class="p">());</span>
     <span class="p">}</span>
@@ -661,11 +661,9 @@ <h3>Tracer<a class="headerlink" href="#tracer" title="Permalink to this headline
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/dynamic_quantization_tutorial.html b/docs/advanced/dynamic_quantization_tutorial.html
index cce204008..faed5e454 100644
--- a/docs/advanced/dynamic_quantization_tutorial.html
+++ b/docs/advanced/dynamic_quantization_tutorial.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>(beta) Dynamic Quantization on an LSTM Word Language Model &mdash; PyTorch Tutorials 1.6.0 documentation</title>
+  <title>(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화 &mdash; PyTorch Tutorials 1.6.0 documentation</title>
   
 
   
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -243,7 +243,7 @@
       </li>
 
         
-      <li>(beta) Dynamic Quantization on an LSTM Word Language Model</li>
+      <li>(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -301,23 +301,22 @@
 <p class="admonition-title">Note</p>
 <p>Click <a class="reference internal" href="#sphx-glr-download-advanced-dynamic-quantization-tutorial-py"><span class="std std-ref">here</span></a> to download the full example code</p>
 </div>
-<div class="sphx-glr-example-title section" id="beta-dynamic-quantization-on-an-lstm-word-language-model">
-<span id="sphx-glr-advanced-dynamic-quantization-tutorial-py"></span><h1>(beta) Dynamic Quantization on an LSTM Word Language Model<a class="headerlink" href="#beta-dynamic-quantization-on-an-lstm-word-language-model" title="Permalink to this headline">¶</a></h1>
+<div class="sphx-glr-example-title section" id="lstm">
+<span id="sphx-glr-advanced-dynamic-quantization-tutorial-py"></span><h1>(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화<a class="headerlink" href="#lstm" title="Permalink to this headline">¶</a></h1>
 <p><strong>Author</strong>: <a class="reference external" href="https://github.com/jamesr66a">James Reed</a></p>
 <p><strong>Edited by</strong>: <a class="reference external" href="https://github.com/SethHWeidman/">Seth Weidman</a></p>
-<div class="section" id="introduction">
-<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
-<p>Quantization involves converting the weights and activations of your model from float
-to int, which can result in smaller model size and faster inference with only a small
-hit to accuracy.</p>
-<p>In this tutorial, we’ll apply the easiest form of quantization -
-<a class="reference external" href="https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic">dynamic quantization</a> -
-to an LSTM-based next word-prediction model, closely following the
-<a class="reference external" href="https://github.com/pytorch/examples/tree/master/word_language_model">word language model</a>
-from the PyTorch examples.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># imports</span>
+<p><strong>번역</strong>: <a class="reference external" href="https://github.com/kypark7/">박경림</a> <a class="reference external" href="https://github.com/kwonmha/">Myungha Kwon</a></p>
+<div class="section" id="id2">
+<h2>시작하기<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
+<p>양자화는 모델의 크기를 줄이고 추론 속도를 높이면서도 정확도는 별로 낮아지지 않도록,
+모델의 가중치와 활성 함수를 실수형에서 정수형으로 변환합니다.</p>
+<p>이 튜토리얼에서는 PyTorch의 <a class="reference external" href="https://github.com/pytorch/examples/tree/master/word_language_model">단어 단위 언어 모델</a>
+예제를 따라하면서, LSTM 기반의 단어 예측 모델에 가장 간단한 양자화 기법인
+<a class="reference external" href="https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic">동적 양자화</a>
+를 적용해 보겠습니다.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 불러오기</span>
 <span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">from</span> <span class="nn">io</span> <span class="k">import</span> <span class="nb">open</span>
+<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="nb">open</span>
 <span class="kn">import</span> <span class="nn">time</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
@@ -326,15 +325,14 @@ <h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to t
 </pre></div>
 </div>
 </div>
-<div class="section" id="define-the-model">
-<h2>1. Define the model<a class="headerlink" href="#define-the-model" title="Permalink to this headline">¶</a></h2>
-<p>Here we define the LSTM model architecture, following the
-<a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/model.py">model</a>
-from the word language model example.</p>
+<div class="section" id="id5">
+<h2>1. 모델 정의하기<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h2>
+<p>단어 단위 언어 모델 예제에서 사용된 <a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/model.py">모델</a> 을
+따라 LSTM 모델 아키텍처를 정의합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LSTMModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;Container module with an encoder, a recurrent module, and a decoder.&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;인코더, 반복 모듈 및 디코더가 있는 컨테이너 모듈.&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">LSTMModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">drop</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">dropout</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">)</span>
@@ -366,15 +364,12 @@ <h2>1. Define the model<a class="headerlink" href="#define-the-model" title="Per
 </pre></div>
 </div>
 </div>
-<div class="section" id="load-in-the-text-data">
-<h2>2. Load in the text data<a class="headerlink" href="#load-in-the-text-data" title="Permalink to this headline">¶</a></h2>
-<p>Next, we load the
-<a class="reference external" href="https://www.google.com/search?q=wikitext+2+data">Wikitext-2 dataset</a> into a <cite>Corpus</cite>,
-again following the
-<a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/data.py">preprocessing</a>
-from the word language model example.</p>
+<div class="section" id="id7">
+<h2>2. 텍스트 데이터 불러오기<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h2>
+<p>다음으로, 단어 단위 언어 모델 예제의 <a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/data.py">전처리</a>
+과정을 따라 <a class="reference external" href="https://www.google.com/search?q=wikitext+2+data">Wikitext-2 데이터셋</a> 을 <cite>Corpus</cite> 인스턴스에 불러옵니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Dictionary</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">word2idx</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">idx2word</span> <span class="o">=</span> <span class="p">[]</span>
 
@@ -384,28 +379,29 @@ <h2>2. Load in the text data<a class="headerlink" href="#load-in-the-text-data"
             <span class="bp">self</span><span class="o">.</span><span class="n">word2idx</span><span class="p">[</span><span class="n">word</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx2word</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">word2idx</span><span class="p">[</span><span class="n">word</span><span class="p">]</span>
 
-    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">idx2word</span><span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">Corpus</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dictionary</span> <span class="o">=</span> <span class="n">Dictionary</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;train.txt&#39;</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">valid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;valid.txt&#39;</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">test</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tokenize</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;test.txt&#39;</span><span class="p">))</span>
 
     <span class="k">def</span> <span class="nf">tokenize</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;Tokenizes a text file.&quot;&quot;&quot;</span>
         <span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
-        <span class="c1"># Add words to the dictionary</span>
+        <span class="sd">&quot;&quot;&quot;텍스트 파일 토큰화&quot;&quot;&quot;</span>
+        <span class="k">assert</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+        <span class="c1"># 사전에 단어 추가</span>
         <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf8&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
             <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
                 <span class="n">words</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="o">+</span> <span class="p">[</span><span class="s1">&#39;&lt;eos&gt;&#39;</span><span class="p">]</span>
                 <span class="k">for</span> <span class="n">word</span> <span class="ow">in</span> <span class="n">words</span><span class="p">:</span>
                     <span class="bp">self</span><span class="o">.</span><span class="n">dictionary</span><span class="o">.</span><span class="n">add_word</span><span class="p">(</span><span class="n">word</span><span class="p">)</span>
 
-        <span class="c1"># Tokenize file content</span>
+        <span class="c1"># 파일 내용 토큰화</span>
         <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf8&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
             <span class="n">idss</span> <span class="o">=</span> <span class="p">[]</span>
             <span class="k">for</span> <span class="n">line</span> <span class="ow">in</span> <span class="n">f</span><span class="p">:</span>
@@ -424,13 +420,11 @@ <h2>2. Load in the text data<a class="headerlink" href="#load-in-the-text-data"
 </pre></div>
 </div>
 </div>
-<div class="section" id="load-the-pre-trained-model">
-<h2>3. Load the pre-trained model<a class="headerlink" href="#load-the-pre-trained-model" title="Permalink to this headline">¶</a></h2>
-<p>This is a tutorial on dynamic quantization, a quantization technique
-that is applied after a model has been trained. Therefore, we’ll simply load some
-pre-trained weights into this model architecture; these weights were obtained
-by training for five epochs using the default settings in the word language model
-example.</p>
+<div class="section" id="id9">
+<h2>3. 사전 학습된 모델 불러오기<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
+<p>이 튜토리얼은 모델이 학습된 후 적용되는 양자화 기술인 동적 양자화에 대한 튜토리얼입니다.
+따라서 우리는 미리 학습된 가중치를 모델 아키텍처에 로드할 것 입니다. 이 가중치는 word
+language 모델 예제의 기본 설정을 사용하여 5개의 epoch 동안 학습하여 얻은 것입니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ntokens</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">corpus</span><span class="o">.</span><span class="n">dictionary</span><span class="p">)</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">LSTMModel</span><span class="p">(</span>
@@ -460,16 +454,15 @@ <h2>3. Load the pre-trained model<a class="headerlink" href="#load-the-pre-train
 )
 </pre></div>
 </div>
-<p>Now let’s generate some text to ensure that the pre-trained model is working
-properly - similarly to before, we follow
-<a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/generate.py">here</a></p>
+<p>이제 사전 학습된 모델이 잘 동작하는지 확인해보기 위해 텍스트를 생성해 보겠습니다.
+지금까지 튜토리얼을 진행했던 방식처럼 <a class="reference external" href="https://github.com/pytorch/examples/blob/master/word_language_model/generate.py">이 예제</a> 를 따라 하겠습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">input_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="n">ntokens</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
 <span class="n">hidden</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">init_hidden</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
 <span class="n">temperature</span> <span class="o">=</span> <span class="mf">1.0</span>
 <span class="n">num_words</span> <span class="o">=</span> <span class="mi">1000</span>
 
 <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">model_data_filepath</span> <span class="o">+</span> <span class="s1">&#39;out.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">outf</span><span class="p">:</span>
-    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>  <span class="c1"># no tracking history</span>
+    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>  <span class="c1"># 기록을 추적하지 않습니다.</span>
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_words</span><span class="p">):</span>
             <span class="n">output</span><span class="p">,</span> <span class="n">hidden</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">hidden</span><span class="p">)</span>
             <span class="n">word_weights</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">div</span><span class="p">(</span><span class="n">temperature</span><span class="p">)</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
@@ -499,78 +492,76 @@ <h2>3. Load the pre-trained model<a class="headerlink" href="#load-the-pre-train
 | Generated 700/1000 words
 | Generated 800/1000 words
 | Generated 900/1000 words
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+b&#39;to&#39; b&#39;engage&#39; b&#39;in&#39; b&#39;human&#39; b&#39;planets&#39; b&#39;.&#39; b&#39;&lt;unk&gt;&#39; b&#39;rabbits&#39; b&#39;ensure&#39; b&#39;that&#39; b&#39;their&#39; b&#39;bird&#39; b&#39;literate&#39; b&#39;history&#39; b&#39;are&#39; b&#39;undergoing&#39; b&#39;Western&#39; b&#39;years&#39; b&#39;by&#39; b&#39;means&#39;
+b&#39;they&#39; b&#39;have&#39; b&#39;.&#39; b&#39;That&#39; b&#39;may&#39; b&#39;be&#39; b&#39;uploaded&#39; b&#39;every&#39; b&#39;appearance&#39; b&#39;regarding&#39; b&#39;these&#39; b&#39;holes&#39; b&#39;are&#39; b&#39;imminent&#39; b&#39;and&#39; b&#39;,&#39; b&#39;able&#39; b&#39;to&#39; b&#39;eat&#39; b&#39;successful&#39;
+b&#39;,&#39; b&#39;and&#39; b&#39;some&#39; b&#39;noted&#39; b&#39;only&#39; b&#39;of&#39; b&#39;a&#39; b&#39;white&#39; b&#39;fox&#39; b&#39;and&#39; b&#39;Roussillon&#39; b&#39;look&#39; b&#39;.&#39; b&#39;According&#39; b&#39;to&#39; b&#39;Kings&#39; b&#39;during&#39; b&#39;56&#39; b&#39;years&#39; b&#39;,&#39;
+b&#39;\xc3\x8dmar&#39; b&#39;Mantell&#39; b&#39;listed&#39; b&#39;&quot;&#39; b&#39;religion&#39; b&#39;&quot;&#39; b&#39;that&#39; b&#39;supported&#39; b&#39;for&#39; b&#39;the&#39; b&#39;modern&#39; b&#39;nurse&#39; b&#39;difference&#39; b&#39;on&#39; b&#39;its&#39; b&#39;face&#39; b&#39;,&#39; b&#39;leaving&#39; b&#39;it&#39; b&#39;Too&#39;
+b&#39;Merit&#39; b&#39;the&#39; b&#39;spots&#39; b&#39;for&#39; b&#39;a&#39; b&#39;number&#39; b&#39;of&#39; b&#39;chicks&#39; b&#39;(&#39; b&#39;Thomson&#39; b&#39;)&#39; b&#39;and&#39; b&#39;&lt;unk&gt;&#39; b&#39;(&#39; b&#39;e.g.&#39; b&#39;camel&#39; b&#39;)&#39; b&#39;.&#39; b&#39;Some&#39; b&#39;Fanny&#39;
+b&#39;,&#39; b&#39;males&#39; b&#39;\xe2\x80\x94&#39; b&#39;or&#39; b&#39;small&#39; b&#39;diversity&#39; b&#39;.&#39; b&#39;&lt;eos&gt;&#39; b&#39;&lt;eos&gt;&#39; b&#39;=&#39; b&#39;=&#39; b&#39;&lt;unk&gt;&#39; b&#39;among&#39; b&#39;grey&#39; b&#39;technique&#39; b&#39;=&#39; b&#39;=&#39; b&#39;&lt;eos&gt;&#39; b&#39;&lt;eos&gt;&#39; b&#39;Naming&#39;
+b&#39;statistics&#39; b&#39;include&#39; b&#39;invisible&#39; b&#39;following&#39; b&#39;Mecca&#39; b&#39;.&#39; b&#39;It&#39; b&#39;may&#39; b&#39;be&#39; b&#39;established&#39; b&#39;into&#39; b&#39;at&#39; b&#39;least&#39; b&#39;enough&#39; b&#39;alone&#39; b&#39;&lt;unk&gt;&#39; b&#39;by&#39; b&#39;their&#39; b&#39;national&#39; b&#39;non&#39;
+b&#39;@-@&#39; b&#39;volume&#39; b&#39;cores&#39; b&#39;(&#39; b&#39;fulfill&#39; b&#39;@-@&#39; b&#39;bird&#39; b&#39;,&#39; b&#39;flowers&#39; b&#39;NATO&#39; b&#39;,&#39; b&#39;and&#39; b&#39;has&#39; b&#39;any&#39; b&#39;results&#39; b&#39;)&#39; b&#39;,&#39; b&#39;this&#39; b&#39;feed&#39; b&#39;on&#39;
+b&#39;,&#39; b&#39;and&#39; b&#39;orchards&#39; b&#39;have&#39; b&#39;incorrect&#39; b&#39;chicks&#39; b&#39;or&#39; b&#39;structures&#39; b&#39;.&#39; b&#39;Because&#39; b&#39;part&#39; b&#39;of&#39; b&#39;these&#39; b&#39;birds&#39; b&#39;will&#39; b&#39;aimed&#39; b&#39;.&#39; b&#39;If&#39; b&#39;the&#39; b&#39;kakapo&#39;
+b&#39;attain&#39; b&#39;well&#39; b&#39;allowed&#39; b&#39;,&#39; b&#39;they&#39; b&#39;are&#39; b&#39;a&#39; b&#39;&quot;&#39; b&#39;&lt;unk&gt;&#39; b&#39;form&#39; b&#39;so&#39; b&#39;since&#39; b&#39;this&#39; b&#39;name&#39; b&#39;about&#39; b&#39;,&#39; b&#39;straight&#39; b&#39;or&#39; b&#39;even&#39; b&#39;evidently&#39;
 </pre></div>
 </div>
-<p>It’s no GPT-2, but it looks like the model has started to learn the structure of
-language!</p>
-<p>We’re almost ready to demonstrate dynamic quantization. We just need to define a few more
-helper functions:</p>
+<p>이 모델이 GPT-2는 아니지만, 언어의 구조를 배우기 시작한 것처럼 보입니다!</p>
+<p>동적 양자화를 시연할 준비가 거의 끝났습니다. 몇 가지 helper 함수를 정의하기만 하면 됩니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">bptt</span> <span class="o">=</span> <span class="mi">25</span>
 <span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">CrossEntropyLoss</span><span class="p">()</span>
 <span class="n">eval_batch_size</span> <span class="o">=</span> <span class="mi">1</span>
 
-<span class="c1"># create test data set</span>
+<span class="c1"># 테스트 데이터셋 만들기</span>
 <span class="k">def</span> <span class="nf">batchify</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">bsz</span><span class="p">):</span>
-    <span class="c1"># Work out how cleanly we can divide the dataset into bsz parts.</span>
+    <span class="c1"># 데이터셋을 bsz 부분으로 얼마나 깔끔하게 나눌 수 있는지 계산합니다.</span>
     <span class="n">nbatch</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">//</span> <span class="n">bsz</span>
-    <span class="c1"># Trim off any extra elements that wouldn&#39;t cleanly fit (remainders).</span>
+    <span class="c1"># 깔끔하게 맞지 않는 추가적인 부분(나머지들)을 잘라냅니다.</span>
     <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">narrow</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">nbatch</span> <span class="o">*</span> <span class="n">bsz</span><span class="p">)</span>
-    <span class="c1"># Evenly divide the data across the bsz batches.</span>
+    <span class="c1"># 데이터에 대하여 bsz 배치들로 동등하게 나눕니다.</span>
     <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">bsz</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span>
 
 <span class="n">test_data</span> <span class="o">=</span> <span class="n">batchify</span><span class="p">(</span><span class="n">corpus</span><span class="o">.</span><span class="n">test</span><span class="p">,</span> <span class="n">eval_batch_size</span><span class="p">)</span>
 
-<span class="c1"># Evaluation functions</span>
+<span class="c1"># 평가 함수들</span>
 <span class="k">def</span> <span class="nf">get_batch</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
     <span class="n">seq_len</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">bptt</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">source</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">i</span><span class="p">)</span>
     <span class="n">data</span> <span class="o">=</span> <span class="n">source</span><span class="p">[</span><span class="n">i</span><span class="p">:</span><span class="n">i</span><span class="o">+</span><span class="n">seq_len</span><span class="p">]</span>
@@ -578,7 +569,7 @@ <h2>3. Load the pre-trained model<a class="headerlink" href="#load-the-pre-train
     <span class="k">return</span> <span class="n">data</span><span class="p">,</span> <span class="n">target</span>
 
 <span class="k">def</span> <span class="nf">repackage_hidden</span><span class="p">(</span><span class="n">h</span><span class="p">):</span>
-  <span class="sd">&quot;&quot;&quot;Wraps hidden states in new Tensors, to detach them from their history.&quot;&quot;&quot;</span>
+  <span class="sd">&quot;&quot;&quot;은닉 상태를 변화도 기록에서 제거된 새로운 tensor로 만듭니다.&quot;&quot;&quot;</span>
 
   <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">h</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
       <span class="k">return</span> <span class="n">h</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
@@ -586,7 +577,7 @@ <h2>3. Load the pre-trained model<a class="headerlink" href="#load-the-pre-train
       <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">repackage_hidden</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">h</span><span class="p">)</span>
 
 <span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">model_</span><span class="p">,</span> <span class="n">data_source</span><span class="p">):</span>
-    <span class="c1"># Turn on evaluation mode which disables dropout.</span>
+    <span class="c1"># Dropout을 중지시키는 평가 모드로 실행합니다.</span>
     <span class="n">model_</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
     <span class="n">total_loss</span> <span class="o">=</span> <span class="mf">0.</span>
     <span class="n">hidden</span> <span class="o">=</span> <span class="n">model_</span><span class="o">.</span><span class="n">init_hidden</span><span class="p">(</span><span class="n">eval_batch_size</span><span class="p">)</span>
@@ -601,14 +592,13 @@ <h2>3. Load the pre-trained model<a class="headerlink" href="#load-the-pre-train
 </pre></div>
 </div>
 </div>
-<div class="section" id="test-dynamic-quantization">
-<h2>4. Test dynamic quantization<a class="headerlink" href="#test-dynamic-quantization" title="Permalink to this headline">¶</a></h2>
-<p>Finally, we can call <code class="docutils literal notranslate"><span class="pre">torch.quantization.quantize_dynamic</span></code> on the model!
-Specifically,</p>
+<div class="section" id="id11">
+<h2>4. 동적 양자화 테스트하기<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h2>
+<p>마지막으로 모델에서 <code class="docutils literal notranslate"><span class="pre">torch.quantization.quantize_dynamic</span></code> 을 호출 할 수 있습니다!
+구체적으로,</p>
 <ul class="simple">
-<li><p>We specify that we want the <code class="docutils literal notranslate"><span class="pre">nn.LSTM</span></code> and <code class="docutils literal notranslate"><span class="pre">nn.Linear</span></code> modules in our
-model to be quantized</p></li>
-<li><p>We specify that we want weights to be converted to <code class="docutils literal notranslate"><span class="pre">int8</span></code> values</p></li>
+<li><p>모델의 <code class="docutils literal notranslate"><span class="pre">nn.LSTM</span></code> 과 <code class="docutils literal notranslate"><span class="pre">nn.Linear</span></code> 모듈을 양자화 하도록 명시합니다.</p></li>
+<li><p>가중치들이 <code class="docutils literal notranslate"><span class="pre">int8</span></code> 값으로 변환되도록 명시합니다.</p></li>
 </ul>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.quantization</span>
 
@@ -627,8 +617,8 @@ <h2>4. Test dynamic quantization<a class="headerlink" href="#test-dynamic-quanti
 )
 </pre></div>
 </div>
-<p>The model looks the same; how has this benefited us? First, we see a
-significant reduction in model size:</p>
+<p>모델은 동일하게 보입니다. 이것이 어떻게 이득을 주는 것일까요? 첫째, 모델 크기가
+상당히 줄어 듭니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">print_size_of_model</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
     <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s2">&quot;temp.p&quot;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Size (MB):&#39;</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getsize</span><span class="p">(</span><span class="s2">&quot;temp.p&quot;</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span><span class="p">)</span>
@@ -639,14 +629,15 @@ <h2>4. Test dynamic quantization<a class="headerlink" href="#test-dynamic-quanti
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size (MB): 113.945504
-Size (MB): 79.739756
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size (MB): 113.94579
+Size (MB): 79.739984
 </pre></div>
 </div>
-<p>Second, we see faster inference time, with no difference in evaluation loss:</p>
-<p>Note: we number of threads to one for single threaded comparison, since quantized
-models run single threaded.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+<p>두 번째로, 평가 손실값은 같으나 추론(inference) 속도가 빨라졌습니다.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 메모: 양자화 된 모델은 단일 스레드로 실행되기 때문에 단일 스레드 비교를 위해</span>
+<span class="c1"># 스레드 수를 1로 설정했습니다.</span>
+
+<span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
 
 <span class="k">def</span> <span class="nf">time_model_evaluation</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_data</span><span class="p">):</span>
     <span class="n">s</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
@@ -660,21 +651,21 @@ <h2>4. Test dynamic quantization<a class="headerlink" href="#test-dynamic-quanti
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>loss: 5.167
-elapsed time (seconds): 180.0
+elapsed time (seconds): 178.6
 loss: 5.168
-elapsed time (seconds): 118.9
+elapsed time (seconds): 118.8
 </pre></div>
 </div>
-<p>Running this locally on a MacBook Pro, without quantization, inference takes about 200 seconds,
-and with quantization it takes just about 100 seconds.</p>
+<p>MacBook Pro에서 로컬로 실행하는 경우, 양자화 없이는 추론(inference)에 약 200초가 걸리고
+양자화를 사용하면 약 100초가 걸립니다.</p>
 </div>
-<div class="section" id="conclusion">
-<h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this headline">¶</a></h2>
-<p>Dynamic quantization can be an easy way to reduce model size while only
-having a limited effect on accuracy.</p>
-<p>Thanks for reading! As always, we welcome any feedback, so please create an issue
-<a class="reference external" href="https://github.com/pytorch/pytorch/issues">here</a> if you have any.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  6.572 seconds)</p>
+<div class="section" id="id12">
+<h2>마치며<a class="headerlink" href="#id12" title="Permalink to this headline">¶</a></h2>
+<p>동적 양자화는 정확도에 제한적인 영향을 미치면서 모델 크기를 줄이는
+쉬운 방법이 될 수 있습니다.</p>
+<p>읽어주셔서 감사합니다. 언제나처럼 어떠한 피드백도 환영이니, 의견이 있다면
+<a class="reference external" href="https://github.com/pytorch/pytorch/issues">여기</a> 에 이슈를 남겨 주세요.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 5 minutes  4.081 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-dynamic-quantization-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/256861ec2cab5f0dc50c523f520dfefd/dynamic_quantization_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">dynamic_quantization_tutorial.py</span></code></a></p>
@@ -737,13 +728,13 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">(beta) Dynamic Quantization on an LSTM Word Language Model</a><ul>
-<li><a class="reference internal" href="#introduction">Introduction</a></li>
-<li><a class="reference internal" href="#define-the-model">1. Define the model</a></li>
-<li><a class="reference internal" href="#load-in-the-text-data">2. Load in the text data</a></li>
-<li><a class="reference internal" href="#load-the-pre-trained-model">3. Load the pre-trained model</a></li>
-<li><a class="reference internal" href="#test-dynamic-quantization">4. Test dynamic quantization</a></li>
-<li><a class="reference internal" href="#conclusion">Conclusion</a></li>
+<li><a class="reference internal" href="#">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a><ul>
+<li><a class="reference internal" href="#id2">시작하기</a></li>
+<li><a class="reference internal" href="#id5">1. 모델 정의하기</a></li>
+<li><a class="reference internal" href="#id7">2. 텍스트 데이터 불러오기</a></li>
+<li><a class="reference internal" href="#id9">3. 사전 학습된 모델 불러오기</a></li>
+<li><a class="reference internal" href="#id11">4. 동적 양자화 테스트하기</a></li>
+<li><a class="reference internal" href="#id12">마치며</a></li>
 </ul>
 </li>
 </ul>
@@ -761,11 +752,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/neural_style_tutorial.html b/docs/advanced/neural_style_tutorial.html
index 3f46def34..f79d23e73 100644
--- a/docs/advanced/neural_style_tutorial.html
+++ b/docs/advanced/neural_style_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -338,14 +338,14 @@ <h2>Importing Packages and Selecting a Device<a class="headerlink" href="#import
 <li><p><code class="docutils literal notranslate"><span class="pre">torchvision.models</span></code> (train or load pre-trained models)</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">copy</span></code> (to deep copy the models; system package)</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 <span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
 
-<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 
 <span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
@@ -460,7 +460,7 @@ <h3>Content Loss<a class="headerlink" href="#content-loss" title="Permalink to t
 parameter of the module.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">ContentLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">ContentLoss</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="c1"># we &#39;detach&#39; the target content from the tree used</span>
         <span class="c1"># to dynamically compute the gradient: this is a stated value,</span>
@@ -520,7 +520,7 @@ <h3>Style Loss<a class="headerlink" href="#style-loss" title="Permalink to this
 error between <span class="math notranslate nohighlight">\(G_{XL}\)</span> and <span class="math notranslate nohighlight">\(G_{SL}\)</span>.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">StyleLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target_feature</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target_feature</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">StyleLoss</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">target</span> <span class="o">=</span> <span class="n">gram_matrix</span><span class="p">(</span><span class="n">target_feature</span><span class="p">)</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
 
@@ -555,7 +555,7 @@ <h2>Importing the Model<a class="headerlink" href="#importing-the-model" title="
 <span class="c1"># create a module to normalize input image so we can easily put it in a</span>
 <span class="c1"># nn.Sequential</span>
 <span class="k">class</span> <span class="nc">Normalization</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Normalization</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="c1"># .view the mean and std to make them [C x 1 x 1] so that they can</span>
         <span class="c1"># directly work with image Tensor of shape [B x C x H x W].</span>
@@ -760,7 +760,7 @@ <h2>Gradient Descent<a class="headerlink" href="#gradient-descent" title="Permal
 Style Loss : 0.265441 Content Loss: 2.358365
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  33.115 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  32.671 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-neural-style-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/b3bcd49c4dd9be3a44c6c5d2e2a8b0cd/neural_style_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">neural_style_tutorial.py</span></code></a></p>
@@ -843,11 +843,10 @@ <h2>Gradient Descent<a class="headerlink" href="#gradient-descent" title="Permal
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/advanced/numpy_extensions_tutorial.html b/docs/advanced/numpy_extensions_tutorial.html
index b7a9c39a6..5cfe32f7c 100644
--- a/docs/advanced/numpy_extensions_tutorial.html
+++ b/docs/advanced/numpy_extensions_tutorial.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Creating Extensions Using numpy and scipy &mdash; PyTorch Tutorials 1.6.0 documentation</title>
+  <title>numpy 와 scipy 를 이용한 확장(Extensions) 만들기 &mdash; PyTorch Tutorials 1.6.0 documentation</title>
   
 
   
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -241,7 +241,7 @@
       </li>
 
         
-      <li>Creating Extensions Using numpy and scipy</li>
+      <li>numpy 와 scipy 를 이용한 확장(Extensions) 만들기</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -299,38 +299,38 @@
 <p class="admonition-title">Note</p>
 <p>Click <a class="reference internal" href="#sphx-glr-download-advanced-numpy-extensions-tutorial-py"><span class="std std-ref">here</span></a> to download the full example code</p>
 </div>
-<div class="sphx-glr-example-title section" id="creating-extensions-using-numpy-and-scipy">
-<span id="sphx-glr-advanced-numpy-extensions-tutorial-py"></span><h1>Creating Extensions Using numpy and scipy<a class="headerlink" href="#creating-extensions-using-numpy-and-scipy" title="Permalink to this headline">¶</a></h1>
+<div class="sphx-glr-example-title section" id="numpy-scipy-extensions">
+<span id="sphx-glr-advanced-numpy-extensions-tutorial-py"></span><h1>numpy 와 scipy 를 이용한 확장(Extensions) 만들기<a class="headerlink" href="#numpy-scipy-extensions" title="Permalink to this headline">¶</a></h1>
 <p><strong>Author</strong>: <a class="reference external" href="https://github.com/apaszke">Adam Paszke</a></p>
 <p><strong>Updated by</strong>: <a class="reference external" href="https://github.com/adam-dziedzic">Adam Dziedzic</a></p>
-<p>In this tutorial, we shall go through two tasks:</p>
-<ol class="arabic">
-<li><p>Create a neural network layer with no parameters.</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>This calls into <strong>numpy</strong> as part of its implementation</p></li>
+<p><strong>번역</strong>: <a class="reference external" href="https://github.com/ajin-jng">Ajin Jeong</a></p>
+<p>이번 튜토리얼에서는 두 가지 작업을 수행할 것입니다:</p>
+<ol class="arabic simple">
+<li><dl class="simple">
+<dt>매개 변수가 없는 신경망 계층(layer) 만들기</dt><dd><ul class="simple">
+<li><p>이는 구현의 일부로 <strong>numpy</strong> 를 호출합니다.</p></li>
 </ul>
-</div></blockquote>
+</dd>
+</dl>
 </li>
-<li><p>Create a neural network layer that has learnable weights</p>
-<blockquote>
-<div><ul class="simple">
-<li><p>This calls into <strong>SciPy</strong> as part of its implementation</p></li>
+<li><dl class="simple">
+<dt>학습 가능한 가중치가 있는 신경망 계층(layer) 만들기</dt><dd><ul class="simple">
+<li><p>이는 구현의 일부로 <strong>Scipy</strong> 를 호출합니다.</p></li>
 </ul>
-</div></blockquote>
+</dd>
+</dl>
 </li>
 </ol>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="k">import</span> <span class="n">Function</span>
+<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="kn">import</span> <span class="n">Function</span>
 </pre></div>
 </div>
-<div class="section" id="parameter-less-example">
-<h2>Parameter-less example<a class="headerlink" href="#parameter-less-example" title="Permalink to this headline">¶</a></h2>
-<p>This layer doesn’t particularly do anything useful or mathematically
-correct.</p>
-<p>It is aptly named BadFFTFunction</p>
-<p><strong>Layer Implementation</strong></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.fft</span> <span class="k">import</span> <span class="n">rfft2</span><span class="p">,</span> <span class="n">irfft2</span>
+<div class="section" id="parameter-less">
+<h2>매개 변수가 없는(Parameter-less) 예시<a class="headerlink" href="#parameter-less" title="Permalink to this headline">¶</a></h2>
+<p>이 계층(layer)은 특별히 유용하거나 수학적으로 올바른 작업을 수행하지 않습니다.</p>
+<p>이름은 대충 BadFFTFunction으로 지었습니다.</p>
+<p><strong>계층(layer) 구현</strong></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy.fft</span> <span class="kn">import</span> <span class="n">rfft2</span><span class="p">,</span> <span class="n">irfft2</span>
 
 
 <span class="k">class</span> <span class="nc">BadFFTFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
@@ -346,15 +346,14 @@ <h2>Parameter-less example<a class="headerlink" href="#parameter-less-example" t
         <span class="n">result</span> <span class="o">=</span> <span class="n">irfft2</span><span class="p">(</span><span class="n">numpy_go</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
 
-<span class="c1"># since this layer does not have any parameters, we can</span>
-<span class="c1"># simply declare this as a function, rather than as an nn.Module class</span>
+<span class="c1"># 이 계층에는 매개 변수가 없으므로 nn.Module 클래스가 아닌 함수로 간단히 선언할 수 있습니다.</span>
 
 
 <span class="k">def</span> <span class="nf">incorrect_fft</span><span class="p">(</span><span class="nb">input</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">BadFFTFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
 </pre></div>
 </div>
-<p><strong>Example usage of the created layer:</strong></p>
+<p><strong>생성된 계층(layer)의 사용 예시:</strong></p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">result</span> <span class="o">=</span> <span class="n">incorrect_fft</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
@@ -363,47 +362,45 @@ <h2>Parameter-less example<a class="headerlink" href="#parameter-less-example" t
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 3.2424,  7.8384,  2.1981,  8.0125,  4.6636],
-        [ 9.9235,  2.4468,  5.1007,  1.5843, 14.9859],
-        [ 3.1496,  5.7176,  0.9066,  1.6388,  5.3236],
-        [ 5.2732, 17.6063,  2.0276,  3.9909,  8.4614],
-        [ 3.7512,  7.5392,  4.9101,  2.8231, 16.7665],
-        [ 5.2732,  4.6648,  4.2121, 11.5095,  8.4614],
-        [ 3.1496,  6.7077,  9.1958,  9.6320,  5.3236],
-        [ 9.9235,  7.0507,  5.4910,  6.9685, 14.9859]],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 1.5501,  7.1151,  8.4364,  3.6474, 14.8662],
+        [ 5.6864,  6.3446,  7.3238,  6.0770, 10.2071],
+        [ 6.4498,  1.0390, 10.2754,  4.8281,  9.5217],
+        [ 5.0410,  6.6596,  9.5980, 13.9414,  5.8046],
+        [ 0.4062,  7.6052,  5.1859, 14.0548,  2.5884],
+        [ 5.0410,  3.2579,  3.7768,  7.5748,  5.8046],
+        [ 6.4498,  1.5078,  2.7242,  5.3578,  9.5217],
+        [ 5.6864,  3.1304, 10.1956, 13.7476, 10.2071]],
        grad_fn=&lt;BadFFTFunctionBackward&gt;)
-tensor([[-0.2921,  1.3271, -0.5736,  1.1381,  0.1384,  0.0339,  0.0091, -0.9687],
-        [-0.9187,  0.4129, -0.0286, -1.2750,  1.0243, -0.8500,  0.6447,  0.9856],
-        [-1.6390,  0.7035,  0.9991,  2.3061, -0.1823,  2.3427, -0.3509,  0.9336],
-        [ 0.6703,  1.1346,  0.3424,  0.8052, -0.7459, -1.6153,  0.3324,  1.0079],
-        [-2.0462,  0.1600, -1.1903,  0.1683,  1.0622,  1.3530, -0.5710,  0.5226],
-        [-0.6224, -1.3238,  0.5718, -0.1133,  0.8483,  0.5005,  0.6194, -1.1472],
-        [ 0.4906, -0.6809, -0.2076, -0.9867,  0.2497, -0.9971,  0.4948, -0.2496],
-        [ 0.4363, -1.0047, -1.5366,  0.0300,  0.7982, -0.5539,  0.4627, -0.1465]],
+tensor([[-0.3161, -1.1879,  0.8249, -0.3031, -0.0397, -0.3313, -0.2853, -0.4113],
+        [ 0.2903, -1.2508, -0.5438, -0.5214,  1.7453, -1.0563,  0.2863,  0.4996],
+        [ 0.0508, -0.8751,  2.0983,  1.7532,  0.4489,  0.1226, -0.8629,  0.7988],
+        [ 0.4952,  1.8440, -1.4548,  0.0079,  0.3158,  0.0358,  0.3163, -1.4703],
+        [ 0.7663,  0.3652, -0.7990,  1.7538, -0.7449,  0.4170, -0.5825, -1.4284],
+        [-0.1767,  0.2966,  0.6400, -0.6092,  2.3086, -0.8897,  0.3803,  0.5017],
+        [ 0.9124, -0.8463,  0.2494,  0.1161,  0.5655, -1.9462,  1.2726, -0.5775],
+        [-1.6538, -1.5879,  1.1116, -0.6050,  0.5170, -0.4244,  0.0721,  1.1520]],
        requires_grad=True)
 </pre></div>
 </div>
 </div>
-<div class="section" id="parametrized-example">
-<h2>Parametrized example<a class="headerlink" href="#parametrized-example" title="Permalink to this headline">¶</a></h2>
-<p>In deep learning literature, this layer is confusingly referred
-to as convolution while the actual operation is cross-correlation
-(the only difference is that filter is flipped for convolution,
-which is not the case for cross-correlation).</p>
-<p>Implementation of a layer with learnable weights, where cross-correlation
-has a filter (kernel) that represents weights.</p>
-<p>The backward pass computes the gradient wrt the input and the gradient wrt the filter.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="k">import</span> <span class="n">flip</span>
+<div class="section" id="parameterized">
+<h2>매개 변수가 있는(Parameterized) 예시<a class="headerlink" href="#parameterized" title="Permalink to this headline">¶</a></h2>
+<p>딥러닝 문헌에서 이 계층(layer)의 실제 연산은 상호 상관(cross-correlation)이지만
+합성곱(convolution)이라고 헷갈리게 부르고 있습니다.
+(합성곱은 필터를 뒤집어서 연산을 하는 반면, 상호 상관은 그렇지 않은 차이가 있습니다)</p>
+<p>학습 가능한 가중치를 가는 필터(커널)를 갖는 상호 상관 계층을 구현해보겠습니다.</p>
+<p>역전파 단계(backward pass)에서는 입력에 대한 기울기(gradient)와 필터에 대한 기울기를 계산합니다.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">numpy</span> <span class="kn">import</span> <span class="n">flip</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="k">import</span> <span class="n">convolve2d</span><span class="p">,</span> <span class="n">correlate2d</span>
-<span class="kn">from</span> <span class="nn">torch.nn.modules.module</span> <span class="k">import</span> <span class="n">Module</span>
-<span class="kn">from</span> <span class="nn">torch.nn.parameter</span> <span class="k">import</span> <span class="n">Parameter</span>
+<span class="kn">from</span> <span class="nn">scipy.signal</span> <span class="kn">import</span> <span class="n">convolve2d</span><span class="p">,</span> <span class="n">correlate2d</span>
+<span class="kn">from</span> <span class="nn">torch.nn.modules.module</span> <span class="kn">import</span> <span class="n">Module</span>
+<span class="kn">from</span> <span class="nn">torch.nn.parameter</span> <span class="kn">import</span> <span class="n">Parameter</span>
 
 
 <span class="k">class</span> <span class="nc">ScipyConv2dFunction</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
     <span class="nd">@staticmethod</span>
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">bias</span><span class="p">):</span>
-        <span class="c1"># detach so we can cast to NumPy</span>
+        <span class="c1"># 분리(detach)하여 NumPy로 변환(cast)할 수 있습니다.</span>
         <span class="nb">input</span><span class="p">,</span> <span class="nb">filter</span><span class="p">,</span> <span class="n">bias</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span> <span class="nb">filter</span><span class="o">.</span><span class="n">detach</span><span class="p">(),</span> <span class="n">bias</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span>
         <span class="n">result</span> <span class="o">=</span> <span class="n">correlate2d</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="nb">filter</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;valid&#39;</span><span class="p">)</span>
         <span class="n">result</span> <span class="o">+=</span> <span class="n">bias</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
@@ -417,14 +414,14 @@ <h2>Parametrized example<a class="headerlink" href="#parametrized-example" title
         <span class="n">grad_output</span> <span class="o">=</span> <span class="n">grad_output</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
         <span class="n">grad_bias</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">grad_output</span><span class="p">,</span> <span class="n">keepdims</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="n">grad_input</span> <span class="o">=</span> <span class="n">convolve2d</span><span class="p">(</span><span class="n">grad_output</span><span class="p">,</span> <span class="nb">filter</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">)</span>
-        <span class="c1"># the previous line can be expressed equivalently as:</span>
+        <span class="c1"># 윗줄은 다음과 같이 표현할 수도 있습니다:</span>
         <span class="c1"># grad_input = correlate2d(grad_output, flip(flip(filter.numpy(), axis=0), axis=1), mode=&#39;full&#39;)</span>
         <span class="n">grad_filter</span> <span class="o">=</span> <span class="n">correlate2d</span><span class="p">(</span><span class="nb">input</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">grad_output</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;valid&#39;</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">grad_input</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">grad_filter</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">from_numpy</span><span class="p">(</span><span class="n">grad_bias</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">ScipyConv2d</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filter_width</span><span class="p">,</span> <span class="n">filter_height</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filter_width</span><span class="p">,</span> <span class="n">filter_height</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">ScipyConv2d</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">filter</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">filter_width</span><span class="p">,</span> <span class="n">filter_height</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
@@ -433,7 +430,7 @@ <h2>Parametrized example<a class="headerlink" href="#parametrized-example" title
         <span class="k">return</span> <span class="n">ScipyConv2dFunction</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">filter</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
 </pre></div>
 </div>
-<p><strong>Example usage:</strong></p>
+<p><strong>사용 예시:</strong></p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">module</span> <span class="o">=</span> <span class="n">ScipyConv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Filter and bias: &quot;</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">parameters</span><span class="p">()))</span>
 <span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -445,43 +442,43 @@ <h2>Parametrized example<a class="headerlink" href="#parametrized-example" title
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Filter and bias:  [Parameter containing:
-tensor([[ 1.1594, -1.5019,  2.3002],
-        [-0.0399, -0.5464,  0.3556],
-        [ 0.9378,  0.6888, -1.6433]], requires_grad=True), Parameter containing:
-tensor([[-0.1342]], requires_grad=True)]
-Output from the convolution:  tensor([[-1.5866, -4.9213,  1.2348, -1.7411,  1.3867, -3.6400, -3.0142,  2.9298],
-        [ 5.0279, -4.7684,  0.9349, -4.7118,  7.2223, -7.0567, -1.1490,  2.9159],
-        [ 1.2355,  0.7761, -0.9240, -3.5258,  5.4679, -3.7748,  6.2502, -0.9549],
-        [-2.9315,  5.2783, -3.9884,  3.0636, -3.9602,  7.4487, -2.9306,  1.1996],
-        [ 4.2224, -5.2260,  1.6353,  3.1985, -5.1410,  7.0212, -1.9695, -2.6567],
-        [ 2.1452, -5.8957,  4.6534, -1.6767,  1.8942, -4.7634,  7.0981,  0.8917],
-        [-3.1319,  2.0006,  3.6807, -4.2530,  5.2136, -5.2570,  2.3453, -4.8116],
-        [ 0.6534,  1.5824, -1.6142,  1.2102, -3.0763,  4.4494, -1.8422,  0.5644]],
+tensor([[ 1.7318,  0.7303,  0.8925],
+        [-0.1013, -0.2586,  0.1423],
+        [ 0.8752, -0.8021, -1.3515]], requires_grad=True), Parameter containing:
+tensor([[0.7393]], requires_grad=True)]
+Output from the convolution:  tensor([[-1.2070, -1.2347, -3.8840, -2.0865, -0.5897,  4.5678,  7.2435,  3.5294],
+        [ 1.3204, -0.2718,  3.9942,  1.1799,  3.2501,  3.4582,  3.3299,  5.1307],
+        [-0.8591,  0.6265,  0.2100, -1.7121,  2.5220, -0.4239,  0.3369, -0.3048],
+        [ 1.7585,  1.7412,  0.3477, -6.7214, -3.8535,  4.3833, -0.9889, -1.3524],
+        [-0.6392,  1.1201,  0.9465,  3.4101,  7.3032,  2.7230, -0.7960,  1.5117],
+        [ 3.3390,  0.2427, -5.0576, -0.0234, -0.1857,  7.6631,  0.5181, -0.1694],
+        [ 3.5709,  2.9849, -1.5485,  0.6234, -5.1039, -0.9237, -1.1887,  1.0532],
+        [ 4.9075,  7.9906,  0.4417,  4.7304,  2.0546,  4.6694,  3.0288,  0.2060]],
        grad_fn=&lt;ScipyConv2dFunctionBackward&gt;)
-Gradient for the input map:  tensor([[ 0.3204,  0.0764, -0.5440,  0.4679,  1.3714, -3.7680,  3.0030, -0.0426,
-         -0.2306,  3.2814],
-        [ 0.0518,  1.7483, -1.5635,  1.4557,  4.8146, -4.6422,  0.1091, -1.0376,
-         -0.7845, -2.4097],
-        [-1.0940,  3.1022, -6.8325,  1.6429, -1.1260,  2.2957, -3.3366,  5.0269,
-         -1.1141, -5.0951],
-        [-0.2152,  1.4700,  1.8167, -5.4662, -0.5164,  0.8981, -1.9527,  4.5233,
-         -0.5912,  3.2236],
-        [-1.7999,  0.5967,  2.4249, -4.5497,  3.8100, -0.5492,  1.5770, -5.9527,
-         -0.7554,  3.6763],
-        [-0.4927,  0.0100, -1.6929, -0.6688,  1.2724,  4.2587, -3.3507, -3.6568,
-          3.1167, -1.3022],
-        [-2.0251,  1.4447, -1.1328,  2.6215, -2.9248, -0.5849, -6.6728,  4.4034,
-         -1.3589, -1.6299],
-        [-1.8930,  1.9998,  0.1931, -8.0635,  3.5877,  1.3187,  3.4759, -0.2619,
-          2.0675, -3.8245],
-        [-1.1161,  0.1658,  2.3411, -1.3095, -0.7334, -2.3646,  0.7730, -0.1118,
-          3.1601, -0.4534],
-        [-1.3985, -1.9474,  3.4416,  1.9089, -3.3054,  3.0016,  1.0033, -3.2258,
-         -2.4228,  2.9648]])
+Gradient for the input map:  tensor([[-1.5639e-01, -2.8550e+00, -1.0226e+00, -2.2953e+00,  2.3711e+00,
+          1.5987e+00,  2.8249e+00,  2.0736e+00,  1.0165e+00,  5.8278e-01],
+        [ 5.3096e-01, -1.4479e+00, -2.2106e+00, -1.2887e+00, -1.1221e+00,
+          1.8156e+00,  6.2694e-01,  1.1915e-01, -4.6073e-01, -3.1541e-01],
+        [-6.7106e-01, -7.4811e-01,  1.3450e+00,  1.2983e-01,  1.2075e+00,
+          1.2204e+00, -8.4251e-01,  1.7919e+00, -5.5791e-01, -3.1286e-01],
+        [-1.3569e+00, -3.4393e+00, -2.4330e+00,  8.4495e-01,  2.2648e+00,
+         -2.2582e+00,  1.6346e-01, -4.3527e+00,  2.7372e-01, -1.0266e-01],
+        [ 7.6037e-02,  1.1122e+00, -1.4549e+00, -2.1135e+00,  5.8307e-01,
+          4.8614e+00,  6.1722e-01,  6.6536e-01, -4.1388e-01, -1.1720e+00],
+        [-3.0112e+00,  9.8146e-01,  2.9347e+00, -1.1867e+00,  1.6878e+00,
+         -9.3850e-01,  2.5720e+00,  1.4011e+00,  8.0337e-02,  7.6082e-01],
+        [ 1.8501e+00,  1.8388e+00, -1.2688e-01,  1.4500e-03,  5.3177e-01,
+         -1.0504e+00,  8.7490e-02, -1.2130e+00,  2.2461e+00,  8.9908e-01],
+        [-2.5281e+00,  1.3529e+00, -1.9791e-01, -2.3832e+00,  3.1050e+00,
+          6.8788e+00,  3.0542e+00, -2.0752e+00,  3.5135e-02,  2.8998e-01],
+        [ 8.8253e-01, -1.6876e-03, -1.7992e+00, -1.4657e+00, -1.5276e-01,
+          1.3057e-01,  2.3409e+00,  9.5714e-01, -2.5217e+00, -1.3817e+00],
+        [-6.7888e-01,  7.3431e-01,  4.7773e-01,  1.0828e+00,  6.5867e-01,
+         -8.0655e-01, -9.4447e-01, -3.3062e+00, -1.2611e+00,  8.4813e-01]])
 </pre></div>
 </div>
-<p><strong>Check the gradients:</strong></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.autograd.gradcheck</span> <span class="k">import</span> <span class="n">gradcheck</span>
+<p><strong>기울기(gradient) 확인:</strong></p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.autograd.gradcheck</span> <span class="kn">import</span> <span class="n">gradcheck</span>
 
 <span class="n">moduleConv</span> <span class="o">=</span> <span class="n">ScipyConv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
 
@@ -494,7 +491,7 @@ <h2>Parametrized example<a class="headerlink" href="#parametrized-example" title
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Are the gradients correct:  True
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.304 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.310 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-numpy-extensions-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/ee55f2537c08fa13e041e479675a6c2c/numpy_extensions_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">numpy_extensions_tutorial.py</span></code></a></p>
@@ -548,9 +545,9 @@ <h2>Parametrized example<a class="headerlink" href="#parametrized-example" title
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">Creating Extensions Using numpy and scipy</a><ul>
-<li><a class="reference internal" href="#parameter-less-example">Parameter-less example</a></li>
-<li><a class="reference internal" href="#parametrized-example">Parametrized example</a></li>
+<li><a class="reference internal" href="#">numpy 와 scipy 를 이용한 확장(Extensions) 만들기</a><ul>
+<li><a class="reference internal" href="#parameter-less">매개 변수가 없는(Parameter-less) 예시</a></li>
+<li><a class="reference internal" href="#parameterized">매개 변수가 있는(Parameterized) 예시</a></li>
 </ul>
 </li>
 </ul>
@@ -568,11 +565,9 @@ <h2>Parametrized example<a class="headerlink" href="#parametrized-example" title
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/rpc_ddp_tutorial.html b/docs/advanced/rpc_ddp_tutorial.html
index 45809486a..fd163dcc8 100644
--- a/docs/advanced/rpc_ddp_tutorial.html
+++ b/docs/advanced/rpc_ddp_tutorial.html
@@ -173,7 +173,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -185,7 +185,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -411,7 +411,7 @@ <h1>Combining Distributed DataParallel with Distributed RPC Framework<a class="h
         <span class="c1"># Run the training loop on trainers.</span>
         <span class="n">futs</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="k">for</span> <span class="n">trainer_rank</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]:</span>
-            <span class="n">trainer_name</span> <span class="o">=</span> <span class="s2">&quot;trainer{}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trainer_rank</span><span class="p">)</span>
+            <span class="n">trainer_name</span> <span class="o">=</span> <span class="s2">&quot;trainer</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">trainer_rank</span><span class="p">)</span>
             <span class="n">fut</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">rpc_async</span><span class="p">(</span>
                     <span class="n">trainer_name</span><span class="p">,</span> <span class="n">_run_trainer</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">emb_rref</span><span class="p">,</span> <span class="n">rank</span><span class="p">))</span>
             <span class="n">futs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fut</span><span class="p">)</span>
@@ -425,7 +425,7 @@ <h1>Combining Distributed DataParallel with Distributed RPC Framework<a class="h
                 <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;gloo&quot;</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 
         <span class="c1"># Initialize RPC.</span>
-        <span class="n">trainer_name</span> <span class="o">=</span> <span class="s2">&quot;trainer{}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rank</span><span class="p">)</span>
+        <span class="n">trainer_name</span> <span class="o">=</span> <span class="s2">&quot;trainer</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">rank</span><span class="p">)</span>
         <span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
                 <span class="n">trainer_name</span><span class="p">,</span>
                 <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span>
@@ -449,7 +449,7 @@ <h1>Combining Distributed DataParallel with Distributed RPC Framework<a class="h
 <span class="k">if</span> <span class="vm">__name__</span><span class="o">==</span><span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
     <span class="c1"># 2 trainers, 1 parameter server, 1 master.</span>
     <span class="n">world_size</span> <span class="o">=</span> <span class="mi">4</span>
-    <span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">run_worker</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="p">),</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+    <span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">run_worker</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="p">),</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Before we discuss details of the Trainer, let’s introduce the <code class="docutils literal notranslate"><span class="pre">HybridModel</span></code> that
@@ -586,7 +586,7 @@ <h1>Combining Distributed DataParallel with Distributed RPC Framework<a class="h
 
                 <span class="c1"># Not necessary to zero grads as each iteration creates a different</span>
                 <span class="c1"># distributed autograd context which hosts different grads</span>
-        <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Training done for epoch {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training done for epoch </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">))</span>
 </pre></div>
 </div>
 <p>Source code for the entire example can be found <a class="reference external" href="https://github.com/pytorch/examples/tree/master/distributed/rpc/ddp_rpc">here</a>.</p>
@@ -656,11 +656,9 @@ <h1>Combining Distributed DataParallel with Distributed RPC Framework<a class="h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/sg_execution_times.html b/docs/advanced/sg_execution_times.html
index 75c9488df..ca9d51feb 100644
--- a/docs/advanced/sg_execution_times.html
+++ b/docs/advanced/sg_execution_times.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -297,12 +297,12 @@
               
   <div class="section" id="computation-times">
 <span id="sphx-glr-advanced-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>09:12.274</strong> total execution time for <strong>advanced</strong> files:</p>
+<p><strong>05:50.282</strong> total execution time for <strong>advanced</strong> files:</p>
 <ul class="simple">
-<li><p><strong>09:12.274</strong>: <a class="reference internal" href="static_quantization_tutorial.html#sphx-glr-advanced-static-quantization-tutorial-py"><span class="std std-ref">(beta) Static Quantization with Eager Mode in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">static_quantization_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="dynamic_quantization_tutorial.html#sphx-glr-advanced-dynamic-quantization-tutorial-py"><span class="std std-ref">(beta) Dynamic Quantization on an LSTM Word Language Model</span></a> (<code class="docutils literal notranslate"><span class="pre">dynamic_quantization_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="neural_style_tutorial.html#sphx-glr-advanced-neural-style-tutorial-py"><span class="std std-ref">Neural Transfer Using PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">neural_style_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="numpy_extensions_tutorial.html#sphx-glr-advanced-numpy-extensions-tutorial-py"><span class="std std-ref">Creating Extensions Using numpy and scipy</span></a> (<code class="docutils literal notranslate"><span class="pre">numpy_extensions_tutorial.py</span></code>)</p></li>
+<li><p><strong>05:04.081</strong>: <a class="reference internal" href="dynamic_quantization_tutorial.html#sphx-glr-advanced-dynamic-quantization-tutorial-py"><span class="std std-ref">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</span></a> (<code class="docutils literal notranslate"><span class="pre">dynamic_quantization_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:32.671</strong>: <a class="reference internal" href="neural_style_tutorial.html#sphx-glr-advanced-neural-style-tutorial-py"><span class="std std-ref">Neural Transfer Using PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">neural_style_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:13.221</strong>: <a class="reference internal" href="static_quantization_tutorial.html#sphx-glr-advanced-static-quantization-tutorial-py"><span class="std std-ref">(beta) Static Quantization with Eager Mode in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">static_quantization_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.310</strong>: <a class="reference internal" href="numpy_extensions_tutorial.html#sphx-glr-advanced-numpy-extensions-tutorial-py"><span class="std std-ref">numpy 와 scipy 를 이용한 확장(Extensions) 만들기</span></a> (<code class="docutils literal notranslate"><span class="pre">numpy_extensions_tutorial.py</span></code>)</p></li>
 <li><p><strong>00:00.000</strong>: <a class="reference internal" href="super_resolution_with_onnxruntime.html#sphx-glr-advanced-super-resolution-with-onnxruntime-py"><span class="std std-ref">(선택) PyTorch 모델을 ONNX으로 변환하고 ONNX 런타임에서 실행하기</span></a> (<code class="docutils literal notranslate"><span class="pre">super_resolution_with_onnxruntime.py</span></code>)</p></li>
 </ul>
 </div>
@@ -364,11 +364,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/static_quantization_tutorial.html b/docs/advanced/static_quantization_tutorial.html
index 60a86288e..da3539fa7 100644
--- a/docs/advanced/static_quantization_tutorial.html
+++ b/docs/advanced/static_quantization_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -323,8 +323,8 @@
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">DataLoader</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span>
 <span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">time</span>
@@ -358,7 +358,7 @@ <h2>1. Model architecture<a class="headerlink" href="#model-architecture" title=
 </ul>
 <p>Note: this code is taken from
 <a class="reference external" href="https://github.com/pytorch/vision/blob/master/torchvision/models/mobilenet.py">here</a>.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="k">import</span> <span class="n">QuantStub</span><span class="p">,</span> <span class="n">DeQuantStub</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="kn">import</span> <span class="n">QuantStub</span><span class="p">,</span> <span class="n">DeQuantStub</span>
 
 <span class="k">def</span> <span class="nf">_make_divisible</span><span class="p">(</span><span class="n">v</span><span class="p">,</span> <span class="n">divisor</span><span class="p">,</span> <span class="n">min_value</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;</span>
@@ -381,7 +381,7 @@ <h2>1. Model architecture<a class="headerlink" href="#model-architecture" title=
 
 
 <span class="k">class</span> <span class="nc">ConvBNReLU</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
         <span class="n">padding</span> <span class="o">=</span> <span class="p">(</span><span class="n">kernel_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">ConvBNReLU</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
             <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">groups</span><span class="o">=</span><span class="n">groups</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
@@ -392,7 +392,7 @@ <h2>1. Model architecture<a class="headerlink" href="#model-architecture" title=
 
 
 <span class="k">class</span> <span class="nc">InvertedResidual</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inp</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">expand_ratio</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inp</span><span class="p">,</span> <span class="n">oup</span><span class="p">,</span> <span class="n">stride</span><span class="p">,</span> <span class="n">expand_ratio</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">InvertedResidual</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">stride</span>
         <span class="k">assert</span> <span class="n">stride</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
@@ -423,7 +423,7 @@ <h2>1. Model architecture<a class="headerlink" href="#model-architecture" title=
 
 
 <span class="k">class</span> <span class="nc">MobileNetV2</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">width_mult</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">inverted_residual_setting</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">round_nearest</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">width_mult</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">inverted_residual_setting</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">round_nearest</span><span class="o">=</span><span class="mi">8</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        MobileNet V2 main class</span>
 
@@ -521,7 +521,7 @@ <h2>2. Helper functions<a class="headerlink" href="#helper-functions" title="Per
 <a class="reference external" href="https://github.com/pytorch/examples/blob/master/imagenet/main.py">here</a>.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">AverageMeter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Computes and stores the average and current value&quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;:f&#39;</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;:f&#39;</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">=</span> <span class="n">fmt</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
@@ -538,7 +538,7 @@ <h2>2. Helper functions<a class="headerlink" href="#helper-functions" title="Per
         <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">n</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">avg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span>
 
-    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">fmtstr</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{name}</span><span class="s1"> {val&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="s1">&#39;} ({avg&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="s1">&#39;})&#39;</span>
         <span class="k">return</span> <span class="n">fmtstr</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
 
@@ -624,12 +624,12 @@ <h3>ImageNet Data<a class="headerlink" href="#imagenet-data" title="Permalink to
 <a class="reference external" href="https://pytorch.org/docs/stable/torchvision/datasets.html#imagenet">here</a>. For example,
 to download the training set and apply some standard transformations to it, you could use:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="kn">as</span> <span class="nn">transforms</span>
+<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
 
 <span class="n">imagenet_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">ImageNet</span><span class="p">(</span>
     <span class="s1">&#39;~/.data/imagenet&#39;</span><span class="p">,</span>
     <span class="n">split</span><span class="o">=</span><span class="s1">&#39;train&#39;</span><span class="p">,</span>
-    <span class="n">download</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
         <span class="n">transforms</span><span class="o">.</span><span class="n">RandomResizedCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
         <span class="n">transforms</span><span class="o">.</span><span class="n">RandomHorizontalFlip</span><span class="p">(),</span>
@@ -710,35 +710,6 @@ <h3>ImageNet Data<a class="headerlink" href="#imagenet-data" title="Permalink to
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1"> Inverted Residual Block: After fusion</span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">,</span><span class="n">float_model</span><span class="o">.</span><span class="n">features</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">conv</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Inverted Residual Block: Before fusion
-
- Sequential(
-  (0): ConvBNReLU(
-    (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)
-    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-    (2): ReLU()
-  )
-  (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)
-  (2): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-)
-
- Inverted Residual Block: After fusion
-
- Sequential(
-  (0): ConvBNReLU(
-    (0): ConvReLU2d(
-      (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32)
-      (1): ReLU()
-    )
-    (1): Identity()
-    (2): Identity()
-  )
-  (1): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1))
-  (2): Identity()
-)
-</pre></div>
-</div>
 <p>Finally to get a “baseline” accuracy, let’s see the accuracy of our un-quantized model
 with fused modules</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">num_eval_batches</span> <span class="o">=</span> <span class="mi">10</span>
@@ -751,12 +722,6 @@ <h3>ImageNet Data<a class="headerlink" href="#imagenet-data" title="Permalink to
 <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">float_model</span><span class="p">),</span> <span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">scripted_float_model_file</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size of baseline model
-Size (MB): 13.998602
-..........Evaluation accuracy on 300 images, 78.00
-</pre></div>
-</div>
 <p>We see 78% accuracy on 300 images, a solid baseline for ImageNet,
 especially considering our model is just 14.0 MB.</p>
 <p>This will be our baseline to compare to. Next, let’s try different quantization methods</p>
@@ -807,51 +772,6 @@ <h2>4. Post-training static quantization<a class="headerlink" href="#post-traini
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Evaluation accuracy on </span><span class="si">%d</span><span class="s1"> images, </span><span class="si">%2.2f</span><span class="s1">&#39;</span><span class="o">%</span><span class="p">(</span><span class="n">num_eval_batches</span> <span class="o">*</span> <span class="n">eval_batch_size</span><span class="p">,</span> <span class="n">top1</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>QConfig(activation=functools.partial(&lt;class &#39;torch.quantization.observer.MinMaxObserver&#39;&gt;, reduce_range=True), weight=functools.partial(&lt;class &#39;torch.quantization.observer.MinMaxObserver&#39;&gt;, dtype=torch.qint8, qscheme=torch.per_tensor_symmetric))
-Post Training Quantization Prepare: Inserting Observers
-
- Inverted Residual Block:After observer insertion
-
- Sequential(
-  (0): ConvBNReLU(
-    (0): ConvReLU2d(
-      (0): Conv2d(
-        32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32
-        (activation_post_process): MinMaxObserver(min_val=tensor([]), max_val=tensor([]))
-      )
-      (1): ReLU(
-        (activation_post_process): MinMaxObserver(min_val=tensor([]), max_val=tensor([]))
-      )
-    )
-    (1): Identity()
-    (2): Identity()
-  )
-  (1): Conv2d(
-    32, 16, kernel_size=(1, 1), stride=(1, 1)
-    (activation_post_process): MinMaxObserver(min_val=tensor([]), max_val=tensor([]))
-  )
-  (2): Identity()
-)
-..........Post Training Quantization: Calibration done
-Post Training Quantization: Convert done
-
- Inverted Residual Block: After fusion and quantization, note fused modules:
-
- Sequential(
-  (0): ConvBNReLU(
-    (0): QuantizedConvReLU2d(32, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.1875331997871399, zero_point=0, padding=(1, 1), groups=32)
-    (1): Identity()
-    (2): Identity()
-  )
-  (1): QuantizedConv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), scale=0.23388975858688354, zero_point=77)
-  (2): Identity()
-)
-Size of model after quantization
-Size (MB): 3.629311
-..........Evaluation accuracy on 300 images, 55.33
-</pre></div>
-</div>
 <p>For this quantized model, we see a significantly lower accuracy of just ~62% on these same 300
 images. Nevertheless, we did reduce the size of our model down to just under 3.6 MB, almost a 4x
 decrease.</p>
@@ -877,11 +797,6 @@ <h2>4. Post-training static quantization<a class="headerlink" href="#post-traini
 <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">per_channel_quantized_model</span><span class="p">),</span> <span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">scripted_quantized_model_file</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>QConfig(activation=functools.partial(&lt;class &#39;torch.quantization.observer.HistogramObserver&#39;&gt;, reduce_range=True), weight=functools.partial(&lt;class &#39;torch.quantization.observer.PerChannelMinMaxObserver&#39;&gt;, dtype=torch.qint8, qscheme=torch.per_channel_symmetric))
-....................Evaluation accuracy on 300 images, 78.67
-</pre></div>
-</div>
 <p>Changing just this quantization configuration method resulted in an increase
 of the accuracy to over 76%! Still, this is 1-2% worse than the baseline of 78% achieved above.
 So lets try quantization aware training.</p>
@@ -949,41 +864,6 @@ <h2>5. Quantization-aware training<a class="headerlink" href="#quantization-awar
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Inverted Residual Block: After preparation for QAT, note fake-quantization modules </span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span><span class="n">qat_model</span><span class="o">.</span><span class="n">features</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">conv</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Inverted Residual Block: After preparation for QAT, note fake-quantization modules
- Sequential(
-  (0): ConvBNReLU(
-    (0): ConvBnReLU2d(
-      32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False
-      (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-      (activation_post_process): FakeQuantize(
-        fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8),            scale=tensor([1.]), zero_point=tensor([0])
-        (activation_post_process): MovingAverageMinMaxObserver(min_val=tensor([]), max_val=tensor([]))
-      )
-      (weight_fake_quant): FakeQuantize(
-        fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8),            scale=tensor([1.]), zero_point=tensor([0])
-        (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))
-      )
-    )
-    (1): Identity()
-    (2): Identity()
-  )
-  (1): ConvBn2d(
-    32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False
-    (bn): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
-    (activation_post_process): FakeQuantize(
-      fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8),            scale=tensor([1.]), zero_point=tensor([0])
-      (activation_post_process): MovingAverageMinMaxObserver(min_val=tensor([]), max_val=tensor([]))
-    )
-    (weight_fake_quant): FakeQuantize(
-      fake_quant_enabled=tensor([1], dtype=torch.uint8), observer_enabled=tensor([1], dtype=torch.uint8),            scale=tensor([1.]), zero_point=tensor([0])
-      (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))
-    )
-  )
-  (2): Identity()
-)
-</pre></div>
-</div>
 <p>Training a quantized model with high accuracy requires accurate modeling of numerics at
 inference. For quantization aware training, therefore, we modify the training loop by:</p>
 <ul class="simple">
@@ -1010,33 +890,6 @@ <h2>5. Quantization-aware training<a class="headerlink" href="#quantization-awar
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">%d</span><span class="s1"> :Evaluation accuracy on </span><span class="si">%d</span><span class="s1"> images, </span><span class="si">%2.2f</span><span class="s1">&#39;</span><span class="o">%</span><span class="p">(</span><span class="n">nepoch</span><span class="p">,</span> <span class="n">num_eval_batches</span> <span class="o">*</span> <span class="n">eval_batch_size</span><span class="p">,</span> <span class="n">top1</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>....................Loss tensor(1.9970, grad_fn=&lt;DivBackward0&gt;)
-Training: * Acc@1 55.667 Acc@5 78.500
-..........Epoch 0 :Evaluation accuracy on 300 images, 74.67
-....................Loss tensor(2.0479, grad_fn=&lt;DivBackward0&gt;)
-Training: * Acc@1 54.000 Acc@5 77.167
-..........Epoch 1 :Evaluation accuracy on 300 images, 77.00
-....................Loss tensor(2.0638, grad_fn=&lt;DivBackward0&gt;)
-Training: * Acc@1 54.833 Acc@5 77.333
-..........Epoch 2 :Evaluation accuracy on 300 images, 75.67
-....................Loss tensor(1.9934, grad_fn=&lt;DivBackward0&gt;)
-Training: * Acc@1 54.000 Acc@5 78.833
-..........Epoch 3 :Evaluation accuracy on 300 images, 76.67
-....................Loss tensor(1.8909, grad_fn=&lt;DivBackward0&gt;)
-Training: * Acc@1 56.167 Acc@5 81.167
-..........Epoch 4 :Evaluation accuracy on 300 images, 77.00
-....................Loss tensor(1.8382, grad_fn=&lt;DivBackward0&gt;)
-Training: * Acc@1 58.167 Acc@5 80.833
-..........Epoch 5 :Evaluation accuracy on 300 images, 76.33
-....................Loss tensor(1.9286, grad_fn=&lt;DivBackward0&gt;)
-Training: * Acc@1 54.500 Acc@5 80.000
-..........Epoch 6 :Evaluation accuracy on 300 images, 74.67
-....................Loss tensor(1.9360, grad_fn=&lt;DivBackward0&gt;)
-Training: * Acc@1 58.000 Acc@5 79.833
-..........Epoch 7 :Evaluation accuracy on 300 images, 74.00
-</pre></div>
-</div>
 <p>Here, we just perform quantization-aware training for a small number of epochs. Nevertheless,
 quantization-aware training yields an accuracy of over 71% on the entire imagenet dataset,
 which is close to the floating point accuracy of 71.9%.</p>
@@ -1077,11 +930,6 @@ <h3>Speedup from quantization<a class="headerlink" href="#speedup-from-quantizat
 <span class="n">run_benchmark</span><span class="p">(</span><span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">scripted_quantized_model_file</span><span class="p">,</span> <span class="n">data_loader_test</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Elapsed time:  14 ms
-Elapsed time:   6 ms
-</pre></div>
-</div>
 <p>Running this locally on a MacBook pro yielded 61 ms for the regular model, and
 just 20 ms for the quantized model, illustrating the typical 2-4x speedup
 we see for quantized models compared to floating point ones.</p>
@@ -1094,7 +942,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 them in PyTorch.</p>
 <p>Thanks for reading! As always, we welcome any feedback, so please create an issue
 <a class="reference external" href="https://github.com/pytorch/pytorch/issues">here</a> if you have any.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 9 minutes  12.274 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-advanced-static-quantization-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/519646ecef5577146f5053a46b9dfa60/static_quantization_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">static_quantization_tutorial.py</span></code></a></p>
@@ -1187,11 +1035,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/super_resolution_with_onnxruntime.html b/docs/advanced/super_resolution_with_onnxruntime.html
index d0ecebcb9..d67493b6c 100644
--- a/docs/advanced/super_resolution_with_onnxruntime.html
+++ b/docs/advanced/super_resolution_with_onnxruntime.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -319,7 +319,7 @@
 <span class="kn">import</span> <span class="nn">io</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
-<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
 <span class="kn">import</span> <span class="nn">torch.utils.model_zoo</span> <span class="k">as</span> <span class="nn">model_zoo</span>
 <span class="kn">import</span> <span class="nn">torch.onnx</span>
 </pre></div>
@@ -340,7 +340,7 @@
 
 
 <span class="k">class</span> <span class="nc">SuperResolutionNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">upscale_factor</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">upscale_factor</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">SuperResolutionNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="n">inplace</span><span class="p">)</span>
@@ -479,7 +479,7 @@ <h2>ONNX 런타임에서 이미지를 입력값으로 모델을 실행하기<a c
 Cr은 빨강색에서 밝기를 뺀 색차 성분을 나타냅니다.
 사람의 눈은 Y 성분에 더 민감하게 반응하기 때문에 저희에게는 현재 이 성분이 중요하고, 이 Y 성분을 변환할 것입니다.
 Y 성분을 뽑아낸 뒤에, 추출한 Y 성분을 모델의 입력값이 될 텐서로 변환합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 <span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
 
 <span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">&quot;./_static/img/cat.jpg&quot;</span><span class="p">)</span>
@@ -616,11 +616,9 @@ <h2>ONNX 런타임에서 이미지를 입력값으로 모델을 실행하기<a c
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/torch-script-parallelism.html b/docs/advanced/torch-script-parallelism.html
index a036f1b6e..df8728637 100644
--- a/docs/advanced/torch-script-parallelism.html
+++ b/docs/advanced/torch-script-parallelism.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -318,7 +318,7 @@ <h2>Basic Syntax<a class="headerlink" href="#basic-syntax" title="Permalink to t
 <span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">neg</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
-<span class="nd">@torch.jit.script</span>
+<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
 <span class="k">def</span> <span class="nf">example</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
     <span class="c1"># Call `foo` using parallelism:</span>
     <span class="c1"># First, we &quot;fork&quot; off a task. This task will run `foo` with argument `x`</span>
@@ -336,7 +336,7 @@ <h2>Basic Syntax<a class="headerlink" href="#basic-syntax" title="Permalink to t
 
     <span class="k">return</span> <span class="n">x_normal</span><span class="p">,</span> <span class="n">x_parallel</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">example</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span> <span class="c1"># (-1., -1.)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">example</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">)))</span> <span class="c1"># (-1., -1.)</span>
 </pre></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">fork()</span></code> takes the callable <code class="docutils literal notranslate"><span class="pre">fn</span></code> and arguments to that callable <code class="docutils literal notranslate"><span class="pre">args</span></code>
@@ -356,7 +356,7 @@ <h2>Basic Syntax<a class="headerlink" href="#basic-syntax" title="Permalink to t
 <span class="k">def</span> <span class="nf">foo</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">neg</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
-<span class="nd">@torch.jit.script</span>
+<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
 <span class="k">def</span> <span class="nf">example</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
     <span class="n">futures</span> <span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">Future</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[]</span>
     <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">100</span><span class="p">):</span>
@@ -368,7 +368,7 @@ <h2>Basic Syntax<a class="headerlink" href="#basic-syntax" title="Permalink to t
 
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">results</span><span class="p">))</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">example</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">([])))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">example</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">([])))</span>
 </pre></div>
 </div>
 <div class="admonition note">
@@ -447,7 +447,7 @@ <h2>Applied Example: Ensemble of Bidirectional LSTMs<a class="headerlink" href="
 <span class="c1"># Let&#39;s see how fast it runs!</span>
 <span class="n">s</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
 <span class="n">ens</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s1">&#39;Inference took&#39;</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">s</span><span class="p">,</span> <span class="s1">&#39; seconds&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Inference took&#39;</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">s</span><span class="p">,</span> <span class="s1">&#39; seconds&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>On my machine, this network runs in <code class="docutils literal notranslate"><span class="pre">2.05</span></code> seconds. We can do a lot better!</p>
@@ -488,6 +488,11 @@ <h2>Aside: Visualizing Parallelism<a class="headerlink" href="#aside-visualizing
 have for visualizing performance. One important tool is the <a class="reference external" href="https://pytorch.org/docs/stable/autograd.html#profiler">PyTorch profiler</a>.</p>
 <p>Let’s use the profiler along with the Chrome trace export functionality to
 visualize the performance of our parallelized model:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">()</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
+    <span class="n">ens</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="n">prof</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="s1">&#39;parallel.json&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
 <p>This snippet of code will write out a file named <code class="docutils literal notranslate"><span class="pre">parallel.json</span></code>. If you
 navigate Google Chrome to <code class="docutils literal notranslate"><span class="pre">chrome://tracing</span></code>, click the <code class="docutils literal notranslate"><span class="pre">Load</span></code> button, and
 load in that JSON file, you should see a timeline like the following:</p>
@@ -618,11 +623,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/torch_script_custom_classes.html b/docs/advanced/torch_script_custom_classes.html
index d6c2b3c66..e11cefdaa 100644
--- a/docs/advanced/torch_script_custom_classes.html
+++ b/docs/advanced/torch_script_custom_classes.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -322,7 +322,7 @@ <h2>Implementing and Binding the Class in C++<a class="headerlink" href="#implem
 <span class="cp">#include</span> <span class="cpf">&lt;vector&gt;</span><span class="cp"></span>
 
 <span class="k">template</span> <span class="o">&lt;</span><span class="k">class</span> <span class="nc">T</span><span class="o">&gt;</span>
-<span class="k">struct</span> <span class="nl">MyStackClass</span> <span class="p">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">CustomClassHolder</span> <span class="p">{</span>
+<span class="k">struct</span> <span class="nc">MyStackClass</span> <span class="o">:</span> <span class="n">torch</span><span class="o">::</span><span class="n">CustomClassHolder</span> <span class="p">{</span>
   <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">T</span><span class="o">&gt;</span> <span class="n">stack_</span><span class="p">;</span>
   <span class="n">MyStackClass</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">T</span><span class="o">&gt;</span> <span class="n">init</span><span class="p">)</span> <span class="o">:</span> <span class="n">stack_</span><span class="p">(</span><span class="n">init</span><span class="p">.</span><span class="n">begin</span><span class="p">(),</span> <span class="n">init</span><span class="p">.</span><span class="n">end</span><span class="p">())</span> <span class="p">{}</span>
 
@@ -494,7 +494,7 @@ <h2>Using the C++ Class from Python and TorchScript<a class="headerlink" href="#
 <span class="c1"># TorchScript</span>
 <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s2">&quot;build/libcustom_class.so&quot;</span><span class="p">)</span>
 <span class="c1"># You can query the loaded libraries like this:</span>
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">loaded_libraries</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">loaded_libraries</span><span class="p">)</span>
 <span class="c1"># prints {&#39;/custom_class_project/build/libcustom_class.so&#39;}</span>
 
 <span class="c1"># We can find and instantiate our custom C++ class in python by using the</span>
@@ -521,7 +521,7 @@ <h2>Using the C++ Class from Python and TorchScript<a class="headerlink" href="#
 <span class="n">MyStackClass</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span>
 
 
-<span class="nd">@torch.jit.script</span>
+<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
 <span class="k">def</span> <span class="nf">do_stacks</span><span class="p">(</span><span class="n">s</span><span class="p">:</span> <span class="n">MyStackClass</span><span class="p">):</span>  <span class="c1"># We can pass a custom class instance</span>
     <span class="c1"># We can instantiate the class</span>
     <span class="n">s2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">my_classes</span><span class="o">.</span><span class="n">MyStackClass</span><span class="p">([</span><span class="s2">&quot;hi&quot;</span><span class="p">,</span> <span class="s2">&quot;mom&quot;</span><span class="p">])</span>
@@ -558,7 +558,7 @@ <h2>Saving, Loading, and Running TorchScript Code Using Custom Classes<a class="
 
 
 <span class="n">scripted_foo</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">Foo</span><span class="p">())</span>
-<span class="k">print</span><span class="p">(</span><span class="n">scripted_foo</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">scripted_foo</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 
 <span class="n">scripted_foo</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;foo.pt&#39;</span><span class="p">)</span>
 </pre></div>
@@ -591,18 +591,18 @@ <h2>Saving, Loading, and Running TorchScript Code Using Custom Classes<a class="
 <span class="cp">#include</span> <span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
 
 <span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
-  <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="n">module</span><span class="p">;</span>
+  <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
   <span class="k">try</span> <span class="p">{</span>
     <span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
-    <span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;foo.pt&quot;</span><span class="p">);</span>
+    <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;foo.pt&quot;</span><span class="p">);</span>
   <span class="p">}</span>
   <span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
     <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;error loading the model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-    <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+    <span class="k">return</span> <span class="mi">-1</span><span class="p">;</span>
   <span class="p">}</span>
 
   <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">c10</span><span class="o">::</span><span class="n">IValue</span><span class="o">&gt;</span> <span class="n">inputs</span> <span class="o">=</span> <span class="p">{</span><span class="s">&quot;foobarbaz&quot;</span><span class="p">};</span>
-  <span class="k">auto</span> <span class="n">output</span> <span class="o">=</span> <span class="n">module</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">).</span><span class="n">toString</span><span class="p">();</span>
+  <span class="k">auto</span> <span class="n">output</span> <span class="o">=</span> <span class="k">module</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">).</span><span class="n">toString</span><span class="p">();</span>
   <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="n">output</span><span class="o">-&gt;</span><span class="n">string</span><span class="p">()</span> <span class="o">&lt;&lt;</span> <span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
 <span class="p">}</span>
 </pre></div>
@@ -722,7 +722,7 @@ <h2>Defining Serialization/Deserialization Methods for Custom C++ Classes<a clas
 <span class="n">scripted_foo</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">&#39;foo.pt&#39;</span><span class="p">)</span>
 <span class="n">loaded</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">&#39;foo.pt&#39;</span><span class="p">)</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">loaded</span><span class="o">.</span><span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">())</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">loaded</span><span class="o">.</span><span class="n">stack</span><span class="o">.</span><span class="n">pop</span><span class="p">())</span>
 </pre></div>
 </div>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>$ python export_attr.py
@@ -924,11 +924,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/advanced/torch_script_custom_ops.html b/docs/advanced/torch_script_custom_ops.html
index d6f3a1ab0..623f964fe 100644
--- a/docs/advanced/torch_script_custom_ops.html
+++ b/docs/advanced/torch_script_custom_ops.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -564,7 +564,7 @@ <h3>Building with CMake<a class="headerlink" href="#building-with-cmake" title="
 Python:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s2">&quot;build/libwarp_perspective.so&quot;</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>If all goes well, this should print something like:</p>
@@ -596,7 +596,7 @@ <h2>Using the TorchScript Custom Operator in Python<a class="headerlink" href="#
 tensors:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s2">&quot;build/libwarp_perspective.so&quot;</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span>
 </pre></div>
 </div>
 <p>producing:</p>
@@ -646,7 +646,7 @@ <h3>Using the Custom Operator with Tracing<a class="headerlink" href="#using-the
 TorchScript compiler can further analyze, optimize and serialize:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">)]</span>
 <span class="n">trace</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">compute</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">trace</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">trace</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Producing:</p>
@@ -670,7 +670,7 @@ <h3>Using the Custom Operator with Tracing<a class="headerlink" href="#using-the
 <p>and then trace it as before:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">)]</span>
 <span class="n">trace</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">compute</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">trace</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">trace</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Producing:</p>
@@ -717,7 +717,7 @@ <h3>Using the Custom Operator with Script<a class="headerlink" href="#using-the-
 </div>
 <p>To convert this function from vanilla PyTorch to TorchScript, we annotate it
 with <code class="docutils literal notranslate"><span class="pre">&#64;torch.jit.script</span></code>:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch.jit.script</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
 <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
   <span class="k">if</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">42</span><span class="p">):</span>
       <span class="n">z</span> <span class="o">=</span> <span class="mi">5</span>
@@ -757,7 +757,7 @@ <h3>Using the Custom Operator with Script<a class="headerlink" href="#using-the-
 function inside of our script code:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s2">&quot;libwarp_perspective.so&quot;</span><span class="p">)</span>
 
-<span class="nd">@torch.jit.script</span>
+<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
 <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
   <span class="k">if</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">42</span><span class="p">):</span>
       <span class="n">z</span> <span class="o">=</span> <span class="mi">5</span>
@@ -842,7 +842,7 @@ <h2>Using the TorchScript Custom Operator in C++<a class="headerlink" href="#usi
 <p>Building on the C++ execution tutorial linked above, let’s start with a minimal
 C++ application in one file, <code class="docutils literal notranslate"><span class="pre">main.cpp</span></code> in a different folder from our
 custom operator, that loads and executes a serialized TorchScript model:</p>
-<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf">&lt;torch/script.h&gt; // One-stop header.</span><span class="cp"></span>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf">&lt;torch/script.h&gt;</span><span class="c1"> // One-stop header.</span><span class="cp"></span>
 
 <span class="cp">#include</span> <span class="cpf">&lt;iostream&gt;</span><span class="cp"></span>
 <span class="cp">#include</span> <span class="cpf">&lt;memory&gt;</span><span class="cp"></span>
@@ -851,17 +851,17 @@ <h2>Using the TorchScript Custom Operator in C++<a class="headerlink" href="#usi
 <span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
   <span class="k">if</span> <span class="p">(</span><span class="n">argc</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">)</span> <span class="p">{</span>
     <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;usage: example-app &lt;path-to-exported-script-module&gt;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-    <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+    <span class="k">return</span> <span class="mi">-1</span><span class="p">;</span>
   <span class="p">}</span>
 
   <span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
-  <span class="n">std</span><span class="o">::</span><span class="n">shared_ptr</span><span class="o">&lt;</span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">script</span><span class="o">::</span><span class="n">Module</span><span class="o">&gt;</span> <span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+  <span class="n">std</span><span class="o">::</span><span class="n">shared_ptr</span><span class="o">&lt;</span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">script</span><span class="o">::</span><span class="n">Module</span><span class="o">&gt;</span> <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
 
   <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">IValue</span><span class="o">&gt;</span> <span class="n">inputs</span><span class="p">;</span>
   <span class="n">inputs</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">}));</span>
   <span class="n">inputs</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">}));</span>
 
-  <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">output</span> <span class="o">=</span> <span class="n">module</span><span class="o">-&gt;</span><span class="n">forward</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">inputs</span><span class="p">)).</span><span class="n">toTensor</span><span class="p">();</span>
+  <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">output</span> <span class="o">=</span> <span class="k">module</span><span class="o">-&gt;</span><span class="n">forward</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">inputs</span><span class="p">)).</span><span class="n">toTensor</span><span class="p">();</span>
 
   <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="n">output</span> <span class="o">&lt;&lt;</span> <span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
 <span class="p">}</span>
@@ -925,7 +925,7 @@ <h2>Using the TorchScript Custom Operator in C++<a class="headerlink" href="#usi
 operator:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s2">&quot;libwarp_perspective.so&quot;</span><span class="p">)</span>
 
-<span class="nd">@torch.jit.script</span>
+<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
 <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
   <span class="k">if</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">42</span><span class="p">):</span>
       <span class="n">z</span> <span class="o">=</span> <span class="mi">5</span>
@@ -1127,11 +1127,11 @@ <h3>Building with JIT compilation<a class="headerlink" href="#building-with-jit-
     <span class="n">name</span><span class="o">=</span><span class="s2">&quot;warp_perspective&quot;</span><span class="p">,</span>
     <span class="n">sources</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;op.cpp&quot;</span><span class="p">],</span>
     <span class="n">extra_ldflags</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-lopencv_core&quot;</span><span class="p">,</span> <span class="s2">&quot;-lopencv_imgproc&quot;</span><span class="p">],</span>
-    <span class="n">is_python_module</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
-    <span class="n">verbose</span><span class="o">=</span><span class="bp">True</span>
+    <span class="n">is_python_module</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span>
 <span class="p">)</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>This should approximately print:</p>
@@ -1175,11 +1175,11 @@ <h3>Building with JIT compilation<a class="headerlink" href="#building-with-jit-
     <span class="n">name</span><span class="o">=</span><span class="s2">&quot;warp_perspective&quot;</span><span class="p">,</span>
     <span class="n">cpp_sources</span><span class="o">=</span><span class="n">op_source</span><span class="p">,</span>
     <span class="n">extra_ldflags</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-lopencv_core&quot;</span><span class="p">,</span> <span class="s2">&quot;-lopencv_imgproc&quot;</span><span class="p">],</span>
-    <span class="n">is_python_module</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
-    <span class="n">verbose</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">is_python_module</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Naturally, it is best practice to only use
@@ -1212,7 +1212,7 @@ <h3>Building with Setuptools<a class="headerlink" href="#building-with-setuptool
             <span class="n">libraries</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;opencv_core&quot;</span><span class="p">,</span> <span class="s2">&quot;opencv_imgproc&quot;</span><span class="p">],</span>
         <span class="p">)</span>
     <span class="p">],</span>
-    <span class="n">cmdclass</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;build_ext&quot;</span><span class="p">:</span> <span class="n">BuildExtension</span><span class="o">.</span><span class="n">with_options</span><span class="p">(</span><span class="n">no_python_abi_suffix</span><span class="o">=</span><span class="bp">True</span><span class="p">)},</span>
+    <span class="n">cmdclass</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;build_ext&quot;</span><span class="p">:</span> <span class="n">BuildExtension</span><span class="o">.</span><span class="n">with_options</span><span class="p">(</span><span class="n">no_python_abi_suffix</span><span class="o">=</span><span class="kc">True</span><span class="p">)},</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -1259,7 +1259,7 @@ <h3>Building with Setuptools<a class="headerlink" href="#building-with-setuptool
 visible to TorchScript:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">load_library</span><span class="p">(</span><span class="s2">&quot;warp_perspective.so&quot;</span><span class="p">)</span>
-<span class="gp">&gt;&gt;&gt; </span><span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">custom</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">custom</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
 <span class="go">&lt;built-in method custom::warp_perspective of PyCapsule object at 0x7ff51c5b7bd0&gt;</span>
 </pre></div>
 </div>
@@ -1354,11 +1354,9 @@ <h3>Building with Setuptools<a class="headerlink" href="#building-with-setuptool
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/Intro_to_TorchScript_tutorial.html b/docs/beginner/Intro_to_TorchScript_tutorial.html
index b382a790f..e8131eb95 100644
--- a/docs/beginner/Intro_to_TorchScript_tutorial.html
+++ b/docs/beginner/Intro_to_TorchScript_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -349,7 +349,7 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
 </ol>
 <p>작은 예제로 시작해 보겟습니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyCell</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
@@ -363,11 +363,11 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[0.7637, 0.5457, 0.5066, 0.8010],
-        [0.7981, 0.7785, 0.7362, 0.8155],
-        [0.8635, 0.5365, 0.7909, 0.8748]]), tensor([[0.7637, 0.5457, 0.5066, 0.8010],
-        [0.7981, 0.7785, 0.7362, 0.8155],
-        [0.8635, 0.5365, 0.7909, 0.8748]]))
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[0.8788, 0.7738, 0.7472, 0.2609],
+        [0.7592, 0.6617, 0.4432, 0.7909],
+        [0.7385, 0.7218, 0.7794, 0.6404]]), tensor([[0.8788, 0.7738, 0.7472, 0.2609],
+        [0.7592, 0.6617, 0.4432, 0.7909],
+        [0.7385, 0.7218, 0.7794, 0.6404]]))
 </pre></div>
 </div>
 <p>우리는 다음 작업을 수행했습니다.:</p>
@@ -386,7 +386,7 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
 함수를 호출합니다.</p>
 <p>좀 더 흥미로운 것을 해봅시다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyCell</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
 
@@ -403,11 +403,11 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>MyCell(
   (linear): Linear(in_features=4, out_features=4, bias=True)
 )
-(tensor([[0.7233, 0.1539, 0.1757, 0.5500],
-        [0.5841, 0.2615, 0.2206, 0.6759],
-        [0.6854, 0.3774, 0.5131, 0.7681]], grad_fn=&lt;TanhBackward&gt;), tensor([[0.7233, 0.1539, 0.1757, 0.5500],
-        [0.5841, 0.2615, 0.2206, 0.6759],
-        [0.6854, 0.3774, 0.5131, 0.7681]], grad_fn=&lt;TanhBackward&gt;))
+(tensor([[ 0.5888,  0.3810,  0.3561,  0.3629],
+        [-0.3124,  0.2061, -0.2721,  0.4088],
+        [-0.2530, -0.6400, -0.1479, -0.2351]], grad_fn=&lt;TanhBackward&gt;), tensor([[ 0.5888,  0.3810,  0.3561,  0.3629],
+        [-0.3124,  0.2061, -0.2721,  0.4088],
+        [-0.2530, -0.6400, -0.1479, -0.2351]], grad_fn=&lt;TanhBackward&gt;))
 </pre></div>
 </div>
 <p>모듈 <code class="docutils literal notranslate"><span class="pre">MyCell</span></code> 을 재정의했지만, 이번에는 <code class="docutils literal notranslate"><span class="pre">self.linear</span></code> 속성을 추가하고
@@ -434,7 +434,7 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
             <span class="k">return</span> <span class="o">-</span><span class="n">x</span>
 
 <span class="k">class</span> <span class="nc">MyCell</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dg</span> <span class="o">=</span> <span class="n">MyDecisionGate</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
@@ -453,11 +453,11 @@ <h2>PyTorch 모델 작성의 기초<a class="headerlink" href="#pytorch" title="
   (dg): MyDecisionGate()
   (linear): Linear(in_features=4, out_features=4, bias=True)
 )
-(tensor([[0.7652, 0.3847, 0.5779, 0.4876],
-        [0.5550, 0.7848, 0.6686, 0.7434],
-        [0.6201, 0.6122, 0.8322, 0.7050]], grad_fn=&lt;TanhBackward&gt;), tensor([[0.7652, 0.3847, 0.5779, 0.4876],
-        [0.5550, 0.7848, 0.6686, 0.7434],
-        [0.6201, 0.6122, 0.8322, 0.7050]], grad_fn=&lt;TanhBackward&gt;))
+(tensor([[ 0.6727,  0.3587,  0.8109,  0.3758],
+        [ 0.0016,  0.1625,  0.4243,  0.8317],
+        [ 0.1875, -0.2585,  0.8365,  0.4886]], grad_fn=&lt;TanhBackward&gt;), tensor([[ 0.6727,  0.3587,  0.8109,  0.3758],
+        [ 0.0016,  0.1625,  0.4243,  0.8317],
+        [ 0.1875, -0.2585,  0.8365,  0.4886]], grad_fn=&lt;TanhBackward&gt;))
 </pre></div>
 </div>
 <p>MyCell 클래스를 다시 정의했지만, 여기선 <code class="docutils literal notranslate"><span class="pre">MyDecisionGate</span></code> 를 정의했습니다.
@@ -482,7 +482,7 @@ <h2>TorchScript의 기초<a class="headerlink" href="#id3" title="Permalink to t
 <div class="section" id="module">
 <h3><code class="docutils literal notranslate"><span class="pre">Module</span></code> 추적<a class="headerlink" href="#module" title="Permalink to this headline">¶</a></h3>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyCell</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
 
@@ -521,9 +521,9 @@ <h3><code class="docutils literal notranslate"><span class="pre">Module</span></
       %h : Float(3:4, 4:1)):
   %19 : __torch__.torch.nn.modules.linear.Linear = prim::GetAttr[name=&quot;linear&quot;](%self.1)
   %21 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%19, %input)
-  %12 : int = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/Intro_to_TorchScript_tutorial.py:184:0
-  %13 : Float(3:4, 4:1) = aten::add(%21, %h, %12) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/Intro_to_TorchScript_tutorial.py:184:0
-  %14 : Float(3:4, 4:1) = aten::tanh(%13) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/Intro_to_TorchScript_tutorial.py:184:0
+  %12 : int = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/Intro_to_TorchScript_tutorial.py:184:0
+  %13 : Float(3:4, 4:1) = aten::add(%21, %h, %12) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/Intro_to_TorchScript_tutorial.py:184:0
+  %14 : Float(3:4, 4:1) = aten::tanh(%13) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/Intro_to_TorchScript_tutorial.py:184:0
   %15 : (Float(3:4, 4:1), Float(3:4, 4:1)) = prim::TupleConstruct(%14, %14)
   return (%15)
 </pre></div>
@@ -561,17 +561,17 @@ <h3><code class="docutils literal notranslate"><span class="pre">Module</span></
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[ 0.2711,  0.3308,  0.7184,  0.8275],
-        [ 0.7674, -0.5661,  0.7969,  0.6040],
-        [ 0.7650, -0.1235,  0.6603,  0.7080]], grad_fn=&lt;TanhBackward&gt;), tensor([[ 0.2711,  0.3308,  0.7184,  0.8275],
-        [ 0.7674, -0.5661,  0.7969,  0.6040],
-        [ 0.7650, -0.1235,  0.6603,  0.7080]], grad_fn=&lt;TanhBackward&gt;))
-(tensor([[ 0.2711,  0.3308,  0.7184,  0.8275],
-        [ 0.7674, -0.5661,  0.7969,  0.6040],
-        [ 0.7650, -0.1235,  0.6603,  0.7080]],
-       grad_fn=&lt;DifferentiableGraphBackward&gt;), tensor([[ 0.2711,  0.3308,  0.7184,  0.8275],
-        [ 0.7674, -0.5661,  0.7969,  0.6040],
-        [ 0.7650, -0.1235,  0.6603,  0.7080]],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(tensor([[ 0.2439,  0.6961,  0.2542, -0.0800],
+        [ 0.5865, -0.0583,  0.0454,  0.3162],
+        [ 0.7924,  0.6700,  0.5091,  0.3922]], grad_fn=&lt;TanhBackward&gt;), tensor([[ 0.2439,  0.6961,  0.2542, -0.0800],
+        [ 0.5865, -0.0583,  0.0454,  0.3162],
+        [ 0.7924,  0.6700,  0.5091,  0.3922]], grad_fn=&lt;TanhBackward&gt;))
+(tensor([[ 0.2439,  0.6961,  0.2542, -0.0800],
+        [ 0.5865, -0.0583,  0.0454,  0.3162],
+        [ 0.7924,  0.6700,  0.5091,  0.3922]],
+       grad_fn=&lt;DifferentiableGraphBackward&gt;), tensor([[ 0.2439,  0.6961,  0.2542, -0.0800],
+        [ 0.5865, -0.0583,  0.0454,  0.3162],
+        [ 0.7924,  0.6700,  0.5091,  0.3922]],
        grad_fn=&lt;DifferentiableGraphBackward&gt;))
 </pre></div>
 </div>
@@ -589,7 +589,7 @@ <h2>스크립팅을 사용하여 모듈 변환<a class="headerlink" href="#id4"
             <span class="k">return</span> <span class="o">-</span><span class="n">x</span>
 
 <span class="k">class</span> <span class="nc">MyCell</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dg</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dg</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyCell</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dg</span> <span class="o">=</span> <span class="n">dg</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">linear</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
@@ -653,7 +653,7 @@ <h3>스크립팅과 추적 혼합<a class="headerlink" href="#id5" title="Permal
 할 것이고, 추적은 스크립트 된 모듈의 코드를 인라인 할 것입니다.</p>
 <p>첫 번째 경우의 예:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyRNNLoop</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyRNNLoop</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">cell</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">MyCell</span><span class="p">(</span><span class="n">scripted_gate</span><span class="p">),</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">h</span><span class="p">))</span>
 
@@ -683,7 +683,7 @@ <h3>스크립팅과 추적 혼합<a class="headerlink" href="#id5" title="Permal
 </div>
 <p>두 번째 경우의 예:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">WrapRNN</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">WrapRNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">loop</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">MyRNNLoop</span><span class="p">())</span>
 
@@ -698,8 +698,8 @@ <h3>스크립팅과 추적 혼합<a class="headerlink" href="#id5" title="Permal
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>def forward(self,
     argument_1: Tensor) -&gt; Tensor:
-  _0, h, = (self.loop).forward(argument_1, )
-  return torch.relu(h)
+  _0, y, = (self.loop).forward(argument_1, )
+  return torch.relu(y)
 </pre></div>
 </div>
 <p>이러한 방식으로, 스크립팅과 추적은 상황에 따라서 따로 사용되거나, 함께
@@ -734,8 +734,8 @@ <h2>모델 저장 및 불러오기<a class="headerlink" href="#id6" title="Perma
 )
 def forward(self,
     argument_1: Tensor) -&gt; Tensor:
-  _0, h, = (self.loop).forward(argument_1, )
-  return torch.relu(h)
+  _0, y, = (self.loop).forward(argument_1, )
+  return torch.relu(y)
 </pre></div>
 </div>
 <p>보시다시피, 직렬화는 모듈 계층과 검사한 코드를 유지합니다. 또한 모델을 로드할
@@ -747,7 +747,7 @@ <h3>더 읽을거리<a class="headerlink" href="#id7" title="Permalink to this h
 <p>튜토리얼을 완료했습니다! 관련 데모를 보려면 TorchScript를 사용하여 기계 번역
 모델을 변환하기 위한 NeurIPS 데모를 확인하십시오:
 <a class="reference external" href="https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ">https://colab.research.google.com/drive/1HiICg6jRkBnr5hvK2-VnMi88Vi9pUzEJ</a></p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.927 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.210 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-intro-to-torchscript-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/07d05907b3ff859aeed5f76f1acc5df4/Intro_to_TorchScript_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">Intro_to_TorchScript_tutorial.py</span></code></a></p>
@@ -842,11 +842,9 @@ <h3>더 읽을거리<a class="headerlink" href="#id7" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/audio_preprocessing_tutorial.html b/docs/beginner/audio_preprocessing_tutorial.html
index 828a44c97..7ff8b6f2f 100644
--- a/docs/beginner/audio_preprocessing_tutorial.html
+++ b/docs/beginner/audio_preprocessing_tutorial.html
@@ -157,7 +157,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">기초부터 시작하는 NLP:  문자-단위 RNN으로 이름 생성하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">기초부터 시작하는 NLP: Sequence to Sequence 네트워크와 Attention을 이용한 번역</a></li>
-<li class="toctree-l1"><a class="reference internal" href="text_sentiment_ngrams_tutorial.html">SyntaxError</a></li>
+<li class="toctree-l1"><a class="reference internal" href="text_sentiment_ngrams_tutorial.html">TorchText로 텍스트 분류하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torchtext_translation_tutorial.html">TorchText로 언어 번역하기</a></li>
 </ul>
 <p class="caption"><span class="caption-text">강화학습</span></p>
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -185,8 +185,8 @@
 </ul>
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">Pruning Tutorial</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -194,7 +194,7 @@
 <p class="caption"><span class="caption-text">병렬 및 분산 학습</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="dist_overview.html">PyTorch Distributed Overview</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">단일 머신을 이용한 모델 병렬화 실습 예제</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">Getting Started with Distributed Data Parallel</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch로 분산 어플리케이션 개발하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
@@ -332,6 +332,12 @@ <h2>Opening a file<a class="headerlink" href="#opening-a-file" title="Permalink
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">waveform</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_001.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of waveform: torch.Size([2, 276858])
+Sample rate of waveform: 44100
+</pre></div>
+</div>
 <p>When you load a file in <code class="docutils literal notranslate"><span class="pre">torchaudio</span></code>, you can optionally specify the backend to use either
 <a class="reference external" href="https://pypi.org/project/sox/">SoX</a> or <a class="reference external" href="https://pypi.org/project/SoundFile/">SoundFile</a>
 via <code class="docutils literal notranslate"><span class="pre">torchaudio.set_audio_backend</span></code>. These backends are loaded lazily when needed.</p>
@@ -375,6 +381,11 @@ <h2>Transformations<a class="headerlink" href="#transformations" title="Permalin
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">specgram</span><span class="o">.</span><span class="n">log2</span><span class="p">()[</span><span class="mi">0</span><span class="p">,:,:]</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_002.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_002.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of spectrogram: torch.Size([2, 201, 1385])
+</pre></div>
+</div>
 <p>Or we can look at the Mel Spectrogram on a log scale.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">specgram</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">MelSpectrogram</span><span class="p">()(</span><span class="n">waveform</span><span class="p">)</span>
 
@@ -384,6 +395,11 @@ <h2>Transformations<a class="headerlink" href="#transformations" title="Permalin
 <span class="n">p</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">specgram</span><span class="o">.</span><span class="n">log2</span><span class="p">()[</span><span class="mi">0</span><span class="p">,:,:]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_003.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_003.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of spectrogram: torch.Size([2, 128, 1385])
+</pre></div>
+</div>
 <p>We can resample the waveform, one channel at a time.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">new_sample_rate</span> <span class="o">=</span> <span class="n">sample_rate</span><span class="o">/</span><span class="mi">10</span>
 
@@ -397,6 +413,11 @@ <h2>Transformations<a class="headerlink" href="#transformations" title="Permalin
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">transformed</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_004.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_004.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of transformed waveform: torch.Size([1, 27686])
+</pre></div>
+</div>
 <p>As another example of transformations, we can encode the signal based on
 Mu-Law enconding. But to do so, we need the signal to be between -1 and
 1. Since the tensor is just a regular PyTorch tensor, we can apply
@@ -405,6 +426,12 @@ <h2>Transformations<a class="headerlink" href="#transformations" title="Permalin
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Min of waveform: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">Max of waveform: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">Mean of waveform: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">waveform</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">waveform</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">waveform</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
 </pre></div>
 </div>
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Min of waveform: -0.572845458984375
+Max of waveform: 0.575958251953125
+Mean of waveform: 9.293758921558037e-05
+</pre></div>
+</div>
 <p>Since the waveform is already between -1 and 1, we do not need to
 normalize it.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">tensor</span><span class="p">):</span>
@@ -425,6 +452,11 @@ <h2>Transformations<a class="headerlink" href="#transformations" title="Permalin
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">transformed</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_005.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_005.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of transformed waveform: torch.Size([2, 276858])
+</pre></div>
+</div>
 <p>And now decode.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">reconstructed</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">transforms</span><span class="o">.</span><span class="n">MuLawDecoding</span><span class="p">()(</span><span class="n">transformed</span><span class="p">)</span>
 
@@ -434,6 +466,11 @@ <h2>Transformations<a class="headerlink" href="#transformations" title="Permalin
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">reconstructed</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_006.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_006.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of recovered waveform: torch.Size([2, 276858])
+</pre></div>
+</div>
 <p>We can finally compare the original waveform with its reconstructed
 version.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Compute median relative difference</span>
@@ -442,6 +479,10 @@ <h2>Transformations<a class="headerlink" href="#transformations" title="Permalin
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Median relative difference between original and MuLaw reconstucted signals: </span><span class="si">{:.2%}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">err</span><span class="p">))</span>
 </pre></div>
 </div>
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Median relative difference between original and MuLaw reconstucted signals: 1.28%
+</pre></div>
+</div>
 </div>
 <div class="section" id="functional">
 <h2>Functional<a class="headerlink" href="#functional" title="Permalink to this headline">¶</a></h2>
@@ -467,6 +508,11 @@ <h2>Functional<a class="headerlink" href="#functional" title="Permalink to this
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">mu_law_encoding_waveform</span><span class="p">[</span><span class="mi">0</span><span class="p">,:]</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_007.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_007.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of transformed waveform: torch.Size([2, 276858])
+</pre></div>
+</div>
 <p>You can see how the output fron <code class="docutils literal notranslate"><span class="pre">torchaudio.functional.mu_law_encoding</span></code> is the same as
 the output from <code class="docutils literal notranslate"><span class="pre">torchaudio.transforms.MuLawEncoding</span></code>.</p>
 <p>Now let’s experiment with a few of the other functionals and visualize their output. Taking our
@@ -478,6 +524,11 @@ <h2>Functional<a class="headerlink" href="#functional" title="Permalink to this
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">computed</span><span class="o">.</span><span class="n">log2</span><span class="p">()[</span><span class="mi">0</span><span class="p">,:,:]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_008.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_008.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of computed deltas: torch.Size([2, 128, 1385])
+</pre></div>
+</div>
 <p>We can take the original waveform and apply different effects to it.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">gain_waveform</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">gain</span><span class="p">(</span><span class="n">waveform</span><span class="p">,</span> <span class="n">gain_db</span><span class="o">=</span><span class="mf">5.0</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Min of gain_waveform: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">Max of gain_waveform: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">Mean of gain_waveform: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">gain_waveform</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">gain_waveform</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">gain_waveform</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
@@ -486,6 +537,15 @@ <h2>Functional<a class="headerlink" href="#functional" title="Permalink to this
 <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Min of dither_waveform: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">Max of dither_waveform: </span><span class="si">{}</span><span class="se">\n</span><span class="s2">Mean of dither_waveform: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">dither_waveform</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">dither_waveform</span><span class="o">.</span><span class="n">max</span><span class="p">(),</span> <span class="n">dither_waveform</span><span class="o">.</span><span class="n">mean</span><span class="p">()))</span>
 </pre></div>
 </div>
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Min of gain_waveform: -1.0186792612075806
+Max of gain_waveform: 1.024214744567871
+Mean of gain_waveform: 0.00016526896797586232
+Min of dither_waveform: -0.572784423828125
+Max of dither_waveform: 0.575927734375
+Mean of dither_waveform: 0.00010744280007202178
+</pre></div>
+</div>
 <p>Another example of the capabilities in <code class="docutils literal notranslate"><span class="pre">torchaudio.functional</span></code> are applying filters to our
 waveform. Applying the lowpass biquad filter to our waveform will output a new waveform with
 the signal of the frequency modified.</p>
@@ -497,6 +557,13 @@ <h2>Functional<a class="headerlink" href="#functional" title="Permalink to this
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lowpass_waveform</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_009.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_009.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Min of lowpass_waveform: -0.5595059990882874
+Max of lowpass_waveform: 0.5595012307167053
+Mean of lowpass_waveform: 9.293764742324129e-05
+</pre></div>
+</div>
 <p>We can also visualize a waveform with the highpass biquad filter.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">highpass_waveform</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">functional</span><span class="o">.</span><span class="n">highpass_biquad</span><span class="p">(</span><span class="n">waveform</span><span class="p">,</span> <span class="n">sample_rate</span><span class="p">,</span> <span class="n">cutoff_freq</span><span class="o">=</span><span class="mi">2000</span><span class="p">)</span>
 
@@ -506,6 +573,13 @@ <h2>Functional<a class="headerlink" href="#functional" title="Permalink to this
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">highpass_waveform</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_010.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_010.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Min of highpass_waveform: -0.11269102990627289
+Max of highpass_waveform: 0.10451897978782654
+Mean of highpass_waveform: 1.722316733676621e-11
+</pre></div>
+</div>
 </div>
 <div class="section" id="migrating-to-torchaudio-from-kaldi">
 <h2>Migrating to torchaudio from Kaldi<a class="headerlink" href="#migrating-to-torchaudio-from-kaldi" title="Permalink to this headline">¶</a></h2>
@@ -547,6 +621,11 @@ <h2>Migrating to torchaudio from Kaldi<a class="headerlink" href="#migrating-to-
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">specgram</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_011.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_011.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of spectrogram: torch.Size([1383, 201])
+</pre></div>
+</div>
 <p>We also support computing the filterbank features from waveforms,
 matching Kaldi’s implementation.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">fbank</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">compliance</span><span class="o">.</span><span class="n">kaldi</span><span class="o">.</span><span class="n">fbank</span><span class="p">(</span><span class="n">waveform</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">)</span>
@@ -557,6 +636,11 @@ <h2>Migrating to torchaudio from Kaldi<a class="headerlink" href="#migrating-to-
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">fbank</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_012.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_012.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of fbank: torch.Size([1383, 23])
+</pre></div>
+</div>
 <p>You can create mel frequency cepstral coefficients from a raw audio signal
 This matches the input/output of Kaldi’s compute-mfcc-feats.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mfcc</span> <span class="o">=</span> <span class="n">torchaudio</span><span class="o">.</span><span class="n">compliance</span><span class="o">.</span><span class="n">kaldi</span><span class="o">.</span><span class="n">mfcc</span><span class="p">(</span><span class="n">waveform</span><span class="p">,</span> <span class="o">**</span><span class="n">params</span><span class="p">)</span>
@@ -567,6 +651,11 @@ <h2>Migrating to torchaudio from Kaldi<a class="headerlink" href="#migrating-to-
 <span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">mfcc</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_013.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_013.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Shape of mfcc: torch.Size([1383, 13])
+</pre></div>
+</div>
 </div>
 <div class="section" id="available-datasets">
 <h2>Available Datasets<a class="headerlink" href="#available-datasets" title="Permalink to this headline">¶</a></h2>
@@ -597,6 +686,13 @@ <h2>Available Datasets<a class="headerlink" href="#available-datasets" title="Pe
 <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">waveform</span><span class="o">.</span><span class="n">t</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span>
 </pre></div>
 </div>
+<img alt="../_images/sphx_glr_audio_preprocessing_tutorial_014.png" class="sphx-glr-single-img" src="../_images/sphx_glr_audio_preprocessing_tutorial_014.png" />
+<p class="sphx-glr-script-out">Out:</p>
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Waveform: tensor([[0.0002, 0.0002, 0.0004,  ..., 0.0010, 0.0003, 0.0005]])
+Sample rate: 8000
+Labels: [1, 1, 0, 1, 0, 1, 0, 0]
+</pre></div>
+</div>
 <p>Now, whenever you ask for a sound file from the dataset, it is loaded in memory only when you ask for it.
 Meaning, the dataset only loads and keeps in memory the items that you want and use, saving on memory.</p>
 </div>
@@ -609,7 +705,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 construct our models. Given that <code class="docutils literal notranslate"><span class="pre">torchaudio</span></code> is built on PyTorch,
 these techniques can be used as building blocks for more advanced audio
 applications, such as speech recognition, while leveraging GPUs.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  11.264 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-audio-preprocessing-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/04cb819f6fb2cbcd58ebcf5ccaeb4f9c/audio_preprocessing_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">audio_preprocessing_tutorial.py</span></code></a></p>
@@ -696,11 +792,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/aws_distributed_training_tutorial.html b/docs/beginner/aws_distributed_training_tutorial.html
index d26ec02cc..bb53adb87 100644
--- a/docs/beginner/aws_distributed_training_tutorial.html
+++ b/docs/beginner/aws_distributed_training_tutorial.html
@@ -157,7 +157,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">기초부터 시작하는 NLP:  문자-단위 RNN으로 이름 생성하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">기초부터 시작하는 NLP: Sequence to Sequence 네트워크와 Attention을 이용한 번역</a></li>
-<li class="toctree-l1"><a class="reference internal" href="text_sentiment_ngrams_tutorial.html">SyntaxError</a></li>
+<li class="toctree-l1"><a class="reference internal" href="text_sentiment_ngrams_tutorial.html">TorchText로 텍스트 분류하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="torchtext_translation_tutorial.html">TorchText로 언어 번역하기</a></li>
 </ul>
 <p class="caption"><span class="caption-text">강화학습</span></p>
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -185,8 +185,8 @@
 </ul>
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">Pruning Tutorial</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -194,7 +194,7 @@
 <p class="caption"><span class="caption-text">병렬 및 분산 학습</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="dist_overview.html">PyTorch Distributed Overview</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">단일 머신을 이용한 모델 병렬화 실습 예제</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">Getting Started with Distributed Data Parallel</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch로 분산 어플리케이션 개발하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
@@ -304,8 +304,8 @@
 <div class="sphx-glr-example-title section" id="advanced-pytorch-1-0-distributed-trainer-with-amazon-aws">
 <span id="sphx-glr-beginner-aws-distributed-training-tutorial-py"></span><h1>(advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS<a class="headerlink" href="#advanced-pytorch-1-0-distributed-trainer-with-amazon-aws" title="Permalink to this headline">¶</a></h1>
 <p><strong>Author</strong>: <a class="reference external" href="https://github.com/inkawhich">Nathan Inkawhich</a></p>
-<p><strong>Edited by</strong>: <a class="reference external" href="https://github.com/teng-li">Teng Li</a></p>
-<p>In this tutorial we will show how to setup, code, and run a PyTorch 1.0
+<p><strong>Edited by</strong>: <a class="reference external" href="https://github.com/teng-li">Teng Li</a>
+In this tutorial we will show how to setup, code, and run a PyTorch 1.0
 distributed trainer across two multi-gpu Amazon AWS nodes. We will start
 with describing the AWS setup, then the PyTorch environment
 configuration, and finally the code for the distributed trainer.
@@ -474,7 +474,7 @@ <h3>Imports<a class="headerlink" href="#imports" title="Permalink to this headli
 <span class="kn">import</span> <span class="nn">torchvision.datasets</span> <span class="k">as</span> <span class="nn">datasets</span>
 <span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
 
-<span class="kn">from</span> <span class="nn">torch.multiprocessing</span> <span class="k">import</span> <span class="n">Pool</span><span class="p">,</span> <span class="n">Process</span>
+<span class="kn">from</span> <span class="nn">torch.multiprocessing</span> <span class="kn">import</span> <span class="n">Pool</span><span class="p">,</span> <span class="n">Process</span>
 </pre></div>
 </div>
 </div>
@@ -488,7 +488,7 @@ <h3>Helper Functions<a class="headerlink" href="#helper-functions" title="Permal
 distributed training specific.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">AverageMeter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Computes and stores the average and current value&quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
 
     <span class="k">def</span> <span class="nf">reset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -1004,11 +1004,10 @@ <h2>Running the Code<a class="headerlink" href="#running-the-code" title="Permal
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/blitz/autograd_tutorial.html b/docs/beginner/blitz/autograd_tutorial.html
index 5d5be1527..54eeb3f8d 100644
--- a/docs/beginner/blitz/autograd_tutorial.html
+++ b/docs/beginner/blitz/autograd_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -363,7 +363,7 @@ <h2>Tensor<a class="headerlink" href="#tensor" title="Permalink to this headline
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;AddBackward0 object at 0x7f843916f0f0&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;AddBackward0 object at 0x7f72ef59e290&gt;
 </pre></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">y</span></code> 에 다른 연산을 수행합니다.</p>
@@ -392,7 +392,7 @@ <h2>Tensor<a class="headerlink" href="#tensor" title="Permalink to this headline
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>False
 True
-&lt;SumBackward0 object at 0x7f843916f470&gt;
+&lt;SumBackward0 object at 0x7f73656a5cd0&gt;
 </pre></div>
 </div>
 </div>
@@ -512,7 +512,7 @@ <h2>변화도(Gradient)<a class="headerlink" href="#gradient" title="Permalink t
 <p><strong>더 읽을거리:</strong></p>
 <p><code class="docutils literal notranslate"><span class="pre">autograd.Function</span></code> 관련 문서는 <a class="reference external" href="https://pytorch.org/docs/stable/autograd.html#function">https://pytorch.org/docs/stable/autograd.html#function</a>
 에서 찾아볼 수 있습니다.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.258 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.240 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-blitz-autograd-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/768abfc6db45b4724b18a56eb470aea1/autograd_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">autograd_tutorial.py</span></code></a></p>
@@ -595,11 +595,10 @@ <h2>변화도(Gradient)<a class="headerlink" href="#gradient" title="Permalink t
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/blitz/cifar10_tutorial.html b/docs/beginner/blitz/cifar10_tutorial.html
index 5acfe384d..c020ae488 100644
--- a/docs/beginner/blitz/cifar10_tutorial.html
+++ b/docs/beginner/blitz/cifar10_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -379,8 +379,7 @@ <h3>1. CIFAR10를 불러오고 정규화하기<a class="headerlink" href="#cifar
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz
-Extracting ./data/cifar-10-python.tar.gz to ./data
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Files already downloaded and verified
 Files already downloaded and verified
 </pre></div>
 </div>
@@ -422,7 +421,7 @@ <h3>2. 합성곱 신경망(Convolution Neural Network) 정의하기<a class="hea
 
 
 <span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -671,7 +670,7 @@ <h2>이제 무엇을 해볼까요?<a class="headerlink" href="#id7" title="Perma
 <span class="c1"># %%%%%%INVISIBLE_CODE_BLOCK%%%%%%</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  0.250 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  21.923 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-blitz-cifar10-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/c51fcdf96d93a8e4b3f2943cb36bab19/cifar10_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">cifar10_tutorial.py</span></code></a></p>
@@ -764,11 +763,9 @@ <h2>이제 무엇을 해볼까요?<a class="headerlink" href="#id7" title="Perma
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/blitz/data_parallel_tutorial.html b/docs/beginner/blitz/data_parallel_tutorial.html
index 6b7135be5..e3cc1faf0 100644
--- a/docs/beginner/blitz/data_parallel_tutorial.html
+++ b/docs/beginner/blitz/data_parallel_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -320,13 +320,13 @@
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">DataParallel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>이것이 튜토리얼의 핵심입니다. 자세한 사항들에 대해서는 아래에서 살펴보겠습니다.</p>
-<div class="section" id="id1">
-<h2>불러오기와 매개변수<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>이것이 튜토리얼의 핵심입니다. 자세한 사항들에 대해서는 아래에서 살펴보겠습니다.
+불러오기와 매개변수
+———————-</p>
 <p>PyTorch 모듈을 불러오고 매개변수를 정의하십시오.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">DataLoader</span>
 
 <span class="c1"># 매개변수와 DataLoaders</span>
 <span class="n">input_size</span> <span class="o">=</span> <span class="mi">5</span>
@@ -340,36 +340,35 @@ <h2>불러오기와 매개변수<a class="headerlink" href="#id1" title="Permali
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:0&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-</div>
 <div class="section" id="dummy">
 <h2>더미(Dummy) 데이터셋<a class="headerlink" href="#dummy" title="Permalink to this headline">¶</a></h2>
 <p>더미(ramdom) 데이터셋을 만들어 봅시다. Getitem 만 구현하면 됩니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">RandomDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">length</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">length</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">len</span> <span class="o">=</span> <span class="n">length</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">length</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">index</span><span class="p">]</span>
 
-    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">len</span>
 
 <span class="n">rand_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">dataset</span><span class="o">=</span><span class="n">RandomDataset</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">data_size</span><span class="p">),</span>
                          <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
-<dl class="simple">
-<dt>간단한 모델</dt><dd><p>데모를 위해 모델은 입력을 받고 선형 연산을 수행하며 출력을 제공합니다. 그러나 <code class="docutils literal notranslate"><span class="pre">DataParallel</span></code> 의 어떤 모델 (CNN, RNN, Capsule Net 등) 에서든 사용할 수 있습니다.</p>
-</dd>
-<dt>우리는 input과 output의 크기를 모니터링하기 위해 모델안에 print 문을 넣었습니다.</dt><dd><p>무엇이 배치 순위 (batch rank) 0 에 프린트 되는지 주의 깊게 봐주시길 바랍니다.</p>
-</dd>
-</dl>
+</div>
+<div class="section" id="id1">
+<h2>간단한 모델<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>데모를 위해 모델은 입력을 받고 선형 연산을 수행하며 출력을 제공합니다. 그러나 <code class="docutils literal notranslate"><span class="pre">DataParallel</span></code> 의 어떤 모델 (CNN, RNN, Capsule Net 등) 에서든 사용할 수 있습니다.</p>
+<p>우리는 input과 output의 크기를 모니터링하기 위해 모델안에 print 문을 넣었습니다.
+무엇이 배치 순위 (batch rank) 0 에 프린트 되는지 주의 깊게 봐주시길 바랍니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
     <span class="c1"># 우리의 모델</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Model</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">)</span>
 
@@ -516,7 +515,7 @@ <h2>요약<a class="headerlink" href="#id7" title="Permalink to this headline">
 사용자에게 결과를 반환하기 전에 모든 결과물들을 수집하여 병합합니다.</p>
 <p>더 많은 정보를 알고싶다면 아래 주소를 참고하세요.
 <a class="reference external" href="https://tutorials.pytorch.kr/beginner/former_torchies/parallelism_tutorial.html">https://tutorials.pytorch.kr/beginner/former_torchies/parallelism_tutorial.html</a></p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.107 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.070 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-blitz-data-parallel-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/e85264945029fe236addcb864bf5f13f/data_parallel_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">data_parallel_tutorial.py</span></code></a></p>
@@ -571,8 +570,8 @@ <h2>요약<a class="headerlink" href="#id7" title="Permalink to this headline">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
 <li><a class="reference internal" href="#">선택 사항: 데이터 병렬 처리 (Data Parallelism)</a><ul>
-<li><a class="reference internal" href="#id1">불러오기와 매개변수</a></li>
 <li><a class="reference internal" href="#dummy">더미(Dummy) 데이터셋</a></li>
+<li><a class="reference internal" href="#id1">간단한 모델</a></li>
 <li><a class="reference internal" href="#id2">모델과 데이터 병렬의 구현</a></li>
 <li><a class="reference internal" href="#id3">모델 실행</a></li>
 <li><a class="reference internal" href="#id4">결과</a><ul>
@@ -599,11 +598,9 @@ <h2>요약<a class="headerlink" href="#id7" title="Permalink to this headline">
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/blitz/neural_networks_tutorial.html b/docs/beginner/blitz/neural_networks_tutorial.html
index 5292b4da0..8d9315cad 100644
--- a/docs/beginner/blitz/neural_networks_tutorial.html
+++ b/docs/beginner/blitz/neural_networks_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -337,7 +337,7 @@ <h2>신경망 정의하기<a class="headerlink" href="#id1" title="Permalink to
 
 <span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="c1"># 입력 이미지 채널 1개, 출력 채널 6개, 3x3의 정사각 컨볼루션 행렬</span>
         <span class="c1"># 컨볼루션 커널 정의</span>
@@ -404,8 +404,8 @@ <h2>신경망 정의하기<a class="headerlink" href="#id1" title="Permalink to
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[ 0.0136, -0.0605,  0.0129, -0.0043, -0.0349,  0.0225,  0.1031,  0.1057,
-         -0.0506, -0.0951]], grad_fn=&lt;AddmmBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-0.0090, -0.0230,  0.0592, -0.0644, -0.0056,  0.0108,  0.0282, -0.0180,
+          0.0124,  0.0470]], grad_fn=&lt;AddmmBackward&gt;)
 </pre></div>
 </div>
 <p>모든 매개변수의 변화도 버퍼(gradient buffer)를 0으로 설정하고, 무작위 값으로
@@ -469,7 +469,7 @@ <h2>손실 함수 (Loss Function)<a class="headerlink" href="#loss-function" tit
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(1.7904, grad_fn=&lt;MseLossBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.9855, grad_fn=&lt;MseLossBackward&gt;)
 </pre></div>
 </div>
 <p>이제 <code class="docutils literal notranslate"><span class="pre">.grad_fn</span></code> 속성을 사용하여 <code class="docutils literal notranslate"><span class="pre">loss</span></code> 를 역방향에서 따라가다보면,
@@ -490,9 +490,9 @@ <h2>손실 함수 (Loss Function)<a class="headerlink" href="#loss-function" tit
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;MseLossBackward object at 0x7f520fa2ad30&gt;
-&lt;AddmmBackward object at 0x7f520fa2ada0&gt;
-&lt;AccumulateGrad object at 0x7f520fa2ada0&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&lt;MseLossBackward object at 0x7f72ef59e750&gt;
+&lt;AddmmBackward object at 0x7f72ef59eed0&gt;
+&lt;AccumulateGrad object at 0x7f72ef59eed0&gt;
 </pre></div>
 </div>
 </div>
@@ -518,7 +518,7 @@ <h2>역전파(Backprop)<a class="headerlink" href="#backprop" title="Permalink t
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv1.bias.grad before backward
 tensor([0., 0., 0., 0., 0., 0.])
 conv1.bias.grad after backward
-tensor([ 0.0086,  0.0034, -0.0079, -0.0114, -0.0105,  0.0007])
+tensor([ 0.0008,  0.0028, -0.0025,  0.0106, -0.0037,  0.0087])
 </pre></div>
 </div>
 <p>지금까지 손실 함수를 어떻게 사용하는지를 살펴봤습니다.</p>
@@ -570,7 +570,7 @@ <h2>가중치 갱신<a class="headerlink" href="#id4" title="Permalink to this h
 것에 유의하세요. 이는 <a class="reference internal" href="#backprop">역전파(Backprop)</a> 섹션에서 설명한 것처럼 변화도가
 누적되기 때문입니다.</p>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.156 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.081 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-blitz-neural-networks-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/fb69edad8880ff72cdc3687bb1dad1e6/neural_networks_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">neural_networks_tutorial.py</span></code></a></p>
@@ -655,11 +655,9 @@ <h2>가중치 갱신<a class="headerlink" href="#id4" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/blitz/sg_execution_times.html b/docs/beginner/blitz/sg_execution_times.html
index f5614b905..35e89cc09 100644
--- a/docs/beginner/blitz/sg_execution_times.html
+++ b/docs/beginner/blitz/sg_execution_times.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -297,13 +297,13 @@
               
   <div class="section" id="computation-times">
 <span id="sphx-glr-beginner-blitz-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.156</strong> total execution time for <strong>beginner_blitz</strong> files:</p>
+<p><strong>01:22.397</strong> total execution time for <strong>beginner_blitz</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:00.156</strong>: <a class="reference internal" href="neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py"><span class="std std-ref">신경망(Neural Networks)</span></a> (<code class="docutils literal notranslate"><span class="pre">neural_networks_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="autograd_tutorial.html#sphx-glr-beginner-blitz-autograd-tutorial-py"><span class="std std-ref">Autograd: 자동 미분</span></a> (<code class="docutils literal notranslate"><span class="pre">autograd_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py"><span class="std std-ref">분류기(Classifier) 학습하기</span></a> (<code class="docutils literal notranslate"><span class="pre">cifar10_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="data_parallel_tutorial.html#sphx-glr-beginner-blitz-data-parallel-tutorial-py"><span class="std std-ref">선택 사항: 데이터 병렬 처리 (Data Parallelism)</span></a> (<code class="docutils literal notranslate"><span class="pre">data_parallel_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py"><span class="std std-ref">PyTorch가 무엇인가요?</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_tutorial.py</span></code>)</p></li>
+<li><p><strong>01:21.923</strong>: <a class="reference internal" href="cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py"><span class="std std-ref">분류기(Classifier) 학습하기</span></a> (<code class="docutils literal notranslate"><span class="pre">cifar10_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.240</strong>: <a class="reference internal" href="autograd_tutorial.html#sphx-glr-beginner-blitz-autograd-tutorial-py"><span class="std std-ref">Autograd: 자동 미분</span></a> (<code class="docutils literal notranslate"><span class="pre">autograd_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.083</strong>: <a class="reference internal" href="tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py"><span class="std std-ref">PyTorch가 무엇인가요?</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.081</strong>: <a class="reference internal" href="neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py"><span class="std std-ref">신경망(Neural Networks)</span></a> (<code class="docutils literal notranslate"><span class="pre">neural_networks_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.070</strong>: <a class="reference internal" href="data_parallel_tutorial.html#sphx-glr-beginner-blitz-data-parallel-tutorial-py"><span class="std std-ref">선택 사항: 데이터 병렬 처리 (Data Parallelism)</span></a> (<code class="docutils literal notranslate"><span class="pre">data_parallel_tutorial.py</span></code>)</p></li>
 </ul>
 </div>
 
@@ -364,11 +364,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/blitz/tensor_tutorial.html b/docs/beginner/blitz/tensor_tutorial.html
index b0e1569a2..e768878ad 100644
--- a/docs/beginner/blitz/tensor_tutorial.html
+++ b/docs/beginner/blitz/tensor_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -315,7 +315,7 @@ <h2>시작하기<a class="headerlink" href="#id1" title="Permalink to this headl
 <div class="section" id="tensors">
 <h3>Tensors<a class="headerlink" href="#tensors" title="Permalink to this headline">¶</a></h3>
 <p>Tensor는 NumPy의 ndarray와 유사하며, GPU를 사용한 연산 가속도 가능합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 </pre></div>
 </div>
@@ -331,11 +331,11 @@ <h3>Tensors<a class="headerlink" href="#tensors" title="Permalink to this headli
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-4.3187e-33,  0.0000e+00,  0.0000e+00],
-        [ 0.0000e+00,  0.0000e+00,  0.0000e+00],
-        [ 0.0000e+00,  0.0000e+00, -2.4925e+03],
-        [ 4.5744e-41,  0.0000e+00,  0.0000e+00],
-        [        nan,  0.0000e+00,  1.6816e-44]])
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-1.0278e-29,  3.0876e-41,  4.4842e-41],
+        [ 0.0000e+00,  8.9683e-44,  0.0000e+00],
+        [ 1.1210e-43,  0.0000e+00, -5.6342e-30],
+        [ 3.0876e-41,  1.5695e-43,  0.0000e+00],
+        [ 0.0000e+00,  0.0000e+00,  4.4842e-41]])
 </pre></div>
 </div>
 <p>무작위로 초기화된 행렬을 생성합니다:</p>
@@ -582,7 +582,7 @@ <h2>CUDA Tensors<a class="headerlink" href="#cuda-tensors" title="Permalink to t
 tensor([1.8994], dtype=torch.float64)
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.145 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.083 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-blitz-tensor-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/6133d4c3ca687bdecb6dda6d3a243c24/tensor_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">tensor_tutorial.py</span></code></a></p>
@@ -674,11 +674,9 @@ <h2>CUDA Tensors<a class="headerlink" href="#cuda-tensors" title="Permalink to t
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/chatbot_tutorial.html b/docs/beginner/chatbot_tutorial.html
index 037ed9685..89fa0c250 100644
--- a/docs/beginner/chatbot_tutorial.html
+++ b/docs/beginner/chatbot_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -375,15 +375,15 @@ <h2>준비 단계<a class="headerlink" href="#id3" title="Permalink to this head
 ZIP 파일 형태의 데이터를 내려받고, 현재 디렉토리 아래에 <code class="docutils literal notranslate"><span class="pre">data/</span></code> 라는
 디렉토리를 만들어서 내려받은 데이터를 옮겨두시기 바랍니다.</p>
 <p>그 다음에는, 몇 가지 필요한 도구들을 import 하겠습니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span>
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span>
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">unicode_literals</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">unicode_literals</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch.jit</span> <span class="k">import</span> <span class="n">script</span><span class="p">,</span> <span class="n">trace</span>
+<span class="kn">from</span> <span class="nn">torch.jit</span> <span class="kn">import</span> <span class="n">script</span><span class="p">,</span> <span class="n">trace</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">optim</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</span>
 <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 <span class="kn">import</span> <span class="nn">csv</span>
 <span class="kn">import</span> <span class="nn">random</span>
@@ -391,7 +391,7 @@ <h2>준비 단계<a class="headerlink" href="#id3" title="Permalink to this head
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">unicodedata</span>
 <span class="kn">import</span> <span class="nn">codecs</span>
-<span class="kn">from</span> <span class="nn">io</span> <span class="k">import</span> <span class="nb">open</span>
+<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="nb">open</span>
 <span class="kn">import</span> <span class="nn">itertools</span>
 <span class="kn">import</span> <span class="nn">math</span>
 
@@ -585,7 +585,7 @@ <h3>데이터 읽고 정리하기<a class="headerlink" href="#id6" title="Permal
 <span class="n">EOS_token</span> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># 문장의 끝(EOS, End Of Sentence)을 나태는 토큰</span>
 
 <span class="k">class</span> <span class="nc">Voc</span><span class="p">:</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">trimmed</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">word2index</span> <span class="o">=</span> <span class="p">{}</span>
@@ -990,7 +990,7 @@ <h3>인코더<a class="headerlink" href="#id9" title="Permalink to this headline
 num_directions, batch_size, hidden_size)</em></p></li>
 </ul>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">EncoderRNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">n_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">n_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">EncoderRNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">n_layers</span> <span class="o">=</span> <span class="n">n_layers</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
@@ -1064,7 +1064,7 @@ <h3>디코더<a class="headerlink" href="#id10" title="Permalink to this headlin
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Luong 어텐션 레이어</span>
 <span class="k">class</span> <span class="nc">Attn</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Attn</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="n">method</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;dot&#39;</span><span class="p">,</span> <span class="s1">&#39;general&#39;</span><span class="p">,</span> <span class="s1">&#39;concat&#39;</span><span class="p">]:</span>
@@ -1137,7 +1137,7 @@ <h3>디코더<a class="headerlink" href="#id10" title="Permalink to this headlin
 num_directions, batch_size, hidden_size)</em></p></li>
 </ul>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LuongAttnDecoderRNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attn_model</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">n_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attn_model</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">n_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">LuongAttnDecoderRNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
         <span class="c1"># 참조를 보존해 둡니다</span>
@@ -1424,7 +1424,7 @@ <h3>탐욕적 디코딩<a class="headerlink" href="#id15" title="Permalink to th
 </ol>
 </div></blockquote>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">GreedySearchDecoder</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">encoder</span><span class="p">,</span> <span class="n">decoder</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">encoder</span><span class="p">,</span> <span class="n">decoder</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">GreedySearchDecoder</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">encoder</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">decoder</span>
@@ -5657,7 +5657,7 @@ <h2>맺음말<a class="headerlink" href="#id20" title="Permalink to this headlin
 보면서 챗봇의 행동을 수정해볼 수 있을 것입니다.</p>
 <p>그 외에도 딥러닝의 멋진 활용 예에 대한 PyTorch 튜토리얼이 있으니 한 번
 확인해 보기 바랍니다!</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  57.931 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  31.890 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-chatbot-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/d90127b0deeb355be3a350521d770206/chatbot_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">chatbot_tutorial.py</span></code></a></p>
@@ -5759,11 +5759,10 @@ <h2>맺음말<a class="headerlink" href="#id20" title="Permalink to this headlin
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/colab.html b/docs/beginner/colab.html
index 15ac20dac..7491bcc34 100644
--- a/docs/beginner/colab.html
+++ b/docs/beginner/colab.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -323,7 +323,7 @@ <h1>Colab에서 Google Drive의 튜토리얼 데이터 사용하기<a class="hea
 </ol>
 <p>이제 Google Drive 상의 파일을 가르키도록 Colab의 파일을 편집해야 합니다.</p>
 <p>Colab에서 <em>corpus_name</em> 으로 시작하는 코드 섹션의 윗 부분에 다음 내용을 추가합니다:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">google.colab</span> <span class="k">import</span> <span class="n">drive</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span>
 <span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s1">&#39;/content/gdrive&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -403,11 +403,9 @@ <h1>Colab에서 Google Drive의 튜토리얼 데이터 사용하기<a class="hea
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/data_loading_tutorial.html b/docs/beginner/data_loading_tutorial.html
index 9e1566758..1be64cfd7 100644
--- a/docs/beginner/data_loading_tutorial.html
+++ b/docs/beginner/data_loading_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -312,15 +312,15 @@
 <li><p><code class="docutils literal notranslate"><span class="pre">scikit-image</span></code>: 이미지 I/O 와 변형을 위해 필요합니다.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">pandas</span></code>: CSV 파일 파싱을 보다 쉽게 해줍니다.</p></li>
 </ul>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">skimage</span> <span class="k">import</span> <span class="n">io</span><span class="p">,</span> <span class="n">transform</span>
+<span class="kn">from</span> <span class="nn">skimage</span> <span class="kn">import</span> <span class="n">io</span><span class="p">,</span> <span class="n">transform</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">DataLoader</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">utils</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">utils</span>
 
 <span class="c1"># 경고 메시지 무시하기</span>
 <span class="kn">import</span> <span class="nn">warnings</span>
@@ -408,7 +408,7 @@ <h2>Dataset 클래스<a class="headerlink" href="#dataset" title="Permalink to t
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">FaceLandmarksDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Face Landmarks dataset.&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">csv_file</span><span class="p">,</span> <span class="n">root_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">csv_file</span><span class="p">,</span> <span class="n">root_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Args:</span>
 <span class="sd">            csv_file (string): csv 파일의 경로</span>
@@ -419,10 +419,10 @@ <h2>Dataset 클래스<a class="headerlink" href="#dataset" title="Permalink to t
         <span class="bp">self</span><span class="o">.</span><span class="n">root_dir</span> <span class="o">=</span> <span class="n">root_dir</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
 
-    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_frame</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
         <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">(</span><span class="n">idx</span><span class="p">):</span>
             <span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
 
@@ -477,18 +477,13 @@ <h2>Transforms<a class="headerlink" href="#transforms" title="Permalink to this
 <p>위에서 볼 수 있었던 한가지 문제점은 샘플들이 다 같은 사이즈가 아니라는 것입니다.
 대부분의 신경망(neural networks)은 고정된 크기의 이미지라고 가정합니다.
 그러므로 우리는 신경망에 주기 전에 처리할 과정을 작성해야 합니다.</p>
-<p>3가지의 transforms 을 만들어 봅시다:
--  <code class="docutils literal notranslate"><span class="pre">Rescale</span></code>: 이미지의 크기를 조절합니다.
--  <code class="docutils literal notranslate"><span class="pre">RandomCrop</span></code>: 이미지를 무작위로 자릅니다.</p>
-<blockquote>
-<div><p>이것을 data augmentation이라 합니다.</p>
-</div></blockquote>
+<p>3가지의 transforms 을 만들어 봅시다:</p>
 <ul class="simple">
-<li><dl class="simple">
-<dt><code class="docutils literal notranslate"><span class="pre">ToTensor</span></code>: numpy 이미지에서 torch 이미지로 변경합니다.</dt><dd><p>(축변환이 필요합니다)</p>
-</dd>
-</dl>
-</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">Rescale</span></code>: 이미지의 크기를 조절합니다.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">RandomCrop</span></code>: 이미지를 무작위로 자릅니다.
+이것을 data augmentation이라 합니다.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">ToTensor</span></code>: numpy 이미지에서 torch 이미지로 변경합니다.
+(축변환이 필요합니다)</p></li>
 </ul>
 <p>간단한 함수대신에 호출 할 수 있는 클래스로 작성 합니다.
 이렇게 한다면, 클래스가 호출 될 때마다 전이(Transform)의 매개변수가 전달 되지 않아도 됩니다.
@@ -508,11 +503,11 @@ <h2>Transforms<a class="headerlink" href="#transforms" title="Permalink to this
 <span class="sd">            int라면 비율을 유지하면서, 길이가 작은 쪽이 output_size가 됩니다.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">output_size</span> <span class="o">=</span> <span class="n">output_size</span>
 
-    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
         <span class="n">image</span><span class="p">,</span> <span class="n">landmarks</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">],</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;landmarks&#39;</span><span class="p">]</span>
 
         <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
@@ -541,7 +536,7 @@ <h2>Transforms<a class="headerlink" href="#transforms" title="Permalink to this
 <span class="sd">                        int라면, 정사각형으로 나올 것 입니다.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">output_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">)</span>
@@ -549,7 +544,7 @@ <h2>Transforms<a class="headerlink" href="#transforms" title="Permalink to this
             <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">output_size</span> <span class="o">=</span> <span class="n">output_size</span>
 
-    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
         <span class="n">image</span><span class="p">,</span> <span class="n">landmarks</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">],</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;landmarks&#39;</span><span class="p">]</span>
 
         <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
@@ -569,7 +564,7 @@ <h2>Transforms<a class="headerlink" href="#transforms" title="Permalink to this
 <span class="k">class</span> <span class="nc">ToTensor</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;numpy array를 tensor(torch)로 변환 시켜줍니다.&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
         <span class="n">image</span><span class="p">,</span> <span class="n">landmarks</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">],</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;landmarks&#39;</span><span class="p">]</span>
 
         <span class="c1"># swap color axis because</span>
@@ -743,7 +738,7 @@ <h2>Afterword: torchvision<a class="headerlink" href="#afterword-torchvision" ti
 :doc:`transfer_learning_tutorial` 문서를 참고해주세요
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  6.401 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  3.695 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-data-loading-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/6042bacf7948939030769777afe22e55/data_loading_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">data_loading_tutorial.py</span></code></a></p>
@@ -822,11 +817,10 @@ <h2>Afterword: torchvision<a class="headerlink" href="#afterword-torchvision" ti
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/dcgan_faces_tutorial.html b/docs/beginner/dcgan_faces_tutorial.html
index ccdbbb738..a84de831b 100644
--- a/docs/beginner/dcgan_faces_tutorial.html
+++ b/docs/beginner/dcgan_faces_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -303,9 +303,9 @@
 </div>
 <div class="sphx-glr-example-title section" id="dcgan-tutorial">
 <span id="sphx-glr-beginner-dcgan-faces-tutorial-py"></span><h1>DCGAN Tutorial<a class="headerlink" href="#dcgan-tutorial" title="Permalink to this headline">¶</a></h1>
-<p><strong>Author</strong>: <a class="reference external" href="https://github.com/inkawhich">Nathan Inkawhich</a></p>
-<div class="section" id="introduction">
-<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
+<p><strong>Author</strong>: <a class="reference external" href="https://github.com/inkawhich">Nathan Inkawhich</a>
+Introduction
+————</p>
 <p>This tutorial will give an introduction to DCGANs through an example. We
 will train a generative adversarial network (GAN) to generate new
 celebrities after showing it pictures of many real celebrities. Most of
@@ -317,7 +317,6 @@ <h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to t
 some time reasoning about what is actually happening under the hood.
 Also, for the sake of time it will help to have a GPU, or two. Lets
 start from the beginning.</p>
-</div>
 <div class="section" id="generative-adversarial-networks">
 <h2>Generative Adversarial Networks<a class="headerlink" href="#generative-adversarial-networks" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="what-is-a-gan">
@@ -397,7 +396,7 @@ <h3>What is a DCGAN?<a class="headerlink" href="#what-is-a-dcgan" title="Permali
 give some tips about how to setup the optimizers, how to calculate the
 loss functions, and how to initialize the model weights, all of which
 will be explained in the coming sections.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
 <span class="c1">#%matplotlib inline</span>
 <span class="kn">import</span> <span class="nn">argparse</span>
 <span class="kn">import</span> <span class="nn">os</span>
@@ -414,7 +413,7 @@ <h3>What is a DCGAN?<a class="headerlink" href="#what-is-a-dcgan" title="Permali
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">matplotlib.animation</span> <span class="k">as</span> <span class="nn">animation</span>
-<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">HTML</span>
+<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">HTML</span>
 
 <span class="c1"># Set random seed for reproducibility</span>
 <span class="n">manualSeed</span> <span class="o">=</span> <span class="mi">999</span>
@@ -604,7 +603,7 @@ <h3>Generator<a class="headerlink" href="#generator" title="Permalink to this he
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Generator Code</span>
 
 <span class="k">class</span> <span class="nc">Generator</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ngpu</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ngpu</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Generator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">ngpu</span> <span class="o">=</span> <span class="n">ngpu</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">main</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
@@ -691,7 +690,7 @@ <h3>Discriminator<a class="headerlink" href="#discriminator" title="Permalink to
 <span class="math notranslate nohighlight">\(G\)</span> and <span class="math notranslate nohighlight">\(D\)</span>.</p>
 <p>Discriminator Code</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Discriminator</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ngpu</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ngpu</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Discriminator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">ngpu</span> <span class="o">=</span> <span class="n">ngpu</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">main</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
@@ -946,165 +945,165 @@ <h3>Training<a class="headerlink" href="#training" title="Permalink to this head
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Starting Training Loop...
 [0/5][0/1583]   Loss_D: 2.2023  Loss_G: 3.9280  D(x): 0.4309    D(G(z)): 0.6160 / 0.0303
-[0/5][50/1583]  Loss_D: 0.0826  Loss_G: 5.6338  D(x): 0.9709    D(G(z)): 0.0124 / 0.0067
-[0/5][100/1583] Loss_D: 0.7950  Loss_G: 24.7427 D(x): 0.7435    D(G(z)): 0.0000 / 0.0000
-[0/5][150/1583] Loss_D: 0.1583  Loss_G: 5.4214  D(x): 0.8993    D(G(z)): 0.0129 / 0.0133
-[0/5][200/1583] Loss_D: 0.7144  Loss_G: 3.6865  D(x): 0.7401    D(G(z)): 0.2470 / 0.0357
-[0/5][250/1583] Loss_D: 0.9668  Loss_G: 3.6454  D(x): 0.6186    D(G(z)): 0.2290 / 0.0363
-[0/5][300/1583] Loss_D: 0.7417  Loss_G: 5.5955  D(x): 0.8856    D(G(z)): 0.3825 / 0.0078
-[0/5][350/1583] Loss_D: 0.9895  Loss_G: 5.3934  D(x): 0.9211    D(G(z)): 0.4614 / 0.0230
-[0/5][400/1583] Loss_D: 0.6032  Loss_G: 5.0149  D(x): 0.8752    D(G(z)): 0.3006 / 0.0157
-[0/5][450/1583] Loss_D: 0.5568  Loss_G: 4.4512  D(x): 0.6881    D(G(z)): 0.0387 / 0.0235
-[0/5][500/1583] Loss_D: 1.3211  Loss_G: 2.5592  D(x): 0.4066    D(G(z)): 0.0111 / 0.1386
-[0/5][550/1583] Loss_D: 0.8746  Loss_G: 6.9872  D(x): 0.9116    D(G(z)): 0.4120 / 0.0020
-[0/5][600/1583] Loss_D: 0.6975  Loss_G: 4.8539  D(x): 0.8068    D(G(z)): 0.2910 / 0.0178
-[0/5][650/1583] Loss_D: 0.5318  Loss_G: 3.7926  D(x): 0.7174    D(G(z)): 0.0664 / 0.0396
-[0/5][700/1583] Loss_D: 0.7665  Loss_G: 2.9768  D(x): 0.6600    D(G(z)): 0.1532 / 0.0851
-[0/5][750/1583] Loss_D: 0.4335  Loss_G: 2.5298  D(x): 0.8293    D(G(z)): 0.1558 / 0.1303
-[0/5][800/1583] Loss_D: 0.5378  Loss_G: 3.5080  D(x): 0.7592    D(G(z)): 0.1596 / 0.0473
-[0/5][850/1583] Loss_D: 0.3222  Loss_G: 4.4557  D(x): 0.8244    D(G(z)): 0.0793 / 0.0212
-[0/5][900/1583] Loss_D: 0.2444  Loss_G: 4.1336  D(x): 0.8682    D(G(z)): 0.0767 / 0.0339
-[0/5][950/1583] Loss_D: 0.5217  Loss_G: 3.8453  D(x): 0.9142    D(G(z)): 0.2932 / 0.0363
-[0/5][1000/1583]        Loss_D: 0.3399  Loss_G: 4.1050  D(x): 0.8943    D(G(z)): 0.1692 / 0.0292
-[0/5][1050/1583]        Loss_D: 0.3916  Loss_G: 5.6400  D(x): 0.9106    D(G(z)): 0.2180 / 0.0065
-[0/5][1100/1583]        Loss_D: 1.5400  Loss_G: 2.9579  D(x): 0.3775    D(G(z)): 0.0071 / 0.1216
-[0/5][1150/1583]        Loss_D: 1.0088  Loss_G: 6.9431  D(x): 0.9443    D(G(z)): 0.5164 / 0.0030
-[0/5][1200/1583]        Loss_D: 0.6902  Loss_G: 2.2776  D(x): 0.6129    D(G(z)): 0.0398 / 0.1503
-[0/5][1250/1583]        Loss_D: 1.8404  Loss_G: 2.5388  D(x): 0.2953    D(G(z)): 0.0068 / 0.1397
-[0/5][1300/1583]        Loss_D: 0.4993  Loss_G: 4.2533  D(x): 0.7369    D(G(z)): 0.1097 / 0.0281
-[0/5][1350/1583]        Loss_D: 3.0276  Loss_G: 0.7344  D(x): 0.1169    D(G(z)): 0.0018 / 0.5360
-[0/5][1400/1583]        Loss_D: 0.4637  Loss_G: 3.1481  D(x): 0.7652    D(G(z)): 0.1141 / 0.0596
-[0/5][1450/1583]        Loss_D: 0.8496  Loss_G: 3.7472  D(x): 0.6935    D(G(z)): 0.2751 / 0.0455
-[0/5][1500/1583]        Loss_D: 0.5104  Loss_G: 2.8548  D(x): 0.7406    D(G(z)): 0.1038 / 0.0913
-[0/5][1550/1583]        Loss_D: 0.5711  Loss_G: 4.6656  D(x): 0.9093    D(G(z)): 0.3275 / 0.0186
-[1/5][0/1583]   Loss_D: 0.7089  Loss_G: 2.5457  D(x): 0.6596    D(G(z)): 0.1435 / 0.1324
-[1/5][50/1583]  Loss_D: 0.3857  Loss_G: 4.1016  D(x): 0.7533    D(G(z)): 0.0435 / 0.0310
-[1/5][100/1583] Loss_D: 0.3092  Loss_G: 3.4889  D(x): 0.8554    D(G(z)): 0.1144 / 0.0486
-[1/5][150/1583] Loss_D: 1.0250  Loss_G: 1.7058  D(x): 0.5571    D(G(z)): 0.2052 / 0.2439
-[1/5][200/1583] Loss_D: 0.5554  Loss_G: 3.0500  D(x): 0.7999    D(G(z)): 0.2088 / 0.0770
-[1/5][250/1583] Loss_D: 0.4799  Loss_G: 4.4238  D(x): 0.8706    D(G(z)): 0.2392 / 0.0182
-[1/5][300/1583] Loss_D: 0.8132  Loss_G: 5.8769  D(x): 0.9121    D(G(z)): 0.4277 / 0.0057
-[1/5][350/1583] Loss_D: 0.4475  Loss_G: 3.7788  D(x): 0.7608    D(G(z)): 0.1005 / 0.0408
-[1/5][400/1583] Loss_D: 0.5012  Loss_G: 4.2276  D(x): 0.8704    D(G(z)): 0.2602 / 0.0230
-[1/5][450/1583] Loss_D: 0.6686  Loss_G: 2.5258  D(x): 0.6470    D(G(z)): 0.1133 / 0.1150
-[1/5][500/1583] Loss_D: 0.9202  Loss_G: 4.9798  D(x): 0.9120    D(G(z)): 0.4918 / 0.0137
-[1/5][550/1583] Loss_D: 1.2522  Loss_G: 5.9922  D(x): 0.8855    D(G(z)): 0.5818 / 0.0060
-[1/5][600/1583] Loss_D: 0.4800  Loss_G: 3.6709  D(x): 0.8176    D(G(z)): 0.2052 / 0.0393
-[1/5][650/1583] Loss_D: 0.4309  Loss_G: 2.9890  D(x): 0.8079    D(G(z)): 0.1540 / 0.0738
-[1/5][700/1583] Loss_D: 0.7635  Loss_G: 1.6731  D(x): 0.5688    D(G(z)): 0.0915 / 0.2545
-[1/5][750/1583] Loss_D: 0.6578  Loss_G: 2.0671  D(x): 0.6013    D(G(z)): 0.0501 / 0.1925
-[1/5][800/1583] Loss_D: 0.8382  Loss_G: 4.3675  D(x): 0.9494    D(G(z)): 0.4870 / 0.0201
-[1/5][850/1583] Loss_D: 0.5184  Loss_G: 2.6880  D(x): 0.7562    D(G(z)): 0.1657 / 0.0911
-[1/5][900/1583] Loss_D: 0.3516  Loss_G: 3.5060  D(x): 0.8713    D(G(z)): 0.1632 / 0.0450
-[1/5][950/1583] Loss_D: 0.7425  Loss_G: 4.4970  D(x): 0.5582    D(G(z)): 0.0131 / 0.0260
-[1/5][1000/1583]        Loss_D: 0.8758  Loss_G: 5.3378  D(x): 0.9405    D(G(z)): 0.4974 / 0.0077
-[1/5][1050/1583]        Loss_D: 0.6241  Loss_G: 2.8049  D(x): 0.7606    D(G(z)): 0.2464 / 0.0852
-[1/5][1100/1583]        Loss_D: 0.4970  Loss_G: 2.6961  D(x): 0.7766    D(G(z)): 0.1689 / 0.0927
-[1/5][1150/1583]        Loss_D: 0.5996  Loss_G: 2.7650  D(x): 0.7765    D(G(z)): 0.2445 / 0.0909
-[1/5][1200/1583]        Loss_D: 0.4698  Loss_G: 1.9781  D(x): 0.7244    D(G(z)): 0.0931 / 0.1837
-[1/5][1250/1583]        Loss_D: 0.6150  Loss_G: 2.2168  D(x): 0.6710    D(G(z)): 0.1319 / 0.1609
-[1/5][1300/1583]        Loss_D: 0.4447  Loss_G: 2.2704  D(x): 0.7479    D(G(z)): 0.1056 / 0.1355
-[1/5][1350/1583]        Loss_D: 0.4616  Loss_G: 2.4254  D(x): 0.7679    D(G(z)): 0.1433 / 0.1192
-[1/5][1400/1583]        Loss_D: 0.5238  Loss_G: 2.1074  D(x): 0.6876    D(G(z)): 0.0947 / 0.1529
-[1/5][1450/1583]        Loss_D: 0.9123  Loss_G: 4.0345  D(x): 0.9140    D(G(z)): 0.4980 / 0.0299
-[1/5][1500/1583]        Loss_D: 0.5739  Loss_G: 2.7996  D(x): 0.8611    D(G(z)): 0.3128 / 0.0828
-[1/5][1550/1583]        Loss_D: 0.4659  Loss_G: 2.4235  D(x): 0.7457    D(G(z)): 0.1222 / 0.1192
-[2/5][0/1583]   Loss_D: 0.6113  Loss_G: 4.4687  D(x): 0.9064    D(G(z)): 0.3592 / 0.0172
-[2/5][50/1583]  Loss_D: 1.1610  Loss_G: 1.7491  D(x): 0.3955    D(G(z)): 0.0208 / 0.2291
-[2/5][100/1583] Loss_D: 0.8690  Loss_G: 0.8140  D(x): 0.5063    D(G(z)): 0.0704 / 0.4988
-[2/5][150/1583] Loss_D: 0.6625  Loss_G: 1.8494  D(x): 0.6238    D(G(z)): 0.0995 / 0.2002
-[2/5][200/1583] Loss_D: 0.5799  Loss_G: 1.4254  D(x): 0.6939    D(G(z)): 0.1434 / 0.2824
-[2/5][250/1583] Loss_D: 0.4409  Loss_G: 3.4459  D(x): 0.8479    D(G(z)): 0.2196 / 0.0437
-[2/5][300/1583] Loss_D: 1.4738  Loss_G: 0.0989  D(x): 0.3115    D(G(z)): 0.0277 / 0.9113
-[2/5][350/1583] Loss_D: 0.6418  Loss_G: 4.1427  D(x): 0.9271    D(G(z)): 0.3899 / 0.0229
-[2/5][400/1583] Loss_D: 0.8741  Loss_G: 4.2575  D(x): 0.9117    D(G(z)): 0.4850 / 0.0226
-[2/5][450/1583] Loss_D: 0.5562  Loss_G: 2.7122  D(x): 0.8010    D(G(z)): 0.2339 / 0.0841
-[2/5][500/1583] Loss_D: 1.8493  Loss_G: 5.9361  D(x): 0.9092    D(G(z)): 0.7555 / 0.0065
-[2/5][550/1583] Loss_D: 0.5284  Loss_G: 2.4050  D(x): 0.7214    D(G(z)): 0.1342 / 0.1266
-[2/5][600/1583] Loss_D: 0.5670  Loss_G: 2.0653  D(x): 0.7360    D(G(z)): 0.1829 / 0.1553
-[2/5][650/1583] Loss_D: 0.6865  Loss_G: 1.8736  D(x): 0.6797    D(G(z)): 0.2057 / 0.1966
-[2/5][700/1583] Loss_D: 1.0360  Loss_G: 3.9221  D(x): 0.7329    D(G(z)): 0.4508 / 0.0283
-[2/5][750/1583] Loss_D: 0.7766  Loss_G: 1.6874  D(x): 0.5435    D(G(z)): 0.0355 / 0.2362
-[2/5][800/1583] Loss_D: 1.6535  Loss_G: 4.8075  D(x): 0.9460    D(G(z)): 0.7547 / 0.0157
-[2/5][850/1583] Loss_D: 1.4429  Loss_G: 0.6234  D(x): 0.3097    D(G(z)): 0.0227 / 0.5773
-[2/5][900/1583] Loss_D: 0.7646  Loss_G: 0.9355  D(x): 0.5527    D(G(z)): 0.0724 / 0.4666
-[2/5][950/1583] Loss_D: 0.5284  Loss_G: 2.0879  D(x): 0.7434    D(G(z)): 0.1672 / 0.1547
-[2/5][1000/1583]        Loss_D: 1.3476  Loss_G: 5.2348  D(x): 0.9368    D(G(z)): 0.6543 / 0.0114
-[2/5][1050/1583]        Loss_D: 1.6248  Loss_G: 0.3698  D(x): 0.2960    D(G(z)): 0.0536 / 0.7191
-[2/5][1100/1583]        Loss_D: 0.9042  Loss_G: 0.8499  D(x): 0.5084    D(G(z)): 0.1050 / 0.4782
-[2/5][1150/1583]        Loss_D: 1.1118  Loss_G: 4.2226  D(x): 0.9462    D(G(z)): 0.5956 / 0.0197
-[2/5][1200/1583]        Loss_D: 0.4847  Loss_G: 2.8643  D(x): 0.8106    D(G(z)): 0.2141 / 0.0750
-[2/5][1250/1583]        Loss_D: 1.4259  Loss_G: 5.3239  D(x): 0.8974    D(G(z)): 0.6834 / 0.0092
-[2/5][1300/1583]        Loss_D: 0.6224  Loss_G: 2.2230  D(x): 0.6235    D(G(z)): 0.0701 / 0.1463
-[2/5][1350/1583]        Loss_D: 0.6478  Loss_G: 1.8250  D(x): 0.7310    D(G(z)): 0.2339 / 0.2023
-[2/5][1400/1583]        Loss_D: 0.8234  Loss_G: 0.8032  D(x): 0.5340    D(G(z)): 0.0925 / 0.5044
-[2/5][1450/1583]        Loss_D: 0.4967  Loss_G: 3.4505  D(x): 0.8940    D(G(z)): 0.2964 / 0.0396
-[2/5][1500/1583]        Loss_D: 1.0110  Loss_G: 4.6629  D(x): 0.9593    D(G(z)): 0.5800 / 0.0142
-[2/5][1550/1583]        Loss_D: 0.9941  Loss_G: 0.7545  D(x): 0.4609    D(G(z)): 0.0617 / 0.5182
-[3/5][0/1583]   Loss_D: 0.8065  Loss_G: 3.5106  D(x): 0.8717    D(G(z)): 0.4386 / 0.0399
-[3/5][50/1583]  Loss_D: 0.7740  Loss_G: 2.8845  D(x): 0.8084    D(G(z)): 0.3749 / 0.0798
-[3/5][100/1583] Loss_D: 0.6291  Loss_G: 1.7428  D(x): 0.6522    D(G(z)): 0.1288 / 0.2092
-[3/5][150/1583] Loss_D: 0.5253  Loss_G: 2.0647  D(x): 0.6976    D(G(z)): 0.1221 / 0.1539
-[3/5][200/1583] Loss_D: 0.6156  Loss_G: 3.8857  D(x): 0.9338    D(G(z)): 0.3921 / 0.0290
-[3/5][250/1583] Loss_D: 0.6142  Loss_G: 1.7707  D(x): 0.6479    D(G(z)): 0.0946 / 0.2184
-[3/5][300/1583] Loss_D: 1.1364  Loss_G: 6.2513  D(x): 0.9616    D(G(z)): 0.6135 / 0.0031
-[3/5][350/1583] Loss_D: 1.2017  Loss_G: 4.6575  D(x): 0.9029    D(G(z)): 0.6096 / 0.0152
-[3/5][400/1583] Loss_D: 0.4947  Loss_G: 1.9186  D(x): 0.7372    D(G(z)): 0.1404 / 0.1725
-[3/5][450/1583] Loss_D: 0.7285  Loss_G: 4.4549  D(x): 0.8461    D(G(z)): 0.3769 / 0.0198
-[3/5][500/1583] Loss_D: 0.9187  Loss_G: 3.8607  D(x): 0.8851    D(G(z)): 0.4983 / 0.0300
-[3/5][550/1583] Loss_D: 0.5433  Loss_G: 2.5528  D(x): 0.8003    D(G(z)): 0.2389 / 0.0968
-[3/5][600/1583] Loss_D: 0.5458  Loss_G: 2.7175  D(x): 0.7251    D(G(z)): 0.1404 / 0.0950
-[3/5][650/1583] Loss_D: 0.4827  Loss_G: 1.9783  D(x): 0.7943    D(G(z)): 0.1859 / 0.1697
-[3/5][700/1583] Loss_D: 0.5091  Loss_G: 2.5401  D(x): 0.7320    D(G(z)): 0.1449 / 0.1049
-[3/5][750/1583] Loss_D: 0.5317  Loss_G: 3.3442  D(x): 0.8557    D(G(z)): 0.2770 / 0.0504
-[3/5][800/1583] Loss_D: 0.5321  Loss_G: 2.1604  D(x): 0.7255    D(G(z)): 0.1474 / 0.1476
-[3/5][850/1583] Loss_D: 0.9059  Loss_G: 1.7570  D(x): 0.5555    D(G(z)): 0.1890 / 0.2207
-[3/5][900/1583] Loss_D: 0.5281  Loss_G: 1.7346  D(x): 0.7056    D(G(z)): 0.1296 / 0.2210
-[3/5][950/1583] Loss_D: 0.6017  Loss_G: 1.8928  D(x): 0.6491    D(G(z)): 0.1104 / 0.1890
-[3/5][1000/1583]        Loss_D: 1.4461  Loss_G: 5.3149  D(x): 0.9513    D(G(z)): 0.6969 / 0.0091
-[3/5][1050/1583]        Loss_D: 0.9067  Loss_G: 4.2314  D(x): 0.8798    D(G(z)): 0.4900 / 0.0217
-[3/5][1100/1583]        Loss_D: 0.4830  Loss_G: 3.2879  D(x): 0.9143    D(G(z)): 0.2861 / 0.0521
-[3/5][1150/1583]        Loss_D: 0.9799  Loss_G: 4.3568  D(x): 0.9335    D(G(z)): 0.5493 / 0.0194
-[3/5][1200/1583]        Loss_D: 0.6657  Loss_G: 2.7578  D(x): 0.8071    D(G(z)): 0.3154 / 0.0824
-[3/5][1250/1583]        Loss_D: 0.5207  Loss_G: 2.9726  D(x): 0.8226    D(G(z)): 0.2518 / 0.0660
-[3/5][1300/1583]        Loss_D: 0.4780  Loss_G: 2.2578  D(x): 0.7069    D(G(z)): 0.0899 / 0.1296
-[3/5][1350/1583]        Loss_D: 0.5935  Loss_G: 1.8699  D(x): 0.6261    D(G(z)): 0.0717 / 0.2062
-[3/5][1400/1583]        Loss_D: 0.6233  Loss_G: 1.7691  D(x): 0.6505    D(G(z)): 0.1019 / 0.2013
-[3/5][1450/1583]        Loss_D: 0.4388  Loss_G: 3.4089  D(x): 0.8971    D(G(z)): 0.2551 / 0.0453
-[3/5][1500/1583]        Loss_D: 0.9709  Loss_G: 4.7050  D(x): 0.9500    D(G(z)): 0.5554 / 0.0130
-[3/5][1550/1583]        Loss_D: 0.6342  Loss_G: 3.2556  D(x): 0.8040    D(G(z)): 0.2954 / 0.0524
-[4/5][0/1583]   Loss_D: 0.5739  Loss_G: 2.0039  D(x): 0.6509    D(G(z)): 0.0861 / 0.1776
-[4/5][50/1583]  Loss_D: 0.7179  Loss_G: 2.4936  D(x): 0.7071    D(G(z)): 0.2464 / 0.1136
-[4/5][100/1583] Loss_D: 0.7975  Loss_G: 1.5050  D(x): 0.5534    D(G(z)): 0.1045 / 0.2735
-[4/5][150/1583] Loss_D: 0.4886  Loss_G: 1.6747  D(x): 0.7249    D(G(z)): 0.1042 / 0.2266
-[4/5][200/1583] Loss_D: 0.8581  Loss_G: 4.8991  D(x): 0.9670    D(G(z)): 0.5085 / 0.0104
-[4/5][250/1583] Loss_D: 0.4161  Loss_G: 3.2271  D(x): 0.7993    D(G(z)): 0.1396 / 0.0598
-[4/5][300/1583] Loss_D: 1.6869  Loss_G: 0.9460  D(x): 0.2610    D(G(z)): 0.0190 / 0.4552
-[4/5][350/1583] Loss_D: 1.2563  Loss_G: 1.1279  D(x): 0.3607    D(G(z)): 0.0329 / 0.3755
-[4/5][400/1583] Loss_D: 0.3870  Loss_G: 2.8560  D(x): 0.7816    D(G(z)): 0.1076 / 0.0782
-[4/5][450/1583] Loss_D: 1.1674  Loss_G: 4.7243  D(x): 0.9426    D(G(z)): 0.6106 / 0.0142
-[4/5][500/1583] Loss_D: 0.6640  Loss_G: 1.5283  D(x): 0.6212    D(G(z)): 0.0940 / 0.2708
-[4/5][550/1583] Loss_D: 0.9988  Loss_G: 4.8699  D(x): 0.8828    D(G(z)): 0.5260 / 0.0129
-[4/5][600/1583] Loss_D: 0.9147  Loss_G: 1.0608  D(x): 0.4642    D(G(z)): 0.0356 / 0.4193
-[4/5][650/1583] Loss_D: 1.0957  Loss_G: 4.8745  D(x): 0.9462    D(G(z)): 0.5873 / 0.0118
-[4/5][700/1583] Loss_D: 1.2180  Loss_G: 0.7225  D(x): 0.3661    D(G(z)): 0.0310 / 0.5431
-[4/5][750/1583] Loss_D: 0.6884  Loss_G: 2.0382  D(x): 0.6267    D(G(z)): 0.1340 / 0.1678
-[4/5][800/1583] Loss_D: 0.6589  Loss_G: 1.6569  D(x): 0.5932    D(G(z)): 0.0635 / 0.2455
-[4/5][850/1583] Loss_D: 0.6937  Loss_G: 1.4475  D(x): 0.5997    D(G(z)): 0.0887 / 0.2829
-[4/5][900/1583] Loss_D: 0.5839  Loss_G: 1.5079  D(x): 0.6957    D(G(z)): 0.1512 / 0.2628
-[4/5][950/1583] Loss_D: 0.4484  Loss_G: 2.9668  D(x): 0.8816    D(G(z)): 0.2508 / 0.0705
-[4/5][1000/1583]        Loss_D: 0.6452  Loss_G: 2.2947  D(x): 0.7452    D(G(z)): 0.2513 / 0.1320
-[4/5][1050/1583]        Loss_D: 0.9155  Loss_G: 0.8871  D(x): 0.4785    D(G(z)): 0.0602 / 0.4657
-[4/5][1100/1583]        Loss_D: 0.6055  Loss_G: 1.9331  D(x): 0.6412    D(G(z)): 0.0889 / 0.1839
-[4/5][1150/1583]        Loss_D: 0.6199  Loss_G: 0.9466  D(x): 0.6283    D(G(z)): 0.0791 / 0.4255
-[4/5][1200/1583]        Loss_D: 0.5185  Loss_G: 3.0360  D(x): 0.8227    D(G(z)): 0.2429 / 0.0626
-[4/5][1250/1583]        Loss_D: 0.5385  Loss_G: 2.6037  D(x): 0.8209    D(G(z)): 0.2500 / 0.0960
-[4/5][1300/1583]        Loss_D: 0.5243  Loss_G: 3.2834  D(x): 0.9036    D(G(z)): 0.3099 / 0.0507
-[4/5][1350/1583]        Loss_D: 0.7415  Loss_G: 3.7208  D(x): 0.8593    D(G(z)): 0.4018 / 0.0332
-[4/5][1400/1583]        Loss_D: 0.8192  Loss_G: 3.8964  D(x): 0.9593    D(G(z)): 0.4971 / 0.0266
-[4/5][1450/1583]        Loss_D: 1.1950  Loss_G: 1.2550  D(x): 0.3697    D(G(z)): 0.0309 / 0.3374
-[4/5][1500/1583]        Loss_D: 1.0767  Loss_G: 3.5240  D(x): 0.9182    D(G(z)): 0.5597 / 0.0474
-[4/5][1550/1583]        Loss_D: 0.7516  Loss_G: 4.6167  D(x): 0.9236    D(G(z)): 0.4403 / 0.0157
+[0/5][50/1583]  Loss_D: 0.5776  Loss_G: 16.6337 D(x): 0.9411    D(G(z)): 0.2981 / 0.0000
+[0/5][100/1583] Loss_D: 0.1787  Loss_G: 10.0289 D(x): 0.9214    D(G(z)): 0.0040 / 0.0001
+[0/5][150/1583] Loss_D: 1.2628  Loss_G: 10.1738 D(x): 0.9816    D(G(z)): 0.6328 / 0.0001
+[0/5][200/1583] Loss_D: 0.4066  Loss_G: 4.7959  D(x): 0.8222    D(G(z)): 0.1213 / 0.0142
+[0/5][250/1583] Loss_D: 0.2658  Loss_G: 3.9123  D(x): 0.8532    D(G(z)): 0.0723 / 0.0354
+[0/5][300/1583] Loss_D: 0.8184  Loss_G: 6.6995  D(x): 0.9124    D(G(z)): 0.4419 / 0.0027
+[0/5][350/1583] Loss_D: 0.9895  Loss_G: 5.0534  D(x): 0.5098    D(G(z)): 0.0180 / 0.0202
+[0/5][400/1583] Loss_D: 0.4253  Loss_G: 5.3106  D(x): 0.8896    D(G(z)): 0.2233 / 0.0106
+[0/5][450/1583] Loss_D: 0.5606  Loss_G: 6.3987  D(x): 0.9130    D(G(z)): 0.3021 / 0.0036
+[0/5][500/1583] Loss_D: 0.5001  Loss_G: 5.2713  D(x): 0.9285    D(G(z)): 0.2849 / 0.0101
+[0/5][550/1583] Loss_D: 0.5006  Loss_G: 3.4762  D(x): 0.7610    D(G(z)): 0.1353 / 0.0509
+[0/5][600/1583] Loss_D: 0.6824  Loss_G: 4.0364  D(x): 0.7306    D(G(z)): 0.2001 / 0.0306
+[0/5][650/1583] Loss_D: 0.7065  Loss_G: 6.9655  D(x): 0.9345    D(G(z)): 0.3913 / 0.0020
+[0/5][700/1583] Loss_D: 0.5570  Loss_G: 3.1944  D(x): 0.7012    D(G(z)): 0.0566 / 0.0652
+[0/5][750/1583] Loss_D: 0.6155  Loss_G: 5.1725  D(x): 0.9450    D(G(z)): 0.3644 / 0.0111
+[0/5][800/1583] Loss_D: 0.9710  Loss_G: 2.6811  D(x): 0.5658    D(G(z)): 0.1158 / 0.1114
+[0/5][850/1583] Loss_D: 0.3815  Loss_G: 3.9660  D(x): 0.7772    D(G(z)): 0.0710 / 0.0342
+[0/5][900/1583] Loss_D: 0.4494  Loss_G: 3.0438  D(x): 0.8048    D(G(z)): 0.1647 / 0.0727
+[0/5][950/1583] Loss_D: 0.4781  Loss_G: 5.3596  D(x): 0.6935    D(G(z)): 0.0215 / 0.0138
+[0/5][1000/1583]        Loss_D: 0.5274  Loss_G: 4.8446  D(x): 0.8350    D(G(z)): 0.2187 / 0.0155
+[0/5][1050/1583]        Loss_D: 0.3903  Loss_G: 5.9931  D(x): 0.9099    D(G(z)): 0.2213 / 0.0047
+[0/5][1100/1583]        Loss_D: 0.3849  Loss_G: 4.5704  D(x): 0.7392    D(G(z)): 0.0109 / 0.0180
+[0/5][1150/1583]        Loss_D: 0.3817  Loss_G: 4.2246  D(x): 0.8328    D(G(z)): 0.1307 / 0.0287
+[0/5][1200/1583]        Loss_D: 0.5055  Loss_G: 3.4494  D(x): 0.7775    D(G(z)): 0.1491 / 0.0596
+[0/5][1250/1583]        Loss_D: 0.5694  Loss_G: 1.8774  D(x): 0.7021    D(G(z)): 0.0683 / 0.2050
+[0/5][1300/1583]        Loss_D: 2.9645  Loss_G: 6.4024  D(x): 0.9607    D(G(z)): 0.8788 / 0.0061
+[0/5][1350/1583]        Loss_D: 0.7374  Loss_G: 2.2333  D(x): 0.5721    D(G(z)): 0.0469 / 0.1633
+[0/5][1400/1583]        Loss_D: 0.4834  Loss_G: 2.8277  D(x): 0.7422    D(G(z)): 0.1025 / 0.0944
+[0/5][1450/1583]        Loss_D: 0.5769  Loss_G: 2.7487  D(x): 0.8246    D(G(z)): 0.2540 / 0.0961
+[0/5][1500/1583]        Loss_D: 0.3146  Loss_G: 4.2885  D(x): 0.8496    D(G(z)): 0.1063 / 0.0247
+[0/5][1550/1583]        Loss_D: 0.4361  Loss_G: 4.8447  D(x): 0.8788    D(G(z)): 0.2288 / 0.0134
+[1/5][0/1583]   Loss_D: 1.8822  Loss_G: 3.7181  D(x): 0.2733    D(G(z)): 0.0055 / 0.0683
+[1/5][50/1583]  Loss_D: 0.6328  Loss_G: 4.4999  D(x): 0.8599    D(G(z)): 0.3022 / 0.0236
+[1/5][100/1583] Loss_D: 0.4821  Loss_G: 3.1979  D(x): 0.7249    D(G(z)): 0.0769 / 0.0756
+[1/5][150/1583] Loss_D: 0.6823  Loss_G: 4.9782  D(x): 0.8888    D(G(z)): 0.3649 / 0.0138
+[1/5][200/1583] Loss_D: 0.3647  Loss_G: 3.1012  D(x): 0.8585    D(G(z)): 0.1618 / 0.0679
+[1/5][250/1583] Loss_D: 0.8459  Loss_G: 1.7260  D(x): 0.5894    D(G(z)): 0.1342 / 0.2480
+[1/5][300/1583] Loss_D: 0.7662  Loss_G: 4.2745  D(x): 0.7942    D(G(z)): 0.3596 / 0.0238
+[1/5][350/1583] Loss_D: 0.7458  Loss_G: 2.1627  D(x): 0.6098    D(G(z)): 0.0858 / 0.1737
+[1/5][400/1583] Loss_D: 0.9870  Loss_G: 1.4827  D(x): 0.4727    D(G(z)): 0.0207 / 0.2935
+[1/5][450/1583] Loss_D: 0.5379  Loss_G: 2.8176  D(x): 0.7165    D(G(z)): 0.0945 / 0.0886
+[1/5][500/1583] Loss_D: 0.4195  Loss_G: 3.1423  D(x): 0.8823    D(G(z)): 0.2239 / 0.0617
+[1/5][550/1583] Loss_D: 0.4509  Loss_G: 2.5207  D(x): 0.7416    D(G(z)): 0.0960 / 0.1109
+[1/5][600/1583] Loss_D: 0.6813  Loss_G: 4.5272  D(x): 0.8108    D(G(z)): 0.3096 / 0.0164
+[1/5][650/1583] Loss_D: 0.5724  Loss_G: 3.6105  D(x): 0.8073    D(G(z)): 0.2329 / 0.0441
+[1/5][700/1583] Loss_D: 0.6276  Loss_G: 2.2359  D(x): 0.6397    D(G(z)): 0.0745 / 0.1401
+[1/5][750/1583] Loss_D: 0.3528  Loss_G: 3.0292  D(x): 0.8245    D(G(z)): 0.1165 / 0.0654
+[1/5][800/1583] Loss_D: 1.5597  Loss_G: 7.4901  D(x): 0.9749    D(G(z)): 0.7081 / 0.0012
+[1/5][850/1583] Loss_D: 1.6685  Loss_G: 1.4540  D(x): 0.3283    D(G(z)): 0.0155 / 0.3347
+[1/5][900/1583] Loss_D: 0.9451  Loss_G: 1.4984  D(x): 0.4886    D(G(z)): 0.0272 / 0.3031
+[1/5][950/1583] Loss_D: 0.6167  Loss_G: 1.9318  D(x): 0.6278    D(G(z)): 0.0689 / 0.1991
+[1/5][1000/1583]        Loss_D: 0.5066  Loss_G: 3.0088  D(x): 0.7632    D(G(z)): 0.1588 / 0.0692
+[1/5][1050/1583]        Loss_D: 0.4756  Loss_G: 4.0927  D(x): 0.8541    D(G(z)): 0.2458 / 0.0239
+[1/5][1100/1583]        Loss_D: 0.5057  Loss_G: 3.5921  D(x): 0.8640    D(G(z)): 0.2681 / 0.0375
+[1/5][1150/1583]        Loss_D: 1.5850  Loss_G: 1.2502  D(x): 0.2747    D(G(z)): 0.0066 / 0.3789
+[1/5][1200/1583]        Loss_D: 0.9067  Loss_G: 1.5964  D(x): 0.5258    D(G(z)): 0.1109 / 0.2676
+[1/5][1250/1583]        Loss_D: 0.4103  Loss_G: 2.6799  D(x): 0.7704    D(G(z)): 0.1122 / 0.0918
+[1/5][1300/1583]        Loss_D: 0.6690  Loss_G: 3.0640  D(x): 0.7549    D(G(z)): 0.2790 / 0.0648
+[1/5][1350/1583]        Loss_D: 0.6313  Loss_G: 2.1532  D(x): 0.7885    D(G(z)): 0.2814 / 0.1465
+[1/5][1400/1583]        Loss_D: 0.6171  Loss_G: 3.2112  D(x): 0.8430    D(G(z)): 0.3155 / 0.0613
+[1/5][1450/1583]        Loss_D: 0.5730  Loss_G: 4.4161  D(x): 0.8979    D(G(z)): 0.3310 / 0.0194
+[1/5][1500/1583]        Loss_D: 0.8083  Loss_G: 4.5388  D(x): 0.8778    D(G(z)): 0.4475 / 0.0163
+[1/5][1550/1583]        Loss_D: 0.5452  Loss_G: 3.2668  D(x): 0.7826    D(G(z)): 0.2243 / 0.0567
+[2/5][0/1583]   Loss_D: 1.0349  Loss_G: 1.7113  D(x): 0.4709    D(G(z)): 0.0650 / 0.2336
+[2/5][50/1583]  Loss_D: 0.6331  Loss_G: 2.1121  D(x): 0.6369    D(G(z)): 0.0666 / 0.1802
+[2/5][100/1583] Loss_D: 0.5541  Loss_G: 2.9218  D(x): 0.7824    D(G(z)): 0.2242 / 0.0754
+[2/5][150/1583] Loss_D: 0.6585  Loss_G: 1.7459  D(x): 0.6087    D(G(z)): 0.0796 / 0.2195
+[2/5][200/1583] Loss_D: 0.6880  Loss_G: 3.7234  D(x): 0.8709    D(G(z)): 0.3754 / 0.0350
+[2/5][250/1583] Loss_D: 0.7289  Loss_G: 4.2876  D(x): 0.9004    D(G(z)): 0.4118 / 0.0203
+[2/5][300/1583] Loss_D: 1.1389  Loss_G: 1.1083  D(x): 0.4015    D(G(z)): 0.0594 / 0.3952
+[2/5][350/1583] Loss_D: 1.0179  Loss_G: 4.4982  D(x): 0.8389    D(G(z)): 0.5134 / 0.0159
+[2/5][400/1583] Loss_D: 0.5610  Loss_G: 3.0215  D(x): 0.6711    D(G(z)): 0.0919 / 0.0736
+[2/5][450/1583] Loss_D: 0.5803  Loss_G: 2.8090  D(x): 0.7648    D(G(z)): 0.2097 / 0.0865
+[2/5][500/1583] Loss_D: 0.7311  Loss_G: 1.8459  D(x): 0.5504    D(G(z)): 0.0527 / 0.2165
+[2/5][550/1583] Loss_D: 0.4800  Loss_G: 2.6273  D(x): 0.7588    D(G(z)): 0.1546 / 0.0966
+[2/5][600/1583] Loss_D: 0.5431  Loss_G: 2.1520  D(x): 0.6823    D(G(z)): 0.1067 / 0.1490
+[2/5][650/1583] Loss_D: 0.6323  Loss_G: 2.0233  D(x): 0.7691    D(G(z)): 0.2674 / 0.1774
+[2/5][700/1583] Loss_D: 0.3883  Loss_G: 2.6608  D(x): 0.7846    D(G(z)): 0.1060 / 0.0924
+[2/5][750/1583] Loss_D: 0.7357  Loss_G: 3.9795  D(x): 0.9296    D(G(z)): 0.4487 / 0.0272
+[2/5][800/1583] Loss_D: 0.6692  Loss_G: 3.8394  D(x): 0.8948    D(G(z)): 0.3863 / 0.0324
+[2/5][850/1583] Loss_D: 2.2030  Loss_G: 0.4439  D(x): 0.1779    D(G(z)): 0.0158 / 0.6806
+[2/5][900/1583] Loss_D: 0.4746  Loss_G: 2.4117  D(x): 0.7298    D(G(z)): 0.1132 / 0.1258
+[2/5][950/1583] Loss_D: 0.5646  Loss_G: 2.4689  D(x): 0.7901    D(G(z)): 0.2471 / 0.1132
+[2/5][1000/1583]        Loss_D: 0.6745  Loss_G: 1.3823  D(x): 0.6465    D(G(z)): 0.1613 / 0.3082
+[2/5][1050/1583]        Loss_D: 1.0826  Loss_G: 0.7337  D(x): 0.4485    D(G(z)): 0.1115 / 0.5195
+[2/5][1100/1583]        Loss_D: 0.8065  Loss_G: 1.4946  D(x): 0.6651    D(G(z)): 0.2578 / 0.2686
+[2/5][1150/1583]        Loss_D: 0.9739  Loss_G: 3.7315  D(x): 0.9388    D(G(z)): 0.5356 / 0.0366
+[2/5][1200/1583]        Loss_D: 0.4648  Loss_G: 2.9908  D(x): 0.8552    D(G(z)): 0.2345 / 0.0683
+[2/5][1250/1583]        Loss_D: 1.5697  Loss_G: 0.8675  D(x): 0.2766    D(G(z)): 0.0311 / 0.4737
+[2/5][1300/1583]        Loss_D: 0.3767  Loss_G: 2.6141  D(x): 0.8178    D(G(z)): 0.1350 / 0.1010
+[2/5][1350/1583]        Loss_D: 0.4897  Loss_G: 1.9802  D(x): 0.7484    D(G(z)): 0.1515 / 0.1677
+[2/5][1400/1583]        Loss_D: 0.6945  Loss_G: 1.6912  D(x): 0.5914    D(G(z)): 0.0782 / 0.2315
+[2/5][1450/1583]        Loss_D: 0.3919  Loss_G: 2.7994  D(x): 0.8482    D(G(z)): 0.1881 / 0.0746
+[2/5][1500/1583]        Loss_D: 1.3548  Loss_G: 3.9970  D(x): 0.8906    D(G(z)): 0.6338 / 0.0308
+[2/5][1550/1583]        Loss_D: 0.5079  Loss_G: 3.4268  D(x): 0.8678    D(G(z)): 0.2712 / 0.0455
+[3/5][0/1583]   Loss_D: 1.7010  Loss_G: 1.2078  D(x): 0.2576    D(G(z)): 0.0343 / 0.3638
+[3/5][50/1583]  Loss_D: 0.8691  Loss_G: 3.7860  D(x): 0.8411    D(G(z)): 0.4495 / 0.0339
+[3/5][100/1583] Loss_D: 0.6276  Loss_G: 1.4785  D(x): 0.6564    D(G(z)): 0.1283 / 0.2731
+[3/5][150/1583] Loss_D: 0.8378  Loss_G: 3.2283  D(x): 0.9196    D(G(z)): 0.4739 / 0.0583
+[3/5][200/1583] Loss_D: 0.4895  Loss_G: 2.4876  D(x): 0.8581    D(G(z)): 0.2556 / 0.1046
+[3/5][250/1583] Loss_D: 1.7910  Loss_G: 0.6341  D(x): 0.2169    D(G(z)): 0.0106 / 0.5612
+[3/5][300/1583] Loss_D: 0.6523  Loss_G: 3.4066  D(x): 0.8970    D(G(z)): 0.3821 / 0.0480
+[3/5][350/1583] Loss_D: 0.8540  Loss_G: 4.7474  D(x): 0.9280    D(G(z)): 0.4898 / 0.0125
+[3/5][400/1583] Loss_D: 1.0608  Loss_G: 5.2535  D(x): 0.9663    D(G(z)): 0.5946 / 0.0076
+[3/5][450/1583] Loss_D: 0.7792  Loss_G: 1.7931  D(x): 0.5596    D(G(z)): 0.1059 / 0.2169
+[3/5][500/1583] Loss_D: 1.0346  Loss_G: 4.5509  D(x): 0.8925    D(G(z)): 0.5427 / 0.0146
+[3/5][550/1583] Loss_D: 0.5411  Loss_G: 1.5521  D(x): 0.6944    D(G(z)): 0.1282 / 0.2553
+[3/5][600/1583] Loss_D: 0.8271  Loss_G: 2.9603  D(x): 0.5829    D(G(z)): 0.1550 / 0.0850
+[3/5][650/1583] Loss_D: 0.5155  Loss_G: 2.6884  D(x): 0.8697    D(G(z)): 0.2816 / 0.0970
+[3/5][700/1583] Loss_D: 0.5647  Loss_G: 1.6044  D(x): 0.6701    D(G(z)): 0.0965 / 0.2382
+[3/5][750/1583] Loss_D: 1.1231  Loss_G: 0.7877  D(x): 0.3974    D(G(z)): 0.0325 / 0.4942
+[3/5][800/1583] Loss_D: 0.4719  Loss_G: 2.0823  D(x): 0.7328    D(G(z)): 0.1170 / 0.1531
+[3/5][850/1583] Loss_D: 0.8078  Loss_G: 1.8972  D(x): 0.6317    D(G(z)): 0.2308 / 0.1849
+[3/5][900/1583] Loss_D: 0.5900  Loss_G: 3.5543  D(x): 0.8926    D(G(z)): 0.3447 / 0.0391
+[3/5][950/1583] Loss_D: 0.6461  Loss_G: 1.4777  D(x): 0.6128    D(G(z)): 0.0963 / 0.2759
+[3/5][1000/1583]        Loss_D: 0.6682  Loss_G: 2.8729  D(x): 0.7866    D(G(z)): 0.3059 / 0.0763
+[3/5][1050/1583]        Loss_D: 0.7241  Loss_G: 2.3558  D(x): 0.6878    D(G(z)): 0.2467 / 0.1181
+[3/5][1100/1583]        Loss_D: 0.4165  Loss_G: 2.1612  D(x): 0.7371    D(G(z)): 0.0721 / 0.1437
+[3/5][1150/1583]        Loss_D: 0.4621  Loss_G: 3.3102  D(x): 0.8843    D(G(z)): 0.2665 / 0.0462
+[3/5][1200/1583]        Loss_D: 1.0649  Loss_G: 1.3972  D(x): 0.4093    D(G(z)): 0.0225 / 0.3092
+[3/5][1250/1583]        Loss_D: 0.5624  Loss_G: 1.9609  D(x): 0.7161    D(G(z)): 0.1631 / 0.1839
+[3/5][1300/1583]        Loss_D: 0.4018  Loss_G: 2.7838  D(x): 0.7705    D(G(z)): 0.1027 / 0.0792
+[3/5][1350/1583]        Loss_D: 0.5299  Loss_G: 2.0948  D(x): 0.6730    D(G(z)): 0.0820 / 0.1576
+[3/5][1400/1583]        Loss_D: 0.6805  Loss_G: 1.5963  D(x): 0.6103    D(G(z)): 0.1051 / 0.2412
+[3/5][1450/1583]        Loss_D: 0.5142  Loss_G: 3.1152  D(x): 0.8444    D(G(z)): 0.2573 / 0.0563
+[3/5][1500/1583]        Loss_D: 0.4676  Loss_G: 2.6659  D(x): 0.8554    D(G(z)): 0.2409 / 0.0899
+[3/5][1550/1583]        Loss_D: 0.9554  Loss_G: 3.5713  D(x): 0.9081    D(G(z)): 0.5174 / 0.0400
+[4/5][0/1583]   Loss_D: 0.3852  Loss_G: 3.0742  D(x): 0.8076    D(G(z)): 0.1352 / 0.0632
+[4/5][50/1583]  Loss_D: 0.6592  Loss_G: 1.7271  D(x): 0.6048    D(G(z)): 0.0763 / 0.2185
+[4/5][100/1583] Loss_D: 0.6205  Loss_G: 1.7958  D(x): 0.6357    D(G(z)): 0.0938 / 0.2112
+[4/5][150/1583] Loss_D: 0.5278  Loss_G: 1.7435  D(x): 0.6978    D(G(z)): 0.1155 / 0.2109
+[4/5][200/1583] Loss_D: 1.9512  Loss_G: 5.6651  D(x): 0.9742    D(G(z)): 0.7847 / 0.0071
+[4/5][250/1583] Loss_D: 1.6376  Loss_G: 2.1186  D(x): 0.2520    D(G(z)): 0.0119 / 0.1696
+[4/5][300/1583] Loss_D: 1.0559  Loss_G: 0.8737  D(x): 0.4640    D(G(z)): 0.0902 / 0.4685
+[4/5][350/1583] Loss_D: 0.9324  Loss_G: 1.1454  D(x): 0.5120    D(G(z)): 0.0830 / 0.3848
+[4/5][400/1583] Loss_D: 0.6158  Loss_G: 1.9987  D(x): 0.6393    D(G(z)): 0.1019 / 0.1721
+[4/5][450/1583] Loss_D: 1.0674  Loss_G: 1.3848  D(x): 0.4674    D(G(z)): 0.1371 / 0.3025
+[4/5][500/1583] Loss_D: 0.8762  Loss_G: 3.3360  D(x): 0.8287    D(G(z)): 0.4394 / 0.0494
+[4/5][550/1583] Loss_D: 0.6076  Loss_G: 2.9584  D(x): 0.7465    D(G(z)): 0.2279 / 0.0763
+[4/5][600/1583] Loss_D: 0.5650  Loss_G: 2.0126  D(x): 0.6785    D(G(z)): 0.1027 / 0.1799
+[4/5][650/1583] Loss_D: 0.4365  Loss_G: 2.9324  D(x): 0.8453    D(G(z)): 0.2123 / 0.0670
+[4/5][700/1583] Loss_D: 1.2922  Loss_G: 2.9594  D(x): 0.8534    D(G(z)): 0.6090 / 0.0812
+[4/5][750/1583] Loss_D: 0.6272  Loss_G: 2.3799  D(x): 0.6625    D(G(z)): 0.1316 / 0.1268
+[4/5][800/1583] Loss_D: 0.9049  Loss_G: 4.8241  D(x): 0.9281    D(G(z)): 0.5057 / 0.0122
+[4/5][850/1583] Loss_D: 0.7825  Loss_G: 0.9592  D(x): 0.5445    D(G(z)): 0.0759 / 0.4315
+[4/5][900/1583] Loss_D: 1.6294  Loss_G: 0.2317  D(x): 0.2495    D(G(z)): 0.0415 / 0.8113
+[4/5][950/1583] Loss_D: 0.6007  Loss_G: 3.0554  D(x): 0.8404    D(G(z)): 0.3076 / 0.0642
+[4/5][1000/1583]        Loss_D: 0.6366  Loss_G: 1.5835  D(x): 0.6825    D(G(z)): 0.1861 / 0.2443
+[4/5][1050/1583]        Loss_D: 0.7229  Loss_G: 1.7450  D(x): 0.5874    D(G(z)): 0.0887 / 0.2260
+[4/5][1100/1583]        Loss_D: 0.7046  Loss_G: 1.6655  D(x): 0.6046    D(G(z)): 0.1187 / 0.2269
+[4/5][1150/1583]        Loss_D: 0.4784  Loss_G: 2.7962  D(x): 0.8168    D(G(z)): 0.2131 / 0.0775
+[4/5][1200/1583]        Loss_D: 0.5378  Loss_G: 2.8802  D(x): 0.8455    D(G(z)): 0.2757 / 0.0763
+[4/5][1250/1583]        Loss_D: 0.4027  Loss_G: 2.7482  D(x): 0.8819    D(G(z)): 0.2180 / 0.0868
+[4/5][1300/1583]        Loss_D: 0.6832  Loss_G: 3.3018  D(x): 0.8493    D(G(z)): 0.3550 / 0.0529
+[4/5][1350/1583]        Loss_D: 1.2466  Loss_G: 4.7552  D(x): 0.9577    D(G(z)): 0.6373 / 0.0135
+[4/5][1400/1583]        Loss_D: 0.6523  Loss_G: 3.8695  D(x): 0.9423    D(G(z)): 0.4044 / 0.0284
+[4/5][1450/1583]        Loss_D: 0.6697  Loss_G: 1.6384  D(x): 0.6064    D(G(z)): 0.0885 / 0.2388
+[4/5][1500/1583]        Loss_D: 0.6431  Loss_G: 1.8224  D(x): 0.6104    D(G(z)): 0.0674 / 0.2032
+[4/5][1550/1583]        Loss_D: 0.6393  Loss_G: 4.0893  D(x): 0.9049    D(G(z)): 0.3792 / 0.0229
 </pre></div>
 </div>
 </div>
@@ -1180,7 +1179,7 @@ <h2>Where to Go Next<a class="headerlink" href="#where-to-go-next" title="Permal
 <li><p>Create GANs that generate
 <a class="reference external" href="https://deepmind.com/blog/wavenet-generative-model-raw-audio/">music</a></p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 21 minutes  12.298 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 21 minutes  35.360 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-dcgan-faces-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/176a0eb17083f2a53b7e388098a15704/dcgan_faces_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">dcgan_faces_tutorial.py</span></code></a></p>
@@ -1244,7 +1243,6 @@ <h2>Where to Go Next<a class="headerlink" href="#where-to-go-next" title="Permal
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
 <li><a class="reference internal" href="#">DCGAN Tutorial</a><ul>
-<li><a class="reference internal" href="#introduction">Introduction</a></li>
 <li><a class="reference internal" href="#generative-adversarial-networks">Generative Adversarial Networks</a><ul>
 <li><a class="reference internal" href="#what-is-a-gan">What is a GAN?</a></li>
 <li><a class="reference internal" href="#what-is-a-dcgan">What is a DCGAN?</a></li>
@@ -1279,11 +1277,10 @@ <h2>Where to Go Next<a class="headerlink" href="#where-to-go-next" title="Permal
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/deep_learning_60min_blitz.html b/docs/beginner/deep_learning_60min_blitz.html
index 1e607c39d..c96374dd4 100644
--- a/docs/beginner/deep_learning_60min_blitz.html
+++ b/docs/beginner/deep_learning_60min_blitz.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -405,11 +405,9 @@ <h1>파이토치(PyTorch)로 딥러닝하기: 60분만에 끝장내기<a class="
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/deep_learning_nlp_tutorial.html b/docs/beginner/deep_learning_nlp_tutorial.html
index 50a0dd348..69554b9d9 100644
--- a/docs/beginner/deep_learning_nlp_tutorial.html
+++ b/docs/beginner/deep_learning_nlp_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -341,7 +341,7 @@ <h1>Deep Learning for NLP with Pytorch<a class="headerlink" href="#deep-learning
 </div></p>
 <p><div class="sphx-glr-thumbcontainer" tooltip="Dynamic versus Static Deep Learning Toolkits. Pytorch is a *dynamic* neural network kit...."><div class="figure align-default" id="id5">
 <img alt="../_images/sphx_glr_advanced_tutorial_thumb.png" src="../_images/sphx_glr_advanced_tutorial_thumb.png" />
-<p class="caption"><span class="caption-text"><a class="reference internal" href="nlp/advanced_tutorial.html#sphx-glr-beginner-nlp-advanced-tutorial-py"><span class="std std-ref">Advanced: Making Dynamic Decisions and the Bi-LSTM CRF</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
+<p class="caption"><span class="caption-text"><a class="reference internal" href="nlp/advanced_tutorial.html#sphx-glr-beginner-nlp-advanced-tutorial-py"><span class="std std-ref">심화 과정 : Bi-LSTM CRF와 동적 결정</span></a></span><a class="headerlink" href="#id5" title="Permalink to this image">¶</a></p>
 </div>
 </div></p>
 <div style='clear:both'></div></div>
@@ -403,11 +403,9 @@ <h1>Deep Learning for NLP with Pytorch<a class="headerlink" href="#deep-learning
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html b/docs/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html
index 269d0ab35..c7c6c0c6b 100644
--- a/docs/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html
+++ b/docs/beginner/deploy_seq2seq_hybrid_frontend_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -301,8 +301,8 @@
 </div>
 <div class="sphx-glr-example-title section" id="deploying-a-seq2seq-model-with-torchscript">
 <span id="sphx-glr-beginner-deploy-seq2seq-hybrid-frontend-tutorial-py"></span><h1>Deploying a Seq2Seq Model with TorchScript<a class="headerlink" href="#deploying-a-seq2seq-model-with-torchscript" title="Permalink to this headline">¶</a></h1>
-<p><strong>Author:</strong> <a class="reference external" href="https://github.com/MatthewInkawhich">Matthew Inkawhich</a></p>
-<p>This tutorial will walk through the process of transitioning a
+<p><strong>Author:</strong> <a class="reference external" href="https://github.com/MatthewInkawhich">Matthew Inkawhich</a>
+This tutorial will walk through the process of transitioning a
 sequence-to-sequence model to TorchScript using the TorchScript
 API. The model that we will convert is the chatbot model from the
 <a class="reference external" href="https://pytorch.org/tutorials/beginner/chatbot_tutorial.html">Chatbot tutorial</a>.
@@ -379,10 +379,10 @@ <h2>Prepare Environment<a class="headerlink" href="#prepare-environment" title="
 <code class="docutils literal notranslate"><span class="pre">MAX_LENGTH</span></code> constant is set correctly. As a reminder, this constant
 defines the maximum allowed sentence length during training and the
 maximum length output that the model is capable of producing.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">absolute_import</span>
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">division</span>
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">unicode_literals</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">unicode_literals</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
@@ -458,7 +458,7 @@ <h2>Data Handling<a class="headerlink" href="#data-handling" title="Permalink to
 sentence of words and returns the corresponding sequence of word
 indexes.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Voc</span><span class="p">:</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">trimmed</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">word2index</span> <span class="o">=</span> <span class="p">{}</span>
@@ -544,7 +544,7 @@ <h3>TorchScript Notes:<a class="headerlink" href="#torchscript-notes" title="Per
 as-is. We will initialize all models towards the end of this document
 before we run evaluations.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">EncoderRNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">n_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">n_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">EncoderRNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">n_layers</span> <span class="o">=</span> <span class="n">n_layers</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
@@ -585,7 +585,7 @@ <h2>Define Decoder’s Attention Module<a class="headerlink" href="#define-decod
 of the query sentence at a particular time-step of decoding.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Luong attention layer</span>
 <span class="k">class</span> <span class="nc">Attn</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">method</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Attn</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="n">method</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;dot&#39;</span><span class="p">,</span> <span class="s1">&#39;general&#39;</span><span class="p">,</span> <span class="s1">&#39;concat&#39;</span><span class="p">]:</span>
@@ -651,7 +651,7 @@ <h2>Define Decoder<a class="headerlink" href="#define-decoder" title="Permalink
 <span class="c1">#</span>
 
 <span class="k">class</span> <span class="nc">LuongAttnDecoderRNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attn_model</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">n_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">attn_model</span><span class="p">,</span> <span class="n">embedding</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">n_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">LuongAttnDecoderRNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
         <span class="c1"># Keep for reference</span>
@@ -792,7 +792,7 @@ <h4>Changes:<a class="headerlink" href="#changes" title="Permalink to this headl
 </li>
 </ul>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">GreedySearchDecoder</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">encoder</span><span class="p">,</span> <span class="n">decoder</span><span class="p">,</span> <span class="n">decoder_n_layers</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">encoder</span><span class="p">,</span> <span class="n">decoder</span><span class="p">,</span> <span class="n">decoder_n_layers</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">GreedySearchDecoder</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">encoder</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">decoder</span> <span class="o">=</span> <span class="n">decoder</span>
@@ -1073,35 +1073,35 @@ <h2>Print Graphs<a class="headerlink" href="#print-graphs" title="Permalink to t
       %input_length.1 : Tensor,
       %max_length.1 : int):
   %51 : bool = prim::Constant[value=0]()
-  %40 : bool = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:566:8
+  %40 : bool = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:566:8
   %20 : None = prim::Constant()
-  %17 : int = prim::Constant[value=4]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:561:68
-  %16 : Device = prim::Constant[value=&quot;cpu&quot;]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:561:48
-  %13 : int = prim::Constant[value=2]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:559:41
-  %12 : int = prim::Constant[value=0]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:559:25
-  %14 : int = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:559:25
+  %17 : int = prim::Constant[value=4]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:561:68
+  %16 : Device = prim::Constant[value=&quot;cpu&quot;]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:561:48
+  %13 : int = prim::Constant[value=2]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:559:41
+  %12 : int = prim::Constant[value=0]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:559:25
+  %14 : int = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:559:25
   %4 : __torch__.EncoderRNN = prim::GetAttr[name=&quot;encoder&quot;](%self)
-  %7 : (Tensor, Tensor) = prim::CallMethod[name=&quot;forward&quot;](%4, %input_seq.1, %input_length.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:557:42
+  %7 : (Tensor, Tensor) = prim::CallMethod[name=&quot;forward&quot;](%4, %input_seq.1, %input_length.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:557:42
   %encoder_outputs.1 : Tensor, %encoder_hidden.1 : Tensor = prim::TupleUnpack(%7)
-  %decoder_hidden.1 : Tensor = aten::slice(%encoder_hidden.1, %12, %12, %13, %14) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:559:25
+  %decoder_hidden.1 : Tensor = aten::slice(%encoder_hidden.1, %12, %12, %13, %14) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:559:25
   %19 : int[] = prim::ListConstruct(%14, %14)
-  %22 : Tensor = aten::ones(%19, %17, %20, %16, %20) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:561:24
-  %decoder_input.1 : Tensor = aten::mul(%22, %14) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:561:24
+  %22 : Tensor = aten::ones(%19, %17, %20, %16, %20) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:561:24
+  %decoder_input.1 : Tensor = aten::mul(%22, %14) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:561:24
   %25 : int[] = prim::ListConstruct(%12)
-  %all_tokens.1 : Tensor = aten::zeros(%25, %17, %20, %16, %20) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:563:21
+  %all_tokens.1 : Tensor = aten::zeros(%25, %17, %20, %16, %20) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:563:21
   %31 : int[] = prim::ListConstruct(%12)
-  %all_scores.1 : Tensor = aten::zeros(%31, %20, %20, %16, %20) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:564:21
-  %all_tokens : Tensor, %all_scores : Tensor, %decoder_hidden : Tensor, %decoder_input : Tensor = prim::Loop(%max_length.1, %40, %all_tokens.1, %all_scores.1, %decoder_hidden.1, %decoder_input.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:566:8
+  %all_scores.1 : Tensor = aten::zeros(%31, %20, %20, %16, %20) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:564:21
+  %all_tokens : Tensor, %all_scores : Tensor, %decoder_hidden : Tensor, %decoder_input : Tensor = prim::Loop(%max_length.1, %40, %all_tokens.1, %all_scores.1, %decoder_hidden.1, %decoder_input.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:566:8
     block0(%41 : int, %all_tokens.6 : Tensor, %all_scores.6 : Tensor, %decoder_hidden.5 : Tensor, %decoder_input.9 : Tensor):
       %42 : __torch__.LuongAttnDecoderRNN = prim::GetAttr[name=&quot;decoder&quot;](%self)
-      %46 : (Tensor, Tensor) = prim::CallMethod[name=&quot;forward&quot;](%42, %decoder_input.9, %decoder_hidden.5, %encoder_outputs.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:568:45
+      %46 : (Tensor, Tensor) = prim::CallMethod[name=&quot;forward&quot;](%42, %decoder_input.9, %decoder_hidden.5, %encoder_outputs.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:568:45
       %decoder_output.1 : Tensor, %decoder_hidden.3 : Tensor = prim::TupleUnpack(%46)
-      %decoder_scores.1 : Tensor, %decoder_input.3 : Tensor = aten::max(%decoder_output.1, %14, %51) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:570:44
+      %decoder_scores.1 : Tensor, %decoder_input.3 : Tensor = aten::max(%decoder_output.1, %14, %51) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:570:44
       %59 : Tensor[] = prim::ListConstruct(%all_tokens.6, %decoder_input.3)
-      %all_tokens.3 : Tensor = aten::cat(%59, %12) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:572:25
+      %all_tokens.3 : Tensor = aten::cat(%59, %12) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:572:25
       %65 : Tensor[] = prim::ListConstruct(%all_scores.6, %decoder_scores.1)
-      %all_scores.3 : Tensor = aten::cat(%65, %12) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:573:25
-      %decoder_input.7 : Tensor = aten::unsqueeze(%decoder_input.3, %12) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:575:28
+      %all_scores.3 : Tensor = aten::cat(%65, %12) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:573:25
+      %decoder_input.7 : Tensor = aten::unsqueeze(%decoder_input.3, %12) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py:575:28
       -&gt; (%40, %all_tokens.3, %all_scores.3, %decoder_hidden.3, %decoder_input.7)
   %73 : (Tensor, Tensor) = prim::TupleConstruct(%all_tokens, %all_scores)
   return (%73)
@@ -1155,7 +1155,7 @@ <h2>Save Model<a class="headerlink" href="#save-model" title="Permalink to this
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">scripted_searcher</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;scripted_chatbot.pth&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.768 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.621 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-deploy-seq2seq-hybrid-frontend-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/b333ee9add2489fe6da06cebde14537a/deploy_seq2seq_hybrid_frontend_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deploy_seq2seq_hybrid_frontend_tutorial.py</span></code></a></p>
@@ -1266,11 +1266,10 @@ <h2>Save Model<a class="headerlink" href="#save-model" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/dist_overview.html b/docs/beginner/dist_overview.html
index 973cc1146..4f2d48905 100644
--- a/docs/beginner/dist_overview.html
+++ b/docs/beginner/dist_overview.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -401,7 +401,7 @@ <h3><code class="docutils literal notranslate"><span class="pre">torch.nn.parall
 of in every forward pass, which also helps to speed up training. DDP is shipped
 with several performance optimization technologies. For a more in-depth
 explanation, please refer to this
-<a class="reference external" href="https://arxiv.org/abs/2006.15704">DDP paper</a> (VLDB‘20).</p>
+<a class="reference external" href="https://arxiv.org/abs/2006.15704">DDP paper</a> (VLDB’20).</p>
 <p>DDP materials are listed below:</p>
 <ol class="arabic simple">
 <li><p><a class="reference external" href="https://pytorch.org/docs/stable/notes/ddp.html">DDP notes</a>
@@ -559,11 +559,9 @@ <h2>General Distributed Training<a class="headerlink" href="#general-distributed
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_autograd/tf_two_layer_net.html b/docs/beginner/examples_autograd/tf_two_layer_net.html
index 7a6a901bb..2a156401b 100644
--- a/docs/beginner/examples_autograd/tf_two_layer_net.html
+++ b/docs/beginner/examples_autograd/tf_two_layer_net.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -452,11 +452,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_autograd/two_layer_net_autograd.html b/docs/beginner/examples_autograd/two_layer_net_autograd.html
index 68477702d..8ff408ba2 100644
--- a/docs/beginner/examples_autograd/two_layer_net_autograd.html
+++ b/docs/beginner/examples_autograd/two_layer_net_autograd.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -448,11 +448,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_autograd/two_layer_net_custom_function.html b/docs/beginner/examples_autograd/two_layer_net_custom_function.html
index 5da79c9e6..306b7efee 100644
--- a/docs/beginner/examples_autograd/two_layer_net_custom_function.html
+++ b/docs/beginner/examples_autograd/two_layer_net_custom_function.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -465,11 +465,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_nn/dynamic_net.html b/docs/beginner/examples_nn/dynamic_net.html
index b5796ced2..5a262b585 100644
--- a/docs/beginner/examples_nn/dynamic_net.html
+++ b/docs/beginner/examples_nn/dynamic_net.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -314,7 +314,7 @@
 
 
 <span class="k">class</span> <span class="nc">DynamicNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        생성자에서 순전파 단계에서 사용할 3개의 nn.Linear 인스턴스를 생성합니다.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -452,11 +452,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_nn/two_layer_net_module.html b/docs/beginner/examples_nn/two_layer_net_module.html
index 4ab02d25e..aa4e82a77 100644
--- a/docs/beginner/examples_nn/two_layer_net_module.html
+++ b/docs/beginner/examples_nn/two_layer_net_module.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -314,7 +314,7 @@
 
 
 <span class="k">class</span> <span class="nc">TwoLayerNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        생성자에서 2개의 nn.Linear 모듈을 생성하고, 멤버 변수로 지정합니다.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -441,11 +441,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_nn/two_layer_net_nn.html b/docs/beginner/examples_nn/two_layer_net_nn.html
index f93c08061..eae8dfb59 100644
--- a/docs/beginner/examples_nn/two_layer_net_nn.html
+++ b/docs/beginner/examples_nn/two_layer_net_nn.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -446,11 +446,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_nn/two_layer_net_optim.html b/docs/beginner/examples_nn/two_layer_net_optim.html
index f10f34bbc..22c05e9cc 100644
--- a/docs/beginner/examples_nn/two_layer_net_optim.html
+++ b/docs/beginner/examples_nn/two_layer_net_optim.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -438,11 +438,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_tensor/two_layer_net_numpy.html b/docs/beginner/examples_tensor/two_layer_net_numpy.html
index 8465e4939..e61ceb592 100644
--- a/docs/beginner/examples_tensor/two_layer_net_numpy.html
+++ b/docs/beginner/examples_tensor/two_layer_net_numpy.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -428,11 +428,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/examples_tensor/two_layer_net_tensor.html b/docs/beginner/examples_tensor/two_layer_net_tensor.html
index ddf2f791c..ee6405ee1 100644
--- a/docs/beginner/examples_tensor/two_layer_net_tensor.html
+++ b/docs/beginner/examples_tensor/two_layer_net_tensor.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -438,11 +438,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/fgsm_tutorial.html b/docs/beginner/fgsm_tutorial.html
index 3f865eb4c..57fa89725 100644
--- a/docs/beginner/fgsm_tutorial.html
+++ b/docs/beginner/fgsm_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -311,9 +311,9 @@
 <p>이 튜토리얼은 ML 모델들의 보안 취약점에 대한 인식을 높이고, 요즘 화두가 되고있는 적대적 머신 러닝에 대한 통찰력을 제공할 것입니다.
 이미지에 눈치챌 수 없는 작은 변화(perturbation)를 추가하면 모델 성능이 크게 달라질 수 있다는 사실에 놀랄 수 있습니다.
 이번 튜토리얼에서는 이미지 분류기의 예제를 통해 위 내용에 대해 살펴볼 것입니다.
-특히 우리는 가장 많이 사용되는 공격 방법 중 하나인 FGSM (Fast Gradient Sign Attack)을 이용해 MNIST 분류기를 속여 볼 것입니다.</p>
-<div class="section" id="id1">
-<h2>위협 모델<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+특히 우리는 가장 많이 사용되는 공격 방법 중 하나인 FGSM (Fast Gradient Sign Attack)을 이용해 MNIST 분류기를 속여 볼 것입니다.
+위협 모델
+————</p>
 <p>상황에 따라 다양한 범주의 적대적 공격이 있는데 각각 목표가 다르고 공격자가 알고 있는 정보
 대한 가정도 다릅니다. 그러나 보통 가장 중요한 목표는 입력 데이터에 최소한의 작은 변화를
 추가하여 이것이 의도적으로 잘못 분류되게 하는 것입니다. 공격자가 가지고 있는 정보에 대한
@@ -327,9 +327,8 @@ <h2>위협 모델<a class="headerlink" href="#id1" title="Permalink to this head
 원래 특정 소스 클래스의 이미지를 다른 특정 대상 클래스로 분류하도록 변경하려고 함을 의미합니다.</p>
 <p>이 경우 FGSM 공격은 <em>오분류</em> 를 목표로 하는 화이트 박스 공격입니다.
 이런 배경 정보를 갖고 공격에 대해 자세히 알아 보겠습니다.</p>
-</div>
-<div class="section" id="id2">
-<h2>빠른 변화도 부호 공격<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id1">
+<h2>빠른 변화도 부호 공격<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
 <p>공격 방법에 있어 초기 방식이면서 가장 유명한 방식은 <em>빠른 변화도 부호 공격 (FGSM)</em> 이라고 하며
 <a class="reference external" href="https://arxiv.org/abs/1412.6572">적대적 예제에 대한 설명과 활용</a> 에서
 이안 갓펠로우가 기고하였습니다.
@@ -356,23 +355,23 @@ <h2>빠른 변화도 부호 공격<a class="headerlink" href="#id2" title="Perma
 <span class="c1"># 이제 본 튜토리얼의 동기가 명확해지길 바라며, 구현으로 넘어가 보겠습니다.</span>
 <span class="c1">#</span>
 
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 <span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 </pre></div>
 </div>
 </div>
-<div class="section" id="id3">
-<h2>구현<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id2">
+<h2>구현<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
 <p>이 섹션에서는 튜토리얼의 입력 매개 변수에 대해 설명하고 공격중인 모델을
 정의한 다음 공격을 코딩하고 일부 테스트를 실행합니다.</p>
-<div class="section" id="id4">
-<h3>입력<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id3">
+<h3>입력<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
 <p>이 학습서에는 입력이 3 개이며 다음과 같이 정의됩니다:</p>
 <ul class="simple">
 <li><p><strong>epsilons</strong> - 실행에 사용할 엡실론의 리스트입니다. 엡실론 0의 값은 원래 테스트 셋의 모델 성능을
@@ -394,15 +393,15 @@ <h3>입력<a class="headerlink" href="#id4" title="Permalink to this headline">
 </pre></div>
 </div>
 </div>
-<div class="section" id="id5">
-<h3>공격을 받는 모델<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id4">
+<h3>공격을 받는 모델<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
 <p>앞서 말한대로, 공격을 받는 모델은 <a class="reference external" href="https://github.com/pytorch/examples/tree/master/mnist">pytorch/examples/mnist</a>
 와 동일한 MNIST 모델입니다. 본인의 MNIST 모델을 학습 및 저장하는 방식으로 하거나 제공된 모델을 다운로드 해 사용하는 식으로 진행할 수 있습니다.
 여기서 <em>Net</em> 정의 및 테스트 데이터 로더는 MNIST 예제에서 복사 하였습니다.
 이 섹션의 목적은 모델과 데이터 로더를 정의한 다음, 모델을 초기화하고 미리 학습된 가중치를 읽어오는 것입니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># LeNet 모델 정의</span>
 <span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
@@ -441,17 +440,7 @@ <h3>공격을 받는 모델<a class="headerlink" href="#id5" title="Permalink to
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz
-Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw
-Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz
-Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw
-Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz
-Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw
-Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz
-Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw
-Processing...
-Done!
-CUDA Available:  True
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>CUDA Available:  True
 </pre></div>
 </div>
 </div>
@@ -478,16 +467,14 @@ <h3>FGSM 공격<a class="headerlink" href="#fgsm" title="Permalink to this headl
 </pre></div>
 </div>
 </div>
-<div class="section" id="id6">
-<h3>테스팅 함수<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id5">
+<h3>테스팅 함수<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
 <p>마지막으로 본 튜토리얼의 핵심 결과는 <code class="docutils literal notranslate"><span class="pre">테스트</span></code> 함수에서 오게 됩니다.
 이 테스트 기능을 호출 할 때마다 MNIST 테스트 셋에서 전체 테스트 단계를 수행하고 최종 정확도를 보고합니다.
 그러나 이 함수에는 <em>엡실론</em> 입력도 필요합니다. 이는 <code class="docutils literal notranslate"><span class="pre">테스트`</span></code> 함수가 <span class="math notranslate nohighlight">\(\epsilon\)</span> 크기에 따라 공격자의 공격을 받는 모델의
-정확도을 보고하기 때문입니다. 더 구체적으로 보면 테스트 셋의 각각의 샘플에서 테스트 함수는 입력 데이터에 대한 손실 변화도( <span class="math notranslate nohighlight">\(data\_grad\)</span> )를 계산하고,</p>
-<blockquote>
-<div><p><a href="#id7"><span class="problematic" id="id8">``</span></a>FGSM 공격``( <span class="math notranslate nohighlight">\(perturbed\_data\)</span> )을 받은 작은 변화가 적용된 이미지를 만들고 나서 작은 변화가 적용된 이미지가 적대적인지 확인을 합니다.</p>
-</div></blockquote>
-<p>추가로 모델의 정확도를 테스트하기 위해서 테스트 함수는 나중에 시각화하여 볼 수 있도록 성공적으로 얻은 적대적 이미지를 저장하고 반환합니다.</p>
+정확도을 보고하기 때문입니다. 더 구체적으로 보면 테스트 셋의 각각의 샘플에서 테스트 함수는 입력 데이터에 대한 손실 변화도( <span class="math notranslate nohighlight">\(data\_grad\)</span> )를 계산하고,
+<code class="docutils literal notranslate"><span class="pre">FGSM</span> <span class="pre">공격</span></code> (<span class="math notranslate nohighlight">\(perturbed\_data\)</span>) 을 받은 작은 변화가 적용된 이미지를 만들고 나서 작은 변화가 적용된 이미지가 적대적인지 확인을 합니다.
+추가로 모델의 정확도를 테스트하기 위해서 테스트 함수는 나중에 시각화하여 볼 수 있도록 성공적으로 얻은 적대적 이미지를 저장하고 반환합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">test</span><span class="p">(</span> <span class="n">model</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">test_loader</span><span class="p">,</span> <span class="n">epsilon</span> <span class="p">):</span>
 
     <span class="c1"># 정확도 카운터</span>
@@ -552,8 +539,8 @@ <h3>테스팅 함수<a class="headerlink" href="#id6" title="Permalink to this h
 </pre></div>
 </div>
 </div>
-<div class="section" id="id9">
-<h3>공격 실행<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id6">
+<h3>공격 실행<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h3>
 <p>구현의 마지막 부분은 공격을 실행하는 것입니다. 여기서 전체 테스트 스텝을 각 <em>엡실론</em> 값에 실행합니다.
 각 엡실론마다 최종 정확도와 성공적인 일부 적대 사례를 저장하여 다음 섹션에 표시합니다.
 엡실론 값이 증가함에 따라 출력된 정확도가 어떻게 감소하는지 보십시오.
@@ -580,8 +567,8 @@ <h3>공격 실행<a class="headerlink" href="#id9" title="Permalink to this head
 </div>
 </div>
 </div>
-<div class="section" id="id10">
-<h2>결과<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id7">
+<h2>결과<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="vs">
 <h3>정확도 vs 엡실론<a class="headerlink" href="#vs" title="Permalink to this headline">¶</a></h3>
 <p>첫 번째 결과는 정확도 vs 엡실론 을 도식화 한 것 입니다.
@@ -604,8 +591,8 @@ <h3>정확도 vs 엡실론<a class="headerlink" href="#vs" title="Permalink to t
 </div>
 <img alt="../_images/sphx_glr_fgsm_tutorial_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_fgsm_tutorial_001.png" />
 </div>
-<div class="section" id="id11">
-<h3>샘플 적대적 예제들<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id8">
+<h3>샘플 적대적 예제들<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h3>
 <p>공짜 점심은 없다는 것을 기억하시나요? 이 경우에는 엡실론이 증가할수록 테스트 정확도는 떨어집니다.
 <strong>그러나</strong> 작은 변화는 더 쉽게 인식할 수 있게 됩니다.
 실제로 정확도 저하와 공격자가 고려해야 하는 이해도 사이에는 상충 관계(tradeoff)가 있습니다.
@@ -635,8 +622,8 @@ <h3>샘플 적대적 예제들<a class="headerlink" href="#id11" title="Permalin
 <img alt="../_images/sphx_glr_fgsm_tutorial_002.png" class="sphx-glr-single-img" src="../_images/sphx_glr_fgsm_tutorial_002.png" />
 </div>
 </div>
-<div class="section" id="id12">
-<h2>다음 단계는?<a class="headerlink" href="#id12" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id9">
+<h2>다음 단계는?<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
 <p>이번 튜토리얼에서 적대적 머신 러닝에 대한 통찰을 얻을 수 있었기를 바랍니다.
 튜토리얼의 내용으로부터 앞으로 더 많은 것들을 알아나갈 수 있습니다.
 튜토리얼의 적대적 공격 예제는 본 분야의 초급 단계이며
@@ -650,7 +637,7 @@ <h2>다음 단계는?<a class="headerlink" href="#id12" title="Permalink to this
 그러나 적대적 머신 러닝 분야에 대해서 많은 것을 알기 위한 최고의 방법은 많이 시도해보는 것입니다.
 NIPS 2017 경쟁에서 소개된 다양한 공격 방법을 직접 구현해 보고, FGSM 과 어떤 점이 다른지 연구해 보세요.
 그리고 나서 직접 만든 공격으로부터 모델을 방어해 보세요.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 6 minutes  12.890 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  31.213 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-fgsm-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/377bf4a7b1761e5f081e057385870d8e/fgsm_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">fgsm_tutorial.py</span></code></a></p>
@@ -714,22 +701,21 @@ <h2>다음 단계는?<a class="headerlink" href="#id12" title="Permalink to this
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
 <li><a class="reference internal" href="#">적대적 예제 생성(Adversarial Example Generation)</a><ul>
-<li><a class="reference internal" href="#id1">위협 모델</a></li>
-<li><a class="reference internal" href="#id2">빠른 변화도 부호 공격</a></li>
-<li><a class="reference internal" href="#id3">구현</a><ul>
-<li><a class="reference internal" href="#id4">입력</a></li>
-<li><a class="reference internal" href="#id5">공격을 받는 모델</a></li>
+<li><a class="reference internal" href="#id1">빠른 변화도 부호 공격</a></li>
+<li><a class="reference internal" href="#id2">구현</a><ul>
+<li><a class="reference internal" href="#id3">입력</a></li>
+<li><a class="reference internal" href="#id4">공격을 받는 모델</a></li>
 <li><a class="reference internal" href="#fgsm">FGSM 공격</a></li>
-<li><a class="reference internal" href="#id6">테스팅 함수</a></li>
-<li><a class="reference internal" href="#id9">공격 실행</a></li>
+<li><a class="reference internal" href="#id5">테스팅 함수</a></li>
+<li><a class="reference internal" href="#id6">공격 실행</a></li>
 </ul>
 </li>
-<li><a class="reference internal" href="#id10">결과</a><ul>
+<li><a class="reference internal" href="#id7">결과</a><ul>
 <li><a class="reference internal" href="#vs">정확도 vs 엡실론</a></li>
-<li><a class="reference internal" href="#id11">샘플 적대적 예제들</a></li>
+<li><a class="reference internal" href="#id8">샘플 적대적 예제들</a></li>
 </ul>
 </li>
-<li><a class="reference internal" href="#id12">다음 단계는?</a></li>
+<li><a class="reference internal" href="#id9">다음 단계는?</a></li>
 </ul>
 </li>
 </ul>
@@ -747,11 +733,10 @@ <h2>다음 단계는?<a class="headerlink" href="#id12" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/former_torchies/autograd_tutorial_old.html b/docs/beginner/former_torchies/autograd_tutorial_old.html
index a3826a660..a020b5f47 100644
--- a/docs/beginner/former_torchies/autograd_tutorial_old.html
+++ b/docs/beginner/former_torchies/autograd_tutorial_old.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -478,11 +478,9 @@ <h2>변화도(Gradient)<a class="headerlink" href="#gradient" title="Permalink t
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/former_torchies/nnft_tutorial.html b/docs/beginner/former_torchies/nnft_tutorial.html
index 67e38ebd8..ba88ab516 100644
--- a/docs/beginner/former_torchies/nnft_tutorial.html
+++ b/docs/beginner/former_torchies/nnft_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -346,7 +346,7 @@ <h2>예제1: 합성곱 신경망(ConvNet)<a class="headerlink" href="#convnet" t
 
 <span class="k">class</span> <span class="nc">MNISTConvNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="c1"># 여기에 모든 모듈을 생성해두고,</span>
         <span class="c1"># 나중에 여기에서 선언해둔 이름으로 사용할 수 있습니다.</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MNISTConvNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
@@ -549,7 +549,7 @@ <h2>예제2: 순환 신경망(Recurrent Nets)<a class="headerlink" href="#recurr
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">RNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
     <span class="c1"># you can also accept arguments in your model constructor</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">RNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
@@ -591,7 +591,7 @@ <h2>예제2: 순환 신경망(Recurrent Nets)<a class="headerlink" href="#recurr
 <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.075 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.084 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-former-torchies-nnft-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/9184ab4adcdfb4d0dbcf2e566b2705d3/nnft_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">nnft_tutorial.py</span></code></a></p>
@@ -666,11 +666,9 @@ <h2>예제2: 순환 신경망(Recurrent Nets)<a class="headerlink" href="#recurr
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/former_torchies/parallelism_tutorial.html b/docs/beginner/former_torchies/parallelism_tutorial.html
index 661005792..d7a7179e8 100644
--- a/docs/beginner/former_torchies/parallelism_tutorial.html
+++ b/docs/beginner/former_torchies/parallelism_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -314,7 +314,7 @@ <h2>DataParallel<a class="headerlink" href="#dataparallel" title="Permalink to t
 
 <span class="k">class</span> <span class="nc">DataParallelModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">block1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">)</span>
 
@@ -341,7 +341,7 @@ <h2>DataParallel<a class="headerlink" href="#dataparallel" title="Permalink to t
 그래도 속성에 접근하고자 한다면 아래와 같이 <code class="docutils literal notranslate"><span class="pre">DataParallel</span></code> 의 서브클래스를
 사용하는 것이 좋습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">MyDataParallel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">DataParallel</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -379,7 +379,7 @@ <h2>모델의 일부는 CPU, 일부는 GPU에서<a class="headerlink" href="#cpu
 
 <span class="k">class</span> <span class="nc">DistributedModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
             <span class="n">embedding</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="mi">1000</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
             <span class="n">rnn</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span>
@@ -412,7 +412,7 @@ <h2>모델의 일부는 CPU, 일부는 GPU에서<a class="headerlink" href="#cpu
 <li><p><a class="reference external" href="https://discuss.pytorch.org/">포럼에서 PyTorch에 대해 얘기하기</a></p></li>
 <li><p><a class="reference external" href="http://pytorch.slack.com/messages/beginner/">Slack에서 다른 사용자와 대화하기</a></p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.057 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.067 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-former-torchies-parallelism-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/9629e4ec46921a9569d03101ecfddaee/parallelism_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">parallelism_tutorial.py</span></code></a></p>
@@ -486,11 +486,9 @@ <h2>모델의 일부는 CPU, 일부는 GPU에서<a class="headerlink" href="#cpu
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/former_torchies/sg_execution_times.html b/docs/beginner/former_torchies/sg_execution_times.html
index c9c87907a..a1086cee8 100644
--- a/docs/beginner/former_torchies/sg_execution_times.html
+++ b/docs/beginner/former_torchies/sg_execution_times.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -297,10 +297,10 @@
               
   <div class="section" id="computation-times">
 <span id="sphx-glr-beginner-former-torchies-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.132</strong> total execution time for <strong>beginner_former_torchies</strong> files:</p>
+<p><strong>00:00.150</strong> total execution time for <strong>beginner_former_torchies</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:00.075</strong>: <a class="reference internal" href="nnft_tutorial.html#sphx-glr-beginner-former-torchies-nnft-tutorial-py"><span class="std std-ref">nn 패키지</span></a> (<code class="docutils literal notranslate"><span class="pre">nnft_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.057</strong>: <a class="reference internal" href="parallelism_tutorial.html#sphx-glr-beginner-former-torchies-parallelism-tutorial-py"><span class="std std-ref">멀티-GPU 예제</span></a> (<code class="docutils literal notranslate"><span class="pre">parallelism_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.084</strong>: <a class="reference internal" href="nnft_tutorial.html#sphx-glr-beginner-former-torchies-nnft-tutorial-py"><span class="std std-ref">nn 패키지</span></a> (<code class="docutils literal notranslate"><span class="pre">nnft_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.067</strong>: <a class="reference internal" href="parallelism_tutorial.html#sphx-glr-beginner-former-torchies-parallelism-tutorial-py"><span class="std std-ref">멀티-GPU 예제</span></a> (<code class="docutils literal notranslate"><span class="pre">parallelism_tutorial.py</span></code>)</p></li>
 <li><p><strong>00:00.000</strong>: <a class="reference internal" href="autograd_tutorial_old.html#sphx-glr-beginner-former-torchies-autograd-tutorial-old-py"><span class="std std-ref">Autograd</span></a> (<code class="docutils literal notranslate"><span class="pre">autograd_tutorial_old.py</span></code>)</p></li>
 <li><p><strong>00:00.000</strong>: <a class="reference internal" href="tensor_tutorial_old.html#sphx-glr-beginner-former-torchies-tensor-tutorial-old-py"><span class="std std-ref">Tensor</span></a> (<code class="docutils literal notranslate"><span class="pre">tensor_tutorial_old.py</span></code>)</p></li>
 </ul>
@@ -363,11 +363,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/former_torchies/tensor_tutorial_old.html b/docs/beginner/former_torchies/tensor_tutorial_old.html
index 5549ce56a..da6adbb5b 100644
--- a/docs/beginner/former_torchies/tensor_tutorial_old.html
+++ b/docs/beginner/former_torchies/tensor_tutorial_old.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -497,11 +497,9 @@ <h2>CUDA Tensor<a class="headerlink" href="#cuda-tensor" title="Permalink to thi
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/former_torchies_tutorial.html b/docs/beginner/former_torchies_tutorial.html
index 46c5ec5eb..110b887c2 100644
--- a/docs/beginner/former_torchies_tutorial.html
+++ b/docs/beginner/former_torchies_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -397,11 +397,9 @@ <h1>Torch 사용자를 위한 PyTorch<a class="headerlink" href="#torch-pytorch"
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html b/docs/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html
index e434bf44a..142233ca5 100644
--- a/docs/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html
+++ b/docs/beginner/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -464,7 +464,7 @@ <h3>Top-Level Module<a class="headerlink" href="#top-level-module" title="Permal
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># This is a demonstration net that calls all of the different types of</span>
 <span class="c1"># methods and functions</span>
 <span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="c1"># Modules must be attributes on the Module because if you want to trace</span>
         <span class="c1"># or script this Module, we must be able to inherit the submodules&#39;</span>
@@ -507,51 +507,51 @@ <h3>Running the Model<a class="headerlink" href="#running-the-model" title="Perm
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>traced_fn graph graph(%x : Float()):
-  %1 : Long() = prim::Constant[value={2}]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:105:0
-  %2 : Float() = aten::mul(%x, %1) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:105:0
-  %3 : Float() = aten::abs(%2) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:105:0
+  %1 : Long() = prim::Constant[value={2}]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:105:0
+  %2 : Float() = aten::mul(%x, %1) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:105:0
+  %3 : Float() = aten::abs(%2) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:105:0
   return (%3)
 
 script_fn graph graph(%x.1 : Tensor):
-  %12 : bool = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:127:4
+  %12 : bool = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:127:4
   %4 : None = prim::Constant()
-  %3 : int = prim::Constant[value=4]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:126:30
-  %1 : int = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:126:20
+  %3 : int = prim::Constant[value=4]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:126:30
+  %1 : int = prim::Constant[value=1]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:126:20
   %2 : int[] = prim::ListConstruct(%1)
-  %z.1 : Tensor = aten::ones(%2, %3, %4, %4, %4) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:126:8
-  %9 : int = aten::Int(%x.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:127:19
-  %z : Tensor = prim::Loop(%9, %12, %z.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:127:4
+  %z.1 : Tensor = aten::ones(%2, %3, %4, %4, %4) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:126:8
+  %9 : int = aten::Int(%x.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:127:19
+  %z : Tensor = prim::Loop(%9, %12, %z.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:127:4
     block0(%i.1 : int, %z.6 : Tensor):
-      %16 : int = aten::add(%i.1, %1) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:128:17
-      %z.3 : Tensor = aten::mul(%z.6, %16) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:128:12
+      %16 : int = aten::add(%i.1, %1) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:128:17
+      %z.3 : Tensor = aten::mul(%z.6, %16) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:128:12
       -&gt; (%12, %z.3)
   return (%z)
 
 TracedModule graph graph(%self : __torch__.TracedModule,
       %x.1 : Float()):
-  %3 : Device = prim::Constant[value=&quot;cpu&quot;]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
-  %4 : int = prim::Constant[value=6]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
-  %5 : bool = prim::Constant[value=0]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
-  %6 : bool = prim::Constant[value=0]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
+  %3 : Device = prim::Constant[value=&quot;cpu&quot;]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
+  %4 : int = prim::Constant[value=6]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
+  %5 : bool = prim::Constant[value=0]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
+  %6 : bool = prim::Constant[value=0]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
   %7 : None = prim::Constant()
-  %x : Float() = aten::to(%x.1, %3, %4, %5, %6, %7) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
-  %9 : Float() = aten::sqrt(%x) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:146:0
-  %10 : Double() = prim::Constant[value={5}]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:146:0
-  %11 : Float() = aten::div(%9, %10) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:146:0
-  %12 : Float() = aten::floor(%11) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:146:0
+  %x : Float() = aten::to(%x.1, %3, %4, %5, %6, %7) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:145:0
+  %9 : Float() = aten::sqrt(%x) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:146:0
+  %10 : Double() = prim::Constant[value={5}]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:146:0
+  %11 : Float() = aten::div(%9, %10) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:146:0
+  %12 : Float() = aten::floor(%11) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:146:0
   return (%12)
 
 ScriptModule graph graph(%self : __torch__.ScriptModule,
       %x.1 : Tensor):
-  %5 : float = prim::Constant[value=2.]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:29
-  %8 : float = prim::Constant[value=0.]() # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:38
-  %r.1 : Tensor = aten::neg(%x.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:165:12
-  %6 : Tensor = aten::fmod(%x.1, %5) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:15
-  %7 : int = aten::Int(%6) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:11
-  %9 : bool = aten::eq(%7, %8) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:11
-  %r : Tensor = prim::If(%9) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:8
+  %5 : float = prim::Constant[value=2.]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:29
+  %8 : float = prim::Constant[value=0.]() # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:38
+  %r.1 : Tensor = aten::neg(%x.1) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:165:12
+  %6 : Tensor = aten::fmod(%x.1, %5) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:15
+  %7 : int = aten::Int(%6) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:11
+  %9 : bool = aten::eq(%7, %8) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:11
+  %r : Tensor = prim::If(%9) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:166:8
     block0():
-      %r.2 : Tensor = aten::div(%x.1, %5) # /home/reserve/Workspace/PyTorch-tutorials-kr/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:167:16
+      %r.2 : Tensor = aten::div(%x.1, %5) # /home/reserve/Workspace/PyTorch-tutorials-kr/v1.6/beginner_source/hybrid_frontend/learning_hybrid_frontend_through_example_tutorial.py:167:16
       -&gt; (%r.2)
     block1():
       -&gt; (%r.1)
@@ -598,7 +598,7 @@ <h3>Tracing the Top-Level Model<a class="headerlink" href="#tracing-the-top-leve
 scripting model <em>is</em> designed to work with both traced and non-traced
 code which means you can call non-traced code from traced functions.
 However, such a model may not be exported to ONNX.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.126 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.093 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-hybrid-frontend-learning-hybrid-frontend-through-example-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/751cefdd3d9e8bae811354ef83e87cba/learning_hybrid_frontend_through_example_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">learning_hybrid_frontend_through_example_tutorial.py</span></code></a></p>
@@ -682,11 +682,10 @@ <h3>Tracing the Top-Level Model<a class="headerlink" href="#tracing-the-top-leve
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/hybrid_frontend/sg_execution_times.html b/docs/beginner/hybrid_frontend/sg_execution_times.html
index 8f605ae81..28f593904 100644
--- a/docs/beginner/hybrid_frontend/sg_execution_times.html
+++ b/docs/beginner/hybrid_frontend/sg_execution_times.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -297,9 +297,9 @@
               
   <div class="section" id="computation-times">
 <span id="sphx-glr-beginner-hybrid-frontend-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.126</strong> total execution time for <strong>beginner_hybrid_frontend</strong> files:</p>
+<p><strong>00:00.093</strong> total execution time for <strong>beginner_hybrid_frontend</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:00.126</strong>: <a class="reference internal" href="learning_hybrid_frontend_through_example_tutorial.html#sphx-glr-beginner-hybrid-frontend-learning-hybrid-frontend-through-example-tutorial-py"><span class="std std-ref">Learning Hybrid Frontend Syntax Through Example</span></a> (<code class="docutils literal notranslate"><span class="pre">learning_hybrid_frontend_through_example_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.093</strong>: <a class="reference internal" href="learning_hybrid_frontend_through_example_tutorial.html#sphx-glr-beginner-hybrid-frontend-learning-hybrid-frontend-through-example-tutorial-py"><span class="std std-ref">Learning Hybrid Frontend Syntax Through Example</span></a> (<code class="docutils literal notranslate"><span class="pre">learning_hybrid_frontend_through_example_tutorial.py</span></code>)</p></li>
 </ul>
 </div>
 
@@ -360,11 +360,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/hybrid_frontend_tutorial.html b/docs/beginner/hybrid_frontend_tutorial.html
index 466d3444b..8c0327adc 100644
--- a/docs/beginner/hybrid_frontend_tutorial.html
+++ b/docs/beginner/hybrid_frontend_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -370,11 +370,9 @@ <h1>Hybrid Frontend Tutorials<a class="headerlink" href="#hybrid-frontend-tutori
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/nlp/advanced_tutorial.html b/docs/beginner/nlp/advanced_tutorial.html
index 32b4da3d2..cc5d91767 100644
--- a/docs/beginner/nlp/advanced_tutorial.html
+++ b/docs/beginner/nlp/advanced_tutorial.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Advanced: Making Dynamic Decisions and the Bi-LSTM CRF &mdash; PyTorch Tutorials 1.6.0 documentation</title>
+  <title>심화 과정 : Bi-LSTM CRF와 동적 결정 &mdash; PyTorch Tutorials 1.6.0 documentation</title>
   
 
   
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -241,7 +241,7 @@
       </li>
 
         
-      <li>Advanced: Making Dynamic Decisions and the Bi-LSTM CRF</li>
+      <li>심화 과정 : Bi-LSTM CRF와 동적 결정</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -299,109 +299,101 @@
 <p class="admonition-title">Note</p>
 <p>Click <a class="reference internal" href="#sphx-glr-download-beginner-nlp-advanced-tutorial-py"><span class="std std-ref">here</span></a> to download the full example code</p>
 </div>
-<div class="sphx-glr-example-title section" id="advanced-making-dynamic-decisions-and-the-bi-lstm-crf">
-<span id="sphx-glr-beginner-nlp-advanced-tutorial-py"></span><h1>Advanced: Making Dynamic Decisions and the Bi-LSTM CRF<a class="headerlink" href="#advanced-making-dynamic-decisions-and-the-bi-lstm-crf" title="Permalink to this headline">¶</a></h1>
-<div class="section" id="dynamic-versus-static-deep-learning-toolkits">
-<h2>Dynamic versus Static Deep Learning Toolkits<a class="headerlink" href="#dynamic-versus-static-deep-learning-toolkits" title="Permalink to this headline">¶</a></h2>
-<p>Pytorch is a <em>dynamic</em> neural network kit. Another example of a dynamic
-kit is <a class="reference external" href="https://github.com/clab/dynet">Dynet</a> (I mention this because
-working with Pytorch and Dynet is similar. If you see an example in
-Dynet, it will probably help you implement it in Pytorch). The opposite
-is the <em>static</em> tool kit, which includes Theano, Keras, TensorFlow, etc.
-The core difference is the following:</p>
+<div class="sphx-glr-example-title section" id="bi-lstm-crf">
+<span id="sphx-glr-beginner-nlp-advanced-tutorial-py"></span><h1>심화 과정 : Bi-LSTM CRF와 동적 결정<a class="headerlink" href="#bi-lstm-crf" title="Permalink to this headline">¶</a></h1>
+<div class="section" id="toolkits">
+<h2>동적, 정적 딥 러닝 툴킷(toolkits) 비교<a class="headerlink" href="#toolkits" title="Permalink to this headline">¶</a></h2>
+<p>Pytorch는 <em>동적</em> 신경망 툴킷입니다. 다른 동적 신경망 툴킷으로는
+<a class="reference external" href="https://github.com/clab/dynet">Dynet</a> 이 있습니다.(이 툴킷을
+예로 든 이유는 사용하는 법이 Pytorch와 비슷하기 때문입니다. Dynet의 예제를 보면
+Pytorch로 구현할 때도 도움이 될 것입니다.) 반대로 <em>정적</em> 툴킷들로
+Theano, Keras, TensorFlow 등이 있습니다. 주요 차이점은 다음과 같습니다:</p>
 <ul class="simple">
-<li><p>In a static toolkit, you define
-a computation graph once, compile it, and then stream instances to it.</p></li>
-<li><p>In a dynamic toolkit, you define a computation graph <em>for each
-instance</em>. It is never compiled and is executed on-the-fly</p></li>
+<li><p>정적 툴킷을 쓸 때는 계산 그래프를 한 번만 정의하고, 컴파일 한 후,
+데이터를 계산 그래프에 넘깁니다.</p></li>
+<li><p>동적 툴킷에서는 <em>각 데이터</em> 의 계산 그래프를 정의하며 컴파일하지
+않고 즉각 실행됩니다.</p></li>
 </ul>
-<p>Without a lot of experience, it is difficult to appreciate the
-difference. One example is to suppose we want to build a deep
-constituent parser. Suppose our model involves roughly the following
-steps:</p>
+<p>경험이 많지 않다면 두 방식의 차이를 알기 어렵습니다. 딥 러닝 기반의
+구구조 분석기(constituent parser)를 예로 들어보겠습니다. 모델은 대략
+다음과 같은 과정을 수행합니다:</p>
 <ul class="simple">
-<li><p>We build the tree bottom up</p></li>
-<li><p>Tag the root nodes (the words of the sentence)</p></li>
-<li><p>From there, use a neural network and the embeddings
-of the words to find combinations that form constituents. Whenever you
-form a new constituent, use some sort of technique to get an embedding
-of the constituent. In this case, our network architecture will depend
-completely on the input sentence. In the sentence “The green cat
-scratched the wall”, at some point in the model, we will want to combine
-the span <span class="math notranslate nohighlight">\((i,j,r) = (1, 3, \text{NP})\)</span> (that is, an NP constituent
-spans word 1 to word 3, in this case “The green cat”).</p></li>
+<li><p>트리를 상향식(bottom-up)으로 만들어 나갑니다.</p></li>
+<li><p>최상위 노드를 태깅합니다. (문장의 각 단어)</p></li>
+<li><p>거기서부터 신경망과 단어들의 임베딩을 이용해 구구조를 이루는 조합을
+찾아냅니다. 새로운 구구조를 생성할 때마다 구구조의 임베딩을 얻기 위한
+어떤 기술이 필요합니다. 지금은 신경망이 오직 입력 문장만 참고할
+것입니다. “The green cat scratched the wall”이란 문장에서, 모델의 어느 시점에
+<span class="math notranslate nohighlight">\((i,j,r) = (1, 3, \text{NP})\)</span> 범위 (단어 1에서부터 단어 3까지가
+NP 구구조라는 뜻이며, 이 문장에서는 “The green cat”) 를 결합하길 원할
+것입니다.</p></li>
 </ul>
-<p>However, another sentence might be “Somewhere, the big fat cat scratched
-the wall”. In this sentence, we will want to form the constituent
-<span class="math notranslate nohighlight">\((2, 4, NP)\)</span> at some point. The constituents we will want to form
-will depend on the instance. If we just compile the computation graph
-once, as in a static toolkit, it will be exceptionally difficult or
-impossible to program this logic. In a dynamic toolkit though, there
-isn’t just 1 pre-defined computation graph. There can be a new
-computation graph for each instance, so this problem goes away.</p>
-<p>Dynamic toolkits also have the advantage of being easier to debug and
-the code more closely resembling the host language (by that I mean that
-Pytorch and Dynet look more like actual Python code than Keras or
-Theano).</p>
+<p>그런데, 또다른 문장 “Somewhere, the big fat cat scratched the wall” 에서는
+어느 시점에 <span class="math notranslate nohighlight">\((2, 4, NP)\)</span> 구구조를 만들기를 원할 것입니다. 우리가
+만들기 원하는 구구조들은 문장에 따라 다릅니다. 만약 정적 툴킷에서처럼
+계산 그래프를 한 번만 컴파일한다면, 이 과정을 프로그래밍하기 매우 어렵거나
+불가능할 것입니다. 하지만 동적 툴킷에서는 하나의 계산 그래프만 있지
+않습니다. 각 문장들마다 새로운 계산 그래프가 있을 수 있기 때문에 이런
+문제가 없습니다.</p>
+<p>동적 틀킷은 디버깅 하기 더 쉽고, 코드가 기반 언어와 더 비슷합니다
+(Pytorch와 Dynet이 Keras 또는 Theano 보다 Python 코드와 더 비슷합니다).</p>
 </div>
-<div class="section" id="bi-lstm-conditional-random-field-discussion">
-<h2>Bi-LSTM Conditional Random Field Discussion<a class="headerlink" href="#bi-lstm-conditional-random-field-discussion" title="Permalink to this headline">¶</a></h2>
-<p>For this section, we will see a full, complicated example of a Bi-LSTM
-Conditional Random Field for named-entity recognition. The LSTM tagger
-above is typically sufficient for part-of-speech tagging, but a sequence
-model like the CRF is really essential for strong performance on NER.
-Familiarity with CRF’s is assumed. Although this name sounds scary, all
-the model is is a CRF but where an LSTM provides the features. This is
-an advanced model though, far more complicated than any earlier model in
-this tutorial. If you want to skip it, that is fine. To see if you’re
-ready, see if you can:</p>
+<div class="section" id="bi-lstm-conditional-rnadom-field">
+<h2>Bi-LSTM Conditional Rnadom Field 설명<a class="headerlink" href="#bi-lstm-conditional-rnadom-field" title="Permalink to this headline">¶</a></h2>
+<p>이 영역에서는 개체명 인식을 수행하는 완성된 Bi-LSTM Conditional Random
+Field 예시를 살펴보겠습니다. 위에 나온 LSTM 태거(tagger)는 일반적으로
+품사 태깅을 하기에 충분합니다. 하지만 CRF 같은 연속된 데이터를 다루는
+모델은 좋은 개체명 인식 모델(NER)에 꼭 필요합니다. 여러분이 CRF를 잘 알고
+있다고 가정하겠습니다. 이름이 무섭게 들릴 수도 있지만, LSTM이 특징을
+제공하는 점을 제외하면 이 모델은 CRF 입니다. 하지만 더 발전된 모델이며,
+이 튜토리얼의 앞부분에 나왔던 모델보다 훨씬 복잡합니다. 넘어가고 싶다면
+넘어가도 괜찮습니다. 이해할 수 있다고 생각한다면, 아래를 읽어보세요:</p>
 <ul class="simple">
-<li><p>Write the recurrence for the viterbi variable at step i for tag k.</p></li>
-<li><p>Modify the above recurrence to compute the forward variables instead.</p></li>
-<li><p>Modify again the above recurrence to compute the forward variables in
-log-space (hint: log-sum-exp)</p></li>
+<li><p>태그 k에 대한 i번째 단계의 비터비(viterbi) 변수를 위해 순환 흐름을 만든다.</p></li>
+<li><p>순방향 변수를 계산하기 위해 위의 순한 흐름을 조정한다.</p></li>
+<li><p>순방향 변수를 로그 공간에서 계산하기 위해 다시 한 번 조정한다.
+(힌트 : 로그-합-지수승)</p></li>
 </ul>
-<p>If you can do those three things, you should be able to understand the
-code below. Recall that the CRF computes a conditional probability. Let
-<span class="math notranslate nohighlight">\(y\)</span> be a tag sequence and <span class="math notranslate nohighlight">\(x\)</span> an input sequence of words.
-Then we compute</p>
+<p>위의 세가지를 할 수 있다면, 아래의 코드를 이해할 수 있을 것입니다.
+CRF는 조건부 확률을 계산한다는 점을 기억하세요. <span class="math notranslate nohighlight">\(y\)</span> 를 연속된
+태그라 하고, <span class="math notranslate nohighlight">\(x\)</span> 를 연속된 입력 단어라 하겠습니다. 그러면 아래의
+식을 계산할 수 있습니다.</p>
 <div class="math notranslate nohighlight">
 \[P(y|x) = \frac{\exp{(\text{Score}(x, y)})}{\sum_{y'} \exp{(\text{Score}(x, y')})}\]</div>
-<p>Where the score is determined by defining some log potentials
-<span class="math notranslate nohighlight">\(\log \psi_i(x,y)\)</span> such that</p>
+<p>점수(score) 함수는 아래와 같이 정의된 로그 포텐셜(potential) <span class="math notranslate nohighlight">\(\log \psi_i(x,y)\)</span>
+함수에 의해 결정됩니다.</p>
 <div class="math notranslate nohighlight">
 \[\text{Score}(x,y) = \sum_i \log \psi_i(x,y)\]</div>
-<p>To make the partition function tractable, the potentials must look only
-at local features.</p>
-<p>In the Bi-LSTM CRF, we define two kinds of potentials: emission and
-transition. The emission potential for the word at index <span class="math notranslate nohighlight">\(i\)</span> comes
-from the hidden state of the Bi-LSTM at timestep <span class="math notranslate nohighlight">\(i\)</span>. The
-transition scores are stored in a <span class="math notranslate nohighlight">\(|T|x|T|\)</span> matrix
-<span class="math notranslate nohighlight">\(\textbf{P}\)</span>, where <span class="math notranslate nohighlight">\(T\)</span> is the tag set. In my
-implementation, <span class="math notranslate nohighlight">\(\textbf{P}_{j,k}\)</span> is the score of transitioning
-to tag <span class="math notranslate nohighlight">\(j\)</span> from tag <span class="math notranslate nohighlight">\(k\)</span>. So:</p>
+<p>분배 함수(partition function)를 단순화하기 위해서, 포텐셜이 주변의
+특징들만 반영한다고 하겠습니다.</p>
+<p>Bi-LSTM CRF 안에 배출(emission), 전이(transition) 두 종류의 포텐셜을
+정의합니다. <span class="math notranslate nohighlight">\(i\)</span> 번째 단어에 대한 배출 포텐셜은 Bi-LSTM의
+<span class="math notranslate nohighlight">\(i\)</span> 번째 시점의 은닉 상태가 결정합니다. 전이 점수는 <span class="math notranslate nohighlight">\(|T|x|T|\)</span>
+형태인 행렬 <span class="math notranslate nohighlight">\(\textbf{P}\)</span> 에 저장되어 있습니다. <span class="math notranslate nohighlight">\(T\)</span> 는
+태그의 집합입니다. 이 구현에서, <span class="math notranslate nohighlight">\(\textbf{P}_{j,k}\)</span> 는 tag <span class="math notranslate nohighlight">\(j\)</span> 에서
+tag <span class="math notranslate nohighlight">\(k\)</span> 로의 전이 점수를 의미합니다. 따라서:</p>
 <div class="math notranslate nohighlight">
 \[\text{Score}(x,y) = \sum_i \log \psi_\text{EMIT}(y_i \rightarrow x_i) + \log \psi_\text{TRANS}(y_{i-1} \rightarrow y_i)\]</div>
 <div class="math notranslate nohighlight">
 \[= \sum_i h_i[y_i] + \textbf{P}_{y_i, y_{i-1}}\]</div>
-<p>where in this second expression, we think of the tags as being assigned
-unique non-negative indices.</p>
-<p>If the above discussion was too brief, you can check out
-<a class="reference external" href="http://www.cs.columbia.edu/%7Emcollins/crf.pdf">this</a> write up from
-Michael Collins on CRFs.</p>
+<p>두 번째 식에서 고유하고 음수가 아닌 인덱스에 의해 태그가 부여됐다고
+간주합니다.</p>
+<p>위의 설명이 너무 간단하다고 생각한다면 CRF에 대한 Michael Collins의
+글을 <a class="reference external" href="http://www.cs.columbia.edu/%7Emcollins/crf.pdf">여기</a> 에서
+읽어보세요.</p>
 </div>
-<div class="section" id="implementation-notes">
-<h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title="Permalink to this headline">¶</a></h2>
-<p>The example below implements the forward algorithm in log space to
-compute the partition function, and the viterbi algorithm to decode.
-Backpropagation will compute the gradients automatically for us. We
-don’t have to do anything by hand.</p>
-<p>The implementation is not optimized. If you understand what is going on,
-you’ll probably quickly see that iterating over the next tag in the
-forward algorithm could probably be done in one big operation. I wanted
-to code to be more readable. If you want to make the relevant change,
-you could probably use this tagger for real tasks.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Author: Robert Guthrie</span>
+<div class="section" id="id1">
+<h2>구현 문서<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>아래의 예시는 로그 공간에서 분배 함수를 계산하기 위한 순방향 알고리즘과
+복호화하기 위한 비터비 알고리즘을 구현한 것입니다. 역전파
+단계에서 변화도는 자동으로 계산될 것입니다. 우리가 직접 할 일은
+없습니다.</p>
+<p>이 구현은 최적의 상태가 아닙니다. 과정을 이해했다면, 순방향 알고리즘
+상에서 다음 태그를 순차적으로 처리하는 과정을 하나의 큰 연산으로 줄일
+수 있다는 것을 아마 빠르게 알 수 있을 것입니다. 이 코드는 가능한 읽기
+쉽게 작성했습니다. 적절하게 수정하면, 이 태거를 실제 문제들에 사용할
+수도 있을 것입니다.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 작성자: Robert Guthrie</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.autograd</span> <span class="k">as</span> <span class="nn">autograd</span>
@@ -411,9 +403,9 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
 <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>Helper functions to make the code more readable.</p>
+<p>코드 가독성을 높여주는 보조 함수들</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">argmax</span><span class="p">(</span><span class="n">vec</span><span class="p">):</span>
-    <span class="c1"># return the argmax as a python int</span>
+    <span class="c1"># argmax를 파이썬 정수형으로 반환합니다.</span>
     <span class="n">_</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">vec</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">idx</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
 
@@ -423,7 +415,7 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
     <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">idxs</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
 
 
-<span class="c1"># Compute log sum exp in a numerically stable way for the forward algorithm</span>
+<span class="c1"># 순방향 알고리즘을 위해 수치적으로 안정적인 방법으로 로그 합 지수승을 계산합니다.</span>
 <span class="k">def</span> <span class="nf">log_sum_exp</span><span class="p">(</span><span class="n">vec</span><span class="p">):</span>
     <span class="n">max_score</span> <span class="o">=</span> <span class="n">vec</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="n">argmax</span><span class="p">(</span><span class="n">vec</span><span class="p">)]</span>
     <span class="n">max_score_broadcast</span> <span class="o">=</span> <span class="n">max_score</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">vec</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">])</span>
@@ -431,10 +423,10 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
         <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">vec</span> <span class="o">-</span> <span class="n">max_score_broadcast</span><span class="p">)))</span>
 </pre></div>
 </div>
-<p>Create model</p>
+<p>모델 생성</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">BiLSTM_CRF</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">tag_to_ix</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">tag_to_ix</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">BiLSTM_CRF</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">embedding_dim</span> <span class="o">=</span> <span class="n">embedding_dim</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span> <span class="o">=</span> <span class="n">hidden_dim</span>
@@ -446,16 +438,15 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
         <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="n">embedding_dim</span><span class="p">,</span> <span class="n">hidden_dim</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
                             <span class="n">num_layers</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">bidirectional</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
-        <span class="c1"># Maps the output of the LSTM into tag space.</span>
+        <span class="c1"># LSTM의 출력을 태그 공간으로 대응시킵니다.</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden2tag</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_dim</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tagset_size</span><span class="p">)</span>
 
-        <span class="c1"># Matrix of transition parameters.  Entry i,j is the score of</span>
-        <span class="c1"># transitioning *to* i *from* j.</span>
+        <span class="c1"># 전이 매개변수 행렬. i, j 성분은 i에서 j로 변할 때의 점수입니다.</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span>
             <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tagset_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tagset_size</span><span class="p">))</span>
 
-        <span class="c1"># These two statements enforce the constraint that we never transfer</span>
-        <span class="c1"># to the start tag and we never transfer from the stop tag</span>
+        <span class="c1"># 이 두 코드는 시작 태그로 전이하지 않고, 정지 태그에서부터</span>
+        <span class="c1"># 전이하지 않도록 강제합니다.</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">START_TAG</span><span class="p">],</span> <span class="p">:]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">10000</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="n">tag_to_ix</span><span class="p">[</span><span class="n">STOP_TAG</span><span class="p">]]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">10000</span>
 
@@ -466,30 +457,28 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
                 <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span> <span class="o">//</span> <span class="mi">2</span><span class="p">))</span>
 
     <span class="k">def</span> <span class="nf">_forward_alg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feats</span><span class="p">):</span>
-        <span class="c1"># Do the forward algorithm to compute the partition function</span>
+        <span class="c1"># 분배 함수를 계산하기 위해 순방향 알고리즘을 수행합니다.</span>
         <span class="n">init_alphas</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tagset_size</span><span class="p">),</span> <span class="o">-</span><span class="mf">10000.</span><span class="p">)</span>
-        <span class="c1"># START_TAG has all of the score.</span>
+        <span class="c1"># START_TAG는 모든 점수를 갖고 있습니다.</span>
         <span class="n">init_alphas</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">START_TAG</span><span class="p">]]</span> <span class="o">=</span> <span class="mf">0.</span>
 
-        <span class="c1"># Wrap in a variable so that we will get automatic backprop</span>
+        <span class="c1"># 자동으로 역전파 되도록 변수로 감쌉니다.</span>
         <span class="n">forward_var</span> <span class="o">=</span> <span class="n">init_alphas</span>
 
-        <span class="c1"># Iterate through the sentence</span>
+        <span class="c1"># 문장의 각 성분을 반복 처리합니다.</span>
         <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="n">feats</span><span class="p">:</span>
-            <span class="n">alphas_t</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># The forward tensors at this timestep</span>
+            <span class="n">alphas_t</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># 현재 시점의 순방향 텐서</span>
             <span class="k">for</span> <span class="n">next_tag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tagset_size</span><span class="p">):</span>
-                <span class="c1"># broadcast the emission score: it is the same regardless of</span>
-                <span class="c1"># the previous tag</span>
+                <span class="c1"># 이전의 태그와 상관없이 배출 점수를 전파합니다.</span>
                 <span class="n">emit_score</span> <span class="o">=</span> <span class="n">feat</span><span class="p">[</span><span class="n">next_tag</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span>
                     <span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tagset_size</span><span class="p">)</span>
-                <span class="c1"># the ith entry of trans_score is the score of transitioning to</span>
-                <span class="c1"># next_tag from i</span>
+                <span class="c1"># trans_score의 i번째 성분은 i로부터 next_tag로 전이할 점수입니다.</span>
                 <span class="n">trans_score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="n">next_tag</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-                <span class="c1"># The ith entry of next_tag_var is the value for the</span>
-                <span class="c1"># edge (i -&gt; next_tag) before we do log-sum-exp</span>
+                <span class="c1"># next_tag_var의 i번째 성분은 로그-합-지수승을 계산하기 전</span>
+                <span class="c1"># i에서 next_tag로 가는 간선의 값입니다.</span>
                 <span class="n">next_tag_var</span> <span class="o">=</span> <span class="n">forward_var</span> <span class="o">+</span> <span class="n">trans_score</span> <span class="o">+</span> <span class="n">emit_score</span>
-                <span class="c1"># The forward variable for this tag is log-sum-exp of all the</span>
-                <span class="c1"># scores.</span>
+                <span class="c1"># 이 태그의 순방향 변수는 모든 점수들의 로그-합-지수승 계산</span>
+                <span class="c1"># 결과입니다.</span>
                 <span class="n">alphas_t</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">log_sum_exp</span><span class="p">(</span><span class="n">next_tag_var</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
             <span class="n">forward_var</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">alphas_t</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">terminal_var</span> <span class="o">=</span> <span class="n">forward_var</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">STOP_TAG</span><span class="p">]]</span>
@@ -505,7 +494,7 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
         <span class="k">return</span> <span class="n">lstm_feats</span>
 
     <span class="k">def</span> <span class="nf">_score_sentence</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feats</span><span class="p">,</span> <span class="n">tags</span><span class="p">):</span>
-        <span class="c1"># Gives the score of a provided tag sequence</span>
+        <span class="c1"># 주어진 태그 순열에 점수를 매깁니다.</span>
         <span class="n">score</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
         <span class="n">tags</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">([</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">START_TAG</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">),</span> <span class="n">tags</span><span class="p">])</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">feat</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">feats</span><span class="p">):</span>
@@ -517,44 +506,43 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
     <span class="k">def</span> <span class="nf">_viterbi_decode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feats</span><span class="p">):</span>
         <span class="n">backpointers</span> <span class="o">=</span> <span class="p">[]</span>
 
-        <span class="c1"># Initialize the viterbi variables in log space</span>
+        <span class="c1"># 비터비 변수를 로그 공간 상에 초기화합니다.</span>
         <span class="n">init_vvars</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">tagset_size</span><span class="p">),</span> <span class="o">-</span><span class="mf">10000.</span><span class="p">)</span>
         <span class="n">init_vvars</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">START_TAG</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span>
 
-        <span class="c1"># forward_var at step i holds the viterbi variables for step i-1</span>
+        <span class="c1"># i 단계의 forward_var는 i-1 단계의 비터비 변수를 갖고 있습니다.</span>
         <span class="n">forward_var</span> <span class="o">=</span> <span class="n">init_vvars</span>
         <span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="n">feats</span><span class="p">:</span>
-            <span class="n">bptrs_t</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># holds the backpointers for this step</span>
-            <span class="n">viterbivars_t</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># holds the viterbi variables for this step</span>
+            <span class="n">bptrs_t</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># 현재 단계의 backpointer를 갖고 있습니다.</span>
+            <span class="n">viterbivars_t</span> <span class="o">=</span> <span class="p">[]</span>  <span class="c1"># 현재 단계의 비터비 변수를 갖고 있습니다.</span>
 
             <span class="k">for</span> <span class="n">next_tag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tagset_size</span><span class="p">):</span>
-                <span class="c1"># next_tag_var[i] holds the viterbi variable for tag i at the</span>
-                <span class="c1"># previous step, plus the score of transitioning</span>
-                <span class="c1"># from tag i to next_tag.</span>
-                <span class="c1"># We don&#39;t include the emission scores here because the max</span>
-                <span class="c1"># does not depend on them (we add them in below)</span>
+                <span class="c1"># next_tag_var[i]는 이전 단계의 태그 i에 대한 비터비 변수와,</span>
+                <span class="c1"># 태그 i에서 next_tag로 전이할 점수를 더한 값을 갖고 있습니다.</span>
+                <span class="c1"># 배출 점수는 argmax와 상관 없기 때문에(아래 코드에서 추가할 것입니다)</span>
+                <span class="c1"># 여기에 포함되지 않습니다.</span>
                 <span class="n">next_tag_var</span> <span class="o">=</span> <span class="n">forward_var</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="n">next_tag</span><span class="p">]</span>
                 <span class="n">best_tag_id</span> <span class="o">=</span> <span class="n">argmax</span><span class="p">(</span><span class="n">next_tag_var</span><span class="p">)</span>
                 <span class="n">bptrs_t</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">best_tag_id</span><span class="p">)</span>
                 <span class="n">viterbivars_t</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">next_tag_var</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">best_tag_id</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
-            <span class="c1"># Now add in the emission scores, and assign forward_var to the set</span>
-            <span class="c1"># of viterbi variables we just computed</span>
+            <span class="c1"># 이제 배출 점수를 더합니다. 그리고 방금 계산한 비터비 변수의</span>
+            <span class="c1"># 집합을 forward_var에 할당합니다.</span>
             <span class="n">forward_var</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">viterbivars_t</span><span class="p">)</span> <span class="o">+</span> <span class="n">feat</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
             <span class="n">backpointers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bptrs_t</span><span class="p">)</span>
 
-        <span class="c1"># Transition to STOP_TAG</span>
+        <span class="c1"># STAP_TAG로의 전이</span>
         <span class="n">terminal_var</span> <span class="o">=</span> <span class="n">forward_var</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">STOP_TAG</span><span class="p">]]</span>
         <span class="n">best_tag_id</span> <span class="o">=</span> <span class="n">argmax</span><span class="p">(</span><span class="n">terminal_var</span><span class="p">)</span>
         <span class="n">path_score</span> <span class="o">=</span> <span class="n">terminal_var</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">best_tag_id</span><span class="p">]</span>
 
-        <span class="c1"># Follow the back pointers to decode the best path.</span>
+        <span class="c1"># 최적의 경로를 구하기 위해 back pointer를 따라갑니다.</span>
         <span class="n">best_path</span> <span class="o">=</span> <span class="p">[</span><span class="n">best_tag_id</span><span class="p">]</span>
         <span class="k">for</span> <span class="n">bptrs_t</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">backpointers</span><span class="p">):</span>
             <span class="n">best_tag_id</span> <span class="o">=</span> <span class="n">bptrs_t</span><span class="p">[</span><span class="n">best_tag_id</span><span class="p">]</span>
             <span class="n">best_path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">best_tag_id</span><span class="p">)</span>
-        <span class="c1"># Pop off the start tag (we dont want to return that to the caller)</span>
+        <span class="c1"># 시작 태그를 빼냅니다 (시작 태그는 반환된 필요가 없습니다)</span>
         <span class="n">start</span> <span class="o">=</span> <span class="n">best_path</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
-        <span class="k">assert</span> <span class="n">start</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">START_TAG</span><span class="p">]</span>  <span class="c1"># Sanity check</span>
+        <span class="k">assert</span> <span class="n">start</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">START_TAG</span><span class="p">]</span>  <span class="c1"># 완결성 검사 (Sanity check)</span>
         <span class="n">best_path</span><span class="o">.</span><span class="n">reverse</span><span class="p">()</span>
         <span class="k">return</span> <span class="n">path_score</span><span class="p">,</span> <span class="n">best_path</span>
 
@@ -564,22 +552,22 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
         <span class="n">gold_score</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_score_sentence</span><span class="p">(</span><span class="n">feats</span><span class="p">,</span> <span class="n">tags</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">forward_score</span> <span class="o">-</span> <span class="n">gold_score</span>
 
-    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sentence</span><span class="p">):</span>  <span class="c1"># dont confuse this with _forward_alg above.</span>
-        <span class="c1"># Get the emission scores from the BiLSTM</span>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sentence</span><span class="p">):</span>  <span class="c1"># 이 함수와 위의 _forward_alg를 헷갈리지 마세요.</span>
+        <span class="c1"># Bi-LSTM으로부터 배출 점수를 얻습니다.</span>
         <span class="n">lstm_feats</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_lstm_features</span><span class="p">(</span><span class="n">sentence</span><span class="p">)</span>
 
-        <span class="c1"># Find the best path, given the features.</span>
+        <span class="c1"># 주어진 특징(배출 점수)들로 최적의 경로를 찾아냅니다.</span>
         <span class="n">score</span><span class="p">,</span> <span class="n">tag_seq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_viterbi_decode</span><span class="p">(</span><span class="n">lstm_feats</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">score</span><span class="p">,</span> <span class="n">tag_seq</span>
 </pre></div>
 </div>
-<p>Run training</p>
+<p>훈련 실행</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">START_TAG</span> <span class="o">=</span> <span class="s2">&quot;&lt;START&gt;&quot;</span>
 <span class="n">STOP_TAG</span> <span class="o">=</span> <span class="s2">&quot;&lt;STOP&gt;&quot;</span>
 <span class="n">EMBEDDING_DIM</span> <span class="o">=</span> <span class="mi">5</span>
 <span class="n">HIDDEN_DIM</span> <span class="o">=</span> <span class="mi">4</span>
 
-<span class="c1"># Make up some training data</span>
+<span class="c1"># 훈련용 데이터를 만듭니다.</span>
 <span class="n">training_data</span> <span class="o">=</span> <span class="p">[(</span>
     <span class="s2">&quot;the wall street journal reported today that apple corporation made money&quot;</span><span class="o">.</span><span class="n">split</span><span class="p">(),</span>
     <span class="s2">&quot;B I I I O O O B I O O&quot;</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
@@ -599,38 +587,39 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
 <span class="n">model</span> <span class="o">=</span> <span class="n">BiLSTM_CRF</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">word_to_ix</span><span class="p">),</span> <span class="n">tag_to_ix</span><span class="p">,</span> <span class="n">EMBEDDING_DIM</span><span class="p">,</span> <span class="n">HIDDEN_DIM</span><span class="p">)</span>
 <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">1e-4</span><span class="p">)</span>
 
-<span class="c1"># Check predictions before training</span>
+<span class="c1"># 훈련 전 예측 결과를 확인합니다.</span>
 <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
     <span class="n">precheck_sent</span> <span class="o">=</span> <span class="n">prepare_sequence</span><span class="p">(</span><span class="n">training_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">word_to_ix</span><span class="p">)</span>
     <span class="n">precheck_tags</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">training_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">precheck_sent</span><span class="p">))</span>
 
 <span class="c1"># Make sure prepare_sequence from earlier in the LSTM section is loaded</span>
+<span class="c1"># 위의 보조 함수 영역에 있는 prepare_sequence 함수가 불러와 졌는지 확인합니다.</span>
 <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span>
-        <span class="mi">300</span><span class="p">):</span>  <span class="c1"># again, normally you would NOT do 300 epochs, it is toy data</span>
+        <span class="mi">300</span><span class="p">):</span>  <span class="c1"># 다시 말하지만, 아마 300 에폭을 실행하진 않을 것입니다. 이것은 연습용 데이터입니다.</span>
     <span class="k">for</span> <span class="n">sentence</span><span class="p">,</span> <span class="n">tags</span> <span class="ow">in</span> <span class="n">training_data</span><span class="p">:</span>
-        <span class="c1"># Step 1. Remember that Pytorch accumulates gradients.</span>
-        <span class="c1"># We need to clear them out before each instance</span>
+        <span class="c1"># 1단계. Pytorch가 변화도를 누적한다는 것을 기억하세요.</span>
+        <span class="c1"># 그것들을 제거합니다.</span>
         <span class="n">model</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
 
-        <span class="c1"># Step 2. Get our inputs ready for the network, that is,</span>
-        <span class="c1"># turn them into Tensors of word indices.</span>
+        <span class="c1"># 2단계. 입력 데이터를 신경망에 사용될 수 있도록 단어</span>
+        <span class="c1"># 인덱스들의 텐서로 변환합니다.</span>
         <span class="n">sentence_in</span> <span class="o">=</span> <span class="n">prepare_sequence</span><span class="p">(</span><span class="n">sentence</span><span class="p">,</span> <span class="n">word_to_ix</span><span class="p">)</span>
         <span class="n">targets</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">tag_to_ix</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tags</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
 
-        <span class="c1"># Step 3. Run our forward pass.</span>
+        <span class="c1"># 3단계. 순방향 계산을 수행합니다.</span>
         <span class="n">loss</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">neg_log_likelihood</span><span class="p">(</span><span class="n">sentence_in</span><span class="p">,</span> <span class="n">targets</span><span class="p">)</span>
 
-        <span class="c1"># Step 4. Compute the loss, gradients, and update the parameters by</span>
-        <span class="c1"># calling optimizer.step()</span>
+        <span class="c1"># 4단계. 손실값, 변화도를 계산하고 optimizer.step()을 호출하여</span>
+        <span class="c1"># 매개변수들을 갱신합니다.</span>
         <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
         <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
 
-<span class="c1"># Check predictions after training</span>
+<span class="c1"># 훈련이 끝난 후 예측 결과를 확인합니다.</span>
 <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
     <span class="n">precheck_sent</span> <span class="o">=</span> <span class="n">prepare_sequence</span><span class="p">(</span><span class="n">training_data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">word_to_ix</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">precheck_sent</span><span class="p">))</span>
-<span class="c1"># We got it!</span>
+<span class="c1"># 다 했습니다!</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
@@ -639,22 +628,20 @@ <h2>Implementation Notes<a class="headerlink" href="#implementation-notes" title
 </pre></div>
 </div>
 </div>
-<div class="section" id="exercise-a-new-loss-function-for-discriminative-tagging">
-<h2>Exercise: A new loss function for discriminative tagging<a class="headerlink" href="#exercise-a-new-loss-function-for-discriminative-tagging" title="Permalink to this headline">¶</a></h2>
-<p>It wasn’t really necessary for us to create a computation graph when
-doing decoding, since we do not backpropagate from the viterbi path
-score. Since we have it anyway, try training the tagger where the loss
-function is the difference between the Viterbi path score and the score
-of the gold-standard path. It should be clear that this function is
-non-negative and 0 when the predicted tag sequence is the correct tag
-sequence. This is essentially <em>structured perceptron</em>.</p>
-<p>This modification should be short, since Viterbi and score_sentence are
-already implemented. This is an example of the shape of the computation
-graph <em>depending on the training instance</em>. Although I haven’t tried
-implementing this in a static toolkit, I imagine that it is possible but
-much less straightforward.</p>
-<p>Pick up some real data and do a comparison!</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  7.666 seconds)</p>
+<div class="section" id="discriminative">
+<h2>연습 : 판별적(discriminative) 태깅을 위한 새로운 손실 함수<a class="headerlink" href="#discriminative" title="Permalink to this headline">¶</a></h2>
+<p>사실 복호화 할 때는 비터비 경로 점수로 역전파를 하지 않기 때문에 계산
+그래프를 만들 필요가 없었습니다. 그러나 이미 만들었으니, 비터비 경로
+점수와 실제 정답 경로 점수의 차이를 손실 함수로 사용해서 태거를
+학습시켜 보세요. 손실 함수의 값은 음수가 아니어야 하며, 예측된 태그
+순열이 정답이라면 손실 함수의 값은 0이어야 합니다. 이것은 본질적으로
+<em>구조화된 퍼셉트론</em> 입니다.</p>
+<p>이미 비터비와 score_sentence 함수가 구현되어 있기 때문에 간단히 수정할
+수 있습니다. 이 모델은 <em>학습 데이터에 따라 변하는</em> 계산 그래프의 한
+예시입니다. 이 모델을 정적 툴킷에서 구현해 보지는 않았는데, 구현이
+가능하지만 덜 직관적일 수 있습니다.</p>
+<p>실제 데이터를 사용해보고 비교해보세요!</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  7.507 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-nlp-advanced-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/2a7469f7250c4f405b1c4f885994ea69/advanced_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">advanced_tutorial.py</span></code></a></p>
@@ -708,11 +695,11 @@ <h2>Exercise: A new loss function for discriminative tagging<a class="headerlink
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">Advanced: Making Dynamic Decisions and the Bi-LSTM CRF</a><ul>
-<li><a class="reference internal" href="#dynamic-versus-static-deep-learning-toolkits">Dynamic versus Static Deep Learning Toolkits</a></li>
-<li><a class="reference internal" href="#bi-lstm-conditional-random-field-discussion">Bi-LSTM Conditional Random Field Discussion</a></li>
-<li><a class="reference internal" href="#implementation-notes">Implementation Notes</a></li>
-<li><a class="reference internal" href="#exercise-a-new-loss-function-for-discriminative-tagging">Exercise: A new loss function for discriminative tagging</a></li>
+<li><a class="reference internal" href="#">심화 과정 : Bi-LSTM CRF와 동적 결정</a><ul>
+<li><a class="reference internal" href="#toolkits">동적, 정적 딥 러닝 툴킷(toolkits) 비교</a></li>
+<li><a class="reference internal" href="#bi-lstm-conditional-rnadom-field">Bi-LSTM Conditional Rnadom Field 설명</a></li>
+<li><a class="reference internal" href="#id1">구현 문서</a></li>
+<li><a class="reference internal" href="#discriminative">연습 : 판별적(discriminative) 태깅을 위한 새로운 손실 함수</a></li>
 </ul>
 </li>
 </ul>
@@ -730,11 +717,10 @@ <h2>Exercise: A new loss function for discriminative tagging<a class="headerlink
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/nlp/deep_learning_tutorial.html b/docs/beginner/nlp/deep_learning_tutorial.html
index 4ac948f01..a9341071d 100644
--- a/docs/beginner/nlp/deep_learning_tutorial.html
+++ b/docs/beginner/nlp/deep_learning_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -510,7 +510,7 @@ <h3>예제: 논리 회귀 Bag-of-Words 분류기<a class="headerlink" href="#bag
 
 <span class="k">class</span> <span class="nc">BoWClassifier</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>  <span class="c1"># nn.Module로 부터 상속 받기 !</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_labels</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">):</span>
         <span class="c1"># calls the init function of nn.Module의 초기화 함수 호출.  Dont get confused by syntax,</span>
         <span class="c1"># 문법에 혼란스러워 하지 마시고 단지 항상 nn.Module 에서 수행하십시오.</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">BoWClassifier</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
@@ -650,7 +650,7 @@ <h3>예제: 논리 회귀 Bag-of-Words 분류기<a class="headerlink" href="#bag
 <p>이제 PyTorch 구성 요소를 만들고 이를 통해 일부 데이터를 전달하고
 그래디언트 업데이트를 수행하는 방법을 살펴 보았습니다.
 우리는 심도있는 NLP가 제공해야하는 것을 더 깊이 파고들 준비가되었습니다.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.493 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.205 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-nlp-deep-learning-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/f1703ff94c4a6544f91c89b37c3a1fcf/deep_learning_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">deep_learning_tutorial.py</span></code></a></p>
@@ -735,11 +735,10 @@ <h3>예제: 논리 회귀 Bag-of-Words 분류기<a class="headerlink" href="#bag
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/nlp/pytorch_tutorial.html b/docs/beginner/nlp/pytorch_tutorial.html
index c3094008d..b6a958731 100644
--- a/docs/beginner/nlp/pytorch_tutorial.html
+++ b/docs/beginner/nlp/pytorch_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -520,7 +520,7 @@ <h2>Computation Graphs and Automatic Differentiation<a class="headerlink" href="
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([5., 7., 9.], grad_fn=&lt;AddBackward0&gt;)
-&lt;AddBackward0 object at 0x7f84774347b8&gt;
+&lt;AddBackward0 object at 0x7f72ef59e5d0&gt;
 </pre></div>
 </div>
 <p>So Tensors know what created them. z knows that it wasn’t read in from
@@ -536,7 +536,7 @@ <h2>Computation Graphs and Automatic Differentiation<a class="headerlink" href="
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(21., grad_fn=&lt;SumBackward0&gt;)
-&lt;SumBackward0 object at 0x7f84774346a0&gt;
+&lt;SumBackward0 object at 0x7f72ef59eed0&gt;
 </pre></div>
 </div>
 <p>So now, what is the derivative of this sum with respect to the first
@@ -605,7 +605,7 @@ <h2>Computation Graphs and Automatic Differentiation<a class="headerlink" href="
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>False False
 None
-&lt;AddBackward0 object at 0x7f848cad27b8&gt;
+&lt;AddBackward0 object at 0x7f72ef4e9c90&gt;
 True
 None
 </pre></div>
@@ -626,7 +626,7 @@ <h2>Computation Graphs and Automatic Differentiation<a class="headerlink" href="
 False
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.074 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.078 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-nlp-pytorch-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/5d90e570f3c24fc2cfe3d8f0f402cd5d/pytorch_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">pytorch_tutorial.py</span></code></a></p>
@@ -705,11 +705,10 @@ <h2>Computation Graphs and Automatic Differentiation<a class="headerlink" href="
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/nlp/sequence_models_tutorial.html b/docs/beginner/nlp/sequence_models_tutorial.html
index 85b9442c9..43e4b5d5f 100644
--- a/docs/beginner/nlp/sequence_models_tutorial.html
+++ b/docs/beginner/nlp/sequence_models_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -441,7 +441,7 @@ <h2>Example: An LSTM for Part-of-Speech Tagging<a class="headerlink" href="#exam
 <p>Create the model:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LSTMTagger</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">tagset_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">hidden_dim</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">tagset_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">LSTMTagger</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_dim</span> <span class="o">=</span> <span class="n">hidden_dim</span>
 
@@ -546,7 +546,7 @@ <h2>Exercise: Augmenting the LSTM part-of-speech tagger with character-level fea
 <li><p>To do a sequence model over characters, you will have to embed characters.
 The character embeddings will be the input to the character LSTM.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.763 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.755 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-nlp-sequence-models-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/00262d1f472765b291ae3a1a5bf86bae/sequence_models_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">sequence_models_tutorial.py</span></code></a></p>
@@ -621,11 +621,10 @@ <h2>Exercise: Augmenting the LSTM part-of-speech tagger with character-level fea
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/nlp/sg_execution_times.html b/docs/beginner/nlp/sg_execution_times.html
index 0eb9b4879..0add09147 100644
--- a/docs/beginner/nlp/sg_execution_times.html
+++ b/docs/beginner/nlp/sg_execution_times.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -297,13 +297,13 @@
               
   <div class="section" id="computation-times">
 <span id="sphx-glr-beginner-nlp-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:01.493</strong> total execution time for <strong>beginner_nlp</strong> files:</p>
+<p><strong>00:09.120</strong> total execution time for <strong>beginner_nlp</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:01.493</strong>: <a class="reference internal" href="deep_learning_tutorial.html#sphx-glr-beginner-nlp-deep-learning-tutorial-py"><span class="std std-ref">PyTorch를 이용한 딥러닝</span></a> (<code class="docutils literal notranslate"><span class="pre">deep_learning_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="advanced_tutorial.html#sphx-glr-beginner-nlp-advanced-tutorial-py"><span class="std std-ref">Advanced: Making Dynamic Decisions and the Bi-LSTM CRF</span></a> (<code class="docutils literal notranslate"><span class="pre">advanced_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="pytorch_tutorial.html#sphx-glr-beginner-nlp-pytorch-tutorial-py"><span class="std std-ref">Introduction to PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">pytorch_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="sequence_models_tutorial.html#sphx-glr-beginner-nlp-sequence-models-tutorial-py"><span class="std std-ref">Sequence Models and Long-Short Term Memory Networks</span></a> (<code class="docutils literal notranslate"><span class="pre">sequence_models_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="word_embeddings_tutorial.html#sphx-glr-beginner-nlp-word-embeddings-tutorial-py"><span class="std std-ref">단어 임베딩: 어휘의 의미를 인코딩하기</span></a> (<code class="docutils literal notranslate"><span class="pre">word_embeddings_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:07.507</strong>: <a class="reference internal" href="advanced_tutorial.html#sphx-glr-beginner-nlp-advanced-tutorial-py"><span class="std std-ref">심화 과정 : Bi-LSTM CRF와 동적 결정</span></a> (<code class="docutils literal notranslate"><span class="pre">advanced_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.755</strong>: <a class="reference internal" href="sequence_models_tutorial.html#sphx-glr-beginner-nlp-sequence-models-tutorial-py"><span class="std std-ref">Sequence Models and Long-Short Term Memory Networks</span></a> (<code class="docutils literal notranslate"><span class="pre">sequence_models_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.575</strong>: <a class="reference internal" href="word_embeddings_tutorial.html#sphx-glr-beginner-nlp-word-embeddings-tutorial-py"><span class="std std-ref">단어 임베딩: 어휘의 의미를 인코딩하기</span></a> (<code class="docutils literal notranslate"><span class="pre">word_embeddings_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.205</strong>: <a class="reference internal" href="deep_learning_tutorial.html#sphx-glr-beginner-nlp-deep-learning-tutorial-py"><span class="std std-ref">PyTorch를 이용한 딥러닝</span></a> (<code class="docutils literal notranslate"><span class="pre">deep_learning_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.078</strong>: <a class="reference internal" href="pytorch_tutorial.html#sphx-glr-beginner-nlp-pytorch-tutorial-py"><span class="std std-ref">Introduction to PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">pytorch_tutorial.py</span></code>)</p></li>
 </ul>
 </div>
 
@@ -364,11 +364,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/nlp/word_embeddings_tutorial.html b/docs/beginner/nlp/word_embeddings_tutorial.html
index 178af75d3..6efc2f721 100644
--- a/docs/beginner/nlp/word_embeddings_tutorial.html
+++ b/docs/beginner/nlp/word_embeddings_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -466,7 +466,7 @@ <h2>예시: N그램 언어 모델링<a class="headerlink" href="#n" title="Perma
 
 <span class="k">class</span> <span class="nc">NGramLanguageModeler</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">context_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="n">context_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">NGramLanguageModeler</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">embeddings</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">linear1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">context_size</span> <span class="o">*</span> <span class="n">embedding_dim</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
@@ -515,7 +515,7 @@ <h2>예시: N그램 언어 모델링<a class="headerlink" href="#n" title="Perma
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[([&#39;When&#39;, &#39;forty&#39;], &#39;winters&#39;), ([&#39;forty&#39;, &#39;winters&#39;], &#39;shall&#39;), ([&#39;winters&#39;, &#39;shall&#39;], &#39;besiege&#39;)]
-[521.9651942253113, 519.424467086792, 516.9015831947327, 514.3950068950653, 511.9028043746948, 509.42428040504456, 506.95850014686584, 504.5070960521698, 502.0665798187256, 499.63560700416565]
+[521.42187666893, 518.924881696701, 516.4445822238922, 513.9798181056976, 511.53030729293823, 509.0956428050995, 506.67354226112366, 504.26296830177307, 501.8644609451294, 499.47719502449036]
 </pre></div>
 </div>
 </div>
@@ -564,7 +564,7 @@ <h2>예시: 단어 임베딩 계산하기: Continuous Bag-of-Words<a class="head
 
 <span class="k">class</span> <span class="nc">CBOW</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">pass</span>
 
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
@@ -586,7 +586,7 @@ <h2>예시: 단어 임베딩 계산하기: Continuous Bag-of-Words<a class="head
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[([&#39;We&#39;, &#39;are&#39;, &#39;to&#39;, &#39;study&#39;], &#39;about&#39;), ([&#39;are&#39;, &#39;about&#39;, &#39;study&#39;, &#39;the&#39;], &#39;to&#39;), ([&#39;about&#39;, &#39;to&#39;, &#39;the&#39;, &#39;idea&#39;], &#39;study&#39;), ([&#39;to&#39;, &#39;study&#39;, &#39;idea&#39;, &#39;of&#39;], &#39;the&#39;), ([&#39;study&#39;, &#39;the&#39;, &#39;of&#39;, &#39;a&#39;], &#39;idea&#39;)]
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.716 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.575 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-nlp-word-embeddings-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../../_downloads/58bdf45884d385ba7031225104b471d3/word_embeddings_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">word_embeddings_tutorial.py</span></code></a></p>
@@ -662,11 +662,10 @@ <h2>예시: 단어 임베딩 계산하기: Continuous Bag-of-Words<a class="head
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/beginner/nn_tutorial.html b/docs/beginner/nn_tutorial.html
index dd0eb4c95..114cb144c 100644
--- a/docs/beginner/nn_tutorial.html
+++ b/docs/beginner/nn_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -305,8 +305,8 @@
 <span id="sphx-glr-beginner-nn-tutorial-py"></span><h1><cite>torch.nn</cite> 이 <em>실제로</em> 무엇인가요?<a class="headerlink" href="#torch-nn" title="Permalink to this headline">¶</a></h1>
 <p>저자: Jeremy Howard, <a class="reference external" href="https://www.fast.ai">fast.ai</a>.</p>
 <p>도움: Rachel Thomas, Francisco Ingham.</p>
-<p>번역: <a class="reference external" href="https://github.com/namdori61">남상호</a></p>
-<p>이 튜토리얼을 스크립트가 아닌 노트북으로 실행하기를 권장합니다. 노트북 (.ipynb) 파일을 다운 받으시려면,
+<p>번역: <a class="reference external" href="https://github.com/namdori61">남상호</a>
+이 튜토리얼을 스크립트가 아닌 노트북으로 실행하기를 권장합니다. 노트북 (.ipynb) 파일을 다운 받으시려면,
 페이지 상단에 있는 링크를 클릭해주세요.</p>
 <p>PyTorch 는 여러분이 신경망(neural network)를 생성하고 학습시키는 것을 도와주기 위해서
 <a class="reference external" href="https://pytorch.org/docs/stable/nn.html">torch.nn</a> ,
@@ -334,7 +334,7 @@ <h2>MNIST 데이터 준비<a class="headerlink" href="#mnist" title="Permalink t
 <a class="reference external" href="http://docs.python-requests.org/en/master/">requests</a> 를 이용하여
 데이터셋을 다운로드 할 것입니다. 우리는 모듈을 사용할 때만 임포트(import) 할 것이므로,
 여러분은 매 포인트마다 정확히 어떤 것이 사용되는지 확인할 수 있습니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="k">import</span> <span class="n">Path</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
 <span class="kn">import</span> <span class="nn">requests</span>
 
 <span class="n">DATA_PATH</span> <span class="o">=</span> <span class="n">Path</span><span class="p">(</span><span class="s2">&quot;data&quot;</span><span class="p">)</span>
@@ -342,7 +342,7 @@ <h2>MNIST 데이터 준비<a class="headerlink" href="#mnist" title="Permalink t
 
 <span class="n">PATH</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
-<span class="n">URL</span> <span class="o">=</span> <span class="s2">&quot;http://deeplearning.net/data/mnist/&quot;</span>
+<span class="n">URL</span> <span class="o">=</span> <span class="s2">&quot;https://github.com/pytorch/tutorials/raw/master/_static/&quot;</span>
 <span class="n">FILENAME</span> <span class="o">=</span> <span class="s2">&quot;mnist.pkl.gz&quot;</span>
 
 <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">PATH</span> <span class="o">/</span> <span class="n">FILENAME</span><span class="p">)</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
@@ -361,7 +361,7 @@ <h2>MNIST 데이터 준비<a class="headerlink" href="#mnist" title="Permalink t
 </div>
 <p>각 이미지는 28 x 28 형태 이고, 784 (=28x28) 크기를 가진 하나의 행으로 저장되어 있습니다.
 하나를 살펴 봅시다; 먼저 우리는 이 이미지를 2d로 재구성해야 합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="k">import</span> <span class="n">pyplot</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 <span class="n">pyplot</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x_train</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)),</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
@@ -453,8 +453,8 @@ <h2>torch.nn 없이 밑바닥부터 신경망 만들기<a class="headerlink" hre
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([-2.3728, -2.5460, -2.5807, -2.6371, -2.6052, -1.8645, -2.4012, -2.1049,
-        -2.0545, -2.1928], grad_fn=&lt;SelectBackward&gt;) torch.Size([64, 10])
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([-2.4213, -2.6816, -2.2776, -2.1147, -2.2795, -1.9980, -2.2832, -1.9899,
+        -2.3805, -2.9871], grad_fn=&lt;SelectBackward&gt;) torch.Size([64, 10])
 </pre></div>
 </div>
 <p>여러분이 보시듯이, <code class="docutils literal notranslate"><span class="pre">preds</span></code> 텐서(tensor)는 텐서 값 외에도, 또한
@@ -475,7 +475,7 @@ <h2>torch.nn 없이 밑바닥부터 신경망 만들기<a class="headerlink" hre
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(2.4268, grad_fn=&lt;NegBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(2.3607, grad_fn=&lt;NegBackward&gt;)
 </pre></div>
 </div>
 <p>또한, 우리 모델의 정확도(accuracy)를 계산하기 위한 함수를 구현합시다.
@@ -492,7 +492,7 @@ <h2>torch.nn 없이 밑바닥부터 신경망 만들기<a class="headerlink" hre
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0625)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.1250)
 </pre></div>
 </div>
 <p>이제 우리는 훈련 루프(training loop)를 실행할 수 있습니다. 매 반복마다, 우리는 다음을 수행할 것입니다:</p>
@@ -516,7 +516,7 @@ <h2>torch.nn 없이 밑바닥부터 신경망 만들기<a class="headerlink" hre
 매 단계마다 다양한 변수 값을 점검할 수 있습니다.
 아래에서 <code class="docutils literal notranslate"><span class="pre">set_trace()</span></code> 를 주석 해제하여 사용해보세요.</p>
 </div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.core.debugger</span> <span class="k">import</span> <span class="n">set_trace</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">IPython.core.debugger</span> <span class="kn">import</span> <span class="n">set_trace</span>
 
 <span class="n">lr</span> <span class="o">=</span> <span class="mf">0.5</span>  <span class="c1"># 학습률(learning rate)</span>
 <span class="n">epochs</span> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># 훈련에 사용할 에포크(epoch) 수</span>
@@ -548,7 +548,7 @@ <h2>torch.nn 없이 밑바닥부터 신경망 만들기<a class="headerlink" hre
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0806, grad_fn=&lt;NegBackward&gt;) tensor(1.)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0815, grad_fn=&lt;NegBackward&gt;) tensor(1.)
 </pre></div>
 </div>
 </div>
@@ -583,7 +583,7 @@ <h2>torch.nn.functional 사용하기<a class="headerlink" href="#torch-nn-functi
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0806, grad_fn=&lt;NllLossBackward&gt;) tensor(1.)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0815, grad_fn=&lt;NllLossBackward&gt;) tensor(1.)
 </pre></div>
 </div>
 </div>
@@ -602,10 +602,10 @@ <h2>nn.Module 을 이용하여 리팩토링 하기<a class="headerlink" href="#n
 <a class="reference external" href="https://docs.python.org/3/tutorial/modules.html">module</a> (소문자 <code class="docutils literal notranslate"><span class="pre">m</span></code>)
 의 개념과 헷갈리지 말아주세요.</p>
 </div>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">nn</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
 
 <span class="k">class</span> <span class="nc">Mnist_Logistic</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">784</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">784</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">bias</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">10</span><span class="p">))</span>
@@ -626,7 +626,7 @@ <h2>nn.Module 을 이용하여 리팩토링 하기<a class="headerlink" href="#n
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(2.3677, grad_fn=&lt;NllLossBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(2.3567, grad_fn=&lt;NllLossBackward&gt;)
 </pre></div>
 </div>
 <p>이전에는 훈련 루프를 위해 이름 별로 각 매개변수(parameter)의 값을 업데이트하고 다음과 같이
@@ -671,7 +671,7 @@ <h2>nn.Module 을 이용하여 리팩토링 하기<a class="headerlink" href="#n
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0809, grad_fn=&lt;NllLossBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0820, grad_fn=&lt;NllLossBackward&gt;)
 </pre></div>
 </div>
 </div>
@@ -685,7 +685,7 @@ <h2>nn.Linear 를 이용하여 리팩토링 하기<a class="headerlink" href="#n
 Pytorch 에는 다양한 유형의 코드를 크게 단순화 할 수 있는 미리 정의된 레이어가 있고 이는 또한
 종종 기존 코드보다 속도를 빠르게 합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Mnist_Logistic</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">lin</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">784</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
 
@@ -699,7 +699,7 @@ <h2>nn.Linear 를 이용하여 리팩토링 하기<a class="headerlink" href="#n
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(2.4012, grad_fn=&lt;NllLossBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(2.2816, grad_fn=&lt;NllLossBackward&gt;)
 </pre></div>
 </div>
 <p>우리는 여전히 이전과 동일한 <code class="docutils literal notranslate"><span class="pre">fit</span></code> 메소드를 사용할 수 있습니다.</p>
@@ -709,7 +709,7 @@ <h2>nn.Linear 를 이용하여 리팩토링 하기<a class="headerlink" href="#n
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0826, grad_fn=&lt;NllLossBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0811, grad_fn=&lt;NllLossBackward&gt;)
 </pre></div>
 </div>
 </div>
@@ -731,7 +731,7 @@ <h2>optim 을 이용하여 리팩토링 하기<a class="headerlink" href="#optim
 </div>
 <p>(<code class="docutils literal notranslate"><span class="pre">optim.zero_grad()</span></code> 는 기울기를 0으로 재설정 해줍니다. 다음 미니 배치에 대한
 기울기를 계산하기 전에 호출해야 합니다.)</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">optim</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</span>
 </pre></div>
 </div>
 <p>나중에 다시 사용할 수 있도록 모델과 옵티마이져를 만드는 작은 함수를 정의합니다.</p>
@@ -759,8 +759,8 @@ <h2>optim 을 이용하여 리팩토링 하기<a class="headerlink" href="#optim
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(2.3108, grad_fn=&lt;NllLossBackward&gt;)
-tensor(0.0825, grad_fn=&lt;NllLossBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(2.3090, grad_fn=&lt;NllLossBackward&gt;)
+tensor(0.0809, grad_fn=&lt;NllLossBackward&gt;)
 </pre></div>
 </div>
 </div>
@@ -777,7 +777,7 @@ <h2>Dataset 을 이용하여 리팩토링하기<a class="headerlink" href="#id7"
 은 텐서를 감싸는(wrapping) Dataset 입니다.
 길이와 인덱싱 방식을 정의함으로써 텐서의 첫 번째 차원을 따라 반복, 인덱싱 및 슬라이스(slice)하는 방법도 제공합니다.
 이렇게하면 훈련 할 때 동일한 라인에서 독립(independent) 변수와 종속(dependent) 변수에 쉽게 액세스 할 수 있습니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">TensorDataset</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">TensorDataset</span>
 </pre></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">x_train</span></code> 및 <code class="docutils literal notranslate"><span class="pre">y_train</span></code> 모두 하나의 <code class="docutils literal notranslate"><span class="pre">TensorDataset</span></code> 에 합쳐질 수 있습니다,
@@ -810,7 +810,7 @@ <h2>Dataset 을 이용하여 리팩토링하기<a class="headerlink" href="#id7"
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0824, grad_fn=&lt;NllLossBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0832, grad_fn=&lt;NllLossBackward&gt;)
 </pre></div>
 </div>
 </div>
@@ -821,7 +821,7 @@ <h2>DataLoader 를 이용하여 리팩토링하기<a class="headerlink" href="#i
 <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> 는 배치들에 대해서 반복하기 쉽게 만들어줍니다.
 <code class="docutils literal notranslate"><span class="pre">train_ds[i*bs</span> <span class="pre">:</span> <span class="pre">i*bs+bs]</span></code> 를 사용하는 대신,
 DataLoader 는 매 미니배치를 자동적으로 제공합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">DataLoader</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
 
 <span class="n">train_ds</span> <span class="o">=</span> <span class="n">TensorDataset</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
 <span class="n">train_dl</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">train_ds</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">bs</span><span class="p">)</span>
@@ -853,7 +853,7 @@ <h2>DataLoader 를 이용하여 리팩토링하기<a class="headerlink" href="#i
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0808, grad_fn=&lt;NllLossBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor(0.0813, grad_fn=&lt;NllLossBackward&gt;)
 </pre></div>
 </div>
 <p>Pytorch의 nn.Module, nn.Parameter, Dataset 및 DataLoader 덕분에 이제 훈련 루프가
@@ -905,8 +905,8 @@ <h2>검증(validation) 추가하기<a class="headerlink" href="#validation" titl
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 tensor(0.3087)
-1 tensor(0.2956)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 tensor(0.2900)
+1 tensor(0.2931)
 </pre></div>
 </div>
 </div>
@@ -962,8 +962,8 @@ <h2>fit() 와 get_data() 생성하기<a class="headerlink" href="#fit-get-data"
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.3331937258243561
-1 0.3399495246887207
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.2963881206870079
+1 0.2952166391849518
 </pre></div>
 </div>
 <p>이러한 기본 3줄의 코드를 사용하여 다양한 모델을 훈련할 수 있습니다.
@@ -980,7 +980,7 @@ <h2>CNN 으로 넘어가기<a class="headerlink" href="#cnn" title="Permalink to
 각 컨볼루션 뒤에는 ReLU가 있습니다. 마지막으로 평균 풀링(average pooling)을 수행합니다.
 (<code class="docutils literal notranslate"><span class="pre">view</span></code> 는 PyTorch의 numpy <code class="docutils literal notranslate"><span class="pre">reshape</span></code> 버전입니다.)</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Mnist_CNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
@@ -1007,8 +1007,8 @@ <h2>CNN 으로 넘어가기<a class="headerlink" href="#cnn" title="Permalink to
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.35547182550430295
-1 0.22808355839252473
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.35901912598609925
+1 0.26533100905418394
 </pre></div>
 </div>
 </div>
@@ -1024,7 +1024,7 @@ <h2>nn.Sequential<a class="headerlink" href="#nn-sequential" title="Permalink to
 예를 들어, PyTorch에는 <cite>view</cite> 레이어가 없으므로 우리의 신경망 용으로 만들어야 합니다.
 <code class="docutils literal notranslate"><span class="pre">Lambda</span></code> 는 <code class="docutils literal notranslate"><span class="pre">Sequential</span></code> 로 신경망을 정의할 때 사용할 수 있는 레이어를 생성할 것입니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Lambda</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">func</span>
 
@@ -1055,8 +1055,8 @@ <h2>nn.Sequential<a class="headerlink" href="#nn-sequential" title="Permalink to
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.36827207362651826
-1 0.2632165398597717
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.349177153468132
+1 0.2569092135667801
 </pre></div>
 </div>
 </div>
@@ -1076,14 +1076,14 @@ <h2>DataLoader 감싸기<a class="headerlink" href="#id11" title="Permalink to t
 
 
 <span class="k">class</span> <span class="nc">WrappedDataLoader</span><span class="p">:</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dl</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dl</span><span class="p">,</span> <span class="n">func</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dl</span> <span class="o">=</span> <span class="n">dl</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">func</span> <span class="o">=</span> <span class="n">func</span>
 
-    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dl</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">batches</span> <span class="o">=</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dl</span><span class="p">)</span>
         <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="n">batches</span><span class="p">:</span>
             <span class="k">yield</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">b</span><span class="p">))</span>
@@ -1115,8 +1115,8 @@ <h2>DataLoader 감싸기<a class="headerlink" href="#id11" title="Permalink to t
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.3833379934310913
-1 0.23371935186386109
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.39713516569137575
+1 0.23623661838769913
 </pre></div>
 </div>
 </div>
@@ -1157,8 +1157,8 @@ <h2>GPU 사용하기<a class="headerlink" href="#gpu" title="Permalink to this h
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.22367399265766144
-1 0.20463330309391023
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0 0.2238931974083185
+1 0.19320880868285895
 </pre></div>
 </div>
 </div>
@@ -1197,7 +1197,7 @@ <h2>마치면서<a class="headerlink" href="#id12" title="Permalink to this head
 <li><p><code class="docutils literal notranslate"><span class="pre">DataLoader</span></code>: 모든 종류의 <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> 을 기반으로 데이터의 배치들을 출력하는 반복자(iterator)를 생성합니다.</p></li>
 </ul>
 </div></blockquote>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  29.780 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  27.610 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-nn-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/f16255c783f9e487235b8eff6c8792b9/nn_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">nn_tutorial.py</span></code></a></p>
@@ -1293,11 +1293,9 @@ <h2>마치면서<a class="headerlink" href="#id12" title="Permalink to this head
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/ptcheat.html b/docs/beginner/ptcheat.html
index 29c978cce..a0840aa2b 100644
--- a/docs/beginner/ptcheat.html
+++ b/docs/beginner/ptcheat.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -308,11 +308,11 @@ <h3>General<a class="headerlink" href="#general" title="Permalink to this headli
 </div>
 <div class="section" id="neural-network-api">
 <h3>Neural Network API<a class="headerlink" href="#neural-network-api" title="Permalink to this headline">¶</a></h3>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.autograd</span> <span class="kn">as</span> <span class="nn">autograd</span>         <span class="c1"># computation graph</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.autograd</span> <span class="k">as</span> <span class="nn">autograd</span>         <span class="c1"># computation graph</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>                  <span class="c1"># tensor node in the computation graph</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="kn">as</span> <span class="nn">nn</span>                     <span class="c1"># neural networks</span>
-<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="kn">as</span> <span class="nn">F</span>           <span class="c1"># layers, activations and more</span>
-<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="kn">as</span> <span class="nn">optim</span>               <span class="c1"># optimizers e.g. gradient descent, ADAM, etc.</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>                     <span class="c1"># neural networks</span>
+<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>           <span class="c1"># layers, activations and more</span>
+<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>               <span class="c1"># optimizers e.g. gradient descent, ADAM, etc.</span>
 <span class="kn">from</span> <span class="nn">torch.jit</span> <span class="kn">import</span> <span class="n">script</span><span class="p">,</span> <span class="n">trace</span>       <span class="c1"># hybrid frontend decorator and tracing jit</span>
 </pre></div>
 </div>
@@ -355,7 +355,7 @@ <h3>Vision<a class="headerlink" href="#vision" title="Permalink to this headline
                                                          <span class="c1"># architectures &amp;</span>
                                                          <span class="c1"># transforms</span>
 
-<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="kn">as</span> <span class="nn">transforms</span>              <span class="c1"># composable transforms</span>
+<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>              <span class="c1"># composable transforms</span>
 </pre></div>
 </div>
 <p>See
@@ -363,7 +363,7 @@ <h3>Vision<a class="headerlink" href="#vision" title="Permalink to this headline
 </div>
 <div class="section" id="distributed-training">
 <h3>Distributed Training<a class="headerlink" href="#distributed-training" title="Permalink to this headline">¶</a></h3>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="kn">as</span> <span class="nn">dist</span>          <span class="c1"># distributed communication</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>          <span class="c1"># distributed communication</span>
 <span class="kn">from</span> <span class="nn">multiprocessing</span> <span class="kn">import</span> <span class="n">Process</span>       <span class="c1"># memory sharing processes</span>
 </pre></div>
 </div>
@@ -381,7 +381,7 @@ <h3>Creation<a class="headerlink" href="#creation" title="Permalink to this head
 <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">L</span><span class="p">)</span>                 <span class="c1"># create tensor from [nested] list or ndarray L</span>
 <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>                       <span class="c1"># clone of x</span>
 <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>           <span class="c1"># code wrap that stops autograd from tracking tensor history</span>
-<span class="n">requires_grad</span><span class="o">=</span><span class="bp">True</span>              <span class="c1"># arg, when set to True, tracks computation</span>
+<span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span>              <span class="c1"># arg, when set to True, tracks computation</span>
                                 <span class="c1"># history for future derivative calculations</span>
 </pre></div>
 </div>
@@ -450,8 +450,8 @@ <h2>Deep Learning<a class="headerlink" href="#deep-learning" title="Permalink to
 
 <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm</span>                                  <span class="c1"># batch norm layer</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">RNN</span><span class="o">/</span><span class="n">LSTM</span><span class="o">/</span><span class="n">GRU</span>                               <span class="c1"># recurrent layers</span>
-<span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>              <span class="c1"># dropout layer for any dimensional input</span>
-<span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>            <span class="c1"># 2-dimensional channel-wise dropout</span>
+<span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>              <span class="c1"># dropout layer for any dimensional input</span>
+<span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>            <span class="c1"># 2-dimensional channel-wise dropout</span>
 <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">num_embeddings</span><span class="p">,</span> <span class="n">embedding_dim</span><span class="p">)</span>   <span class="c1"># (tensor-wise) mapping from</span>
                                               <span class="c1"># indices to embedding vectors</span>
 </pre></div>
@@ -635,11 +635,9 @@ <h3>Also see<a class="headerlink" href="#also-see" title="Permalink to this head
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/pytorch_with_examples.html b/docs/beginner/pytorch_with_examples.html
index 0bfd70d31..f862ab455 100644
--- a/docs/beginner/pytorch_with_examples.html
+++ b/docs/beginner/pytorch_with_examples.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -320,7 +320,7 @@ <h1>예제로 배우는 파이토치(PyTorch)<a class="headerlink" href="#pytorc
 살펴볼 수 있습니다.</p>
 </div>
 <div class="contents local topic" id="table-of-contents">
-<p class="topic-title first">Table of Contents</p>
+<p class="topic-title">Table of Contents</p>
 <ul class="simple">
 <li><p><a class="reference internal" href="#tensors" id="id14">Tensors</a></p>
 <ul>
@@ -783,7 +783,7 @@ <h3><a class="toc-backref" href="#id23">PyTorch: 사용자 정의 nn.Module</a><
 
 
 <span class="k">class</span> <span class="nc">TwoLayerNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        생성자에서 2개의 nn.Linear 모듈을 생성하고, 멤버 변수로 지정합니다.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -849,7 +849,7 @@ <h3><a class="toc-backref" href="#id24">PyTorch: 제어 흐름(Control Flow) + 
 
 
 <span class="k">class</span> <span class="nc">DynamicNet</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">D_in</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">D_out</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        생성자에서 순전파 단계에서 사용할 3개의 nn.Linear 인스턴스를 생성합니다.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -1068,11 +1068,9 @@ <h3><a class="toc-backref" href="#id28"><cite>nn</cite> 모듈</a><a class="head
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/saving_loading_models.html b/docs/beginner/saving_loading_models.html
index 8878abfa8..55a08f0c9 100644
--- a/docs/beginner/saving_loading_models.html
+++ b/docs/beginner/saving_loading_models.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -375,14 +375,14 @@ <h3>예제:<a class="headerlink" href="#id3" title="Permalink to this headline">
 <span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.9</span><span class="p">)</span>
 
 <span class="c1"># 모델의 state_dict 출력</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Model&#39;s state_dict:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Model&#39;s state_dict:&quot;</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">param_tensor</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">():</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">param_tensor</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()[</span><span class="n">param_tensor</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">param_tensor</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()[</span><span class="n">param_tensor</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
 
 <span class="c1"># 옵티마이저의 state_dict 출력</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Optimizer&#39;s state_dict:&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Optimizer&#39;s state_dict:&quot;</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">var_name</span> <span class="ow">in</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">():</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">var_name</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()[</span><span class="n">var_name</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">var_name</span><span class="p">,</span> <span class="s2">&quot;</span><span class="se">\t</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()[</span><span class="n">var_name</span><span class="p">])</span>
 </pre></div>
 </div>
 <p><strong>출력:</strong></p>
@@ -579,7 +579,7 @@ <h3>저장하기:<a class="headerlink" href="#id10" title="Permalink to this hea
 <div class="section" id="id11">
 <h3>불러오기:<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h3>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">modelB</span> <span class="o">=</span> <span class="n">TheModelBClass</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-<span class="n">modelB</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">PATH</span><span class="p">),</span> <span class="n">strict</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+<span class="n">modelB</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">PATH</span><span class="p">),</span> <span class="n">strict</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>부분적으로 모델을 불러오거나, 모델의 일부를 불러오는 것은 전이학습 또는
@@ -776,11 +776,9 @@ <h3><code class="docutils literal notranslate"><span class="pre">torch.nn.DataPa
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/sg_execution_times.html b/docs/beginner/sg_execution_times.html
index 583fabd0f..c4285eff2 100644
--- a/docs/beginner/sg_execution_times.html
+++ b/docs/beginner/sg_execution_times.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -297,22 +297,22 @@
               
   <div class="section" id="computation-times">
 <span id="sphx-glr-beginner-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:00.343</strong> total execution time for <strong>beginner</strong> files:</p>
+<p><strong>37:29.864</strong> total execution time for <strong>beginner</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:00.239</strong>: <a class="reference internal" href="torchtext_translation_tutorial.html#sphx-glr-beginner-torchtext-translation-tutorial-py"><span class="std std-ref">TorchText로 언어 번역하기</span></a> (<code class="docutils literal notranslate"><span class="pre">torchtext_translation_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.054</strong>: <a class="reference internal" href="aws_distributed_training_tutorial.html#sphx-glr-beginner-aws-distributed-training-tutorial-py"><span class="std std-ref">(advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS</span></a> (<code class="docutils literal notranslate"><span class="pre">aws_distributed_training_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.051</strong>: <a class="reference internal" href="audio_preprocessing_tutorial.html#sphx-glr-beginner-audio-preprocessing-tutorial-py"><span class="std std-ref">torchaudio Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">audio_preprocessing_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="Intro_to_TorchScript_tutorial.html#sphx-glr-beginner-intro-to-torchscript-tutorial-py"><span class="std std-ref">TorchScript 소개</span></a> (<code class="docutils literal notranslate"><span class="pre">Intro_to_TorchScript_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="chatbot_tutorial.html#sphx-glr-beginner-chatbot-tutorial-py"><span class="std std-ref">챗봇 튜토리얼</span></a> (<code class="docutils literal notranslate"><span class="pre">chatbot_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="data_loading_tutorial.html#sphx-glr-beginner-data-loading-tutorial-py"><span class="std std-ref">사용자 정의 Dataset, Dataloader, Transforms 작성하기</span></a> (<code class="docutils literal notranslate"><span class="pre">data_loading_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="dcgan_faces_tutorial.html#sphx-glr-beginner-dcgan-faces-tutorial-py"><span class="std std-ref">DCGAN Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">dcgan_faces_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="deploy_seq2seq_hybrid_frontend_tutorial.html#sphx-glr-beginner-deploy-seq2seq-hybrid-frontend-tutorial-py"><span class="std std-ref">Deploying a Seq2Seq Model with TorchScript</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_seq2seq_hybrid_frontend_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="fgsm_tutorial.html#sphx-glr-beginner-fgsm-tutorial-py"><span class="std std-ref">적대적 예제 생성(Adversarial Example Generation)</span></a> (<code class="docutils literal notranslate"><span class="pre">fgsm_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="nn_tutorial.html#sphx-glr-beginner-nn-tutorial-py"><span class="std std-ref">torch.nn 이 실제로 무엇인가요?</span></a> (<code class="docutils literal notranslate"><span class="pre">nn_tutorial.py</span></code>)</p></li>
+<li><p><strong>21:35.360</strong>: <a class="reference internal" href="dcgan_faces_tutorial.html#sphx-glr-beginner-dcgan-faces-tutorial-py"><span class="std std-ref">DCGAN Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">dcgan_faces_tutorial.py</span></code>)</p></li>
+<li><p><strong>04:31.890</strong>: <a class="reference internal" href="chatbot_tutorial.html#sphx-glr-beginner-chatbot-tutorial-py"><span class="std std-ref">챗봇 튜토리얼</span></a> (<code class="docutils literal notranslate"><span class="pre">chatbot_tutorial.py</span></code>)</p></li>
+<li><p><strong>04:31.213</strong>: <a class="reference internal" href="fgsm_tutorial.html#sphx-glr-beginner-fgsm-tutorial-py"><span class="std std-ref">적대적 예제 생성(Adversarial Example Generation)</span></a> (<code class="docutils literal notranslate"><span class="pre">fgsm_tutorial.py</span></code>)</p></li>
+<li><p><strong>03:03.735</strong>: <a class="reference internal" href="transformer_tutorial.html#sphx-glr-beginner-transformer-tutorial-py"><span class="std std-ref">nn.Transformer 와 TorchText 로 시퀀스-투-시퀀스(Sequence-to-Sequence) 모델링하기</span></a> (<code class="docutils literal notranslate"><span class="pre">transformer_tutorial.py</span></code>)</p></li>
+<li><p><strong>01:35.676</strong>: <a class="reference internal" href="text_sentiment_ngrams_tutorial.html#sphx-glr-beginner-text-sentiment-ngrams-tutorial-py"><span class="std std-ref">TorchText로 텍스트 분류하기</span></a> (<code class="docutils literal notranslate"><span class="pre">text_sentiment_ngrams_tutorial.py</span></code>)</p></li>
+<li><p><strong>01:28.172</strong>: <a class="reference internal" href="transfer_learning_tutorial.html#sphx-glr-beginner-transfer-learning-tutorial-py"><span class="std std-ref">컴퓨터 비전(Vision)을 위한 전이학습(Transfer Learning)</span></a> (<code class="docutils literal notranslate"><span class="pre">transfer_learning_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:27.610</strong>: <a class="reference internal" href="nn_tutorial.html#sphx-glr-beginner-nn-tutorial-py"><span class="std std-ref">torch.nn 이 실제로 무엇인가요?</span></a> (<code class="docutils literal notranslate"><span class="pre">nn_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:11.264</strong>: <a class="reference internal" href="audio_preprocessing_tutorial.html#sphx-glr-beginner-audio-preprocessing-tutorial-py"><span class="std std-ref">torchaudio Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">audio_preprocessing_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:03.695</strong>: <a class="reference internal" href="data_loading_tutorial.html#sphx-glr-beginner-data-loading-tutorial-py"><span class="std std-ref">사용자 정의 Dataset, Dataloader, Transforms 작성하기</span></a> (<code class="docutils literal notranslate"><span class="pre">data_loading_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.621</strong>: <a class="reference internal" href="deploy_seq2seq_hybrid_frontend_tutorial.html#sphx-glr-beginner-deploy-seq2seq-hybrid-frontend-tutorial-py"><span class="std std-ref">Deploying a Seq2Seq Model with TorchScript</span></a> (<code class="docutils literal notranslate"><span class="pre">deploy_seq2seq_hybrid_frontend_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.344</strong>: <a class="reference internal" href="torchtext_translation_tutorial.html#sphx-glr-beginner-torchtext-translation-tutorial-py"><span class="std std-ref">TorchText로 언어 번역하기</span></a> (<code class="docutils literal notranslate"><span class="pre">torchtext_translation_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.210</strong>: <a class="reference internal" href="Intro_to_TorchScript_tutorial.html#sphx-glr-beginner-intro-to-torchscript-tutorial-py"><span class="std std-ref">TorchScript 소개</span></a> (<code class="docutils literal notranslate"><span class="pre">Intro_to_TorchScript_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.072</strong>: <a class="reference internal" href="aws_distributed_training_tutorial.html#sphx-glr-beginner-aws-distributed-training-tutorial-py"><span class="std std-ref">(advanced) PyTorch 1.0 Distributed Trainer with Amazon AWS</span></a> (<code class="docutils literal notranslate"><span class="pre">aws_distributed_training_tutorial.py</span></code>)</p></li>
 <li><p><strong>00:00.000</strong>: <a class="reference internal" href="saving_loading_models.html#sphx-glr-beginner-saving-loading-models-py"><span class="std std-ref">모델 저장하기 &amp; 불러오기</span></a> (<code class="docutils literal notranslate"><span class="pre">saving_loading_models.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="text_sentiment_ngrams_tutorial.html#sphx-glr-beginner-text-sentiment-ngrams-tutorial-py"><span class="std std-ref">TorchText로 텍스트 분류하기</span></a> (<code class="docutils literal notranslate"><span class="pre">text_sentiment_ngrams_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="transfer_learning_tutorial.html#sphx-glr-beginner-transfer-learning-tutorial-py"><span class="std std-ref">컴퓨터 비전(Vision)을 위한 전이학습(Transfer Learning)</span></a> (<code class="docutils literal notranslate"><span class="pre">transfer_learning_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="transformer_tutorial.html#sphx-glr-beginner-transformer-tutorial-py"><span class="std std-ref">nn.Transformer 와 TorchText 로 시퀀스-투-시퀀스(Sequence-to-Sequence) 모델링하기</span></a> (<code class="docutils literal notranslate"><span class="pre">transformer_tutorial.py</span></code>)</p></li>
 </ul>
 </div>
 
@@ -373,11 +373,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/text_sentiment_ngrams_tutorial.html b/docs/beginner/text_sentiment_ngrams_tutorial.html
index d097e050c..778bc68b7 100644
--- a/docs/beginner/text_sentiment_ngrams_tutorial.html
+++ b/docs/beginner/text_sentiment_ngrams_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -334,7 +334,7 @@ <h2>ngrams를 이용하여 데이터 불러오기<a class="headerlink" href="#ng
 데이터셋 안의 예제 텍스트는 각각의(single) 단어들에 bi-grams 문자열이 더해진 리스트가 될 것입니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torchtext</span>
-<span class="kn">from</span> <span class="nn">torchtext.datasets</span> <span class="k">import</span> <span class="n">text_classification</span>
+<span class="kn">from</span> <span class="nn">torchtext.datasets</span> <span class="kn">import</span> <span class="n">text_classification</span>
 <span class="n">NGRAMS</span> <span class="o">=</span> <span class="mi">2</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="s1">&#39;./.data&#39;</span><span class="p">):</span>
@@ -362,7 +362,7 @@ <h2>모델 정의하기<a class="headerlink" href="#id3" title="Permalink to thi
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 <span class="k">class</span> <span class="nc">TextSentiment</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="n">num_class</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="n">num_class</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">embedding</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">EmbeddingBag</span><span class="p">(</span><span class="n">vocab_size</span><span class="p">,</span> <span class="n">embed_dim</span><span class="p">,</span> <span class="n">sparse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">embed_dim</span><span class="p">,</span> <span class="n">num_class</span><span class="p">)</span>
@@ -435,7 +435,7 @@ <h2>모델을 학습하고 결과를 평가하는 함수 정의하기<a class="h
 읽어올 수 있습니다 (이에 대한 튜토리얼은 <a class="reference external" href="https://tutorials.pytorch.kr/beginner/data_loading_tutorial.html">이 문서</a>
 를 참고하시기 바랍니다). 우리는 여기서 <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> 를 이용하여
 AG_NEWS 데이터셋을 읽어오고, 이를 모델로 넘겨 학습과 검증을 진행합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">DataLoader</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
 
 <span class="k">def</span> <span class="nf">train_func</span><span class="p">(</span><span class="n">sub_train_</span><span class="p">):</span>
 
@@ -493,7 +493,7 @@ <h2>데이터셋을 분할하고 모델 수행하기<a class="headerlink" href="
 <a class="reference external" href="https://pytorch.org/docs/master/_modules/torch/optim/lr_scheduler.html#StepLR">StepLR</a>
 을 사용합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data.dataset</span> <span class="k">import</span> <span class="n">random_split</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data.dataset</span> <span class="kn">import</span> <span class="n">random_split</span>
 <span class="n">N_EPOCHS</span> <span class="o">=</span> <span class="mi">5</span>
 <span class="n">min_valid_loss</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;inf&#39;</span><span class="p">)</span>
 
@@ -516,26 +516,26 @@ <h2>데이터셋을 분할하고 모델 수행하기<a class="headerlink" href="
     <span class="n">secs</span> <span class="o">=</span> <span class="n">secs</span> <span class="o">%</span> <span class="mi">60</span>
 
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch: </span><span class="si">%d</span><span class="s1">&#39;</span> <span class="o">%</span><span class="p">(</span><span class="n">epoch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">),</span> <span class="s2">&quot; | time in </span><span class="si">%d</span><span class="s2"> minutes, </span><span class="si">%d</span><span class="s2"> seconds&quot;</span> <span class="o">%</span><span class="p">(</span><span class="n">mins</span><span class="p">,</span> <span class="n">secs</span><span class="p">))</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Loss: </span><span class="si">{train_loss:.4f}</span><span class="s1">(train)</span><span class="se">\t</span><span class="s1">|</span><span class="se">\t</span><span class="s1">Acc: {train_acc * 100:.1f}%(train)&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Loss: </span><span class="si">{valid_loss:.4f}</span><span class="s1">(valid)</span><span class="se">\t</span><span class="s1">|</span><span class="se">\t</span><span class="s1">Acc: {valid_acc * 100:.1f}%(valid)&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Loss: </span><span class="si">{</span><span class="n">train_loss</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">(train)</span><span class="se">\t</span><span class="s1">|</span><span class="se">\t</span><span class="s1">Acc: </span><span class="si">{</span><span class="n">train_acc</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">%(train)&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Loss: </span><span class="si">{</span><span class="n">valid_loss</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">(valid)</span><span class="se">\t</span><span class="s1">|</span><span class="se">\t</span><span class="s1">Acc: </span><span class="si">{</span><span class="n">valid_acc</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">%(valid)&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch: 1  | time in 0 minutes, 18 seconds
-        Loss: 0.0261(train)     |       Acc: 84.8%(train)
-        Loss: 0.0001(valid)     |       Acc: 89.8%(valid)
-Epoch: 2  | time in 0 minutes, 18 seconds
-        Loss: 0.0119(train)     |       Acc: 93.7%(train)
-        Loss: 0.0001(valid)     |       Acc: 91.0%(valid)
-Epoch: 3  | time in 0 minutes, 18 seconds
-        Loss: 0.0068(train)     |       Acc: 96.5%(train)
-        Loss: 0.0002(valid)     |       Acc: 90.8%(valid)
-Epoch: 4  | time in 0 minutes, 18 seconds
-        Loss: 0.0039(train)     |       Acc: 98.1%(train)
-        Loss: 0.0002(valid)     |       Acc: 91.2%(valid)
-Epoch: 5  | time in 0 minutes, 18 seconds
-        Loss: 0.0022(train)     |       Acc: 99.0%(train)
-        Loss: 0.0002(valid)     |       Acc: 91.4%(valid)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch: 1  | time in 0 minutes, 11 seconds
+        Loss: 0.0262(train)     |       Acc: 84.7%(train)
+        Loss: 0.0000(valid)     |       Acc: 90.2%(valid)
+Epoch: 2  | time in 0 minutes, 12 seconds
+        Loss: 0.0119(train)     |       Acc: 93.6%(train)
+        Loss: 0.0000(valid)     |       Acc: 90.2%(valid)
+Epoch: 3  | time in 0 minutes, 13 seconds
+        Loss: 0.0069(train)     |       Acc: 96.4%(train)
+        Loss: 0.0000(valid)     |       Acc: 91.5%(valid)
+Epoch: 4  | time in 0 minutes, 14 seconds
+        Loss: 0.0039(train)     |       Acc: 98.0%(train)
+        Loss: 0.0000(valid)     |       Acc: 91.2%(valid)
+Epoch: 5  | time in 0 minutes, 14 seconds
+        Loss: 0.0023(train)     |       Acc: 99.0%(train)
+        Loss: 0.0000(valid)     |       Acc: 91.2%(valid)
 </pre></div>
 </div>
 <p>이 모델을 GPU 상에서 수행했을 때 다음과 같은 결과를 얻었습니다.</p>
@@ -569,12 +569,12 @@ <h2>데이터셋을 분할하고 모델 수행하기<a class="headerlink" href="
 <h2>평가 데이터로 모델 평가하기<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h2>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Checking the results of test dataset...&#39;</span><span class="p">)</span>
 <span class="n">test_loss</span><span class="p">,</span> <span class="n">test_acc</span> <span class="o">=</span> <span class="n">test</span><span class="p">(</span><span class="n">test_dataset</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Loss: </span><span class="si">{test_loss:.4f}</span><span class="s1">(test)</span><span class="se">\t</span><span class="s1">|</span><span class="se">\t</span><span class="s1">Acc: {test_acc * 100:.1f}%(test)&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Loss: </span><span class="si">{</span><span class="n">test_loss</span><span class="si">:</span><span class="s1">.4f</span><span class="si">}</span><span class="s1">(test)</span><span class="se">\t</span><span class="s1">|</span><span class="se">\t</span><span class="s1">Acc: </span><span class="si">{</span><span class="n">test_acc</span> <span class="o">*</span> <span class="mi">100</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">%(test)&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Checking the results of test dataset...
-        Loss: 0.0003(test)      |       Acc: 90.8%(test)
+        Loss: 0.0003(test)      |       Acc: 89.7%(test)
 </pre></div>
 </div>
 <p>평가 데이터셋을 통한 결과를 확인합니다…</p>
@@ -593,8 +593,8 @@ <h2>임의의 뉴스로 평가하기<a class="headerlink" href="#id9" title="Per
 <a class="reference external" href="https://pytorch.org/text/datasets.html?highlight=ag_news#torchtext.datasets.AG_NEWS">여기에</a>
 나와 있습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">re</span>
-<span class="kn">from</span> <span class="nn">torchtext.data.utils</span> <span class="k">import</span> <span class="n">ngrams_iterator</span>
-<span class="kn">from</span> <span class="nn">torchtext.data.utils</span> <span class="k">import</span> <span class="n">get_tokenizer</span>
+<span class="kn">from</span> <span class="nn">torchtext.data.utils</span> <span class="kn">import</span> <span class="n">ngrams_iterator</span>
+<span class="kn">from</span> <span class="nn">torchtext.data.utils</span> <span class="kn">import</span> <span class="n">get_tokenizer</span>
 
 <span class="n">ag_news_label</span> <span class="o">=</span> <span class="p">{</span><span class="mi">1</span> <span class="p">:</span> <span class="s2">&quot;World&quot;</span><span class="p">,</span>
                  <span class="mi">2</span> <span class="p">:</span> <span class="s2">&quot;Sports&quot;</span><span class="p">,</span>
@@ -635,7 +635,7 @@ <h2>임의의 뉴스로 평가하기<a class="headerlink" href="#id9" title="Per
 <p>이 튜토리얼에서 사용한 예제 코드는
 <a class="reference external" href="https://github.com/pytorch/text/tree/master/examples/text_classification">여기에서</a>
 확인하실 수 있습니다.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  20.286 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  35.676 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-text-sentiment-ngrams-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/f003f262713c341f497ab4d8dd9be880/text_sentiment_ngrams_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">text_sentiment_ngrams_tutorial.py</span></code></a></p>
@@ -724,11 +724,9 @@ <h2>임의의 뉴스로 평가하기<a class="headerlink" href="#id9" title="Per
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/torchtext_translation_tutorial.html b/docs/beginner/torchtext_translation_tutorial.html
index 89d031b71..78a3fb00d 100644
--- a/docs/beginner/torchtext_translation_tutorial.html
+++ b/docs/beginner/torchtext_translation_tutorial.html
@@ -31,7 +31,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="강화 학습 (DQN) 튜토리얼" href="../intermediate/reinforcement_q_learning.html" />
-    <link rel="prev" title="SyntaxError" href="text_sentiment_ngrams_tutorial.html" /> 
+    <link rel="prev" title="TorchText로 텍스트 분류하기" href="text_sentiment_ngrams_tutorial.html" /> 
 
   
   <script src="../_static/js/modernizr.min.js"></script>
@@ -157,7 +157,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_classification_tutorial.html">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/char_rnn_generation_tutorial.html">기초부터 시작하는 NLP:  문자-단위 RNN으로 이름 생성하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/seq2seq_translation_tutorial.html">기초부터 시작하는 NLP: Sequence to Sequence 네트워크와 Attention을 이용한 번역</a></li>
-<li class="toctree-l1"><a class="reference internal" href="text_sentiment_ngrams_tutorial.html">SyntaxError</a></li>
+<li class="toctree-l1"><a class="reference internal" href="text_sentiment_ngrams_tutorial.html">TorchText로 텍스트 분류하기</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">TorchText로 언어 번역하기</a></li>
 </ul>
 <p class="caption"><span class="caption-text">강화학습</span></p>
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -185,8 +185,8 @@
 </ul>
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">Pruning Tutorial</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -194,7 +194,7 @@
 <p class="caption"><span class="caption-text">병렬 및 분산 학습</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="dist_overview.html">PyTorch Distributed Overview</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">Single-Machine Model Parallel Best Practices</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/model_parallel_tutorial.html">단일 머신을 이용한 모델 병렬화 실습 예제</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/ddp_tutorial.html">Getting Started with Distributed Data Parallel</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dist_tuto.html">PyTorch로 분산 어플리케이션 개발하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/rpc_tutorial.html">Getting Started with Distributed RPC Framework</a></li>
@@ -341,8 +341,8 @@ <h2><cite>Field</cite> 와 <cite>TranslationDataset</cite><a class="headerlink"
 </div>
 <p>Spacy가 설치되어 있다면, 다음 코드는 <code class="docutils literal notranslate"><span class="pre">TranslationDataset</span></code> 에 있는 각 문장을 <code class="docutils literal notranslate"><span class="pre">Field</span></code> 에 정의된
 내용을 기반으로 토큰화할 것입니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchtext.datasets</span> <span class="k">import</span> <span class="n">Multi30k</span>
-<span class="kn">from</span> <span class="nn">torchtext.data</span> <span class="k">import</span> <span class="n">Field</span><span class="p">,</span> <span class="n">BucketIterator</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchtext.datasets</span> <span class="kn">import</span> <span class="n">Multi30k</span>
+<span class="kn">from</span> <span class="nn">torchtext.data</span> <span class="kn">import</span> <span class="n">Field</span><span class="p">,</span> <span class="n">BucketIterator</span>
 
 <span class="n">SRC</span> <span class="o">=</span> <span class="n">Field</span><span class="p">(</span><span class="n">tokenize</span> <span class="o">=</span> <span class="s2">&quot;spacy&quot;</span><span class="p">,</span>
             <span class="n">tokenizer_language</span><span class="o">=</span><span class="s2">&quot;de&quot;</span><span class="p">,</span>
@@ -413,16 +413,16 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module</span
 에서 확인할 수 있으며 이 튜토리얼의 모델이 사용하는 “attention” 은 Transformer 모델에서 제안하는
 멀티 헤드 셀프 어텐션(multi-headed self-attention) 과는 다르다는 점을 알려드립니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">Tuple</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
 
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
 <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">Tensor</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">Tensor</span>
 
 
 <span class="k">class</span> <span class="nc">Encoder</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                  <span class="n">input_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                  <span class="n">emb_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                  <span class="n">enc_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -457,7 +457,7 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module</span
 
 
 <span class="k">class</span> <span class="nc">Attention</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                  <span class="n">enc_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                  <span class="n">dec_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                  <span class="n">attn_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
@@ -491,7 +491,7 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module</span
 
 
 <span class="k">class</span> <span class="nc">Decoder</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                  <span class="n">output_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                  <span class="n">emb_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
                  <span class="n">enc_hid_dim</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -561,7 +561,7 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module</span
 
 
 <span class="k">class</span> <span class="nc">Seq2Seq</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
                  <span class="n">encoder</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
                  <span class="n">decoder</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
                  <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
@@ -641,7 +641,7 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module</span
     <span class="k">return</span> <span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)</span>
 
 
-<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;The model has {count_parameters(model):,} trainable parameters&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;The model has </span><span class="si">{</span><span class="n">count_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span><span class="si">:</span><span class="s1">,</span><span class="si">}</span><span class="s1"> trainable parameters&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>참고 : 언어 번역의 성능 점수를 기록하려면, <code class="docutils literal notranslate"><span class="pre">nn.CrossEntropyLoss</span></code> 함수가 단순한
@@ -742,13 +742,13 @@ <h2><code class="docutils literal notranslate"><span class="pre">nn.Module</span
 
     <span class="n">epoch_mins</span><span class="p">,</span> <span class="n">epoch_secs</span> <span class="o">=</span> <span class="n">epoch_time</span><span class="p">(</span><span class="n">start_time</span><span class="p">,</span> <span class="n">end_time</span><span class="p">)</span>
 
-    <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;Epoch: {epoch+1:02} | Time: </span><span class="si">{epoch_mins}</span><span class="s1">m </span><span class="si">{epoch_secs}</span><span class="s1">s&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Train Loss: </span><span class="si">{train_loss:.3f}</span><span class="s1"> | Train PPL: {math.exp(train_loss):7.3f}&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1"> Val. Loss: </span><span class="si">{valid_loss:.3f}</span><span class="s1"> |  Val. PPL: {math.exp(valid_loss):7.3f}&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;Epoch: </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">:</span><span class="s1">02</span><span class="si">}</span><span class="s1"> | Time: </span><span class="si">{</span><span class="n">epoch_mins</span><span class="si">}</span><span class="s1">m </span><span class="si">{</span><span class="n">epoch_secs</span><span class="si">}</span><span class="s1">s&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1">Train Loss: </span><span class="si">{</span><span class="n">train_loss</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> | Train PPL: </span><span class="si">{</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">train_loss</span><span class="p">)</span><span class="si">:</span><span class="s1">7.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;</span><span class="se">\t</span><span class="s1"> Val. Loss: </span><span class="si">{</span><span class="n">valid_loss</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> |  Val. PPL: </span><span class="si">{</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">valid_loss</span><span class="p">)</span><span class="si">:</span><span class="s1">7.3f</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
 
 <span class="n">test_loss</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">test_iterator</span><span class="p">,</span> <span class="n">criterion</span><span class="p">)</span>
 
-<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">&#39;| Test Loss: </span><span class="si">{test_loss:.3f}</span><span class="s1"> | Test PPL: {math.exp(test_loss):7.3f} |&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;| Test Loss: </span><span class="si">{</span><span class="n">test_loss</span><span class="si">:</span><span class="s1">.3f</span><span class="si">}</span><span class="s1"> | Test PPL: </span><span class="si">{</span><span class="n">math</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">test_loss</span><span class="p">)</span><span class="si">:</span><span class="s1">7.3f</span><span class="si">}</span><span class="s1"> |&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -785,7 +785,7 @@ <h2>다음 단계<a class="headerlink" href="#id1" title="Permalink to this head
         <a href="../intermediate/reinforcement_q_learning.html" class="btn btn-neutral float-right" title="강화 학습 (DQN) 튜토리얼" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
-        <a href="text_sentiment_ngrams_tutorial.html" class="btn btn-neutral" title="SyntaxError" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
+        <a href="text_sentiment_ngrams_tutorial.html" class="btn btn-neutral" title="TorchText로 텍스트 분류하기" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
       
     </div>
   
@@ -846,11 +846,9 @@ <h2>다음 단계<a class="headerlink" href="#id1" title="Permalink to this head
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/transfer_learning_tutorial.html b/docs/beginner/transfer_learning_tutorial.html
index 9a4b6b4a0..2115a14ce 100644
--- a/docs/beginner/transfer_learning_tutorial.html
+++ b/docs/beginner/transfer_learning_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -332,15 +332,15 @@
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># License: BSD</span>
 <span class="c1"># Author: Sasank Chilamkurthy</span>
 
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
-<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="k">import</span> <span class="n">lr_scheduler</span>
+<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">lr_scheduler</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">transforms</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">transforms</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">time</span>
 <span class="kn">import</span> <span class="nn">os</span>
@@ -562,130 +562,130 @@ <h3>학습 및 평가하기<a class="headerlink" href="#id7" title="Permalink to
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 0/24
 ----------
-train Loss: 0.6187 Acc: 0.6967
-val Loss: 0.2015 Acc: 0.9150
+train Loss: 0.6242 Acc: 0.6967
+val Loss: 0.1517 Acc: 0.9477
 
 Epoch 1/24
 ----------
-train Loss: 0.4809 Acc: 0.8033
-val Loss: 0.2493 Acc: 0.9020
+train Loss: 0.4996 Acc: 0.7541
+val Loss: 0.4502 Acc: 0.8105
 
 Epoch 2/24
 ----------
-train Loss: 0.3885 Acc: 0.8566
-val Loss: 0.2393 Acc: 0.9150
+train Loss: 0.4637 Acc: 0.7910
+val Loss: 0.2307 Acc: 0.9020
 
 Epoch 3/24
 ----------
-train Loss: 0.3952 Acc: 0.8074
-val Loss: 0.1715 Acc: 0.9281
+train Loss: 0.6726 Acc: 0.7623
+val Loss: 0.3258 Acc: 0.8562
 
 Epoch 4/24
 ----------
-train Loss: 0.5041 Acc: 0.8033
-val Loss: 0.2204 Acc: 0.9216
+train Loss: 0.5452 Acc: 0.7869
+val Loss: 0.3261 Acc: 0.8693
 
 Epoch 5/24
 ----------
-train Loss: 0.3896 Acc: 0.8238
-val Loss: 0.3936 Acc: 0.8431
+train Loss: 0.4464 Acc: 0.8033
+val Loss: 0.3025 Acc: 0.8627
 
 Epoch 6/24
 ----------
-train Loss: 0.2696 Acc: 0.8975
-val Loss: 0.4542 Acc: 0.8562
+train Loss: 0.3586 Acc: 0.8443
+val Loss: 0.3206 Acc: 0.8824
 
 Epoch 7/24
 ----------
-train Loss: 0.2941 Acc: 0.8730
-val Loss: 0.2716 Acc: 0.8889
+train Loss: 0.3695 Acc: 0.8607
+val Loss: 0.1933 Acc: 0.9412
 
 Epoch 8/24
 ----------
-train Loss: 0.2940 Acc: 0.8852
-val Loss: 0.2243 Acc: 0.9346
+train Loss: 0.3549 Acc: 0.8402
+val Loss: 0.1907 Acc: 0.9216
 
 Epoch 9/24
 ----------
-train Loss: 0.3518 Acc: 0.8607
-val Loss: 0.2499 Acc: 0.9477
+train Loss: 0.3785 Acc: 0.8320
+val Loss: 0.1714 Acc: 0.9281
 
 Epoch 10/24
 ----------
-train Loss: 0.3104 Acc: 0.8689
-val Loss: 0.2123 Acc: 0.9412
+train Loss: 0.2648 Acc: 0.8648
+val Loss: 0.1770 Acc: 0.9216
 
 Epoch 11/24
 ----------
-train Loss: 0.2920 Acc: 0.8730
-val Loss: 0.2278 Acc: 0.9216
+train Loss: 0.4072 Acc: 0.8197
+val Loss: 0.1647 Acc: 0.9346
 
 Epoch 12/24
 ----------
-train Loss: 0.3360 Acc: 0.8484
-val Loss: 0.2184 Acc: 0.9281
+train Loss: 0.2219 Acc: 0.9016
+val Loss: 0.1836 Acc: 0.9281
 
 Epoch 13/24
 ----------
-train Loss: 0.3281 Acc: 0.8648
-val Loss: 0.2382 Acc: 0.9150
+train Loss: 0.1602 Acc: 0.9508
+val Loss: 0.1589 Acc: 0.9412
 
 Epoch 14/24
 ----------
-train Loss: 0.3046 Acc: 0.8730
-val Loss: 0.2306 Acc: 0.9281
+train Loss: 0.2877 Acc: 0.8730
+val Loss: 0.1560 Acc: 0.9412
 
 Epoch 15/24
 ----------
-train Loss: 0.3191 Acc: 0.8811
-val Loss: 0.2126 Acc: 0.9281
+train Loss: 0.3343 Acc: 0.8811
+val Loss: 0.1614 Acc: 0.9346
 
 Epoch 16/24
 ----------
-train Loss: 0.2875 Acc: 0.8607
-val Loss: 0.2093 Acc: 0.9281
+train Loss: 0.3086 Acc: 0.8566
+val Loss: 0.1646 Acc: 0.9216
 
 Epoch 17/24
 ----------
-train Loss: 0.1985 Acc: 0.8934
-val Loss: 0.2165 Acc: 0.9346
+train Loss: 0.3062 Acc: 0.8484
+val Loss: 0.1623 Acc: 0.9281
 
 Epoch 18/24
 ----------
-train Loss: 0.2579 Acc: 0.9016
-val Loss: 0.1919 Acc: 0.9477
+train Loss: 0.3259 Acc: 0.8689
+val Loss: 0.1656 Acc: 0.9281
 
 Epoch 19/24
 ----------
-train Loss: 0.3008 Acc: 0.8811
-val Loss: 0.1988 Acc: 0.9346
+train Loss: 0.3035 Acc: 0.8689
+val Loss: 0.1796 Acc: 0.9346
 
 Epoch 20/24
 ----------
-train Loss: 0.3490 Acc: 0.8443
-val Loss: 0.2175 Acc: 0.9281
+train Loss: 0.3052 Acc: 0.8730
+val Loss: 0.1562 Acc: 0.9346
 
 Epoch 21/24
 ----------
-train Loss: 0.2834 Acc: 0.8689
-val Loss: 0.2097 Acc: 0.9346
+train Loss: 0.2268 Acc: 0.8934
+val Loss: 0.1727 Acc: 0.9281
 
 Epoch 22/24
 ----------
-train Loss: 0.2497 Acc: 0.8934
-val Loss: 0.2152 Acc: 0.9216
+train Loss: 0.3171 Acc: 0.8811
+val Loss: 0.1573 Acc: 0.9412
 
 Epoch 23/24
 ----------
-train Loss: 0.2447 Acc: 0.8975
-val Loss: 0.2203 Acc: 0.9216
+train Loss: 0.3242 Acc: 0.8811
+val Loss: 0.1605 Acc: 0.9281
 
 Epoch 24/24
 ----------
-train Loss: 0.4095 Acc: 0.8156
-val Loss: 0.2290 Acc: 0.9216
+train Loss: 0.2511 Acc: 0.9016
+val Loss: 0.1697 Acc: 0.9346
 
-Training complete in 0m 52s
+Training complete in 0m 55s
 Best val Acc: 0.947712
 </pre></div>
 </div>
@@ -734,131 +734,131 @@ <h3>학습 및 평가하기<a class="headerlink" href="#id9" title="Permalink to
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Epoch 0/24
 ----------
-train Loss: 0.5976 Acc: 0.6967
-val Loss: 0.2412 Acc: 0.9020
+train Loss: 0.5146 Acc: 0.7336
+val Loss: 0.4898 Acc: 0.7647
 
 Epoch 1/24
 ----------
-train Loss: 0.4837 Acc: 0.7869
-val Loss: 0.2396 Acc: 0.8889
+train Loss: 0.5920 Acc: 0.7254
+val Loss: 0.4466 Acc: 0.7974
 
 Epoch 2/24
 ----------
-train Loss: 0.4622 Acc: 0.7910
-val Loss: 0.1960 Acc: 0.9477
+train Loss: 0.6526 Acc: 0.7377
+val Loss: 0.1819 Acc: 0.9412
 
 Epoch 3/24
 ----------
-train Loss: 0.4613 Acc: 0.8238
-val Loss: 0.2043 Acc: 0.9412
+train Loss: 0.5290 Acc: 0.7828
+val Loss: 0.3363 Acc: 0.8562
 
 Epoch 4/24
 ----------
-train Loss: 0.4293 Acc: 0.8238
-val Loss: 0.2762 Acc: 0.9281
+train Loss: 0.4220 Acc: 0.8566
+val Loss: 0.2524 Acc: 0.9216
 
 Epoch 5/24
 ----------
-train Loss: 0.5115 Acc: 0.7787
-val Loss: 0.2344 Acc: 0.9346
+train Loss: 0.4839 Acc: 0.8156
+val Loss: 0.4798 Acc: 0.8366
 
 Epoch 6/24
 ----------
-train Loss: 0.5646 Acc: 0.7910
-val Loss: 0.2532 Acc: 0.9216
+train Loss: 0.6587 Acc: 0.7500
+val Loss: 0.2595 Acc: 0.9281
 
 Epoch 7/24
 ----------
-train Loss: 0.3448 Acc: 0.8689
-val Loss: 0.2001 Acc: 0.9477
+train Loss: 0.3498 Acc: 0.8443
+val Loss: 0.2111 Acc: 0.9477
 
 Epoch 8/24
 ----------
-train Loss: 0.3882 Acc: 0.8156
-val Loss: 0.1953 Acc: 0.9346
+train Loss: 0.2621 Acc: 0.8893
+val Loss: 0.2684 Acc: 0.9150
 
 Epoch 9/24
 ----------
-train Loss: 0.2684 Acc: 0.8770
-val Loss: 0.2195 Acc: 0.9281
+train Loss: 0.3621 Acc: 0.8238
+val Loss: 0.2172 Acc: 0.9346
 
 Epoch 10/24
 ----------
-train Loss: 0.3095 Acc: 0.8607
-val Loss: 0.2034 Acc: 0.9346
+train Loss: 0.3308 Acc: 0.8566
+val Loss: 0.2424 Acc: 0.9216
 
 Epoch 11/24
 ----------
-train Loss: 0.2747 Acc: 0.8975
-val Loss: 0.2088 Acc: 0.9412
+train Loss: 0.3407 Acc: 0.8402
+val Loss: 0.2167 Acc: 0.9346
 
 Epoch 12/24
 ----------
-train Loss: 0.2216 Acc: 0.9016
-val Loss: 0.2122 Acc: 0.9412
+train Loss: 0.4458 Acc: 0.7910
+val Loss: 0.2035 Acc: 0.9412
 
 Epoch 13/24
 ----------
-train Loss: 0.3659 Acc: 0.8361
-val Loss: 0.1938 Acc: 0.9477
+train Loss: 0.3229 Acc: 0.8689
+val Loss: 0.2088 Acc: 0.9412
 
 Epoch 14/24
 ----------
-train Loss: 0.3955 Acc: 0.8156
-val Loss: 0.2018 Acc: 0.9412
+train Loss: 0.3990 Acc: 0.8156
+val Loss: 0.2386 Acc: 0.9281
 
 Epoch 15/24
 ----------
-train Loss: 0.3046 Acc: 0.8893
-val Loss: 0.2003 Acc: 0.9412
+train Loss: 0.2491 Acc: 0.8934
+val Loss: 0.2184 Acc: 0.9412
 
 Epoch 16/24
 ----------
-train Loss: 0.3016 Acc: 0.8689
-val Loss: 0.1852 Acc: 0.9477
+train Loss: 0.3227 Acc: 0.8770
+val Loss: 0.2143 Acc: 0.9346
 
 Epoch 17/24
 ----------
-train Loss: 0.3477 Acc: 0.8566
-val Loss: 0.1968 Acc: 0.9542
+train Loss: 0.3036 Acc: 0.8730
+val Loss: 0.2005 Acc: 0.9412
 
 Epoch 18/24
 ----------
-train Loss: 0.3123 Acc: 0.8811
-val Loss: 0.2131 Acc: 0.9412
+train Loss: 0.2984 Acc: 0.8852
+val Loss: 0.1928 Acc: 0.9346
 
 Epoch 19/24
 ----------
-train Loss: 0.3373 Acc: 0.8648
-val Loss: 0.1921 Acc: 0.9412
+train Loss: 0.3429 Acc: 0.8607
+val Loss: 0.1999 Acc: 0.9346
 
 Epoch 20/24
 ----------
-train Loss: 0.3246 Acc: 0.8443
-val Loss: 0.1861 Acc: 0.9412
+train Loss: 0.3248 Acc: 0.8484
+val Loss: 0.2091 Acc: 0.9346
 
 Epoch 21/24
 ----------
-train Loss: 0.3668 Acc: 0.8197
-val Loss: 0.2590 Acc: 0.9216
+train Loss: 0.3253 Acc: 0.8648
+val Loss: 0.2012 Acc: 0.9477
 
 Epoch 22/24
 ----------
-train Loss: 0.3521 Acc: 0.8525
-val Loss: 0.2046 Acc: 0.9281
+train Loss: 0.3281 Acc: 0.8484
+val Loss: 0.2022 Acc: 0.9346
 
 Epoch 23/24
 ----------
-train Loss: 0.3124 Acc: 0.8484
-val Loss: 0.1996 Acc: 0.9477
+train Loss: 0.3193 Acc: 0.8689
+val Loss: 0.2099 Acc: 0.9346
 
 Epoch 24/24
 ----------
-train Loss: 0.3181 Acc: 0.8648
-val Loss: 0.2445 Acc: 0.9216
+train Loss: 0.4175 Acc: 0.7951
+val Loss: 0.1894 Acc: 0.9346
 
-Training complete in 0m 30s
-Best val Acc: 0.954248
+Training complete in 0m 31s
+Best val Acc: 0.947712
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">visualize_model</span><span class="p">(</span><span class="n">model_conv</span><span class="p">)</span>
@@ -874,7 +874,7 @@ <h3>학습 및 평가하기<a class="headerlink" href="#id9" title="Permalink to
 <h2>더 배워볼 내용<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h2>
 <p>전이학습의 응용 사례(application)들을 더 알아보려면,
 <a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html"><span class="doc">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</span></a> 을 참조해보세요.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  28.563 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 1 minutes  28.172 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-transfer-learning-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/d923ca53b1bfbeb3c222ae46d65d485e/transfer_learning_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">transfer_learning_tutorial.py</span></code></a></p>
@@ -972,11 +972,9 @@ <h2>더 배워볼 내용<a class="headerlink" href="#id10" title="Permalink to t
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/beginner/transformer_tutorial.html b/docs/beginner/transformer_tutorial.html
index 0808e7f83..d548a9016 100644
--- a/docs/beginner/transformer_tutorial.html
+++ b/docs/beginner/transformer_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -338,9 +338,9 @@ <h2>모델 정의하기<a class="headerlink" href="#id1" title="Permalink to thi
 
 <span class="k">class</span> <span class="nc">TransformerModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhead</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">TransformerModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="kn">from</span> <span class="nn">torch.nn</span> <span class="k">import</span> <span class="n">TransformerEncoder</span><span class="p">,</span> <span class="n">TransformerEncoderLayer</span>
+        <span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">TransformerEncoder</span><span class="p">,</span> <span class="n">TransformerEncoderLayer</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="s1">&#39;Transformer&#39;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">src_mask</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pos_encoder</span> <span class="o">=</span> <span class="n">PositionalEncoding</span><span class="p">(</span><span class="n">ninp</span><span class="p">,</span> <span class="n">dropout</span><span class="p">)</span>
@@ -381,7 +381,7 @@ <h2>모델 정의하기<a class="headerlink" href="#id1" title="Permalink to thi
 여기에서, 우리는 다른 주파수(frequency) 의 <code class="docutils literal notranslate"><span class="pre">sine</span></code> 과 <code class="docutils literal notranslate"><span class="pre">cosine</span></code> 함수를 사용합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">PositionalEncoding</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">max_len</span><span class="o">=</span><span class="mi">5000</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">d_model</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">max_len</span><span class="o">=</span><span class="mi">5000</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">PositionalEncoding</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
 
@@ -418,7 +418,7 @@ <h2>데이터 로드하고 배치 만들기<a class="headerlink" href="#id2" tit
 \end{bmatrix}\end{split}\]</div>
 <p>이 컬럼들은 모델에 의해서 독립적으로 취급되며, 이것은 더 효율적인 배치 프로세싱(batch processing) 이 가능하지만, <code class="docutils literal notranslate"><span class="pre">G</span></code> 와 <code class="docutils literal notranslate"><span class="pre">F</span></code> 의 의존성이 학습될 수 없다는 것을 의미합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchtext</span>
-<span class="kn">from</span> <span class="nn">torchtext.data.utils</span> <span class="k">import</span> <span class="n">get_tokenizer</span>
+<span class="kn">from</span> <span class="nn">torchtext.data.utils</span> <span class="kn">import</span> <span class="n">get_tokenizer</span>
 <span class="n">TEXT</span> <span class="o">=</span> <span class="n">torchtext</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Field</span><span class="p">(</span><span class="n">tokenize</span><span class="o">=</span><span class="n">get_tokenizer</span><span class="p">(</span><span class="s2">&quot;basic_english&quot;</span><span class="p">),</span>
                             <span class="n">init_token</span><span class="o">=</span><span class="s1">&#39;&lt;sos&gt;&#39;</span><span class="p">,</span>
                             <span class="n">eos_token</span><span class="o">=</span><span class="s1">&#39;&lt;eos&gt;&#39;</span><span class="p">,</span>
@@ -444,11 +444,6 @@ <h2>데이터 로드하고 배치 만들기<a class="headerlink" href="#id2" tit
 <span class="n">test_data</span> <span class="o">=</span> <span class="n">batchify</span><span class="p">(</span><span class="n">test_txt</span><span class="p">,</span> <span class="n">eval_batch_size</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>downloading wikitext-2-v1.zip
-extracting
-</pre></div>
-</div>
 <div class="section" id="input-target">
 <h3>입력(input) 과 타겟(target) 시퀀스를 생성하기 위한 함수들<a class="headerlink" href="#input-target" title="Permalink to this headline">¶</a></h3>
 <p><code class="docutils literal notranslate"><span class="pre">get_batch()</span></code> 함수는 트랜스포머 모델을 위한 입력과 타겟 시퀀스를 생성합니다.
@@ -566,62 +561,62 @@ <h2>모델 실행하기<a class="headerlink" href="#id3" title="Permalink to thi
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>| epoch   1 |   200/ 2981 batches | lr 5.00 | ms/batch 18.69 | loss  8.09 | ppl  3266.73
-| epoch   1 |   400/ 2981 batches | lr 5.00 | ms/batch 16.77 | loss  6.80 | ppl   897.40
-| epoch   1 |   600/ 2981 batches | lr 5.00 | ms/batch 16.77 | loss  6.37 | ppl   586.12
-| epoch   1 |   800/ 2981 batches | lr 5.00 | ms/batch 16.92 | loss  6.22 | ppl   502.44
-| epoch   1 |  1000/ 2981 batches | lr 5.00 | ms/batch 16.94 | loss  6.12 | ppl   453.27
-| epoch   1 |  1200/ 2981 batches | lr 5.00 | ms/batch 16.94 | loss  6.09 | ppl   441.95
-| epoch   1 |  1400/ 2981 batches | lr 5.00 | ms/batch 16.95 | loss  6.04 | ppl   418.67
-| epoch   1 |  1600/ 2981 batches | lr 5.00 | ms/batch 16.95 | loss  6.05 | ppl   422.17
-| epoch   1 |  1800/ 2981 batches | lr 5.00 | ms/batch 17.34 | loss  5.96 | ppl   388.03
-| epoch   1 |  2000/ 2981 batches | lr 5.00 | ms/batch 18.16 | loss  5.96 | ppl   385.80
-| epoch   1 |  2200/ 2981 batches | lr 5.00 | ms/batch 18.16 | loss  5.85 | ppl   347.74
-| epoch   1 |  2400/ 2981 batches | lr 5.00 | ms/batch 18.17 | loss  5.89 | ppl   362.37
-| epoch   1 |  2600/ 2981 batches | lr 5.00 | ms/batch 18.17 | loss  5.90 | ppl   363.60
-| epoch   1 |  2800/ 2981 batches | lr 5.00 | ms/batch 18.17 | loss  5.80 | ppl   329.67
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>| epoch   1 |   200/ 2981 batches | lr 5.00 | ms/batch 19.38 | loss  7.96 | ppl  2858.17
+| epoch   1 |   400/ 2981 batches | lr 5.00 | ms/batch 18.37 | loss  6.78 | ppl   881.44
+| epoch   1 |   600/ 2981 batches | lr 5.00 | ms/batch 18.43 | loss  6.36 | ppl   577.87
+| epoch   1 |   800/ 2981 batches | lr 5.00 | ms/batch 18.51 | loss  6.23 | ppl   508.39
+| epoch   1 |  1000/ 2981 batches | lr 5.00 | ms/batch 18.53 | loss  6.12 | ppl   453.27
+| epoch   1 |  1200/ 2981 batches | lr 5.00 | ms/batch 18.50 | loss  6.09 | ppl   441.19
+| epoch   1 |  1400/ 2981 batches | lr 5.00 | ms/batch 18.55 | loss  6.04 | ppl   420.90
+| epoch   1 |  1600/ 2981 batches | lr 5.00 | ms/batch 18.57 | loss  6.04 | ppl   421.19
+| epoch   1 |  1800/ 2981 batches | lr 5.00 | ms/batch 19.66 | loss  5.95 | ppl   385.53
+| epoch   1 |  2000/ 2981 batches | lr 5.00 | ms/batch 19.76 | loss  5.95 | ppl   385.53
+| epoch   1 |  2200/ 2981 batches | lr 5.00 | ms/batch 19.75 | loss  5.85 | ppl   347.73
+| epoch   1 |  2400/ 2981 batches | lr 5.00 | ms/batch 19.74 | loss  5.89 | ppl   362.05
+| epoch   1 |  2600/ 2981 batches | lr 5.00 | ms/batch 19.75 | loss  5.90 | ppl   363.78
+| epoch   1 |  2800/ 2981 batches | lr 5.00 | ms/batch 19.75 | loss  5.80 | ppl   330.93
 -----------------------------------------------------------------------------------------
-| end of epoch   1 | time: 54.73s | valid loss  5.73 | valid ppl   308.84
+| end of epoch   1 | time: 59.52s | valid loss  5.75 | valid ppl   314.46
 -----------------------------------------------------------------------------------------
-| epoch   2 |   200/ 2981 batches | lr 4.51 | ms/batch 18.26 | loss  5.81 | ppl   332.94
-| epoch   2 |   400/ 2981 batches | lr 4.51 | ms/batch 18.17 | loss  5.78 | ppl   323.96
-| epoch   2 |   600/ 2981 batches | lr 4.51 | ms/batch 18.17 | loss  5.61 | ppl   272.42
-| epoch   2 |   800/ 2981 batches | lr 4.51 | ms/batch 18.17 | loss  5.63 | ppl   279.10
-| epoch   2 |  1000/ 2981 batches | lr 4.51 | ms/batch 18.17 | loss  5.59 | ppl   267.91
-| epoch   2 |  1200/ 2981 batches | lr 4.51 | ms/batch 18.17 | loss  5.61 | ppl   274.49
-| epoch   2 |  1400/ 2981 batches | lr 4.51 | ms/batch 18.16 | loss  5.62 | ppl   275.56
-| epoch   2 |  1600/ 2981 batches | lr 4.51 | ms/batch 18.16 | loss  5.66 | ppl   286.39
-| epoch   2 |  1800/ 2981 batches | lr 4.51 | ms/batch 18.17 | loss  5.59 | ppl   267.40
-| epoch   2 |  2000/ 2981 batches | lr 4.51 | ms/batch 18.17 | loss  5.61 | ppl   273.92
-| epoch   2 |  2200/ 2981 batches | lr 4.51 | ms/batch 18.17 | loss  5.51 | ppl   246.77
-| epoch   2 |  2400/ 2981 batches | lr 4.51 | ms/batch 18.16 | loss  5.57 | ppl   263.34
-| epoch   2 |  2600/ 2981 batches | lr 4.51 | ms/batch 18.16 | loss  5.59 | ppl   267.07
-| epoch   2 |  2800/ 2981 batches | lr 4.51 | ms/batch 18.16 | loss  5.51 | ppl   247.44
+| epoch   2 |   200/ 2981 batches | lr 4.51 | ms/batch 19.81 | loss  5.80 | ppl   328.95
+| epoch   2 |   400/ 2981 batches | lr 4.51 | ms/batch 19.75 | loss  5.77 | ppl   320.30
+| epoch   2 |   600/ 2981 batches | lr 4.51 | ms/batch 19.76 | loss  5.59 | ppl   268.46
+| epoch   2 |   800/ 2981 batches | lr 4.51 | ms/batch 19.76 | loss  5.64 | ppl   281.18
+| epoch   2 |  1000/ 2981 batches | lr 4.51 | ms/batch 19.29 | loss  5.59 | ppl   266.60
+| epoch   2 |  1200/ 2981 batches | lr 4.51 | ms/batch 18.87 | loss  5.61 | ppl   274.28
+| epoch   2 |  1400/ 2981 batches | lr 4.51 | ms/batch 18.87 | loss  5.63 | ppl   277.96
+| epoch   2 |  1600/ 2981 batches | lr 4.51 | ms/batch 18.87 | loss  5.66 | ppl   285.90
+| epoch   2 |  1800/ 2981 batches | lr 4.51 | ms/batch 18.87 | loss  5.59 | ppl   267.43
+| epoch   2 |  2000/ 2981 batches | lr 4.51 | ms/batch 18.88 | loss  5.61 | ppl   274.47
+| epoch   2 |  2200/ 2981 batches | lr 4.51 | ms/batch 18.87 | loss  5.50 | ppl   244.10
+| epoch   2 |  2400/ 2981 batches | lr 4.51 | ms/batch 18.87 | loss  5.57 | ppl   261.26
+| epoch   2 |  2600/ 2981 batches | lr 4.51 | ms/batch 18.88 | loss  5.58 | ppl   265.01
+| epoch   2 |  2800/ 2981 batches | lr 4.51 | ms/batch 18.86 | loss  5.50 | ppl   244.25
 -----------------------------------------------------------------------------------------
-| end of epoch   2 | time: 56.59s | valid loss  5.60 | valid ppl   269.14
+| end of epoch   2 | time: 59.49s | valid loss  5.57 | valid ppl   263.65
 -----------------------------------------------------------------------------------------
-| epoch   3 |   200/ 2981 batches | lr 4.29 | ms/batch 18.26 | loss  5.56 | ppl   259.23
-| epoch   3 |   400/ 2981 batches | lr 4.29 | ms/batch 18.17 | loss  5.55 | ppl   258.15
-| epoch   3 |   600/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.36 | ppl   213.79
-| epoch   3 |   800/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.42 | ppl   225.04
-| epoch   3 |  1000/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.38 | ppl   216.22
-| epoch   3 |  1200/ 2981 batches | lr 4.29 | ms/batch 18.17 | loss  5.41 | ppl   223.60
-| epoch   3 |  1400/ 2981 batches | lr 4.29 | ms/batch 18.17 | loss  5.43 | ppl   227.95
-| epoch   3 |  1600/ 2981 batches | lr 4.29 | ms/batch 18.17 | loss  5.48 | ppl   238.99
-| epoch   3 |  1800/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.40 | ppl   221.25
-| epoch   3 |  2000/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.43 | ppl   228.06
-| epoch   3 |  2200/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.32 | ppl   205.05
-| epoch   3 |  2400/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.39 | ppl   218.15
-| epoch   3 |  2600/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.42 | ppl   225.01
-| epoch   3 |  2800/ 2981 batches | lr 4.29 | ms/batch 18.16 | loss  5.34 | ppl   208.39
+| epoch   3 |   200/ 2981 batches | lr 4.29 | ms/batch 18.96 | loss  5.53 | ppl   252.84
+| epoch   3 |   400/ 2981 batches | lr 4.29 | ms/batch 18.88 | loss  5.54 | ppl   255.62
+| epoch   3 |   600/ 2981 batches | lr 4.29 | ms/batch 18.89 | loss  5.36 | ppl   212.21
+| epoch   3 |   800/ 2981 batches | lr 4.29 | ms/batch 18.86 | loss  5.42 | ppl   225.51
+| epoch   3 |  1000/ 2981 batches | lr 4.29 | ms/batch 18.87 | loss  5.37 | ppl   215.34
+| epoch   3 |  1200/ 2981 batches | lr 4.29 | ms/batch 18.88 | loss  5.40 | ppl   222.34
+| epoch   3 |  1400/ 2981 batches | lr 4.29 | ms/batch 18.88 | loss  5.43 | ppl   228.15
+| epoch   3 |  1600/ 2981 batches | lr 4.29 | ms/batch 18.89 | loss  5.46 | ppl   235.81
+| epoch   3 |  1800/ 2981 batches | lr 4.29 | ms/batch 18.89 | loss  5.40 | ppl   220.84
+| epoch   3 |  2000/ 2981 batches | lr 4.29 | ms/batch 18.87 | loss  5.44 | ppl   231.04
+| epoch   3 |  2200/ 2981 batches | lr 4.29 | ms/batch 18.88 | loss  5.32 | ppl   203.49
+| epoch   3 |  2400/ 2981 batches | lr 4.29 | ms/batch 18.89 | loss  5.39 | ppl   219.21
+| epoch   3 |  2600/ 2981 batches | lr 4.29 | ms/batch 18.88 | loss  5.42 | ppl   226.01
+| epoch   3 |  2800/ 2981 batches | lr 4.29 | ms/batch 18.89 | loss  5.34 | ppl   207.68
 -----------------------------------------------------------------------------------------
-| end of epoch   3 | time: 56.56s | valid loss  5.50 | valid ppl   244.15
+| end of epoch   3 | time: 58.73s | valid loss  5.51 | valid ppl   246.08
 -----------------------------------------------------------------------------------------
 </pre></div>
 </div>
 </div>
 <div class="section" id="test-dataset">
-<h2>평가 데이터셋(test dataset) 으로 모델을 평가하기<a class="headerlink" href="#test-dataset" title="Permalink to this headline">¶</a></h2>
+<h2>평가 데이터셋(test dataset)으로 모델을 평가하기<a class="headerlink" href="#test-dataset" title="Permalink to this headline">¶</a></h2>
 <p>평가 데이터셋에 대한 결과를 확인하기 위해서 최고의 모델을 적용합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">test_loss</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">best_model</span><span class="p">,</span> <span class="n">test_data</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;=&#39;</span> <span class="o">*</span> <span class="mi">89</span><span class="p">)</span>
@@ -632,11 +627,11 @@ <h2>평가 데이터셋(test dataset) 으로 모델을 평가하기<a class="hea
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>=========================================================================================
-| End of training | test loss  5.40 | test ppl   221.98
+| End of training | test loss  5.42 | test ppl   224.82
 =========================================================================================
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  57.025 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  3.735 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-beginner-transformer-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/aa3898eb04d468790e00cb42405b1c23/transformer_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">transformer_tutorial.py</span></code></a></p>
@@ -707,7 +702,7 @@ <h2>평가 데이터셋(test dataset) 으로 모델을 평가하기<a class="hea
 </li>
 <li><a class="reference internal" href="#instance">인스턴스(instance) 초기화하기</a></li>
 <li><a class="reference internal" href="#id3">모델 실행하기</a></li>
-<li><a class="reference internal" href="#test-dataset">평가 데이터셋(test dataset) 으로 모델을 평가하기</a></li>
+<li><a class="reference internal" href="#test-dataset">평가 데이터셋(test dataset)으로 모델을 평가하기</a></li>
 </ul>
 </li>
 </ul>
@@ -725,11 +720,10 @@ <h2>평가 데이터셋(test dataset) 으로 모델을 평가하기<a class="hea
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/genindex.html b/docs/genindex.html
index c79d76485..cdfffd023 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -1,7 +1,6 @@
 
 
 
-
 <!DOCTYPE html>
 <!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
 <!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
@@ -173,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -185,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -355,11 +354,9 @@ <h1 id="index">Index</h1>
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
-         <script type="text/javascript" src="_static/jquery.js"></script>
-         <script type="text/javascript" src="_static/underscore.js"></script>
-         <script type="text/javascript" src="_static/doctools.js"></script>
-         <script type="text/javascript" src="_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="_static/jquery.js"></script>
+         <script src="_static/underscore.js"></script>
+         <script src="_static/doctools.js"></script>
      
 
   
diff --git a/docs/index.html b/docs/index.html
index 369633931..271706c81 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -173,7 +173,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -185,7 +185,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -545,6 +545,27 @@ <h4>nn.Transformer 와 TorchText 로 시퀀스-투-시퀀스 모델링하기</h4
 </div></p>
 <p><div class="col-md-12 tutorials-card-container" data-tags=Text>
 
+<div class="card tutorials-card" link=intermediate/char_rnn_classification_tutorial>
+
+<div class="card-body">
+
+<div class="card-title-container">
+    <h4>기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</h4>
+</div>
+
+<p class="card-summary">torchtext를 사용하지 않고 기본적인 문자-단위 RNN을 사용하여 단어를 분류하는 모델을 기초부터 만들고 학습합니다. 총 3개로 이뤄진 튜토리얼 시리즈의 첫번째 편입니다.</p>
+
+<p class="tags">Text</p>
+
+<div class="tutorials-image"><img src='_static/img/thumbnails/cropped/NLP-From-Scratch-Classifying-Names-with-a-Character-Level-RNN.png'></div>
+
+</div>
+
+</div>
+
+</div></p>
+<p><div class="col-md-12 tutorials-card-container" data-tags=Text>
+
 <div class="card tutorials-card" link=intermediate/char_rnn_generation_tutorial.html>
 
 <div class="card-body">
@@ -1347,11 +1368,9 @@ <h3>(비공식) 한국어 튜토리얼 저장소(GitHub)</h3>
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
-         <script type="text/javascript" src="_static/jquery.js"></script>
-         <script type="text/javascript" src="_static/underscore.js"></script>
-         <script type="text/javascript" src="_static/doctools.js"></script>
-         <script type="text/javascript" src="_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="_static/jquery.js"></script>
+         <script src="_static/underscore.js"></script>
+         <script src="_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/char_rnn_classification_tutorial.html b/docs/intermediate/char_rnn_classification_tutorial.html
index 11ec94627..448b1da40 100644
--- a/docs/intermediate/char_rnn_classification_tutorial.html
+++ b/docs/intermediate/char_rnn_classification_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -356,10 +356,13 @@ <h2>데이터 준비<a class="headerlink" href="#id2" title="Permalink to this h
 (그러나, 유니코드에서 ASCII로 변환해야 함).</p>
 <p>각 언어 별로 이름 목록 사전 <code class="docutils literal notranslate"><span class="pre">{language:</span> <span class="pre">[names</span> <span class="pre">...]}</span></code> 을 만듭니다.
 일반 변수 “category” 와 “line” (우리의 경우 언어와 이름)은 이후의 확장성을 위해 사용됩니다.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
 <p>역자 주:  “line” 에 입력을 “category”에 클래스를 적용하여 다른 문제에도 활용 할 수 있습니다.
 여기서는 “line”에 이름(ex. Robert )를 입력으로 “category”에 클래스(ex. english)로 사용합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">unicode_literals</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
-<span class="kn">from</span> <span class="nn">io</span> <span class="k">import</span> <span class="nb">open</span>
+</div>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">unicode_literals</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
+<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="nb">open</span>
 <span class="kn">import</span> <span class="nn">glob</span>
 <span class="kn">import</span> <span class="nn">os</span>
 
@@ -402,7 +405,7 @@ <h2>데이터 준비<a class="headerlink" href="#id2" title="Permalink to this h
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&#39;data/names/Scottish.txt&#39;, &#39;data/names/Arabic.txt&#39;, &#39;data/names/German.txt&#39;, &#39;data/names/Vietnamese.txt&#39;, &#39;data/names/French.txt&#39;, &#39;data/names/Japanese.txt&#39;, &#39;data/names/Spanish.txt&#39;, &#39;data/names/Chinese.txt&#39;, &#39;data/names/Dutch.txt&#39;, &#39;data/names/Italian.txt&#39;, &#39;data/names/Russian.txt&#39;, &#39;data/names/Irish.txt&#39;, &#39;data/names/Portuguese.txt&#39;, &#39;data/names/Polish.txt&#39;, &#39;data/names/Czech.txt&#39;, &#39;data/names/Korean.txt&#39;, &#39;data/names/Greek.txt&#39;, &#39;data/names/English.txt&#39;]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&#39;data/names/Korean.txt&#39;, &#39;data/names/Italian.txt&#39;, &#39;data/names/English.txt&#39;, &#39;data/names/Polish.txt&#39;, &#39;data/names/Scottish.txt&#39;, &#39;data/names/Dutch.txt&#39;, &#39;data/names/French.txt&#39;, &#39;data/names/Chinese.txt&#39;, &#39;data/names/Japanese.txt&#39;, &#39;data/names/Czech.txt&#39;, &#39;data/names/Portuguese.txt&#39;, &#39;data/names/Russian.txt&#39;, &#39;data/names/Vietnamese.txt&#39;, &#39;data/names/Irish.txt&#39;, &#39;data/names/Greek.txt&#39;, &#39;data/names/Spanish.txt&#39;, &#39;data/names/Arabic.txt&#39;, &#39;data/names/German.txt&#39;]
 Slusarski
 </pre></div>
 </div>
@@ -488,7 +491,7 @@ <h2>네트워크 생성<a class="headerlink" href="#id3" title="Permalink to thi
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 
 <span class="k">class</span> <span class="nc">RNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">RNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
@@ -533,9 +536,9 @@ <h2>네트워크 생성<a class="headerlink" href="#id3" title="Permalink to thi
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-2.8646, -2.9245, -2.9756, -2.9451, -2.7820, -3.0002, -2.8055, -2.8977,
-         -2.8573, -2.8691, -3.0234, -2.9436, -2.9146, -2.8814, -2.7751, -2.9309,
-         -2.8228, -2.8563]], grad_fn=&lt;LogSoftmaxBackward&gt;)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[-2.9074, -2.8636, -2.9943, -2.8746, -2.9909, -3.0183, -2.8108, -2.9739,
+         -2.8045, -2.7645, -2.9492, -2.8440, -2.8136, -2.9600, -2.8511, -2.9029,
+         -2.8978, -2.8525]], grad_fn=&lt;LogSoftmaxBackward&gt;)
 </pre></div>
 </div>
 <p>보시다시피 출력은 <code class="docutils literal notranslate"><span class="pre">&lt;1</span> <span class="pre">x</span> <span class="pre">n_categories&gt;</span></code> Tensor이고, 모든 항목은
@@ -559,7 +562,7 @@ <h3>학습 준비<a class="headerlink" href="#id5" title="Permalink to this head
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&#39;Czech&#39;, 14)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(&#39;Czech&#39;, 9)
 </pre></div>
 </div>
 <p>학습 예시(하나의 이름과 그 언어)를 얻는 빠른 방법도 필요합니다.:</p>
@@ -581,16 +584,16 @@ <h3>학습 준비<a class="headerlink" href="#id5" title="Permalink to this head
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>category = German / line = Kohler
-category = Greek / line = Sortras
-category = Arabic / line = Sabbagh
-category = Russian / line = Aduloff
-category = German / line = Kirchner
-category = Italian / line = Adessi
-category = Italian / line = Zunino
-category = Korean / line = Sin
-category = Scottish / line = Wilson
-category = Irish / line = Aonghuis
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>category = Italian / line = Catalano
+category = Korean / line = Chong
+category = Czech / line = Coma
+category = Arabic / line = Morcos
+category = French / line = Pan
+category = Korean / line = Kwang
+category = Japanese / line = Deushi
+category = Czech / line = Kratschmar
+category = Korean / line = Hyun
+category = Scottish / line = Wallace
 </pre></div>
 </div>
 </div>
@@ -680,26 +683,26 @@ <h3>네트워크 학습<a class="headerlink" href="#id6" title="Permalink to thi
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>5000 5% (0m 6s) 1.8359 Giannakopoulos / Greek ✓
-10000 10% (0m 13s) 3.2191 Bischoffs / Greek ✗ (German)
-15000 15% (0m 20s) 2.3342 Blackmore / Dutch ✗ (English)
-20000 20% (0m 26s) 1.7032 Escarra / Czech ✗ (Spanish)
-25000 25% (0m 33s) 2.9125 Sachs / Arabic ✗ (German)
-30000 30% (0m 40s) 1.2964 Freitas / Portuguese ✓
-35000 35% (0m 47s) 0.7054 an / Vietnamese ✓
-40000 40% (0m 53s) 0.9241 Xie / Chinese ✓
-45000 45% (1m 0s) 3.3471 Goncharuk / Portuguese ✗ (Russian)
-50000 50% (1m 7s) 0.6080 Rzehak / Czech ✓
-55000 55% (1m 14s) 1.2832 Chu / Vietnamese ✗ (Korean)
-60000 60% (1m 20s) 1.4585 Lafrentz / German ✓
-65000 65% (1m 27s) 0.4726 Winther / German ✓
-70000 70% (1m 34s) 1.8208 Roosevelt / French ✗ (Dutch)
-75000 75% (1m 40s) 0.9230 Tong / Chinese ✗ (Vietnamese)
-80000 80% (1m 47s) 0.5606 Bakushinsky / Russian ✓
-85000 85% (1m 54s) 2.1081 Kalb / Scottish ✗ (Arabic)
-90000 90% (2m 1s) 1.6164 Duryagin / Irish ✗ (Russian)
-95000 95% (2m 7s) 0.5838 Handal / Arabic ✓
-100000 100% (2m 14s) 1.3830 Chai / Arabic ✗ (Chinese)
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>5000 5% (0m 6s) 2.8952 Gebara / Japanese ✗ (Spanish)
+10000 10% (0m 13s) 2.6886 OwYang / Japanese ✗ (Chinese)
+15000 15% (0m 19s) 0.4028 Kinoshita / Japanese ✓
+20000 20% (0m 26s) 2.1028 Belo / Spanish ✗ (Portuguese)
+25000 25% (0m 33s) 2.1260 Chin / Korean ✗ (Chinese)
+30000 30% (0m 39s) 2.9334 Germain / Irish ✗ (French)
+35000 35% (0m 46s) 0.0224 Brisimitzakis / Greek ✓
+40000 40% (0m 53s) 1.9806 Antonowitsch / Russian ✗ (Czech)
+45000 45% (0m 59s) 1.3402 Koury / Arabic ✓
+50000 50% (1m 6s) 0.5164 Kwong / Chinese ✓
+55000 55% (1m 13s) 1.1304 Jedynak / Czech ✗ (Polish)
+60000 60% (1m 19s) 0.1538 Zheng / Chinese ✓
+65000 65% (1m 26s) 0.3045 Vaskin / Russian ✓
+70000 70% (1m 32s) 2.6335 Christou / Scottish ✗ (Greek)
+75000 75% (1m 39s) 1.1952 Walkenhorst / Dutch ✗ (German)
+80000 80% (1m 46s) 1.0503 Patsevich / Russian ✓
+85000 85% (1m 52s) 0.2133 Leggieri / Italian ✓
+90000 90% (1m 59s) 3.3590 Hoang / German ✗ (Vietnamese)
+95000 95% (2m 6s) 1.0874 Clayton / English ✓
+100000 100% (2m 12s) 0.6804 Giang / Vietnamese ✓
 </pre></div>
 </div>
 </div>
@@ -796,19 +799,19 @@ <h3>사용자 입력으로 실행<a class="headerlink" href="#id9" title="Permal
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&gt; Dovesky
-(-0.37) Russian
-(-1.40) Czech
-(-3.81) English
+(-0.25) Russian
+(-1.85) Czech
+(-3.93) English
 
 &gt; Jackson
-(-0.29) Scottish
-(-2.28) English
-(-2.45) Russian
+(-0.41) Scottish
+(-1.74) English
+(-2.98) Russian
 
 &gt; Satoshi
-(-0.35) Arabic
-(-1.61) Japanese
-(-3.29) Italian
+(-0.37) Italian
+(-2.24) Portuguese
+(-3.01) Japanese
 </pre></div>
 </div>
 <p><a class="reference external" href="https://github.com/spro/practical-pytorch/tree/master/char-rnn-classification">실용 PyTorch 저장소</a>
@@ -851,7 +854,7 @@ <h2>연습<a class="headerlink" href="#id10" title="Permalink to this headline">
 </ul>
 </li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  23.553 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  21.579 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-char-rnn-classification-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/37c8905519d3fd3f437b783a48d06eac/char_rnn_classification_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">char_rnn_classification_tutorial.py</span></code></a></p>
@@ -948,11 +951,9 @@ <h2>연습<a class="headerlink" href="#id10" title="Permalink to this headline">
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/char_rnn_generation_tutorial.html b/docs/intermediate/char_rnn_generation_tutorial.html
index 409479659..8529440df 100644
--- a/docs/intermediate/char_rnn_generation_tutorial.html
+++ b/docs/intermediate/char_rnn_generation_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -366,8 +366,8 @@ <h2>데이터 준비<a class="headerlink" href="#id2" title="Permalink to this h
 요약하면, 줄마다 이름이 적힌 텍스트 파일 <code class="docutils literal notranslate"><span class="pre">data/names/[Language].txt</span></code> 있습니다.
 이것을 어레이로 분리하고, Unicode를 ASCII로 변경하고,
 사전 <code class="docutils literal notranslate"><span class="pre">{language:</span> <span class="pre">[names</span> <span class="pre">...]}</span></code> 을 만들어서 마무리합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">unicode_literals</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
-<span class="kn">from</span> <span class="nn">io</span> <span class="k">import</span> <span class="nb">open</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">unicode_literals</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
+<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="nb">open</span>
 <span class="kn">import</span> <span class="nn">glob</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">unicodedata</span>
@@ -412,7 +412,7 @@ <h2>데이터 준비<a class="headerlink" href="#id2" title="Permalink to this h
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span># categories: 18 [&#39;Scottish&#39;, &#39;Arabic&#39;, &#39;German&#39;, &#39;Vietnamese&#39;, &#39;French&#39;, &#39;Japanese&#39;, &#39;Spanish&#39;, &#39;Chinese&#39;, &#39;Dutch&#39;, &#39;Italian&#39;, &#39;Russian&#39;, &#39;Irish&#39;, &#39;Portuguese&#39;, &#39;Polish&#39;, &#39;Czech&#39;, &#39;Korean&#39;, &#39;Greek&#39;, &#39;English&#39;]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span># categories: 18 [&#39;Korean&#39;, &#39;Italian&#39;, &#39;English&#39;, &#39;Polish&#39;, &#39;Scottish&#39;, &#39;Dutch&#39;, &#39;French&#39;, &#39;Chinese&#39;, &#39;Japanese&#39;, &#39;Czech&#39;, &#39;Portuguese&#39;, &#39;Russian&#39;, &#39;Vietnamese&#39;, &#39;Irish&#39;, &#39;Greek&#39;, &#39;Spanish&#39;, &#39;Arabic&#39;, &#39;German&#39;]
 O&#39;Neal
 </pre></div>
 </div>
@@ -440,7 +440,7 @@ <h2>네트워크 생성<a class="headerlink" href="#id4" title="Permalink to thi
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 
 <span class="k">class</span> <span class="nc">RNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">RNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
 
@@ -604,26 +604,26 @@ <h3>네트워크 학습<a class="headerlink" href="#id7" title="Permalink to thi
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0m 17s (5000 5%) 3.2079
-0m 31s (10000 10%) 3.0993
-0m 46s (15000 15%) 2.6456
-1m 1s (20000 20%) 1.9240
-1m 16s (25000 25%) 3.0209
-1m 31s (30000 30%) 1.0877
-1m 46s (35000 35%) 2.3249
-2m 1s (40000 40%) 2.5350
-2m 15s (45000 45%) 2.3017
-2m 30s (50000 50%) 2.3729
-2m 45s (55000 55%) 2.1775
-3m 0s (60000 60%) 2.3064
-3m 15s (65000 65%) 3.0474
-3m 30s (70000 70%) 1.9051
-3m 44s (75000 75%) 2.4201
-3m 59s (80000 80%) 2.8119
-4m 14s (85000 85%) 2.2757
-4m 29s (90000 90%) 2.0652
-4m 44s (95000 95%) 2.0606
-4m 59s (100000 100%) 1.2818
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>0m 14s (5000 5%) 2.6873
+0m 29s (10000 10%) 3.2346
+0m 43s (15000 15%) 2.8100
+0m 57s (20000 20%) 2.7549
+1m 12s (25000 25%) 3.4142
+1m 26s (30000 30%) 2.7237
+1m 41s (35000 35%) 2.7037
+1m 55s (40000 40%) 2.4760
+2m 10s (45000 45%) 1.7889
+2m 24s (50000 50%) 1.8619
+2m 39s (55000 55%) 2.1363
+2m 53s (60000 60%) 1.4708
+3m 8s (65000 65%) 2.6330
+3m 22s (70000 70%) 2.3061
+3m 37s (75000 75%) 1.6835
+3m 51s (80000 80%) 2.2866
+4m 6s (85000 85%) 1.0852
+4m 20s (90000 90%) 2.9085
+4m 35s (95000 95%) 2.4591
+4m 49s (100000 100%) 1.5600
 </pre></div>
 </div>
 </div>
@@ -703,18 +703,18 @@ <h2>네트워크 샘플링<a class="headerlink" href="#id9" title="Permalink to
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Rovanton
-Uartan
-Shallon
-Gerten
-Eerten
-Roune
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Romankin
+Uarinok
+Shakan
+Gerter
+Eringer
+Roure
 Salla
-Palla
-Aran
+Pora
+Artan
 Chan
 Han
-Iuni
+Iun
 </pre></div>
 </div>
 </div>
@@ -737,7 +737,7 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 </ul>
 </li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  59.343 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 4 minutes  50.128 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-char-rnn-generation-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/322506af160d5e2056afd75de1fd34ee/char_rnn_generation_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">char_rnn_generation_tutorial.py</span></code></a></p>
@@ -828,11 +828,9 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/ddp_tutorial.html b/docs/intermediate/ddp_tutorial.html
index 74f598c0f..3f69ceba3 100644
--- a/docs/intermediate/ddp_tutorial.html
+++ b/docs/intermediate/ddp_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -359,10 +359,10 @@ <h2>Basic Use Case<a class="headerlink" href="#basic-use-case" title="Permalink
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">tempfile</span>
 <span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="kn">as</span> <span class="nn">dist</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="kn">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="kn">as</span> <span class="nn">optim</span>
-<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="kn">as</span> <span class="nn">mp</span>
+<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
+<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
 
 <span class="kn">from</span> <span class="nn">torch.nn.parallel</span> <span class="kn">import</span> <span class="n">DistributedDataParallel</span> <span class="k">as</span> <span class="n">DDP</span>
 
@@ -395,7 +395,7 @@ <h2>Basic Use Case<a class="headerlink" href="#basic-use-case" title="Permalink
 
 
 <span class="k">def</span> <span class="nf">demo_basic</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Running basic DDP example on rank {rank}.&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Running basic DDP example on rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
     <span class="n">setup</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">)</span>
 
     <span class="c1"># create model and move it to GPU with id rank</span>
@@ -418,7 +418,7 @@ <h2>Basic Use Case<a class="headerlink" href="#basic-use-case" title="Permalink
     <span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">demo_fn</span><span class="p">,</span>
              <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,),</span>
              <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span>
-             <span class="n">join</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+             <span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>As you can see, DDP wraps lower-level distributed communication details and
@@ -461,7 +461,7 @@ <h2>Save and Load Checkpoints<a class="headerlink" href="#save-and-load-checkpoi
 same machine using the same set of devices. For more advanced failure recovery
 and elasticity support, please refer to <a class="reference external" href="https://pytorch.org/elastic">TorchElastic</a>.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">demo_checkpoint</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Running DDP checkpoint example on rank {rank}.&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Running DDP checkpoint example on rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
     <span class="n">setup</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">)</span>
 
     <span class="n">model</span> <span class="o">=</span> <span class="n">ToyModel</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">rank</span><span class="p">)</span>
@@ -527,7 +527,7 @@ <h2>Combine DDP with Model Parallelism<a class="headerlink" href="#combine-ddp-w
 must NOT be set. Input and output data will be placed in proper devices by
 either the application or the model <code class="docutils literal notranslate"><span class="pre">forward()</span></code> method.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">demo_model_parallel</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Running DDP with model parallel example on rank {rank}.&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Running DDP with model parallel example on rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
     <span class="n">setup</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">)</span>
 
     <span class="c1"># setup mp_model and devices for this process</span>
@@ -552,7 +552,7 @@ <h2>Combine DDP with Model Parallelism<a class="headerlink" href="#combine-ddp-w
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
     <span class="n">n_gpus</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device_count</span><span class="p">()</span>
     <span class="k">if</span> <span class="n">n_gpus</span> <span class="o">&lt;</span> <span class="mi">8</span><span class="p">:</span>
-      <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Requires at least 8 GPUs to run, but got {n_gpus}.&quot;</span><span class="p">)</span>
+      <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Requires at least 8 GPUs to run, but got </span><span class="si">{</span><span class="n">n_gpus</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
       <span class="n">run_demo</span><span class="p">(</span><span class="n">demo_basic</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
       <span class="n">run_demo</span><span class="p">(</span><span class="n">demo_checkpoint</span><span class="p">,</span> <span class="mi">8</span><span class="p">)</span>
@@ -635,11 +635,9 @@ <h2>Combine DDP with Model Parallelism<a class="headerlink" href="#combine-ddp-w
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/dist_pipeline_parallel_tutorial.html b/docs/intermediate/dist_pipeline_parallel_tutorial.html
index 15bce3ff3..deb7fcb57 100644
--- a/docs/intermediate/dist_pipeline_parallel_tutorial.html
+++ b/docs/intermediate/dist_pipeline_parallel_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -352,7 +352,7 @@ <h2>Step 1: Partition ResNet50 Model<a class="headerlink" href="#step-1-partitio
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">threading</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="kn">as</span> <span class="nn">nn</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 
 <span class="kn">from</span> <span class="nn">torchvision.models.resnet</span> <span class="kn">import</span> <span class="n">Bottleneck</span>
 
@@ -360,12 +360,12 @@ <h2>Step 1: Partition ResNet50 Model<a class="headerlink" href="#step-1-partitio
 
 
 <span class="k">def</span> <span class="nf">conv1x1</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="n">in_planes</span><span class="p">,</span> <span class="n">out_planes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="n">stride</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 
 
 <span class="k">class</span> <span class="nc">ResNetBase</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block</span><span class="p">,</span> <span class="n">inplanes</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
-                <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">width_per_group</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">norm_layer</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
+                <span class="n">groups</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">width_per_group</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">norm_layer</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">ResNetBase</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">_lock</span> <span class="o">=</span> <span class="n">threading</span><span class="o">.</span><span class="n">Lock</span><span class="p">()</span>
@@ -378,7 +378,7 @@ <h2>Step 1: Partition ResNet50 Model<a class="headerlink" href="#step-1-partitio
 
     <span class="k">def</span> <span class="nf">_make_layer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">planes</span><span class="p">,</span> <span class="n">blocks</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
         <span class="n">norm_layer</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_norm_layer</span>
-        <span class="n">downsample</span> <span class="o">=</span> <span class="bp">None</span>
+        <span class="n">downsample</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="n">previous_dilation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span>
         <span class="k">if</span> <span class="n">stride</span> <span class="o">!=</span> <span class="mi">1</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span> <span class="o">!=</span> <span class="n">planes</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_block</span><span class="o">.</span><span class="n">expansion</span><span class="p">:</span>
             <span class="n">downsample</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
@@ -416,9 +416,9 @@ <h2>Step 1: Partition ResNet50 Model<a class="headerlink" href="#step-1-partitio
 
         <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_norm_layer</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">inplanes</span><span class="p">),</span>
-            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
             <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_make_layer</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
@@ -533,8 +533,8 @@ <h2>Step 3: Define The Training Loop<a class="headerlink" href="#step-3-define-t
 regular local training, except that it uses <code class="docutils literal notranslate"><span class="pre">dist_autograd</span></code> to launch
 backward and provides the <code class="docutils literal notranslate"><span class="pre">context_id</span></code> for both backward and optimizer
 <code class="docutils literal notranslate"><span class="pre">step()</span></code>.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed.autograd</span> <span class="kn">as</span> <span class="nn">dist_autograd</span>
-<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="kn">as</span> <span class="nn">optim</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.distributed.autograd</span> <span class="k">as</span> <span class="nn">dist_autograd</span>
+<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
 <span class="kn">from</span> <span class="nn">torch.distributed.optim</span> <span class="kn">import</span> <span class="n">DistributedOptimizer</span>
 
 <span class="n">num_batches</span> <span class="o">=</span> <span class="mi">3</span>
@@ -558,7 +558,7 @@ <h2>Step 3: Define The Training Loop<a class="headerlink" href="#step-3-define-t
                         <span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
 
     <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_batches</span><span class="p">):</span>
-        <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Processing batch {i}&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Processing batch </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
         <span class="c1"># generate random inputs and labels</span>
         <span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">image_w</span><span class="p">,</span> <span class="n">image_h</span><span class="p">)</span>
         <span class="n">labels</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">)</span> \
@@ -580,7 +580,7 @@ <h2>Step 4: Launch RPC Processes<a class="headerlink" href="#step-4-launch-rpc-p
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">time</span>
 
-<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="kn">as</span> <span class="nn">mp</span>
+<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
 
 
 <span class="k">def</span> <span class="nf">run_worker</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">num_split</span><span class="p">):</span>
@@ -598,7 +598,7 @@ <h2>Step 4: Launch RPC Processes<a class="headerlink" href="#step-4-launch-rpc-p
         <span class="n">run_master</span><span class="p">(</span><span class="n">num_split</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
-            <span class="n">f</span><span class="s2">&quot;worker{rank}&quot;</span><span class="p">,</span>
+            <span class="sa">f</span><span class="s2">&quot;worker</span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
             <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span>
             <span class="n">world_size</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span>
             <span class="n">rpc_backend_options</span><span class="o">=</span><span class="n">options</span>
@@ -613,9 +613,9 @@ <h2>Step 4: Launch RPC Processes<a class="headerlink" href="#step-4-launch-rpc-p
     <span class="n">world_size</span> <span class="o">=</span> <span class="mi">3</span>
     <span class="k">for</span> <span class="n">num_split</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">]:</span>
         <span class="n">tik</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-        <span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">run_worker</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="n">num_split</span><span class="p">),</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+        <span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">run_worker</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="n">num_split</span><span class="p">),</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="n">tok</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-        <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;number of splits = {num_split}, execution time = {tok - tik}&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;number of splits = </span><span class="si">{</span><span class="n">num_split</span><span class="si">}</span><span class="s2">, execution time = </span><span class="si">{</span><span class="n">tok</span> <span class="o">-</span> <span class="n">tik</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>The output below shows the speedup attained by increasing the number of splits
@@ -715,11 +715,9 @@ <h2>Step 4: Launch RPC Processes<a class="headerlink" href="#step-4-launch-rpc-p
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/dist_tuto.html b/docs/intermediate/dist_tuto.html
index 8e92f4026..339a5fe73 100644
--- a/docs/intermediate/dist_tuto.html
+++ b/docs/intermediate/dist_tuto.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -331,7 +331,7 @@ <h2>설정(Setup)<a class="headerlink" href="#setup" title="Permalink to this he
 <span class="c1">#!/usr/bin/env python</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="kn">as</span> <span class="nn">dist</span>
+<span class="kn">import</span> <span class="nn">torch.distributed</span> <span class="k">as</span> <span class="nn">dist</span>
 <span class="kn">from</span> <span class="nn">torch.multiprocessing</span> <span class="kn">import</span> <span class="n">Process</span>
 
 <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
@@ -369,15 +369,15 @@ <h2>설정(Setup)<a class="headerlink" href="#setup" title="Permalink to this he
 프로세스가 자신의 위치를 공유함으로써 서로 통신할 수 있도록 합니다.</p>
 </div>
 <div class="section" id="point-to-point">
-<h2>지점-대-지점 간(Point-to-Point) 통신<a class="headerlink" href="#point-to-point" title="Permalink to this headline">¶</a></h2>
+<h2>점-대-점 간(Point-to-Point) 통신<a class="headerlink" href="#point-to-point" title="Permalink to this headline">¶</a></h2>
 <div class="figure align-center" id="id2">
 <a class="reference internal image-reference" href="../_images/send_recv.png"><img alt="송신과 수신" src="../_images/send_recv.png" style="width: 100%;" /></a>
 <p class="caption"><span class="caption-text">송신과 수신</span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
 </div>
-<p>하나의 프로세스에서 다른 프로세스로 데이터를 전송하는 것을 지점-대-지점 간 통신이라고 합니다.
+<p>하나의 프로세스에서 다른 프로세스로 데이터를 전송하는 것을 점-대-점 간 통신이라고 합니다.
 지점간 통신은  <code class="docutils literal notranslate"><span class="pre">send</span></code> 와 <code class="docutils literal notranslate"><span class="pre">recv</span></code> 함수 또는 즉시 응답하는(<em>immediate</em> counter-parts)
 <code class="docutils literal notranslate"><span class="pre">isend</span></code> 와 <code class="docutils literal notranslate"><span class="pre">irecv</span></code> 를 사용합니다.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;블로킹(blocking) 지점-대-지점 간 통신&quot;&quot;&quot;</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;블로킹(blocking) 점-대-점 간 통신&quot;&quot;&quot;</span>
 
 <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
     <span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
@@ -388,7 +388,7 @@ <h2>지점-대-지점 간(Point-to-Point) 통신<a class="headerlink" href="#poi
     <span class="k">else</span><span class="p">:</span>
         <span class="c1"># Receive tensor from process 0</span>
         <span class="n">dist</span><span class="o">.</span><span class="n">recv</span><span class="p">(</span><span class="n">tensor</span><span class="o">=</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Rank &#39;</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="s1">&#39; has data &#39;</span><span class="p">,</span> <span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Rank &#39;</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="s1">&#39; has data &#39;</span><span class="p">,</span> <span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
 </div>
 <p>위 예제에서 두 프로세스는 값이 0인 Tensor로 시작한 후, 0번 프로세스가 Tensor의 값을
@@ -397,22 +397,22 @@ <h2>지점-대-지점 간(Point-to-Point) 통신<a class="headerlink" href="#poi
 <p>또한 <code class="docutils literal notranslate"><span class="pre">send</span></code>/<code class="docutils literal notranslate"><span class="pre">recv</span></code> 는 모두 <strong>블로킹</strong> 입니다: 두 프로세스는 통신이 완료될 때까지
 멈춰있습니다. 반면에 즉시 응답하는 것이 <strong>논-블로킹</strong> 입니다; 스크립트는 실행을
 계속하고 메소드는 <code class="docutils literal notranslate"><span class="pre">wait()</span></code> 를 선택할 수 있는 <code class="docutils literal notranslate"><span class="pre">Work</span></code> 객체를 반환합니다.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;논-블로킹(non-blocking) 지점-대-지점 간 통신&quot;&quot;&quot;</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot;논-블로킹(non-blocking) 점-대-점 간 통신&quot;&quot;&quot;</span>
 
 <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
     <span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-    <span class="n">req</span> <span class="o">=</span> <span class="bp">None</span>
+    <span class="n">req</span> <span class="o">=</span> <span class="kc">None</span>
     <span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
         <span class="n">tensor</span> <span class="o">+=</span> <span class="mi">1</span>
         <span class="c1"># Send the tensor to process 1</span>
         <span class="n">req</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">isend</span><span class="p">(</span><span class="n">tensor</span><span class="o">=</span><span class="n">tensor</span><span class="p">,</span> <span class="n">dst</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-        <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Rank 0 started sending&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Rank 0 started sending&#39;</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="c1"># Receive tensor from process 0</span>
         <span class="n">req</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">irecv</span><span class="p">(</span><span class="n">tensor</span><span class="o">=</span><span class="n">tensor</span><span class="p">,</span> <span class="n">src</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Rank 1 started receiving&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Rank 1 started receiving&#39;</span><span class="p">)</span>
     <span class="n">req</span><span class="o">.</span><span class="n">wait</span><span class="p">()</span>
-    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Rank &#39;</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="s1">&#39; has data &#39;</span><span class="p">,</span> <span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Rank &#39;</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="s1">&#39; has data &#39;</span><span class="p">,</span> <span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
 </div>
 <p>즉시 응답하는 함수들을 사용할 때는 보내고 받는 Tensor에 대한 사용법에 주의해야 합니다.
@@ -424,7 +424,7 @@ <h2>지점-대-지점 간(Point-to-Point) 통신<a class="headerlink" href="#poi
 </ul>
 <p>그러나, <code class="docutils literal notranslate"><span class="pre">req.wait()</span></code> 를 실행한 후에는 통신이 이루어진 것을 보장받을 수 있기 때문에,
 <code class="docutils literal notranslate"><span class="pre">tensor[0]</span></code> 에 저장된 값은 1.0이 됩니다.</p>
-<p>지점-대-지점 간 통신은 프로세스 간 통신에 대한 세밀한 제어를 원할 때 유용합니다.
+<p>점-대-점 간 통신은 프로세스 간 통신에 대한 세밀한 제어를 원할 때 유용합니다.
 <a class="reference external" href="https://github.com/baidu-research/baidu-allreduce">바이두(Baidu)의 DeepSpeech</a> 나
 <a class="reference external" href="https://research.fb.com/publications/imagenet1kin1h/">페이스북(Facebook)의 대규모 실험</a>
 에서 사용하는 것과 같은 멋진 알고리즘을 구현할 때 사용할 수 있습니다.
@@ -473,7 +473,7 @@ <h2>집합 통신(Collective Communication)<a class="headerlink" href="#collecti
 </tr>
 </tbody>
 </table>
-<p>지점-대-지점 간 통신과 달리 집합 통신은 <strong>그룹</strong> 의 모든 프로세스에 걸친 통신 패턴을
+<p>점-대-점 간 통신과 달리 집합 통신은 <strong>그룹</strong> 의 모든 프로세스에 걸친 통신 패턴을
 허용합니다. 그룹은 모든 프로세스의 부분 집합입니다. 그룹을 생성하기 위해서는
 <code class="docutils literal notranslate"><span class="pre">dist.new_group(group)</span></code> 에 순서(rank) 목록을 전달합니다. 기본적으로, 집합 통신은
 <strong>월드(world)</strong> 라고 부르는 전체 프로세스에서 실행됩니다. 예를 들어, 모든 프로세스에
@@ -481,11 +481,11 @@ <h2>집합 통신(Collective Communication)<a class="headerlink" href="#collecti
 사용하면 됩니다.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot; All-Reduce 예제 &quot;&quot;&quot;</span>
 <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot; 간단한 지점-대-지점 간 통신 &quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot; 간단한 점-대-점 간 통신 &quot;&quot;&quot;</span>
     <span class="n">group</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">new_group</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
     <span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
     <span class="n">dist</span><span class="o">.</span><span class="n">all_reduce</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">op</span><span class="o">=</span><span class="n">dist</span><span class="o">.</span><span class="n">reduce_op</span><span class="o">.</span><span class="n">SUM</span><span class="p">,</span> <span class="n">group</span><span class="o">=</span><span class="n">group</span><span class="p">)</span>
-    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Rank &#39;</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="s1">&#39; has data &#39;</span><span class="p">,</span> <span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Rank &#39;</span><span class="p">,</span> <span class="n">rank</span><span class="p">,</span> <span class="s1">&#39; has data &#39;</span><span class="p">,</span> <span class="n">tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
 </pre></div>
 </div>
 <p>그룹 내의 모든 Tensor들의 합이 필요하기 떄문에, <code class="docutils literal notranslate"><span class="pre">dist.reduce_op.SUM</span></code> 을
@@ -575,7 +575,7 @@ <h2>분산 학습(Distributed Training)<a class="headerlink" href="#distributed-
 <p>위 코드를 사용하여 어떤 데이터셋도 몇 줄의 코드로 간단히 분할할 수 있습니다:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot; MNIST 데이터셋 분할 &quot;&quot;&quot;</span>
 <span class="k">def</span> <span class="nf">partition_dataset</span><span class="p">():</span>
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="s1">&#39;./data&#39;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="s1">&#39;./data&#39;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                              <span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
                                  <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                                  <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,))</span>
@@ -587,7 +587,7 @@ <h2>분산 학습(Distributed Training)<a class="headerlink" href="#distributed-
     <span class="n">partition</span> <span class="o">=</span> <span class="n">partition</span><span class="o">.</span><span class="n">use</span><span class="p">(</span><span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">())</span>
     <span class="n">train_set</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">partition</span><span class="p">,</span>
                                          <span class="n">batch_size</span><span class="o">=</span><span class="n">bsz</span><span class="p">,</span>
-                                         <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+                                         <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">train_set</span><span class="p">,</span> <span class="n">bsz</span>
 </pre></div>
 </div>
@@ -617,7 +617,7 @@ <h2>분산 학습(Distributed Training)<a class="headerlink" href="#distributed-
             <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
             <span class="n">average_gradients</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
             <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
-        <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Rank &#39;</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">(),</span> <span class="s1">&#39;, epoch &#39;</span><span class="p">,</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Rank &#39;</span><span class="p">,</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">(),</span> <span class="s1">&#39;, epoch &#39;</span><span class="p">,</span>
               <span class="n">epoch</span><span class="p">,</span> <span class="s1">&#39;: &#39;</span><span class="p">,</span> <span class="n">epoch_loss</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -640,7 +640,7 @@ <h2>분산 학습(Distributed Training)<a class="headerlink" href="#distributed-
 <div class="section" id="ring-allreduce">
 <h3>사용자 정의 링-올리듀스(Ring-Allreduce)<a class="headerlink" href="#ring-allreduce" title="Permalink to this headline">¶</a></h3>
 <p>추가로 DeepSpeech의 효율적인 링 올리듀스(ring allreduce)를 구현하고 싶다고 가정해보겠습니다.
-이것은 지점-대-지점 집합 통신(point-to-point collectives)으로 쉽게 구현할 수 있습니다.</p>
+이것은 점-대-점 집합 통신(point-to-point collectives)으로 쉽게 구현할 수 있습니다.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="sd">&quot;&quot;&quot; 링-리듀스(ring-reduce) 구현 &quot;&quot;&quot;</span>
 <span class="k">def</span> <span class="nf">allreduce</span><span class="p">(</span><span class="n">send</span><span class="p">,</span> <span class="n">recv</span><span class="p">):</span>
    <span class="n">rank</span> <span class="o">=</span> <span class="n">dist</span><span class="o">.</span><span class="n">get_rank</span><span class="p">()</span>
@@ -711,7 +711,7 @@ <h3>통신 백엔드(Communication Backends)<a class="headerlink" href="#communi
 로 사용률을 모니터링할 수 있습니다.</p>
 <p><strong>MPI 백엔드</strong></p>
 <p>MPI(Message Passing Interface)는 고성능 컴퓨팅 분야의 표준 도구입니다.
-이는 지점-대-지점 간 통신과 집합 통신을 허용하며 <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> 의 API에
+이는 점-대-점 간 통신과 집합 통신을 허용하며 <code class="docutils literal notranslate"><span class="pre">torch.distributed</span></code> 의 API에
 영감을 주었습니다. 다양한 목적에 따라 최적화된 몇몇 MPI 구현체들(예.
 <a class="reference external" href="https://www.open-mpi.org/">Open-MPI</a>,
 <a class="reference external" href="http://mvapich.cse.ohio-state.edu/">MVAPICH2</a>,
@@ -869,7 +869,7 @@ <h3>초기화 방법(Initialization Methods)<a class="headerlink" href="#initial
               <ul>
 <li><a class="reference internal" href="#">PyTorch로 분산 어플리케이션 개발하기</a><ul>
 <li><a class="reference internal" href="#setup">설정(Setup)</a></li>
-<li><a class="reference internal" href="#point-to-point">지점-대-지점 간(Point-to-Point) 통신</a></li>
+<li><a class="reference internal" href="#point-to-point">점-대-점 간(Point-to-Point) 통신</a></li>
 <li><a class="reference internal" href="#collective-communication">집합 통신(Collective Communication)</a></li>
 <li><a class="reference internal" href="#distributed-training">분산 학습(Distributed Training)</a><ul>
 <li><a class="reference internal" href="#ring-allreduce">사용자 정의 링-올리듀스(Ring-Allreduce)</a></li>
@@ -897,11 +897,10 @@ <h3>초기화 방법(Initialization Methods)<a class="headerlink" href="#initial
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/intermediate/dynamic_quantization_bert_tutorial.html b/docs/intermediate/dynamic_quantization_bert_tutorial.html
index aa60709d2..081b13019 100644
--- a/docs/intermediate/dynamic_quantization_bert_tutorial.html
+++ b/docs/intermediate/dynamic_quantization_bert_tutorial.html
@@ -31,7 +31,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="(beta) Static Quantization with Eager Mode in PyTorch" href="../advanced/static_quantization_tutorial.html" />
-    <link rel="prev" title="(beta) Dynamic Quantization on an LSTM Word Language Model" href="../advanced/dynamic_quantization_tutorial.html" /> 
+    <link rel="prev" title="(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화" href="../advanced/dynamic_quantization_tutorial.html" /> 
 
   
   <script src="../_static/js/modernizr.min.js"></script>
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -302,16 +302,16 @@ <h1>(베타) BERT 모델 동적 양자화하기<a class="headerlink" href="#bert
 <div class="admonition tip">
 <p class="admonition-title">Tip</p>
 <p>이 튜토리얼을 따라 하기 위해, 이
-<a class="reference external" href="https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads/dynamic_quantization_bert_tutorial.ipynb">Colab Version</a> 을 사용하길 권장합니다.
+<a class="reference external" href="https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads/dynamic_quantization_bert_tutorial.ipynb">Colab 버전</a> 을 사용하길 권장합니다.
 그러면 아래에 설명된 정보들을 이용해 실험할 수 있습니다.</p>
 </div>
-<p><strong>Author</strong>: <a class="reference external" href="https://github.com/jianyuh">Jianyu Huang</a></p>
-<p><strong>Reviewed by</strong>: <a class="reference external" href="https://github.com/raghuramank100">Raghuraman Krishnamoorthi</a></p>
-<p><strong>Edited by</strong>: <a class="reference external" href="https://github.com/jlin27">Jessica Lin</a></p>
-<p><strong>번역</strong>: <a class="reference external" href="https://github.com/kwonmha">Myungha Kwon</a></p>
+<p><strong>Author</strong>: <a class="reference external" href="https://github.com/jianyuh">Jianyu Huang</a>
+<strong>Reviewed by</strong>: <a class="reference external" href="https://github.com/raghuramank100">Raghuraman Krishnamoorthi</a>
+<strong>Edited by</strong>: <a class="reference external" href="https://github.com/jlin27">Jessica Lin</a>
+<strong>번역</strong>: <a class="reference external" href="https://github.com/kwonmha">Myungha Kwon</a></p>
 <div class="section" id="id1">
 <h2>시작하기<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
-<p>이 튜토리얼에서는 <a class="reference external" href="https://github.com/huggingface/transformers">HuggingFace Transformers</a> 예제들을 따라 하면서 BERT
+<p>이 튜토리얼에서는 <a class="reference external" href="https://github.com/huggingface/transformers">HuggingFace Transformers</a> 예제들을 따라하면서 BERT
 모델을 동적으로 양자화할 것입니다. BERT 처럼 유명하면서도 최고 성능을
 내는 모델을 어떻게 동적으로 양자화된 모델로 변환하는지 한 단계씩 설명하겠습니다.</p>
 <ul class="simple">
@@ -361,7 +361,7 @@ <h3>1.2 필요한 모듈 불러오기<a class="headerlink" href="#id6" title="Pe
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span>
 
 <span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">random</span>
 <span class="kn">import</span> <span class="nn">sys</span>
@@ -380,20 +380,20 @@ <h3>1.2 필요한 모듈 불러오기<a class="headerlink" href="#id6" title="Pe
 
 <span class="c1"># 로깅 준비</span>
 <span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">format</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> - </span><span class="si">%(levelname)s</span><span class="s1"> - </span><span class="si">%(name)s</span><span class="s1"> -   </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span>
+<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> - </span><span class="si">%(levelname)s</span><span class="s1"> - </span><span class="si">%(name)s</span><span class="s1"> -   </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span>
                     <span class="n">datefmt</span> <span class="o">=</span> <span class="s1">&#39;%m/</span><span class="si">%d</span><span class="s1">/%Y %H:%M:%S&#39;</span><span class="p">,</span>
                     <span class="n">level</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">WARN</span><span class="p">)</span>
 
 <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;transformers.modeling_utils&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span>
-   <span class="n">logging</span><span class="o">.</span><span class="n">WARN</span><span class="p">)</span>  <span class="c1"># 로깅 줄이기</span>
+                    <span class="n">logging</span><span class="o">.</span><span class="n">WARN</span><span class="p">)</span>  <span class="c1"># 로깅 줄이기</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>쓰레드 한 개를 사용할 때의 FP32와 INT8의 성능을 비교하기 위해 쓰레드의 수를 1로 설정합니다.
 이 튜토리얼의 끝부분에서는 PyTorch를 적절하게 병렬적으로 빌드하여 쓰레드 수를 다르게 설정할 수 있습니다.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__config__</span><span class="o">.</span><span class="n">parallel_info</span><span class="p">())</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__config__</span><span class="o">.</span><span class="n">parallel_info</span><span class="p">())</span>
 </pre></div>
 </div>
 </div>
@@ -412,19 +412,21 @@ <h3>1.3 보조 함수 알아보기<a class="headerlink" href="#id7" title="Perma
 <li><p>토큰이 첫번째 문장에 속하는지 두번째 문장에 속하는지 알려주는 토큰 타입 id 생성하기</p></li>
 </ul>
 <p><a class="reference external" href="https://github.com/huggingface/transformers/blob/master/transformers/data/processors/glue.py">glue_compute_metrics</a> 함수는
-정밀도와 지현율의 가중 평균인 <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html">F1 점수</a> 를
+정밀도와 재현율의 가중 평균인 <a class="reference external" href="https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html">F1 점수</a> 를
 계산하는 행렬을 갖고 있습니다. F1 점수가 가장 좋을 때는 1이며, 가장 나쁠 때는 0입니다.
 정밀도와 재현율은 F1 점수를 계산할 때 동일한 비중을 갖습니다.</p>
 <ul class="simple">
 <li><p>F1 점수를 구하는 식 :</p></li>
 </ul>
-<div class="math notranslate nohighlight">
+<blockquote>
+<div><div class="math notranslate nohighlight">
 \[F1 = 2 * (\text{정밀도} * \text{재현율}) / (\text{정밀도} + \text{재현율})\]</div>
+</div></blockquote>
 </div>
 <div class="section" id="id9">
 <h3>1.4 데이터셋 다운로드<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h3>
 <p>MRPC 문제를 풀어보기 전에 <a class="reference external" href="https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e">이 스크립트</a> 를
-실행해 <a class="reference external" href="https://gluebenchmark.com/tasks">GLUE 데이터셋</a> 을 다운로드 받고 ‘’glue_data’’
+실행해 <a class="reference external" href="https://gluebenchmark.com/tasks">GLUE 데이터셋</a> 을 다운로드 받고 <code class="docutils literal notranslate"><span class="pre">glue_data</span></code>
 폴더에 저장합니다.</p>
 <div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python download_glue_data.py --data_dir<span class="o">=</span><span class="s1">&#39;glue_data&#39;</span> --tasks<span class="o">=</span><span class="s1">&#39;MRPC&#39;</span>
 </pre></div>
@@ -486,14 +488,14 @@ <h3>2.1 전역 환경 설정하기<a class="headerlink" href="#id15" title="Perm
 <span class="n">configs</span><span class="o">.</span><span class="n">output_mode</span> <span class="o">=</span> <span class="n">output_modes</span><span class="p">[</span><span class="n">configs</span><span class="o">.</span><span class="n">task_name</span><span class="p">]</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">label_list</span> <span class="o">=</span> <span class="n">configs</span><span class="o">.</span><span class="n">processor</span><span class="o">.</span><span class="n">get_labels</span><span class="p">()</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="s2">&quot;bert&quot;</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-<span class="n">configs</span><span class="o">.</span><span class="n">do_lower_case</span> <span class="o">=</span> <span class="bp">True</span>
+<span class="n">configs</span><span class="o">.</span><span class="n">do_lower_case</span> <span class="o">=</span> <span class="kc">True</span>
 
 <span class="c1"># 장비 종류, 배치 크기, 분산 학습 방식, 캐싱 방식 설정</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">per_gpu_eval_batch_size</span> <span class="o">=</span> <span class="mi">8</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">n_gpu</span> <span class="o">=</span> <span class="mi">0</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-<span class="n">configs</span><span class="o">.</span><span class="n">overwrite_cache</span> <span class="o">=</span> <span class="bp">False</span>
+<span class="n">configs</span><span class="o">.</span><span class="n">overwrite_cache</span> <span class="o">=</span> <span class="kc">False</span>
 
 
 <span class="c1"># 재현을 위한 랜덤 시드 설정</span>
@@ -540,32 +542,35 @@ <h3>2.3 토큰화, 평가 함수 정의하기<a class="headerlink" href="#id17"
 <span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">):</span>
     <span class="c1"># MNLI의 두 평가 결과(일치, 불일치)를 처리하기 위한 반복문</span>
     <span class="n">eval_task_names</span> <span class="o">=</span> <span class="p">(</span><span class="s2">&quot;mnli&quot;</span><span class="p">,</span> <span class="s2">&quot;mnli-mm&quot;</span><span class="p">)</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">task_name</span> <span class="o">==</span> <span class="s2">&quot;mnli&quot;</span> <span class="k">else</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">task_name</span><span class="p">,)</span>
-    <span class="n">eval_outputs_dirs</span> <span class="o">=</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">output_dir</span> <span class="o">+</span> <span class="s1">&#39;-MM&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">task_name</span> <span class="o">==</span> <span class="s2">&quot;mnli&quot;</span> <span class="k">else</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,)</span>
+    <span class="n">eval_outputs_dirs</span> <span class="o">=</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">output_dir</span> <span class="o">+</span> <span class="s1">&#39;-MM&#39;</span><span class="p">)</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">task_name</span> <span class="o">==</span> <span class="s2">&quot;mnli&quot;</span>
+                            <span class="k">else</span> <span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,)</span>
 
     <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
     <span class="k">for</span> <span class="n">eval_task</span><span class="p">,</span> <span class="n">eval_output_dir</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eval_task_names</span><span class="p">,</span> <span class="n">eval_outputs_dirs</span><span class="p">):</span>
-        <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">load_and_cache_examples</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">eval_task</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">evaluate</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+        <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">load_and_cache_examples</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">eval_task</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">evaluate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">eval_output_dir</span><span class="p">)</span> <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="ow">in</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]:</span>
             <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">eval_output_dir</span><span class="p">)</span>
 
         <span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">per_gpu_eval_batch_size</span> <span class="o">*</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">n_gpu</span><span class="p">)</span>
         <span class="c1"># DistributedSampler는 무작위로 표본을 추출합니다</span>
-        <span class="n">eval_sampler</span> <span class="o">=</span> <span class="n">SequentialSampler</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span> <span class="k">else</span> <span class="n">DistributedSampler</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span>
-        <span class="n">eval_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">eval_sampler</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">)</span>
+        <span class="n">eval_sampler</span> <span class="o">=</span> <span class="n">SequentialSampler</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span>
+                        <span class="k">else</span> <span class="n">DistributedSampler</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">)</span>
+        <span class="n">eval_dataloader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">,</span> <span class="n">sampler</span><span class="o">=</span><span class="n">eval_sampler</span><span class="p">,</span>
+                                        <span class="n">batch_size</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">)</span>
 
         <span class="c1"># 다중 gpu로 평가</span>
         <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">n_gpu</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">DataParallel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 
         <span class="c1"># 평가 실행!</span>
-        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Running evaluation {} *****&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Running evaluation </span><span class="si">{}</span><span class="s2"> *****&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
         <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;  Num examples = </span><span class="si">%d</span><span class="s2">&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">))</span>
         <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;  Batch size = </span><span class="si">%d</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">)</span>
         <span class="n">eval_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
         <span class="n">nb_eval_steps</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">preds</span> <span class="o">=</span> <span class="bp">None</span>
-        <span class="n">out_label_ids</span> <span class="o">=</span> <span class="bp">None</span>
+        <span class="n">preds</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">out_label_ids</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">eval_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Evaluating&quot;</span><span class="p">):</span>
             <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
             <span class="n">batch</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">)</span>
@@ -575,18 +580,22 @@ <h3>2.3 토큰화, 평가 함수 정의하기<a class="headerlink" href="#id17"
                           <span class="s1">&#39;attention_mask&#39;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
                           <span class="s1">&#39;labels&#39;</span><span class="p">:</span>         <span class="n">batch</span><span class="p">[</span><span class="mi">3</span><span class="p">]}</span>
                 <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="o">!=</span> <span class="s1">&#39;distilbert&#39;</span><span class="p">:</span>
-                    <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;token_type_ids&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bert&#39;</span><span class="p">,</span> <span class="s1">&#39;xlnet&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="bp">None</span>  <span class="c1"># XLM, DistilBERT and RoBERTa 모델들은 segment_ids를 사용하지 않습니다</span>
+                    <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;token_type_ids&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bert&#39;</span><span class="p">,</span> <span class="s1">&#39;xlnet&#39;</span><span class="p">]</span>
+                                                <span class="k">else</span> <span class="kc">None</span>
+                                                <span class="c1"># XLM, DistilBERT and RoBERTa 모델들은 segment_ids를</span>
+                                                <span class="c1"># 사용하지 않습니다</span>
                 <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">)</span>
                 <span class="n">tmp_eval_loss</span><span class="p">,</span> <span class="n">logits</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
 
                 <span class="n">eval_loss</span> <span class="o">+=</span> <span class="n">tmp_eval_loss</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
             <span class="n">nb_eval_steps</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="k">if</span> <span class="n">preds</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">preds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">preds</span> <span class="o">=</span> <span class="n">logits</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
                 <span class="n">out_label_ids</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
             <span class="k">else</span><span class="p">:</span>
                 <span class="n">preds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">preds</span><span class="p">,</span> <span class="n">logits</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-                <span class="n">out_label_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out_label_ids</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                <span class="n">out_label_ids</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out_label_ids</span><span class="p">,</span> <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;labels&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span>
+                                            <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
 
         <span class="n">eval_loss</span> <span class="o">=</span> <span class="n">eval_loss</span> <span class="o">/</span> <span class="n">nb_eval_steps</span>
         <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">output_mode</span> <span class="o">==</span> <span class="s2">&quot;classification&quot;</span><span class="p">:</span>
@@ -598,7 +607,7 @@ <h3>2.3 토큰화, 평가 함수 정의하기<a class="headerlink" href="#id17"
 
         <span class="n">output_eval_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">eval_output_dir</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="s2">&quot;eval_results.txt&quot;</span><span class="p">)</span>
         <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">output_eval_file</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">writer</span><span class="p">:</span>
-            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Eval results {} *****&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
+            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Eval results </span><span class="si">{}</span><span class="s2"> *****&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
             <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
                 <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;  </span><span class="si">%s</span><span class="s2"> = </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span>
                 <span class="n">writer</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> = </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">])))</span>
@@ -606,16 +615,17 @@ <h3>2.3 토큰화, 평가 함수 정의하기<a class="headerlink" href="#id17"
     <span class="k">return</span> <span class="n">results</span>
 
 
-<span class="k">def</span> <span class="nf">load_and_cache_examples</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">evaluate</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">load_and_cache_examples</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">evaluate</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">evaluate</span><span class="p">:</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>  <span class="c1"># 분산 학습 프로세스들 중 처음 프로세스 한 개만 데이터를 처리하고 다른 프로세스들은 캐시를 이용하도록 합니다.</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>  <span class="c1"># 분산 학습 프로세스들 중 처음 프로세스 한 개만 데이터를 처리하고 다른</span>
+                                     <span class="c1"># 프로세스들은 캐시를 이용하도록 합니다.</span>
 
     <span class="n">processor</span> <span class="o">=</span> <span class="n">processors</span><span class="p">[</span><span class="n">task</span><span class="p">]()</span>
     <span class="n">output_mode</span> <span class="o">=</span> <span class="n">output_modes</span><span class="p">[</span><span class="n">task</span><span class="p">]</span>
     <span class="c1"># 캐시 또는 데이터셋 파일로부터 데이터 특징을 불러옵니다.</span>
-    <span class="n">cached_features_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;cached_{}_{}_{}_{}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+    <span class="n">cached_features_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;cached_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
         <span class="s1">&#39;dev&#39;</span> <span class="k">if</span> <span class="n">evaluate</span> <span class="k">else</span> <span class="s1">&#39;train&#39;</span><span class="p">,</span>
-        <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="bp">None</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)))</span><span class="o">.</span><span class="n">pop</span><span class="p">(),</span>
+        <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)))</span><span class="o">.</span><span class="n">pop</span><span class="p">(),</span>
         <span class="nb">str</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">max_seq_length</span><span class="p">),</span>
         <span class="nb">str</span><span class="p">(</span><span class="n">task</span><span class="p">)))</span>
     <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">cached_features_file</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">overwrite_cache</span><span class="p">:</span>
@@ -627,22 +637,27 @@ <h3>2.3 토큰화, 평가 함수 정의하기<a class="headerlink" href="#id17"
         <span class="k">if</span> <span class="n">task</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;mnli&#39;</span><span class="p">,</span> <span class="s1">&#39;mnli-mm&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;roberta&#39;</span><span class="p">]:</span>
             <span class="c1"># 해결책(사전학습된 RoBERTa 모델에서는 라벨 인덱스 순서가 바뀌어 있습니다.)</span>
             <span class="n">label_list</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">label_list</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">label_list</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">label_list</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">examples</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_dev_examples</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span> <span class="k">if</span> <span class="n">evaluate</span> <span class="k">else</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_train_examples</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span>
+        <span class="n">examples</span> <span class="o">=</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_dev_examples</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span> <span class="k">if</span> <span class="n">evaluate</span>
+                    <span class="k">else</span> <span class="n">processor</span><span class="o">.</span><span class="n">get_train_examples</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">)</span>
         <span class="n">features</span> <span class="o">=</span> <span class="n">convert_examples_to_features</span><span class="p">(</span><span class="n">examples</span><span class="p">,</span>
                                                 <span class="n">tokenizer</span><span class="p">,</span>
                                                 <span class="n">label_list</span><span class="o">=</span><span class="n">label_list</span><span class="p">,</span>
                                                 <span class="n">max_length</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">max_seq_length</span><span class="p">,</span>
                                                 <span class="n">output_mode</span><span class="o">=</span><span class="n">output_mode</span><span class="p">,</span>
-                                                <span class="n">pad_on_left</span><span class="o">=</span><span class="nb">bool</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;xlnet&#39;</span><span class="p">]),</span>                 <span class="c1"># xlnet의 경우 앞쪽에 패딩합니다.</span>
-                                                <span class="n">pad_token</span><span class="o">=</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">([</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span><span class="p">])[</span><span class="mi">0</span><span class="p">],</span>
-                                                <span class="n">pad_token_segment_id</span><span class="o">=</span><span class="mi">4</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;xlnet&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span>
+                                                <span class="n">pad_on_left</span><span class="o">=</span><span class="nb">bool</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;xlnet&#39;</span><span class="p">]),</span>
+                                                <span class="c1"># xlnet의 경우 앞쪽에 패딩합니다.</span>
+                                                <span class="n">pad_token</span><span class="o">=</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">convert_tokens_to_ids</span><span class="p">(</span>
+                                                    <span class="p">[</span><span class="n">tokenizer</span><span class="o">.</span><span class="n">pad_token</span><span class="p">])[</span><span class="mi">0</span><span class="p">],</span>
+                                                <span class="n">pad_token_segment_id</span><span class="o">=</span><span class="mi">4</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span>
+                                                                        <span class="p">[</span><span class="s1">&#39;xlnet&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">,</span>
         <span class="p">)</span>
         <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="ow">in</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]:</span>
             <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Saving features into cached file </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">cached_features_file</span><span class="p">)</span>
             <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">cached_features_file</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">evaluate</span><span class="p">:</span>
-        <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>  <span class="c1"># 분산 학습 프로세스들 중 처음 프로세스 한 개만 데이터를 처리하고 다른 프로세스들은 캐시를 이용하도록 합니다.</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>  <span class="c1"># 분산 학습 프로세스들 중 처음 프로세스 한 개만 데이터를 처리하고 다른</span>
+                                     <span class="c1"># 프로세스들은 캐시를 이용하도록 합니다.</span>
 
     <span class="c1"># 텐서로 변환하고 데이터셋을 빌드합니다.</span>
     <span class="n">all_input_ids</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">f</span><span class="o">.</span><span class="n">input_ids</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">features</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
@@ -670,7 +685,7 @@ <h2>3. 동적 양자화 적용하기<a class="headerlink" href="#id19" title="Pe
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">quantized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">quantize_dynamic</span><span class="p">(</span>
     <span class="n">model</span><span class="p">,</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">},</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">qint8</span>
 <span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="section" id="id20">
@@ -679,7 +694,7 @@ <h3>3.1 모델 크기 확인하기<a class="headerlink" href="#id20" title="Perm
 알 수 있습니다(FP32 형식의 모델 크기 : 438MB; INT8 형식의 모델 크기 : 181MB):</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">print_size_of_model</span><span class="p">(</span><span class="n">model</span><span class="p">):</span>
     <span class="n">torch</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="s2">&quot;temp.p&quot;</span><span class="p">)</span>
-    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Size (MB):&#39;</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getsize</span><span class="p">(</span><span class="s2">&quot;temp.p&quot;</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Size (MB):&#39;</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getsize</span><span class="p">(</span><span class="s2">&quot;temp.p&quot;</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span><span class="p">)</span>
     <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;temp.p&#39;</span><span class="p">)</span>
 
 <span class="n">print_size_of_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
@@ -701,8 +716,8 @@ <h3>3.2 추론 정확도와 속도 평가하기<a class="headerlink" href="#id21
     <span class="n">result</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">configs</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
     <span class="n">eval_end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
     <span class="n">eval_duration_time</span> <span class="o">=</span> <span class="n">eval_end_time</span> <span class="o">-</span> <span class="n">eval_start_time</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Evaluate total time (seconds): {0:.1f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">eval_duration_time</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluate total time (seconds): </span><span class="si">{0:.1f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">eval_duration_time</span><span class="p">))</span>
 
 <span class="c1"># 기존 FP32 BERT 모델 평가</span>
 <span class="n">time_model_evaluation</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">configs</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">)</span>
@@ -776,7 +791,7 @@ <h2>참고 자료<a class="headerlink" href="#id27" title="Permalink to this hea
         <a href="../advanced/static_quantization_tutorial.html" class="btn btn-neutral float-right" title="(beta) Static Quantization with Eager Mode in PyTorch" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
-        <a href="../advanced/dynamic_quantization_tutorial.html" class="btn btn-neutral" title="(beta) Dynamic Quantization on an LSTM Word Language Model" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
+        <a href="../advanced/dynamic_quantization_tutorial.html" class="btn btn-neutral" title="(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
       
     </div>
   
@@ -855,11 +870,10 @@ <h2>참고 자료<a class="headerlink" href="#id27" title="Permalink to this hea
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/intermediate/flask_rest_api_tutorial.html b/docs/intermediate/flask_rest_api_tutorial.html
index 886d0ed7d..b31bf6f43 100644
--- a/docs/intermediate/flask_rest_api_tutorial.html
+++ b/docs/intermediate/flask_rest_api_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -339,14 +339,14 @@ <h2>API 정의<a class="headerlink" href="#api" title="Permalink to this headlin
 <div class="section" id="dependencies">
 <h2>의존성(Dependencies)<a class="headerlink" href="#dependencies" title="Permalink to this headline">¶</a></h2>
 <p>아래 명령어를 실행하여 필요한 패키지들을 설치합니다:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip install Flask==1.0.3 torchvision-0.3.0
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ pip install Flask==1.0.3 torchvision==0.3.0
 </pre></div>
 </div>
 </div>
 <div class="section" id="id2">
 <h2>간단한 웹 서버<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
 <p>Flask의 문서를 참고하여 아래와 같은 코드로 간단한 웹 서버를 구성합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">flask</span> <span class="k">import</span> <span class="n">Flask</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span>
 <span class="n">app</span> <span class="o">=</span> <span class="n">Flask</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
 
 
@@ -373,7 +373,7 @@ <h2>간단한 웹 서버<a class="headerlink" href="#id2" title="Permalink to th
 </div>
 <p>또한, ImageNet 분류 ID와 이름을 포함하는 JSON을 회신하도록 응답 형식을 변경하겠습니다.
 이제 <code class="docutils literal notranslate"><span class="pre">app.py</span></code> 는 아래와 같이 변경되었습니다:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">flask</span> <span class="k">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span>
 <span class="n">app</span> <span class="o">=</span> <span class="n">Flask</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
 
 <span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">&#39;/predict&#39;</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;POST&#39;</span><span class="p">])</span>
@@ -398,7 +398,7 @@ <h3>이미지 준비하기<a class="headerlink" href="#id3" title="Permalink to
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">io</span>
 
 <span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
-<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 
 <span class="k">def</span> <span class="nf">transform_image</span><span class="p">(</span><span class="n">image_bytes</span><span class="p">):</span>
     <span class="n">my_transforms</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span><span class="n">transforms</span><span class="o">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">255</span><span class="p">),</span>
@@ -455,7 +455,7 @@ <h3>예측(Prediction)<a class="headerlink" href="#prediction" title="Permalink
 이 예제에서는 미리 학습된 모델을 사용하지만, 직접 만든 모델에 대해서도
 이와 동일한 방법을 사용할 수 있습니다. 모델을 읽어오는 것은 이
 <a class="reference internal" href="../beginner/saving_loading_models.html"><span class="doc">튜토리얼</span></a> 을 참고하세요.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">models</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">models</span>
 
 <span class="c1"># 이미 학습된 가중치를 사용하기 위해 `pretrained` 에 `True` 값을 전달합니다:</span>
 <span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
@@ -529,13 +529,13 @@ <h2>모델을 API 서버에 통합하기<a class="headerlink" href="#id7" title=
 <code class="docutils literal notranslate"><span class="pre">predict</span></code> 메소드를 수정해야 합니다:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">request</span>
 
-<span class="nd">@app.route</span><span class="p">(</span><span class="s1">&#39;/predict&#39;</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;POST&#39;</span><span class="p">])</span>
+<span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">&#39;/predict&#39;</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;POST&#39;</span><span class="p">])</span>
 <span class="k">def</span> <span class="nf">predict</span><span class="p">():</span>
     <span class="k">if</span> <span class="n">request</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;POST&#39;</span><span class="p">:</span>
         <span class="c1"># we will get the file from the request</span>
-        <span class="nb">file</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">files</span><span class="p">[</span><span class="s1">&#39;file&#39;</span><span class="p">]</span>
+        <span class="n">file</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">files</span><span class="p">[</span><span class="s1">&#39;file&#39;</span><span class="p">]</span>
         <span class="c1"># convert that to bytes</span>
-        <span class="n">img_bytes</span> <span class="o">=</span> <span class="nb">file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="n">img_bytes</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
         <span class="n">class_id</span><span class="p">,</span> <span class="n">class_name</span> <span class="o">=</span> <span class="n">get_prediction</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">img_bytes</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">jsonify</span><span class="p">({</span><span class="s1">&#39;class_id&#39;</span><span class="p">:</span> <span class="n">class_id</span><span class="p">,</span> <span class="s1">&#39;class_name&#39;</span><span class="p">:</span> <span class="n">class_name</span><span class="p">})</span>
 </pre></div>
@@ -546,14 +546,14 @@ <h2>모델을 API 서버에 통합하기<a class="headerlink" href="#id7" title=
 <span class="kn">import</span> <span class="nn">json</span>
 
 <span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">models</span>
-<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="kn">as</span> <span class="nn">transforms</span>
+<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
 <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 <span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span><span class="p">,</span> <span class="n">request</span>
 
 
 <span class="n">app</span> <span class="o">=</span> <span class="n">Flask</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
 <span class="n">imagenet_class_index</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">&#39;&lt;PATH/TO/.json/FILE&gt;/imagenet_class_index.json&#39;</span><span class="p">))</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">densenet121</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
 
 
@@ -576,11 +576,11 @@ <h2>모델을 API 서버에 통합하기<a class="headerlink" href="#id7" title=
     <span class="k">return</span> <span class="n">imagenet_class_index</span><span class="p">[</span><span class="n">predicted_idx</span><span class="p">]</span>
 
 
-<span class="nd">@app.route</span><span class="p">(</span><span class="s1">&#39;/predict&#39;</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;POST&#39;</span><span class="p">])</span>
+<span class="nd">@app</span><span class="o">.</span><span class="n">route</span><span class="p">(</span><span class="s1">&#39;/predict&#39;</span><span class="p">,</span> <span class="n">methods</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;POST&#39;</span><span class="p">])</span>
 <span class="k">def</span> <span class="nf">predict</span><span class="p">():</span>
     <span class="k">if</span> <span class="n">request</span><span class="o">.</span><span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;POST&#39;</span><span class="p">:</span>
-        <span class="nb">file</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">files</span><span class="p">[</span><span class="s1">&#39;file&#39;</span><span class="p">]</span>
-        <span class="n">img_bytes</span> <span class="o">=</span> <span class="nb">file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="n">file</span> <span class="o">=</span> <span class="n">request</span><span class="o">.</span><span class="n">files</span><span class="p">[</span><span class="s1">&#39;file&#39;</span><span class="p">]</span>
+        <span class="n">img_bytes</span> <span class="o">=</span> <span class="n">file</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
         <span class="n">class_id</span><span class="p">,</span> <span class="n">class_name</span> <span class="o">=</span> <span class="n">get_prediction</span><span class="p">(</span><span class="n">image_bytes</span><span class="o">=</span><span class="n">img_bytes</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">jsonify</span><span class="p">({</span><span class="s1">&#39;class_id&#39;</span><span class="p">:</span> <span class="n">class_id</span><span class="p">,</span> <span class="s1">&#39;class_name&#39;</span><span class="p">:</span> <span class="n">class_name</span><span class="p">})</span>
 
@@ -637,7 +637,7 @@ <h2>다음 단계<a class="headerlink" href="#id8" title="Permalink to this head
 <li><p>마지막으로 이 문서 상단에 링크된, PyTorch 모델을 배포하는 다른 튜토리얼들을
 읽어보는 것을 권장합니다.</p></li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.126 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.767 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-flask-rest-api-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/b45d95b1d38fe556c77d1ee548809d28/flask_rest_api_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">flask_rest_api_tutorial.py</span></code></a></p>
@@ -728,11 +728,9 @@ <h2>다음 단계<a class="headerlink" href="#id8" title="Permalink to this head
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/memory_format_tutorial.html b/docs/intermediate/memory_format_tutorial.html
index f6e6b7e47..ef101a061 100644
--- a/docs/intermediate/memory_format_tutorial.html
+++ b/docs/intermediate/memory_format_tutorial.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>(beta) Channels Last Memory Format in PyTorch &mdash; PyTorch Tutorials 1.6.0 documentation</title>
+  <title>(실험용) PyTorch를 사용한 Channels Last Memory Format &mdash; PyTorch Tutorials 1.6.0 documentation</title>
   
 
   
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -243,7 +243,7 @@
       </li>
 
         
-      <li>(beta) Channels Last Memory Format in PyTorch</li>
+      <li>(실험용) PyTorch를 사용한 Channels Last Memory Format</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -301,45 +301,47 @@
 <p class="admonition-title">Note</p>
 <p>Click <a class="reference internal" href="#sphx-glr-download-intermediate-memory-format-tutorial-py"><span class="std std-ref">here</span></a> to download the full example code</p>
 </div>
-<div class="sphx-glr-example-title section" id="beta-channels-last-memory-format-in-pytorch">
-<span id="sphx-glr-intermediate-memory-format-tutorial-py"></span><h1>(beta) Channels Last Memory Format in PyTorch<a class="headerlink" href="#beta-channels-last-memory-format-in-pytorch" title="Permalink to this headline">¶</a></h1>
+<div class="sphx-glr-example-title section" id="pytorch-channels-last-memory-format">
+<span id="sphx-glr-intermediate-memory-format-tutorial-py"></span><h1>(실험용) PyTorch를 사용한 Channels Last Memory Format<a class="headerlink" href="#pytorch-channels-last-memory-format" title="Permalink to this headline">¶</a></h1>
 <p><strong>Author</strong>: <a class="reference external" href="https://github.com/VitalyFedyunin">Vitaly Fedyunin</a></p>
-<div class="section" id="what-is-channels-last">
-<h2>What is Channels Last<a class="headerlink" href="#what-is-channels-last" title="Permalink to this headline">¶</a></h2>
-<p>Channels Last memory format is an alternative way of ordering NCHW tensors in memory preserving dimensions ordering. Channels Last tensors ordered in such a way that channels become the densest dimension (aka storing images pixel-per-pixel).</p>
-<p>For example, classic (contiguous) storage of NCHW tensor (in our case it is two 2x2 images with 3 color channels) look like this:</p>
+<p><strong>번역</strong>: <a class="reference external" href="https://github.com/potatochips178">Choi Yoonjeong</a></p>
+<div class="section" id="channels-last">
+<h2>Channels Last가 무엇인가요<a class="headerlink" href="#channels-last" title="Permalink to this headline">¶</a></h2>
+<p>Channels Last 메모리 형식(memory format)은 차원 순서를 유지하면서 메모리 상의 NCHW 텐서(tensor)를 정렬하는 또 다른 방식입니다.
+Channels Last 텐서는 채널(Channel)이 가장 밀도가 높은(densest) 차원으로 정렬(예. 이미지를 픽셀x픽셀로 저장)됩니다.</p>
+<p>예를 들어, (2개의 2 x 2 이미지에 3개의 채널이 존재하는 경우) 전형적인(연속적인) NCHW 텐서의 저장 방식은 다음과 같습니다:</p>
 <div class="figure align-default">
 <img alt="classic_memory_format" src="../_images/classic_memory_format.png" />
 </div>
-<p>Channels Last memory format orders data differently:</p>
+<p>Channels Last 메모리 형식은 데이터를 다르게 정렬합니다:</p>
 <div class="figure align-default">
 <img alt="channels_last_memory_format" src="../_images/channels_last_memory_format.png" />
 </div>
-<p>Pytorch supports memory formats (and provides back compatibility with existing models including eager, JIT, and TorchScript) by utilizing  existing strides structure.
-For example, 10x3x16x16 batch in Channels Last format will have strides equal to (768, 1, 48, 3).</p>
-<p>Channels Last memory format is implemented for 4D NCWH Tensors only.</p>
+<p>PyTorch는 기존의 스트라이드(strides) 구조를 사용함으로써 메모리 형식을 지원(하며, eager, JIT 및 TorchScript를 포함한
+기존의 모델들과 하위 호환성을 제공)합니다. 예를 들어, Channels Last 형식에서 10x3x16x16 배치(batch)는 (768, 1, 48, 3)와
+같은 폭(strides)을 가지고 있게 됩니다.
+Channels Last 메모리 형식은 오직 4D NCWH Tensors에서만 실행할 수 있습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="n">N</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">W</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="memory-format-api">
-<h2>Memory Format API<a class="headerlink" href="#memory-format-api" title="Permalink to this headline">¶</a></h2>
-<p>Here is how to convert tensors between contiguous and channels
-last memory formats.</p>
-<p>Classic PyTorch contiguous tensor</p>
+<h2>메모리 형식(Memory Format) API<a class="headerlink" href="#memory-format-api" title="Permalink to this headline">¶</a></h2>
+<p>연속 메모리 형식과 channels last 메모리 형식 간에 텐서를 변환하는 방법은 다음과 같습니다.</p>
+<p>전형적인 PyTorch의 연속적인 텐서(tensor)</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Ouputs: (3072, 1024, 32, 1)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1024, 32, 1)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(3072, 1024, 32, 1)
 </pre></div>
 </div>
-<p>Conversion operator</p>
+<p>변환 연산자</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1"># Outputs: (10, 3, 32, 32) as dimensions order preserved</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Outputs: (3072, 1, 96, 3)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1"># 결과: (10, 3, 32, 32) 차원 순서는 보존함</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1, 96, 3)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
@@ -347,89 +349,88 @@ <h2>Memory Format API<a class="headerlink" href="#memory-format-api" title="Perm
 (3072, 1, 96, 3)
 </pre></div>
 </div>
-<p>Back to contiguous</p>
+<p>연속적인 형식으로 되돌리기</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Outputs: (3072, 1024, 32, 1)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1024, 32, 1)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(3072, 1024, 32, 1)
 </pre></div>
 </div>
-<p>Alternative option</p>
+<p>다른 방식</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Ouputs: (3072, 1, 96, 3)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1, 96, 3)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(3072, 1, 96, 3)
 </pre></div>
 </div>
-<p>Format checks</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">))</span> <span class="c1"># Ouputs: True</span>
+<p>형식(format) 확인</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">))</span> <span class="c1"># 결과: True</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>True
 </pre></div>
 </div>
-<p>Create as Channels Last</p>
+<p>Channels Last 방식으로 생성하기</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="n">N</span><span class="p">,</span> <span class="n">C</span><span class="p">,</span> <span class="n">H</span><span class="p">,</span> <span class="n">W</span><span class="p">,</span> <span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Ouputs: (3072, 1, 96, 3)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1, 96, 3)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(3072, 1, 96, 3)
 </pre></div>
 </div>
-<p><code class="docutils literal notranslate"><span class="pre">clone</span></code> preserves memory format</p>
+<p><code class="docutils literal notranslate"><span class="pre">clone</span></code> 은 메모리 형식을 보존합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Ouputs: (3072, 1, 96, 3)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1, 96, 3)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(3072, 1, 96, 3)
 </pre></div>
 </div>
-<p><code class="docutils literal notranslate"><span class="pre">to</span></code>, <code class="docutils literal notranslate"><span class="pre">cuda</span></code>, <code class="docutils literal notranslate"><span class="pre">float</span></code> … preserves memory format</p>
+<p><code class="docutils literal notranslate"><span class="pre">to</span></code>, <code class="docutils literal notranslate"><span class="pre">cuda</span></code>, <code class="docutils literal notranslate"><span class="pre">float</span></code> … 등도 메모리 형식을 보존합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
     <span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Ouputs: (3072, 1, 96, 3)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1, 96, 3)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(3072, 1, 96, 3)
 </pre></div>
 </div>
-<p><code class="docutils literal notranslate"><span class="pre">empty_like</span></code>, <code class="docutils literal notranslate"><span class="pre">*_like</span></code> operators preserves memory format</p>
+<p><code class="docutils literal notranslate"><span class="pre">empty_like</span></code>, <code class="docutils literal notranslate"><span class="pre">*_like</span></code> 연산자도 메모리 형식을 보존합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Ouputs: (3072, 1, 96, 3)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1, 96, 3)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(3072, 1, 96, 3)
 </pre></div>
 </div>
-<p>Pointwise operators preserves memory format</p>
+<p>Pointwise 연산자도 메모리 형식을 보존합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">z</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="n">y</span>
-<span class="nb">print</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># Ouputs: (3072, 1, 96, 3)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">z</span><span class="o">.</span><span class="n">stride</span><span class="p">())</span> <span class="c1"># 결과: (3072, 1, 96, 3)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>(3072, 1, 96, 3)
 </pre></div>
 </div>
-<p>Conv, Batchnorm modules support Channels Last
-(only works for CudNN &gt;= 7.6)</p>
+<p>Conv, Batchnorm 모듈은 Channels Last를 지원합니다. (단, CudNN &gt;=7.6 에서만 동작)</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">backends</span><span class="o">.</span><span class="n">cudnn</span><span class="o">.</span><span class="n">version</span><span class="p">()</span> <span class="o">&gt;=</span> <span class="mi">7603</span><span class="p">:</span>
     <span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="s2">&quot;cuda&quot;</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
     <span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
 
     <span class="nb">input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span>
-    <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span> <span class="c1"># Module parameters need to be Channels Last</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span> <span class="c1"># 모듈 인자들은 Channels Last로 변환이 필요합니다</span>
 
     <span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">))</span> <span class="c1"># Ouputs: True</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">))</span> <span class="c1"># 결과: True</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
@@ -437,11 +438,11 @@ <h2>Memory Format API<a class="headerlink" href="#memory-format-api" title="Perm
 </pre></div>
 </div>
 </div>
-<div class="section" id="performance-gains">
-<h2>Performance Gains<a class="headerlink" href="#performance-gains" title="Permalink to this headline">¶</a></h2>
-<p>The most significant performance gains are observed on Nvidia’s hardware with
-Tensor Cores support. We were able to archive over 22%  perf gains while running ‘
-AMP (Automated Mixed Precision) training scripts supplied by Nvidia <a class="reference external" href="https://github.com/NVIDIA/apex">https://github.com/NVIDIA/apex</a>.</p>
+<div class="section" id="id1">
+<h2>성능 향상<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>Tensor Cores를 지원하는 Nvidia의 하드웨어에서 가장 의미심장한 성능 향상을 보였습니다.
+Nvidia가 제공하는 AMP(Automated Mixed Precision) 학습 스크립트로 22% 이상의 성능 향상을 확인할 수 있었습니다.
+<a class="reference external" href="https://github.com/NVIDIA/apex">https://github.com/NVIDIA/apex</a></p>
 <p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">main_amp.py</span> <span class="pre">-a</span> <span class="pre">resnet50</span> <span class="pre">--b</span> <span class="pre">200</span> <span class="pre">--workers</span> <span class="pre">16</span> <span class="pre">--opt-level</span> <span class="pre">O2</span>&#160; <span class="pre">./data</span></code></p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># opt_level = O2</span>
 <span class="c1"># keep_batchnorm_fp32 = None &lt;class &#39;NoneType&#39;&gt;</span>
@@ -476,7 +477,7 @@ <h2>Performance Gains<a class="headerlink" href="#performance-gains" title="Perm
 <span class="c1"># Epoch: [0][80/125] Time 0.260 (0.335) Speed 770.324 (597.659) Loss 2.2505953312 (1.0879) Prec@1 50.500 (52.938) Prec@5 100.000 (100.000)</span>
 </pre></div>
 </div>
-<p>Passing <code class="docutils literal notranslate"><span class="pre">--channels-last</span> <span class="pre">true</span></code> allows running a model in Channels Last format with observed 22% perf gain.</p>
+<p><code class="docutils literal notranslate"><span class="pre">--channels-last</span> <span class="pre">true</span></code> 인자를 전달하여 Channels Last 형식으로 모델을 실행하면 22%의 성능 향상을 보입니다.</p>
 <p><code class="docutils literal notranslate"><span class="pre">python</span> <span class="pre">main_amp.py</span> <span class="pre">-a</span> <span class="pre">resnet50</span> <span class="pre">--b</span> <span class="pre">200</span> <span class="pre">--workers</span> <span class="pre">16</span> <span class="pre">--opt-level</span> <span class="pre">O2</span> <span class="pre">--channels-last</span> <span class="pre">true</span> <span class="pre">./data</span></code></p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># opt_level = O2</span>
 <span class="c1"># keep_batchnorm_fp32 = None &lt;class &#39;NoneType&#39;&gt;</span>
@@ -515,26 +516,25 @@ <h2>Performance Gains<a class="headerlink" href="#performance-gains" title="Perm
 <span class="c1"># Epoch: [0][80/125] Time 0.198 (0.269) Speed 1011.827 (743.883) Loss 2.8196096420 (2.4011) Prec@1 47.500 (50.938) Prec@5 100.000 (100.000)</span>
 </pre></div>
 </div>
-<p>The following list of models has the full support of Channels Last and showing 8%-35% perf gains on Volta devices:
+<p>아래 목록의 모델들은 Channels Last 형식을 전적으로 지원(full support)하며 Volta 장비에서 8%-35%의 성능 향상을 보입니다:
 <code class="docutils literal notranslate"><span class="pre">alexnet</span></code>, <code class="docutils literal notranslate"><span class="pre">mnasnet0_5</span></code>, <code class="docutils literal notranslate"><span class="pre">mnasnet0_75</span></code>, <code class="docutils literal notranslate"><span class="pre">mnasnet1_0</span></code>, <code class="docutils literal notranslate"><span class="pre">mnasnet1_3</span></code>, <code class="docutils literal notranslate"><span class="pre">mobilenet_v2</span></code>, <code class="docutils literal notranslate"><span class="pre">resnet101</span></code>, <code class="docutils literal notranslate"><span class="pre">resnet152</span></code>, <code class="docutils literal notranslate"><span class="pre">resnet18</span></code>, <code class="docutils literal notranslate"><span class="pre">resnet34</span></code>, <code class="docutils literal notranslate"><span class="pre">resnet50</span></code>, <code class="docutils literal notranslate"><span class="pre">resnext50_32x4d</span></code>, <code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x0_5</span></code>, <code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x1_0</span></code>, <code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x1_5</span></code>, <code class="docutils literal notranslate"><span class="pre">shufflenet_v2_x2_0</span></code>, <code class="docutils literal notranslate"><span class="pre">squeezenet1_0</span></code>, <code class="docutils literal notranslate"><span class="pre">squeezenet1_1</span></code>, <code class="docutils literal notranslate"><span class="pre">vgg11</span></code>, <code class="docutils literal notranslate"><span class="pre">vgg11_bn</span></code>, <code class="docutils literal notranslate"><span class="pre">vgg13</span></code>, <code class="docutils literal notranslate"><span class="pre">vgg13_bn</span></code>, <code class="docutils literal notranslate"><span class="pre">vgg16</span></code>, <code class="docutils literal notranslate"><span class="pre">vgg16_bn</span></code>, <code class="docutils literal notranslate"><span class="pre">vgg19</span></code>, <code class="docutils literal notranslate"><span class="pre">vgg19_bn</span></code>, <code class="docutils literal notranslate"><span class="pre">wide_resnet101_2</span></code>, <code class="docutils literal notranslate"><span class="pre">wide_resnet50_2</span></code></p>
 </div>
-<div class="section" id="converting-existing-models">
-<h2>Converting existing models<a class="headerlink" href="#converting-existing-models" title="Permalink to this headline">¶</a></h2>
-<p>Channels Last support not limited by existing models, as any model can be converted to Channels Last and propagate format through the graph as soon as input formatted correctly.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># Need to be done once, after model initialization (or load)</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span> <span class="c1"># Replace with your model</span>
+<div class="section" id="id2">
+<h2>기존 모델들 변환하기<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
+<p>Channels Last 지원은 기존 모델이 무엇이냐에 따라 제한되지 않으며, 어떠한 모델도 Channels Last로 변환할 수 있으며
+입력(input)의 형식만 맞춰주면 (신경망) 그래프를 통해 바로 전파(propagate)할 수 있습니다.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 모델을 초기화한(또는 불러온) 이후, 한 번 실행이 필요합니다.</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span> <span class="c1"># 원하는 모델로 교체하기</span>
 
-<span class="c1"># Need to be done for every input</span>
-<span class="nb">input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span> <span class="c1"># Replace with your input</span>
+<span class="c1"># 모든 입력에 대해서 실행이 필요합니다.</span>
+<span class="nb">input</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span> <span class="c1"># 원하는 입력으로 교체하기</span>
 <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>However, not all operators fully converted to support Channels Last (usually returning
-contiguous output instead). That means you need to verify the list of used operators
-against supported operators list <a class="reference external" href="https://github.com/pytorch/pytorch/wiki/Operators-with-Channels-Last-support">https://github.com/pytorch/pytorch/wiki/Operators-with-Channels-Last-support</a>,
-or introduce memory format checks into eager execution mode and run your model.</p>
-<p>After running the code below, operators will raise an exception if the output of the
-operator doesn’t match the memory format of the input.</p>
+<p>그러나, 모든 연산자들이 Channels Last를 지원하도록 완전히 바뀐 것은 아닙니다(일반적으로는 연속적인 출력을 대신 반환합니다).
+즉, Channel Last 지원 연산자 목록 <a class="reference external" href="https://github.com/pytorch/pytorch/wiki/Operators-with-Channels-Last-support">https://github.com/pytorch/pytorch/wiki/Operators-with-Channels-Last-support</a> 에서 사용한 연산자들이 존재하는지 확인하거나,
+eager 실행 모드에서 메모리 형식 검사를 도입하고 모델을 실행해야 합니다.</p>
+<p>아래 코드에서, 연산자들의 출력이 입력의 메모리 형식과 일치하지 않으면 예외(exception)를 발생시킵니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">contains_cl</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
     <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
@@ -604,25 +604,28 @@ <h2>Converting existing models<a class="headerlink" href="#converting-existing-m
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Optional
-&#39;_Optional&#39; object has no attribute &#39;__name__&#39;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>List
+__name__
+Optional
+&#39;_SpecialForm&#39; object has no attribute &#39;__name__&#39;
+Set
+__name__
 </pre></div>
 </div>
-<p>If you found an operator that doesn’t support Channels Last tensors
-and you want to contribute, feel free to use following developers
-guide <a class="reference external" href="https://github.com/pytorch/pytorch/wiki/Writing-memory-format-aware-operators">https://github.com/pytorch/pytorch/wiki/Writing-memory-format-aware-operators</a>.</p>
+<p>만약 Channels Last 텐서를 지원하지 않는 연산자를 발견하였고, 기여하기를 원한다면
+다음 개발 문서를 참고해주세요.
+<a class="reference external" href="https://github.com/pytorch/pytorch/wiki/Writing-memory-format-aware-operators">https://github.com/pytorch/pytorch/wiki/Writing-memory-format-aware-operators</a></p>
 </div>
-<div class="section" id="work-to-do">
-<h2>Work to do<a class="headerlink" href="#work-to-do" title="Permalink to this headline">¶</a></h2>
-<p>There are still many things to do, such as:</p>
+<div class="section" id="id3">
+<h2>해야할 일<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h2>
+<p>다음과 같이 여전히 해야 할 일이 많이 남아있습니다:</p>
 <ul class="simple">
-<li><p>Resolving ambiguity of N1HW and NC11 Tensors;</p></li>
-<li><p>Testing of Distributed Training support;</p></li>
-<li><p>Improving operators coverage.</p></li>
+<li><p>N1HW와 NC11 Tensors의 모호성 해결하기;</p></li>
+<li><p>분산 학습을 지원하는지 확인하기;</p></li>
+<li><p>연산자 범위(operators coverage) 개선(improve)하기</p></li>
 </ul>
-<p>If you have feedback and/or suggestions for improvement, please let us
-know by creating <a class="reference external" href="https://github.com/pytorch/pytorch/issues">an issue</a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.100 seconds)</p>
+<p>개선할 부분에 대한 피드백 또는 제안이 있다면 <a class="reference external" href="https://github.com/pytorch/pytorch/issues">이슈를 만들어</a> 알려주세요.</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.105 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-memory-format-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/591028d309d0401740cd71eb6b14bf93/memory_format_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">memory_format_tutorial.py</span></code></a></p>
@@ -685,12 +688,12 @@ <h2>Work to do<a class="headerlink" href="#work-to-do" title="Permalink to this
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">(beta) Channels Last Memory Format in PyTorch</a><ul>
-<li><a class="reference internal" href="#what-is-channels-last">What is Channels Last</a></li>
-<li><a class="reference internal" href="#memory-format-api">Memory Format API</a></li>
-<li><a class="reference internal" href="#performance-gains">Performance Gains</a></li>
-<li><a class="reference internal" href="#converting-existing-models">Converting existing models</a></li>
-<li><a class="reference internal" href="#work-to-do">Work to do</a></li>
+<li><a class="reference internal" href="#">(실험용) PyTorch를 사용한 Channels Last Memory Format</a><ul>
+<li><a class="reference internal" href="#channels-last">Channels Last가 무엇인가요</a></li>
+<li><a class="reference internal" href="#memory-format-api">메모리 형식(Memory Format) API</a></li>
+<li><a class="reference internal" href="#id1">성능 향상</a></li>
+<li><a class="reference internal" href="#id2">기존 모델들 변환하기</a></li>
+<li><a class="reference internal" href="#id3">해야할 일</a></li>
 </ul>
 </li>
 </ul>
@@ -708,11 +711,9 @@ <h2>Work to do<a class="headerlink" href="#work-to-do" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/model_parallel_tutorial.html b/docs/intermediate/model_parallel_tutorial.html
index 5317a1b9e..9e6b50bf7 100644
--- a/docs/intermediate/model_parallel_tutorial.html
+++ b/docs/intermediate/model_parallel_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -335,7 +335,7 @@ <h2>Basic Usage<a class="headerlink" href="#basic-usage" title="Permalink to thi
 
 
 <span class="k">class</span> <span class="nc">ToyModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">ToyModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">net1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cuda:0&#39;</span><span class="p">)</span> <span class="c1"># 첫 번째 층을 첫 번째 GPU에 할당</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
@@ -368,13 +368,13 @@ <h2>기존에 존재하는 모듈에 모델 병렬 처리 적용해보기<a clas
 <p>기존에 단일 GPU에 존재하는 모듈을 여러 GPU에 할당하는 것은  단지 몇 줄의 코드를 수정하는 것으로도 쉽게 가능합니다.
 아래에 있는 코드들은 ResNet50 모델을 분할하는 방법입니다. 이 아이디어는, 기존에 존재하는 ResNet 모듈을 상속받아 설계할 때, 2개의 GPU에 층을 나누어 설계하는 방식으로 진행됩니다.
 그 후, 2개 GPU에서 계산되는 중간 산출물 텐서값을 적절히 배치하기 위헤 순전파 메소드를 수정합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchvision.models.resnet</span> <span class="k">import</span> <span class="n">ResNet</span><span class="p">,</span> <span class="n">Bottleneck</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torchvision.models.resnet</span> <span class="kn">import</span> <span class="n">ResNet</span><span class="p">,</span> <span class="n">Bottleneck</span>
 
 <span class="n">num_classes</span> <span class="o">=</span> <span class="mi">1000</span>
 
 
 <span class="k">class</span> <span class="nc">ModelParallelResNet50</span><span class="p">(</span><span class="n">ResNet</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">ModelParallelResNet50</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
             <span class="n">Bottleneck</span><span class="p">,</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="n">num_classes</span><span class="o">=</span><span class="n">num_classes</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
@@ -510,7 +510,7 @@ <h2>입력 텐서값을 분할하는 파이프라인을 설계하여 학습 시
 과정입니다. 아래의 과정을 실행할 때, PyTorch가 CUDA 연산을 비동기적으로 이용하기 때문에,
 프로세스를 실행하는 스레드를 여러개 생성할 필요가 없습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">PipelineParallelResNet50</span><span class="p">(</span><span class="n">ModelParallelResNet50</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">split_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">split_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">PipelineParallelResNet50</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">split_size</span> <span class="o">=</span> <span class="n">split_size</span>
 
@@ -690,11 +690,9 @@ <h2>입력 텐서값을 분할하는 파이프라인을 설계하여 학습 시
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/named_tensor_tutorial.html b/docs/intermediate/named_tensor_tutorial.html
index 18b4ee3bf..e84390086 100644
--- a/docs/intermediate/named_tensor_tutorial.html
+++ b/docs/intermediate/named_tensor_tutorial.html
@@ -30,7 +30,7 @@
   <link rel="stylesheet" href="../_static/gallery.css" type="text/css" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="(beta) Channels Last Memory Format in PyTorch" href="memory_format_tutorial.html" />
+    <link rel="next" title="(실험용) PyTorch를 사용한 Channels Last Memory Format" href="memory_format_tutorial.html" />
     <link rel="prev" title="(선택) PyTorch 모델을 ONNX으로 변환하고 ONNX 런타임에서 실행하기" href="../advanced/super_resolution_with_onnxruntime.html" /> 
 
   
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -391,9 +391,9 @@ <h2>Basics: named dimensions<a class="headerlink" href="#basics-named-dimensions
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[ 0.3771, -1.1817, -1.6455]],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[ 0.6522, -1.4478,  0.0423]],
 
-        [[ 0.9752,  0.5266,  1.5375]]])
+        [[ 0.4302,  1.6664,  0.3224]]])
 (None, None, None)
 </pre></div>
 </div>
@@ -649,8 +649,8 @@ <h3>Autograd support<a class="headerlink" href="#autograd-support" title="Permal
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([-0.4352, -0.4562, -0.6570])
-tensor([-0.4352, -0.4562, -0.6570])
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([ 1.0185, -1.0310,  0.3990])
+tensor([ 1.0185, -1.0310,  0.3990])
 </pre></div>
 </div>
 </div>
@@ -704,7 +704,7 @@ <h3>Longer example: Multi-head attention<a class="headerlink" href="#longer-exam
 
 
 <span class="k">class</span> <span class="nc">MultiHeadAttention</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_heads</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n_heads</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MultiHeadAttention</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">n_heads</span> <span class="o">=</span> <span class="n">n_heads</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">=</span> <span class="n">dim</span>
@@ -730,7 +730,7 @@ <h3>Longer example: Multi-head attention<a class="headerlink" href="#longer-exam
 
         <span class="n">dim</span> <span class="o">=</span> <span class="n">query</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="s1">&#39;D&#39;</span><span class="p">)</span>
         <span class="k">assert</span> <span class="n">dim</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">,</span> \
-            <span class="n">f</span><span class="s1">&#39;Dimensions do not match: </span><span class="si">{dim}</span><span class="s1"> query vs </span><span class="si">{self.dim}</span><span class="s1"> configured&#39;</span>
+            <span class="sa">f</span><span class="s1">&#39;Dimensions do not match: </span><span class="si">{</span><span class="n">dim</span><span class="si">}</span><span class="s1"> query vs </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="si">}</span><span class="s1"> configured&#39;</span>
         <span class="k">assert</span> <span class="n">mask</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s1">&#39;Mask is None, please specify a mask&#39;</span>
         <span class="n">n_heads</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">n_heads</span>
         <span class="n">dim_per_head</span> <span class="o">=</span> <span class="n">dim</span> <span class="o">//</span> <span class="n">n_heads</span>
@@ -880,7 +880,7 @@ <h3>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <p>Thank you for reading! Named tensors are still very much in development;
 if you have feedback and/or suggestions for improvement, please let us
 know by creating <a class="reference external" href="https://github.com/pytorch/pytorch/issues">an issue</a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.179 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.101 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-named-tensor-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/b7ab75b9f73be677521a0654a1837732/named_tensor_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">named_tensor_tutorial.py</span></code></a></p>
@@ -902,7 +902,7 @@ <h3>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="memory_format_tutorial.html" class="btn btn-neutral float-right" title="(beta) Channels Last Memory Format in PyTorch" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
+        <a href="memory_format_tutorial.html" class="btn btn-neutral float-right" title="(실험용) PyTorch를 사용한 Channels Last Memory Format" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
         <a href="../advanced/super_resolution_with_onnxruntime.html" class="btn btn-neutral" title="(선택) PyTorch 모델을 ONNX으로 변환하고 ONNX 런타임에서 실행하기" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
@@ -978,11 +978,9 @@ <h3>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
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-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/pruning_tutorial.html b/docs/intermediate/pruning_tutorial.html
index 176cd5990..bf1b7260c 100644
--- a/docs/intermediate/pruning_tutorial.html
+++ b/docs/intermediate/pruning_tutorial.html
@@ -30,7 +30,7 @@
   <link rel="stylesheet" href="../_static/gallery.css" type="text/css" />
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
-    <link rel="next" title="(beta) Dynamic Quantization on an LSTM Word Language Model" href="../advanced/dynamic_quantization_tutorial.html" />
+    <link rel="next" title="(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화" href="../advanced/dynamic_quantization_tutorial.html" />
     <link rel="prev" title="Dispatcher in C++" href="../advanced/dispatcher.html" /> 
 
   
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul class="current">
 <li class="toctree-l1 current"><a class="current reference internal" href="#">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -320,7 +320,7 @@
 <h2>요구사항<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">&quot;torch&gt;=1.4&quot;</span></code></p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
 <span class="kn">import</span> <span class="nn">torch.nn.utils.prune</span> <span class="k">as</span> <span class="nn">prune</span>
 <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 </pre></div>
@@ -333,7 +333,7 @@ <h2>딥러닝 모델 생성<a class="headerlink" href="#id3" title="Permalink to
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
 
 <span class="k">class</span> <span class="nc">LeNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">LeNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="c1"># 1개 채널 수의 이미지를 입력값으로 이용하여 6개 채널 수의 출력값을 계산하는 방식</span>
         <span class="c1"># Convolution 연산을 진행하는 커널(필터)의 크기는 3x3 을 이용</span>
@@ -359,42 +359,42 @@ <h2>딥러닝 모델 생성<a class="headerlink" href="#id3" title="Permalink to
 <div class="section" id="id4">
 <h2>모듈 점검<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h2>
 <p>가지치기 기법이 적용되지 않은 LeNet 모델의 <code class="docutils literal notranslate"><span class="pre">conv1</span></code> 층을 점검해봅시다.
-여기에는 2개의 파라미터값들인 <a href="#id5"><span class="problematic" id="id6">``</span></a>가중치``값과 <a href="#id7"><span class="problematic" id="id8">``</span></a>편향``값을이 포함될 것이며, 버퍼는 존재하지 않을 것입니다.</p>
+여기에는 2개의 파라미터값들인 <code class="docutils literal notranslate"><span class="pre">가중치</span></code> 값과 <code class="docutils literal notranslate"><span class="pre">편향</span></code> 값이 포함될 것이며, 버퍼는 존재하지 않을 것입니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">module</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">conv1</span>
 <span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">()))</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;weight&#39;, Parameter containing:
-tensor([[[[ 0.3161, -0.2212,  0.0417],
-          [ 0.2488,  0.2415,  0.2071],
-          [-0.2412, -0.2400, -0.2016]]],
+tensor([[[[ 0.1220,  0.1574, -0.2430],
+          [ 0.2315,  0.2777, -0.2998],
+          [ 0.2914, -0.2449, -0.0627]]],
 
 
-        [[[ 0.0419,  0.3322, -0.2106],
-          [ 0.1776, -0.1845, -0.3134],
-          [-0.0708,  0.1921,  0.3095]]],
+        [[[ 0.0202, -0.3315,  0.3249],
+          [-0.1553, -0.3050, -0.2590],
+          [ 0.2829, -0.1247,  0.1029]]],
 
 
-        [[[-0.2070,  0.0723,  0.2876],
-          [ 0.2209,  0.2077,  0.2369],
-          [ 0.2108,  0.0861, -0.2279]]],
+        [[[ 0.2747,  0.2280, -0.2064],
+          [ 0.0613, -0.2235,  0.2610],
+          [-0.2872, -0.2117, -0.0720]]],
 
 
-        [[[-0.2799, -0.1527, -0.0388],
-          [-0.2043,  0.1220,  0.1032],
-          [-0.0755,  0.1281,  0.1077]]],
+        [[[ 0.0972, -0.2510, -0.0740],
+          [-0.2693,  0.0858,  0.2765],
+          [-0.2819, -0.0532,  0.1224]]],
 
 
-        [[[ 0.2035,  0.2245, -0.1129],
-          [ 0.3257, -0.0385, -0.0115],
-          [-0.3146, -0.2145, -0.1947]]],
+        [[[-0.2743,  0.2629, -0.0047],
+          [-0.0743, -0.2141,  0.1626],
+          [ 0.0976,  0.1006, -0.3228]]],
 
 
-        [[[-0.1426,  0.2370, -0.1089],
-          [-0.2491,  0.1282,  0.1067],
-          [ 0.2159, -0.1725,  0.0723]]]], device=&#39;cuda:0&#39;, requires_grad=True)), (&#39;bias&#39;, Parameter containing:
-tensor([-0.1214, -0.0749, -0.2656, -0.1519, -0.1021,  0.1425], device=&#39;cuda:0&#39;,
+        [[[ 0.0517,  0.1638,  0.1589],
+          [-0.0781, -0.2169, -0.2271],
+          [ 0.1213, -0.1946, -0.0012]]]], device=&#39;cuda:0&#39;, requires_grad=True)), (&#39;bias&#39;, Parameter containing:
+tensor([-0.2996, -0.1588, -0.1239,  0.0091,  0.1408,  0.0922], device=&#39;cuda:0&#39;,
        requires_grad=True))]
 </pre></div>
 </div>
@@ -406,10 +406,11 @@ <h2>모듈 점검<a class="headerlink" href="#id4" title="Permalink to this head
 </pre></div>
 </div>
 </div>
-<div class="section" id="id9">
-<h2>모듈 가지치기 기법 적용 예제<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id5">
+<h2>모듈 가지치기 기법 적용 예제<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h2>
 <p>모듈에 대해 가지치기 기법을 적용하기 위해 (이번 예제에서는, LeNet 모델의 <code class="docutils literal notranslate"><span class="pre">conv1</span></code> 층)
-첫 번째로는, <code class="docutils literal notranslate"><span class="pre">torch.nn.utils.prune</span></code> (또는 <code class="docutils literal notranslate"><span class="pre">BasePruningMethod</span></code> 의 서브 클래스로 직접 <a class="reference external" href="torch-nn-utils-prune">구현</a> )
+첫 번째로는, <code class="docutils literal notranslate"><span class="pre">torch.nn.utils.prune</span></code> (또는 <code class="docutils literal notranslate"><span class="pre">BasePruningMethod</span></code> 의 서브 클래스로 직접
+<a class="reference external" href="torch-nn-utils-prune">구현</a> )
 내 존재하는 가지치기 기법을 선택합니다.
 그 후, 해당 모듈 내에서 가지치기 기법을 적용하고자 하는 모듈과 파라미터를 지정합니다.
 마지막으로, 가지치기 기법에 적당한 키워드 인자값을 이용하여 가지치기 매개변수를 지정합니다.
@@ -429,36 +430,36 @@ <h2>모듈 가지치기 기법 적용 예제<a class="headerlink" href="#id9" ti
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;bias&#39;, Parameter containing:
-tensor([-0.1214, -0.0749, -0.2656, -0.1519, -0.1021,  0.1425], device=&#39;cuda:0&#39;,
+tensor([-0.2996, -0.1588, -0.1239,  0.0091,  0.1408,  0.0922], device=&#39;cuda:0&#39;,
        requires_grad=True)), (&#39;weight_orig&#39;, Parameter containing:
-tensor([[[[ 0.3161, -0.2212,  0.0417],
-          [ 0.2488,  0.2415,  0.2071],
-          [-0.2412, -0.2400, -0.2016]]],
+tensor([[[[ 0.1220,  0.1574, -0.2430],
+          [ 0.2315,  0.2777, -0.2998],
+          [ 0.2914, -0.2449, -0.0627]]],
 
 
-        [[[ 0.0419,  0.3322, -0.2106],
-          [ 0.1776, -0.1845, -0.3134],
-          [-0.0708,  0.1921,  0.3095]]],
+        [[[ 0.0202, -0.3315,  0.3249],
+          [-0.1553, -0.3050, -0.2590],
+          [ 0.2829, -0.1247,  0.1029]]],
 
 
-        [[[-0.2070,  0.0723,  0.2876],
-          [ 0.2209,  0.2077,  0.2369],
-          [ 0.2108,  0.0861, -0.2279]]],
+        [[[ 0.2747,  0.2280, -0.2064],
+          [ 0.0613, -0.2235,  0.2610],
+          [-0.2872, -0.2117, -0.0720]]],
 
 
-        [[[-0.2799, -0.1527, -0.0388],
-          [-0.2043,  0.1220,  0.1032],
-          [-0.0755,  0.1281,  0.1077]]],
+        [[[ 0.0972, -0.2510, -0.0740],
+          [-0.2693,  0.0858,  0.2765],
+          [-0.2819, -0.0532,  0.1224]]],
 
 
-        [[[ 0.2035,  0.2245, -0.1129],
-          [ 0.3257, -0.0385, -0.0115],
-          [-0.3146, -0.2145, -0.1947]]],
+        [[[-0.2743,  0.2629, -0.0047],
+          [-0.0743, -0.2141,  0.1626],
+          [ 0.0976,  0.1006, -0.3228]]],
 
 
-        [[[-0.1426,  0.2370, -0.1089],
-          [-0.2491,  0.1282,  0.1067],
-          [ 0.2159, -0.1725,  0.0723]]]], device=&#39;cuda:0&#39;, requires_grad=True))]
+        [[[ 0.0517,  0.1638,  0.1589],
+          [-0.0781, -0.2169, -0.2271],
+          [ 0.1213, -0.1946, -0.0012]]]], device=&#39;cuda:0&#39;, requires_grad=True))]
 </pre></div>
 </div>
 <p>위에서 선택한 가지치기 기법에 의해 생성되는 가지치기 마스크는 초기 파라미터  <code class="docutils literal notranslate"><span class="pre">name</span></code> 에 <code class="docutils literal notranslate"><span class="pre">weight_mask</span></code>
@@ -497,42 +498,42 @@ <h2>모듈 가지치기 기법 적용 예제<a class="headerlink" href="#id9" ti
           [1., 1., 0.]]]], device=&#39;cuda:0&#39;))]
 </pre></div>
 </div>
-<p>수정이 되지 않은 상태에서 순전파를 진행하기 위해서는 <code class="docutils literal notranslate"><span class="pre">가중치``값</span> <span class="pre">속성이</span> <span class="pre">존재해야</span> <span class="pre">합니다.</span>
-<span class="pre">``torch.nn.utils.prune</span></code>  내 구현된 가지치기 기법은 가지치기 기법이 적용된 가중치값들을 이용하여
+<p>수정이 되지 않은 상태에서 순전파를 진행하기 위해서는 <code class="docutils literal notranslate"><span class="pre">가중치</span></code> 값 속성이 존재해야 합니다.
+<code class="docutils literal notranslate"><span class="pre">torch.nn.utils.prune</span></code> 내 구현된 가지치기 기법은 가지치기 기법이 적용된 가중치값들을 이용하여
 (기존의 가중치값에 가지치기 기법이 적용된) 순전파를 진행하고, <code class="docutils literal notranslate"><span class="pre">weight</span></code> 속성값에 가지치기 기법이 적용된 가중치값들을 저장합니다.
 이제 가중치값들은 <code class="docutils literal notranslate"><span class="pre">module</span></code> 의 매개변수가 아니라 하나의 속성값으로 취급되는 점을 주의하세요.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[[ 0.0000, -0.2212,  0.0000],
-          [ 0.2488,  0.0000,  0.0000],
-          [-0.2412, -0.2400, -0.2016]]],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[[ 0.0000,  0.1574, -0.0000],
+          [ 0.2315,  0.0000, -0.0000],
+          [ 0.2914, -0.2449, -0.0627]]],
 
 
-        [[[ 0.0419,  0.0000, -0.2106],
-          [ 0.1776, -0.1845, -0.0000],
-          [-0.0708,  0.0000,  0.3095]]],
+        [[[ 0.0202, -0.0000,  0.3249],
+          [-0.1553, -0.3050, -0.0000],
+          [ 0.2829, -0.0000,  0.1029]]],
 
 
-        [[[-0.2070,  0.0000,  0.0000],
-          [ 0.0000,  0.2077,  0.2369],
-          [ 0.2108,  0.0861, -0.2279]]],
+        [[[ 0.2747,  0.0000, -0.0000],
+          [ 0.0000, -0.2235,  0.2610],
+          [-0.2872, -0.2117, -0.0720]]],
 
 
-        [[[-0.2799, -0.0000, -0.0000],
-          [-0.2043,  0.1220,  0.1032],
-          [-0.0755,  0.1281,  0.1077]]],
+        [[[ 0.0972, -0.0000, -0.0000],
+          [-0.2693,  0.0858,  0.2765],
+          [-0.2819, -0.0532,  0.1224]]],
 
 
-        [[[ 0.2035,  0.0000, -0.1129],
-          [ 0.3257, -0.0385, -0.0115],
-          [-0.0000, -0.2145, -0.1947]]],
+        [[[-0.2743,  0.0000, -0.0047],
+          [-0.0743, -0.2141,  0.1626],
+          [ 0.0000,  0.1006, -0.3228]]],
 
 
-        [[[-0.1426,  0.2370, -0.1089],
-          [-0.2491,  0.1282,  0.0000],
-          [ 0.2159, -0.1725,  0.0000]]]], device=&#39;cuda:0&#39;,
+        [[[ 0.0517,  0.1638,  0.1589],
+          [-0.0781, -0.2169, -0.0000],
+          [ 0.1213, -0.1946, -0.0000]]]], device=&#39;cuda:0&#39;,
        grad_fn=&lt;MulBackward0&gt;)
 </pre></div>
 </div>
@@ -545,7 +546,7 @@ <h2>모듈 가지치기 기법 적용 예제<a class="headerlink" href="#id9" ti
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>OrderedDict([(0, &lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f587a5c0550&gt;)])
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>OrderedDict([(3, &lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f72ee46fc90&gt;)])
 </pre></div>
 </div>
 <p>완결성을 위해, 편향값에 대해서도 가지치기 기법을 적용할 수 있으며,
@@ -563,35 +564,35 @@ <h2>모듈 가지치기 기법 적용 예제<a class="headerlink" href="#id9" ti
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;weight_orig&#39;, Parameter containing:
-tensor([[[[ 0.3161, -0.2212,  0.0417],
-          [ 0.2488,  0.2415,  0.2071],
-          [-0.2412, -0.2400, -0.2016]]],
+tensor([[[[ 0.1220,  0.1574, -0.2430],
+          [ 0.2315,  0.2777, -0.2998],
+          [ 0.2914, -0.2449, -0.0627]]],
 
 
-        [[[ 0.0419,  0.3322, -0.2106],
-          [ 0.1776, -0.1845, -0.3134],
-          [-0.0708,  0.1921,  0.3095]]],
+        [[[ 0.0202, -0.3315,  0.3249],
+          [-0.1553, -0.3050, -0.2590],
+          [ 0.2829, -0.1247,  0.1029]]],
 
 
-        [[[-0.2070,  0.0723,  0.2876],
-          [ 0.2209,  0.2077,  0.2369],
-          [ 0.2108,  0.0861, -0.2279]]],
+        [[[ 0.2747,  0.2280, -0.2064],
+          [ 0.0613, -0.2235,  0.2610],
+          [-0.2872, -0.2117, -0.0720]]],
 
 
-        [[[-0.2799, -0.1527, -0.0388],
-          [-0.2043,  0.1220,  0.1032],
-          [-0.0755,  0.1281,  0.1077]]],
+        [[[ 0.0972, -0.2510, -0.0740],
+          [-0.2693,  0.0858,  0.2765],
+          [-0.2819, -0.0532,  0.1224]]],
 
 
-        [[[ 0.2035,  0.2245, -0.1129],
-          [ 0.3257, -0.0385, -0.0115],
-          [-0.3146, -0.2145, -0.1947]]],
+        [[[-0.2743,  0.2629, -0.0047],
+          [-0.0743, -0.2141,  0.1626],
+          [ 0.0976,  0.1006, -0.3228]]],
 
 
-        [[[-0.1426,  0.2370, -0.1089],
-          [-0.2491,  0.1282,  0.1067],
-          [ 0.2159, -0.1725,  0.0723]]]], device=&#39;cuda:0&#39;, requires_grad=True)), (&#39;bias_orig&#39;, Parameter containing:
-tensor([-0.1214, -0.0749, -0.2656, -0.1519, -0.1021,  0.1425], device=&#39;cuda:0&#39;,
+        [[[ 0.0517,  0.1638,  0.1589],
+          [-0.0781, -0.2169, -0.2271],
+          [ 0.1213, -0.1946, -0.0012]]]], device=&#39;cuda:0&#39;, requires_grad=True)), (&#39;bias_orig&#39;, Parameter containing:
+tensor([-0.2996, -0.1588, -0.1239,  0.0091,  0.1408,  0.0922], device=&#39;cuda:0&#39;,
        requires_grad=True))]
 </pre></div>
 </div>
@@ -626,14 +627,14 @@ <h2>모듈 가지치기 기법 적용 예제<a class="headerlink" href="#id9" ti
 
         [[[1., 1., 1.],
           [1., 1., 0.],
-          [1., 1., 0.]]]], device=&#39;cuda:0&#39;)), (&#39;bias_mask&#39;, tensor([0., 0., 1., 1., 0., 1.], device=&#39;cuda:0&#39;))]
+          [1., 1., 0.]]]], device=&#39;cuda:0&#39;)), (&#39;bias_mask&#39;, tensor([1., 1., 0., 0., 1., 0.], device=&#39;cuda:0&#39;))]
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([-0.0000, -0.0000, -0.2656, -0.1519, -0.0000,  0.1425], device=&#39;cuda:0&#39;,
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([-0.2996, -0.1588, -0.0000,  0.0000,  0.1408,  0.0000], device=&#39;cuda:0&#39;,
        grad_fn=&lt;MulBackward0&gt;)
 </pre></div>
 </div>
@@ -641,12 +642,12 @@ <h2>모듈 가지치기 기법 적용 예제<a class="headerlink" href="#id9" ti
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>OrderedDict([(0, &lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f587a5c0550&gt;), (1, &lt;torch.nn.utils.prune.L1Unstructured object at 0x7f587a5c0710&gt;)])
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>OrderedDict([(3, &lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f72ee46fc90&gt;), (4, &lt;torch.nn.utils.prune.L1Unstructured object at 0x7f72ee46f890&gt;)])
 </pre></div>
 </div>
 </div>
-<div class="section" id="id11">
-<h2>가지치기 기법 반복 적용<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id7">
+<h2>가지치기 기법 반복 적용<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h2>
 <p>모듈 내 같은 파라미터값에 대해 가지치기 기법이 여러번 적용될 수 있으며, 다양한 가지치기 기법의 조합이 적용된 것과 동일하게 적용될 수 있습니다.
 새로운 마스크와 이전의 마스크의 결합은 <code class="docutils literal notranslate"><span class="pre">PruningContainer</span></code> 의 <code class="docutils literal notranslate"><span class="pre">compute_mask</span></code> 메소드를 통해 처리할 수 있습니다.</p>
 <p>예를 들어, 만약 <code class="docutils literal notranslate"><span class="pre">module.weight</span></code> 값에 가지치기 기법을 적용하고 싶을 때, 텐서의 0번째 축의 L2 norm값을 기준으로 구조화된 가지치기 기법을 적용합니다.
@@ -660,34 +661,34 @@ <h2>가지치기 기법 반복 적용<a class="headerlink" href="#id11" title="P
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[[ 0.0000, -0.2212,  0.0000],
-          [ 0.2488,  0.0000,  0.0000],
-          [-0.2412, -0.2400, -0.2016]]],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[[ 0.0000,  0.0000, -0.0000],
+          [ 0.0000,  0.0000, -0.0000],
+          [ 0.0000, -0.0000, -0.0000]]],
 
 
-        [[[ 0.0000,  0.0000, -0.0000],
-          [ 0.0000, -0.0000, -0.0000],
-          [-0.0000,  0.0000,  0.0000]]],
+        [[[ 0.0202, -0.0000,  0.3249],
+          [-0.1553, -0.3050, -0.0000],
+          [ 0.2829, -0.0000,  0.1029]]],
 
 
-        [[[-0.2070,  0.0000,  0.0000],
-          [ 0.0000,  0.2077,  0.2369],
-          [ 0.2108,  0.0861, -0.2279]]],
+        [[[ 0.2747,  0.0000, -0.0000],
+          [ 0.0000, -0.2235,  0.2610],
+          [-0.2872, -0.2117, -0.0720]]],
 
 
-        [[[-0.0000, -0.0000, -0.0000],
+        [[[ 0.0000, -0.0000, -0.0000],
           [-0.0000,  0.0000,  0.0000],
-          [-0.0000,  0.0000,  0.0000]]],
+          [-0.0000, -0.0000,  0.0000]]],
 
 
-        [[[ 0.2035,  0.0000, -0.1129],
-          [ 0.3257, -0.0385, -0.0115],
-          [-0.0000, -0.2145, -0.1947]]],
+        [[[-0.2743,  0.0000, -0.0047],
+          [-0.0743, -0.2141,  0.1626],
+          [ 0.0000,  0.1006, -0.3228]]],
 
 
-        [[[-0.0000,  0.0000, -0.0000],
-          [-0.0000,  0.0000,  0.0000],
-          [ 0.0000, -0.0000,  0.0000]]]], device=&#39;cuda:0&#39;,
+        [[[ 0.0000,  0.0000,  0.0000],
+          [-0.0000, -0.0000, -0.0000],
+          [ 0.0000, -0.0000, -0.0000]]]], device=&#39;cuda:0&#39;,
        grad_fn=&lt;MulBackward0&gt;)
 </pre></div>
 </div>
@@ -700,12 +701,12 @@ <h2>가지치기 기법 반복 적용<a class="headerlink" href="#id11" title="P
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f587a5c0550&gt;, &lt;torch.nn.utils.prune.LnStructured object at 0x7f587a5c09b0&gt;]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[&lt;torch.nn.utils.prune.RandomUnstructured object at 0x7f72ee46fc90&gt;, &lt;torch.nn.utils.prune.LnStructured object at 0x7f72ee46f750&gt;]
 </pre></div>
 </div>
 </div>
-<div class="section" id="id12">
-<h2>가지치기 기법이 적용된 모델의 직렬화<a class="headerlink" href="#id12" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id8">
+<h2>가지치기 기법이 적용된 모델의 직렬화<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h2>
 <p>마스크 버퍼들과 가지치기 기법이 적용된 텐서 계산에 사용된 기존의 파라미터를 포함하여 관련된 모든 텐서값들은
 필요한 경우 모델의 <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> 에 저장되기 떄문에, 쉽게 직렬화하여 저장할 수 있습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
@@ -716,8 +717,8 @@ <h2>가지치기 기법이 적용된 모델의 직렬화<a class="headerlink" hr
 </pre></div>
 </div>
 </div>
-<div class="section" id="id13">
-<h2>가지치기 기법의 재-파라미터화 제거<a class="headerlink" href="#id13" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id9">
+<h2>가지치기 기법의 재-파라미터화 제거<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
 <p>가지치기 기법이 적용된 것을 영구적으로 만들기 위해서, 재-파라미터화 관점의
 <code class="docutils literal notranslate"><span class="pre">weight_orig</span></code> 와 <code class="docutils literal notranslate"><span class="pre">weight_mask</span></code> 값을 제거하고, <code class="docutils literal notranslate"><span class="pre">forward_pre_hook</span></code> 값을 제거합니다.
 제거하기 위해 <code class="docutils literal notranslate"><span class="pre">torch.nn.utils.prune</span></code> 내 <code class="docutils literal notranslate"><span class="pre">remove</span></code> 함수를 이용할 수 있습니다.
@@ -729,35 +730,35 @@ <h2>가지치기 기법의 재-파라미터화 제거<a class="headerlink" href=
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;weight_orig&#39;, Parameter containing:
-tensor([[[[ 0.3161, -0.2212,  0.0417],
-          [ 0.2488,  0.2415,  0.2071],
-          [-0.2412, -0.2400, -0.2016]]],
+tensor([[[[ 0.1220,  0.1574, -0.2430],
+          [ 0.2315,  0.2777, -0.2998],
+          [ 0.2914, -0.2449, -0.0627]]],
 
 
-        [[[ 0.0419,  0.3322, -0.2106],
-          [ 0.1776, -0.1845, -0.3134],
-          [-0.0708,  0.1921,  0.3095]]],
+        [[[ 0.0202, -0.3315,  0.3249],
+          [-0.1553, -0.3050, -0.2590],
+          [ 0.2829, -0.1247,  0.1029]]],
 
 
-        [[[-0.2070,  0.0723,  0.2876],
-          [ 0.2209,  0.2077,  0.2369],
-          [ 0.2108,  0.0861, -0.2279]]],
+        [[[ 0.2747,  0.2280, -0.2064],
+          [ 0.0613, -0.2235,  0.2610],
+          [-0.2872, -0.2117, -0.0720]]],
 
 
-        [[[-0.2799, -0.1527, -0.0388],
-          [-0.2043,  0.1220,  0.1032],
-          [-0.0755,  0.1281,  0.1077]]],
+        [[[ 0.0972, -0.2510, -0.0740],
+          [-0.2693,  0.0858,  0.2765],
+          [-0.2819, -0.0532,  0.1224]]],
 
 
-        [[[ 0.2035,  0.2245, -0.1129],
-          [ 0.3257, -0.0385, -0.0115],
-          [-0.3146, -0.2145, -0.1947]]],
+        [[[-0.2743,  0.2629, -0.0047],
+          [-0.0743, -0.2141,  0.1626],
+          [ 0.0976,  0.1006, -0.3228]]],
 
 
-        [[[-0.1426,  0.2370, -0.1089],
-          [-0.2491,  0.1282,  0.1067],
-          [ 0.2159, -0.1725,  0.0723]]]], device=&#39;cuda:0&#39;, requires_grad=True)), (&#39;bias_orig&#39;, Parameter containing:
-tensor([-0.1214, -0.0749, -0.2656, -0.1519, -0.1021,  0.1425], device=&#39;cuda:0&#39;,
+        [[[ 0.0517,  0.1638,  0.1589],
+          [-0.0781, -0.2169, -0.2271],
+          [ 0.1213, -0.1946, -0.0012]]]], device=&#39;cuda:0&#39;, requires_grad=True)), (&#39;bias_orig&#39;, Parameter containing:
+tensor([-0.2996, -0.1588, -0.1239,  0.0091,  0.1408,  0.0922], device=&#39;cuda:0&#39;,
        requires_grad=True))]
 </pre></div>
 </div>
@@ -765,16 +766,16 @@ <h2>가지치기 기법의 재-파라미터화 제거<a class="headerlink" href=
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;weight_mask&#39;, tensor([[[[0., 1., 0.],
-          [1., 0., 0.],
-          [1., 1., 1.]]],
-
-
-        [[[0., 0., 0.],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;weight_mask&#39;, tensor([[[[0., 0., 0.],
           [0., 0., 0.],
           [0., 0., 0.]]],
 
 
+        [[[1., 0., 1.],
+          [1., 1., 0.],
+          [1., 0., 1.]]],
+
+
         [[[1., 0., 0.],
           [0., 1., 1.],
           [1., 1., 1.]]],
@@ -792,41 +793,41 @@ <h2>가지치기 기법의 재-파라미터화 제거<a class="headerlink" href=
 
         [[[0., 0., 0.],
           [0., 0., 0.],
-          [0., 0., 0.]]]], device=&#39;cuda:0&#39;)), (&#39;bias_mask&#39;, tensor([0., 0., 1., 1., 0., 1.], device=&#39;cuda:0&#39;))]
+          [0., 0., 0.]]]], device=&#39;cuda:0&#39;)), (&#39;bias_mask&#39;, tensor([1., 1., 0., 0., 1., 0.], device=&#39;cuda:0&#39;))]
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[[ 0.0000, -0.2212,  0.0000],
-          [ 0.2488,  0.0000,  0.0000],
-          [-0.2412, -0.2400, -0.2016]]],
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([[[[ 0.0000,  0.0000, -0.0000],
+          [ 0.0000,  0.0000, -0.0000],
+          [ 0.0000, -0.0000, -0.0000]]],
 
 
-        [[[ 0.0000,  0.0000, -0.0000],
-          [ 0.0000, -0.0000, -0.0000],
-          [-0.0000,  0.0000,  0.0000]]],
+        [[[ 0.0202, -0.0000,  0.3249],
+          [-0.1553, -0.3050, -0.0000],
+          [ 0.2829, -0.0000,  0.1029]]],
 
 
-        [[[-0.2070,  0.0000,  0.0000],
-          [ 0.0000,  0.2077,  0.2369],
-          [ 0.2108,  0.0861, -0.2279]]],
+        [[[ 0.2747,  0.0000, -0.0000],
+          [ 0.0000, -0.2235,  0.2610],
+          [-0.2872, -0.2117, -0.0720]]],
 
 
-        [[[-0.0000, -0.0000, -0.0000],
+        [[[ 0.0000, -0.0000, -0.0000],
           [-0.0000,  0.0000,  0.0000],
-          [-0.0000,  0.0000,  0.0000]]],
+          [-0.0000, -0.0000,  0.0000]]],
 
 
-        [[[ 0.2035,  0.0000, -0.1129],
-          [ 0.3257, -0.0385, -0.0115],
-          [-0.0000, -0.2145, -0.1947]]],
+        [[[-0.2743,  0.0000, -0.0047],
+          [-0.0743, -0.2141,  0.1626],
+          [ 0.0000,  0.1006, -0.3228]]],
 
 
-        [[[-0.0000,  0.0000, -0.0000],
-          [-0.0000,  0.0000,  0.0000],
-          [ 0.0000, -0.0000,  0.0000]]]], device=&#39;cuda:0&#39;,
+        [[[ 0.0000,  0.0000,  0.0000],
+          [-0.0000, -0.0000, -0.0000],
+          [ 0.0000, -0.0000, -0.0000]]]], device=&#39;cuda:0&#39;,
        grad_fn=&lt;MulBackward0&gt;)
 </pre></div>
 </div>
@@ -837,48 +838,48 @@ <h2>가지치기 기법의 재-파라미터화 제거<a class="headerlink" href=
 </div>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;bias_orig&#39;, Parameter containing:
-tensor([-0.1214, -0.0749, -0.2656, -0.1519, -0.1021,  0.1425], device=&#39;cuda:0&#39;,
+tensor([-0.2996, -0.1588, -0.1239,  0.0091,  0.1408,  0.0922], device=&#39;cuda:0&#39;,
        requires_grad=True)), (&#39;weight&#39;, Parameter containing:
-tensor([[[[ 0.0000, -0.2212,  0.0000],
-          [ 0.2488,  0.0000,  0.0000],
-          [-0.2412, -0.2400, -0.2016]]],
+tensor([[[[ 0.0000,  0.0000, -0.0000],
+          [ 0.0000,  0.0000, -0.0000],
+          [ 0.0000, -0.0000, -0.0000]]],
 
 
-        [[[ 0.0000,  0.0000, -0.0000],
-          [ 0.0000, -0.0000, -0.0000],
-          [-0.0000,  0.0000,  0.0000]]],
+        [[[ 0.0202, -0.0000,  0.3249],
+          [-0.1553, -0.3050, -0.0000],
+          [ 0.2829, -0.0000,  0.1029]]],
 
 
-        [[[-0.2070,  0.0000,  0.0000],
-          [ 0.0000,  0.2077,  0.2369],
-          [ 0.2108,  0.0861, -0.2279]]],
+        [[[ 0.2747,  0.0000, -0.0000],
+          [ 0.0000, -0.2235,  0.2610],
+          [-0.2872, -0.2117, -0.0720]]],
 
 
-        [[[-0.0000, -0.0000, -0.0000],
+        [[[ 0.0000, -0.0000, -0.0000],
           [-0.0000,  0.0000,  0.0000],
-          [-0.0000,  0.0000,  0.0000]]],
+          [-0.0000, -0.0000,  0.0000]]],
 
 
-        [[[ 0.2035,  0.0000, -0.1129],
-          [ 0.3257, -0.0385, -0.0115],
-          [-0.0000, -0.2145, -0.1947]]],
+        [[[-0.2743,  0.0000, -0.0047],
+          [-0.0743, -0.2141,  0.1626],
+          [ 0.0000,  0.1006, -0.3228]]],
 
 
-        [[[-0.0000,  0.0000, -0.0000],
-          [-0.0000,  0.0000,  0.0000],
-          [ 0.0000, -0.0000,  0.0000]]]], device=&#39;cuda:0&#39;, requires_grad=True))]
+        [[[ 0.0000,  0.0000,  0.0000],
+          [-0.0000, -0.0000, -0.0000],
+          [ 0.0000, -0.0000, -0.0000]]]], device=&#39;cuda:0&#39;, requires_grad=True))]
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">named_buffers</span><span class="p">()))</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;bias_mask&#39;, tensor([0., 0., 1., 1., 0., 1.], device=&#39;cuda:0&#39;))]
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[(&#39;bias_mask&#39;, tensor([1., 1., 0., 0., 1., 0.], device=&#39;cuda:0&#39;))]
 </pre></div>
 </div>
 </div>
-<div class="section" id="id14">
-<h2>모델 내 여러 파라미터값들에 대하여 가지치기 기법 적용<a class="headerlink" href="#id14" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id10">
+<h2>모델 내 여러 파라미터값들에 대하여 가지치기 기법 적용<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h2>
 <p>가지치기 기법을 적용하고 싶은 파라미터값들을 지정함으로써, 이번 예제에서 볼 수 있는 것 처럼,
 신경망 모델 내 여러 텐서값들에 대해서 쉽게 가지치기 기법을 적용할 수 있습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">new_model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
@@ -898,8 +899,8 @@ <h2>모델 내 여러 파라미터값들에 대하여 가지치기 기법 적용
 </pre></div>
 </div>
 </div>
-<div class="section" id="id15">
-<h2>전역 범위에 대한 가지치기 기법 적용<a class="headerlink" href="#id15" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id11">
+<h2>전역 범위에 대한 가지치기 기법 적용<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h2>
 <p>지금까지, “지역 변수” 에 대해서만 가지치기 기법을 적용하는 방법을 살펴보았습니다.
 (즉, 가중치 규모, 활성화 정도, 경사값 등의 각 항목의 통계량을 바탕으로 모델 내 텐서값 하나씩 가지치기 기법을 적용하는 방식)
 그러나, 범용적이고 아마 더 강력한 방법은 각 층에서 가장 낮은 20%의 연결을 제거하는것 대신에, 전체 모델에 대해서 가장 낮은 20% 연결을 한번에 제거하는 것입니다.
@@ -975,11 +976,11 @@ <h2>전역 범위에 대한 가지치기 기법 적용<a class="headerlink" href
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sparsity in conv1.weight: 7.41%
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Sparsity in conv1.weight: 3.70%
 Sparsity in conv2.weight: 9.49%
-Sparsity in fc1.weight: 22.00%
-Sparsity in fc2.weight: 12.28%
-Sparsity in fc3.weight: 9.76%
+Sparsity in fc1.weight: 22.04%
+Sparsity in fc2.weight: 12.08%
+Sparsity in fc3.weight: 10.48%
 Global sparsity: 20.00%
 </pre></div>
 </div>
@@ -997,8 +998,8 @@ <h2><code class="docutils literal notranslate"><span class="pre">torch.nn.utils.
 이러한 방식은, 가지치기 기법을 반복적으로 적용해야 하는 경우 마스크를 결합하는 방법을 결정하기 위해 필요합니다.
 즉, 이미 가지치기 기법이 적용된 모델에 대해서 가지치기 기법을 적용할 때,
 기존의 가지치기 기법이 적용되지 않은 파라미터 값에 대해 가지치기 기법이 영향을 미칠 것으로 예상됩니다.
-<code class="docutils literal notranslate"><span class="pre">PRUNING_TYPE``을</span> <span class="pre">지정한다면,</span> <span class="pre">가지치기</span> <span class="pre">기법을</span> <span class="pre">적용하기</span> <span class="pre">위해</span> <span class="pre">파라미터</span> <span class="pre">값을</span> <span class="pre">올바르게</span> <span class="pre">제거하는</span>
-<span class="pre">``PruningContainer</span></code> (마스크 가지치기 기법을 반복적으로 적용하는 것을 처리하는)를 가능하게 합니다.
+<code class="docutils literal notranslate"><span class="pre">PRUNING_TYPE</span></code> 을 지정한다면, 가지치기 기법을 적용하기 위해 파라미터 값을 올바르게 제거하는
+<code class="docutils literal notranslate"><span class="pre">PruningContainer</span></code> (마스크 가지치기 기법을 반복적으로 적용하는 것을 처리하는)를 가능하게 합니다.
 예를 들어, 다른 모든 항목이 존재하는 텐서를 가지치기 기법을 구현하고 싶을 때,
 (또는, 텐서가 이전에 가지치기 기법에 의해 제거되었거나 남아있는 텐서에 대해)
 한 층의 개별 연결에 작용하며 전체 유닛/채널 (<code class="docutils literal notranslate"><span class="pre">'structured'</span></code>), 또는 다른 파라미터 간
@@ -1050,7 +1051,7 @@ <h2><code class="docutils literal notranslate"><span class="pre">torch.nn.utils.
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>tensor([0., 1., 0., 1., 0., 1., 0., 1., 0., 1.])
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  2.629 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.170 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-pruning-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/ef3541eb2ef78e22efa65b3d6f4ba737/pruning_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">pruning_tutorial.py</span></code></a></p>
@@ -1071,7 +1072,7 @@ <h2><code class="docutils literal notranslate"><span class="pre">torch.nn.utils.
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="../advanced/dynamic_quantization_tutorial.html" class="btn btn-neutral float-right" title="(beta) Dynamic Quantization on an LSTM Word Language Model" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
+        <a href="../advanced/dynamic_quantization_tutorial.html" class="btn btn-neutral float-right" title="(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화" accesskey="n" rel="next">Next <img src="../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
         <a href="../advanced/dispatcher.html" class="btn btn-neutral" title="Dispatcher in C++" accesskey="p" rel="prev"><img src="../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
@@ -1117,12 +1118,12 @@ <h2><code class="docutils literal notranslate"><span class="pre">torch.nn.utils.
 <li><a class="reference internal" href="#id2">요구사항</a></li>
 <li><a class="reference internal" href="#id3">딥러닝 모델 생성</a></li>
 <li><a class="reference internal" href="#id4">모듈 점검</a></li>
-<li><a class="reference internal" href="#id9">모듈 가지치기 기법 적용 예제</a></li>
-<li><a class="reference internal" href="#id11">가지치기 기법 반복 적용</a></li>
-<li><a class="reference internal" href="#id12">가지치기 기법이 적용된 모델의 직렬화</a></li>
-<li><a class="reference internal" href="#id13">가지치기 기법의 재-파라미터화 제거</a></li>
-<li><a class="reference internal" href="#id14">모델 내 여러 파라미터값들에 대하여 가지치기 기법 적용</a></li>
-<li><a class="reference internal" href="#id15">전역 범위에 대한 가지치기 기법 적용</a></li>
+<li><a class="reference internal" href="#id5">모듈 가지치기 기법 적용 예제</a></li>
+<li><a class="reference internal" href="#id7">가지치기 기법 반복 적용</a></li>
+<li><a class="reference internal" href="#id8">가지치기 기법이 적용된 모델의 직렬화</a></li>
+<li><a class="reference internal" href="#id9">가지치기 기법의 재-파라미터화 제거</a></li>
+<li><a class="reference internal" href="#id10">모델 내 여러 파라미터값들에 대하여 가지치기 기법 적용</a></li>
+<li><a class="reference internal" href="#id11">전역 범위에 대한 가지치기 기법 적용</a></li>
 <li><a class="reference internal" href="#torch-nn-utils-prune"><code class="docutils literal notranslate"><span class="pre">torch.nn.utils.prune</span></code> 에서 확장된 맞춤형 가지치기 기법</a></li>
 </ul>
 </li>
@@ -1141,11 +1142,9 @@ <h2><code class="docutils literal notranslate"><span class="pre">torch.nn.utils.
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/quantized_transfer_learning_tutorial.html b/docs/intermediate/quantized_transfer_learning_tutorial.html
index 7ecb44cd4..bcf990306 100644
--- a/docs/intermediate/quantized_transfer_learning_tutorial.html
+++ b/docs/intermediate/quantized_transfer_learning_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -305,27 +305,26 @@ <h1>(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이
 <a class="reference external" href="https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads/quantized_transfer_learning_tutorial.ipynb">Colab 버전</a>.
 이를 통해 아래에 제시된 정보로 실험을 해 볼 수 있습니다.</p>
 </div>
-<p><strong>Author</strong>: <a class="reference external" href="https://github.com/z-a-f">Zafar Takhirov</a></p>
-<p><strong>Reviewed by</strong>: <a class="reference external" href="https://github.com/raghuramank100">Raghuraman Krishnamoorthi</a></p>
-<p><strong>Edited by</strong>: <a class="reference external" href="https://github.com/jlin27">Jessica Lin</a></p>
-<p><strong>번역</strong>: <a class="reference external" href="https://github.com/jjeamin">정재민</a></p>
+<p><strong>Author</strong>: <a class="reference external" href="https://github.com/z-a-f">Zafar Takhirov</a>
+<strong>Reviewed by</strong>: <a class="reference external" href="https://github.com/raghuramank100">Raghuraman Krishnamoorthi</a>
+<strong>Edited by</strong>: <a class="reference external" href="https://github.com/jlin27">Jessica Lin</a>
+<strong>번역</strong>: <a class="reference external" href="https://github.com/jjeamin">정재민</a></p>
 <p>이 튜토리얼은 <a class="reference external" href="https://chsasank.github.io/">Sasank Chilamkurthy</a> 가 작성한 원본
 <a class="reference external" href="https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html">PyTorch Transfer Learning</a> 을 기반으로 합니다.</p>
 <p>전이학습(Transfer learning)은 다른 데이터셋에 적용하기 위해서 미리 학습된 모델을 사용하는 기술을 말합니다.
 전이학습을 사용하는 2가지 주요 방법이 있습니다.</p>
-<ol class="arabic simple">
+<ol class="arabic">
 <li><p><strong>고정 된 특징 추출기로써 ConvNet</strong>: 여기서는 마지막 몇개의 계층(일명 “헤드(the head)”, 일반적으로 완전히 연결된 계층)
 을 제외하고 네트워크의 모든 매개 변수 가중치를 <a class="reference external" href="https://arxiv.org/abs/1706.04983">“고정(freeze)”</a> 합니다.
 마지막 계층은 임의의 가중치로 초기화된 새로운 계층으로 대체되며 오직 이 계층만 학습됩니다.</p></li>
-</ol>
-<p>2. <strong>ConvNet 미세조정(Finetuning)</strong>: 랜덤 초기화 대신, 미리 학습된 네트워크를 이용하여 모델을 초기화합니다.
-이후 평소처럼 학습을 진행하지만 다른 데이터셋을 사용합니다.</p>
-<blockquote>
-<div><p>평소처럼 학습이 진행되지만 다른 데이터셋을 사용합니다.</p>
+<li><p><strong>ConvNet 미세조정(Finetuning)</strong>: 랜덤 초기화 대신, 미리 학습된 네트워크를 이용하여 모델을 초기화합니다.
+이후 평소처럼 학습을 진행하지만 다른 데이터셋을 사용합니다.
+평소처럼 학습이 진행되지만 다른 데이터셋을 사용합니다.</p>
 <p>출력의 수가 다를 수 있기 때문에, 일반적으로 신경망에서 헤드(또는 그 일부)는 교체됩니다.
 이 방법에서는 학습률을 더 작은 수로 설정하는 것이 일반적입니다.
 이는 네트워크가 이미 학습되었기 때문이며 새로운 데이터셋으로 “미세조정(finetuning)”하려면 약간의 변경만이 필요합니다.</p>
-</div></blockquote>
+</li>
+</ol>
 <p>또한 위의 두 방법을 결합할 수도 있습니다.
 먼저 특징 추출기를 고정(freeze)하고 헤드(the head)를 학습시킵니다.
 그런 다음 특징 추출기(또는 그 일부)를 고정해제(unfreeze)하고 학습률을
@@ -337,8 +336,8 @@ <h2>파트 0. 요구사항<a class="headerlink" href="#id2" title="Permalink to
 데이터 불러오기 / 시각화와 같은 “요구사항(prerequisites)”을 검토하겠습니다.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Imports</span>
 <span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">time</span>
 
@@ -398,7 +397,7 @@ <h3>데이터 불러오기<a class="headerlink" href="#id4" title="Permalink to
                                           <span class="n">data_transforms</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
                   <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;val&#39;</span><span class="p">]}</span>
 <span class="n">dataloaders</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">image_datasets</span><span class="p">[</span><span class="n">x</span><span class="p">],</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span>
-                                              <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
+                                              <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
               <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;val&#39;</span><span class="p">]}</span>
 <span class="n">dataset_sizes</span> <span class="o">=</span> <span class="p">{</span><span class="n">x</span><span class="p">:</span> <span class="nb">len</span><span class="p">(</span><span class="n">image_datasets</span><span class="p">[</span><span class="n">x</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;val&#39;</span><span class="p">]}</span>
 <span class="n">class_names</span> <span class="o">=</span> <span class="n">image_datasets</span><span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">classes</span>
@@ -412,19 +411,19 @@ <h3>일부 이미지 시각화하기<a class="headerlink" href="#id6" title="Per
 <p>데이터 보강을 이해하기 위해 일부 학습용 이미지를 시각화 해보겠습니다.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
 
-<span class="k">def</span> <span class="nf">imshow</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)):</span>
+<span class="k">def</span> <span class="nf">imshow</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">)):</span>
   <span class="sd">&quot;&quot;&quot;Imshow for Tensor.&quot;&quot;&quot;</span>
   <span class="n">inp</span> <span class="o">=</span> <span class="n">inp</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
   <span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.485</span><span class="p">,</span> <span class="mf">0.456</span><span class="p">,</span> <span class="mf">0.406</span><span class="p">])</span>
   <span class="n">std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.229</span><span class="p">,</span> <span class="mf">0.224</span><span class="p">,</span> <span class="mf">0.225</span><span class="p">])</span>
   <span class="n">inp</span> <span class="o">=</span> <span class="n">std</span> <span class="o">*</span> <span class="n">inp</span> <span class="o">+</span> <span class="n">mean</span>
   <span class="n">inp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-  <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
+  <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
     <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">inp</span><span class="p">)</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
   <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
-  <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
+  <span class="k">if</span> <span class="n">title</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
     <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
 
 <span class="c1"># 학습 데이터의 배치를 얻습니다.</span>
@@ -453,7 +452,7 @@ <h3>모델 학습을 위한 지원 함수<a class="headerlink" href="#id7" title
 <span class="sd">    model: 학습할 모델</span>
 <span class="sd">    criterion: 최적화 기준(손실)</span>
 <span class="sd">    optimizer: 학습에 사용할 옵티마이저</span>
-<span class="sd">    scheduler: ``torch.optim.lr_scheduler``의 인스턴스</span>
+<span class="sd">    scheduler: ``torch.optim.lr_scheduler`` 의 인스턴스</span>
 <span class="sd">    num_epochs: 에폭의 수</span>
 <span class="sd">    device: 학습을 동작시킬 장치. &#39;cpu&#39; 또는 &#39;cuda&#39;여야 합니다.</span>
 <span class="sd">  &quot;&quot;&quot;</span>
@@ -463,8 +462,8 @@ <h3>모델 학습을 위한 지원 함수<a class="headerlink" href="#id7" title
   <span class="n">best_acc</span> <span class="o">=</span> <span class="mf">0.0</span>
 
   <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_epochs</span><span class="p">):</span>
-    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Epoch {}/{}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">num_epochs</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;-&#39;</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Epoch </span><span class="si">{}</span><span class="s1">/</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">,</span> <span class="n">num_epochs</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&#39;</span> <span class="o">*</span> <span class="mi">10</span><span class="p">)</span>
 
     <span class="c1"># 각 에폭에는 학습 및 검증 단계가 있습니다.</span>
     <span class="k">for</span> <span class="n">phase</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;train&#39;</span><span class="p">,</span> <span class="s1">&#39;val&#39;</span><span class="p">]:</span>
@@ -505,7 +504,7 @@ <h3>모델 학습을 위한 지원 함수<a class="headerlink" href="#id7" title
       <span class="n">epoch_loss</span> <span class="o">=</span> <span class="n">running_loss</span> <span class="o">/</span> <span class="n">dataset_sizes</span><span class="p">[</span><span class="n">phase</span><span class="p">]</span>
       <span class="n">epoch_acc</span> <span class="o">=</span> <span class="n">running_corrects</span><span class="o">.</span><span class="n">double</span><span class="p">()</span> <span class="o">/</span> <span class="n">dataset_sizes</span><span class="p">[</span><span class="n">phase</span><span class="p">]</span>
 
-      <span class="k">print</span><span class="p">(</span><span class="s1">&#39;{} Loss: {:.4f} Acc: {:.4f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+      <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{}</span><span class="s1"> Loss: </span><span class="si">{:.4f}</span><span class="s1"> Acc: </span><span class="si">{:.4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
         <span class="n">phase</span><span class="p">,</span> <span class="n">epoch_loss</span><span class="p">,</span> <span class="n">epoch_acc</span><span class="p">))</span>
 
       <span class="c1"># 모델 복사하기</span>
@@ -513,12 +512,12 @@ <h3>모델 학습을 위한 지원 함수<a class="headerlink" href="#id7" title
         <span class="n">best_acc</span> <span class="o">=</span> <span class="n">epoch_acc</span>
         <span class="n">best_model_wts</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
 
-    <span class="k">print</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">()</span>
 
   <span class="n">time_elapsed</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">-</span> <span class="n">since</span>
-  <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Training complete in {:.0f}m {:.0f}s&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+  <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Training complete in </span><span class="si">{:.0f}</span><span class="s1">m </span><span class="si">{:.0f}</span><span class="s1">s&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
     <span class="n">time_elapsed</span> <span class="o">//</span> <span class="mi">60</span><span class="p">,</span> <span class="n">time_elapsed</span> <span class="o">%</span> <span class="mi">60</span><span class="p">))</span>
-  <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Best val Acc: {:4f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_acc</span><span class="p">))</span>
+  <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Best val Acc: </span><span class="si">{:4f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_acc</span><span class="p">))</span>
 
   <span class="c1"># 최적의 모델 가중치 불러오기</span>
   <span class="n">model</span><span class="o">.</span><span class="n">load_state_dict</span><span class="p">(</span><span class="n">best_model_wts</span><span class="p">)</span>
@@ -546,7 +545,7 @@ <h3>모델 예측을 시각화하기 위한 지원 함수<a class="headerlink" h
       <span class="k">for</span> <span class="n">jdx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">imgs</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">0</span><span class="p">]):</span>
         <span class="n">imshow</span><span class="p">(</span><span class="n">imgs</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">jdx</span><span class="p">],</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="n">current_row</span><span class="p">,</span> <span class="n">current_col</span><span class="p">])</span>
         <span class="n">ax</span><span class="p">[</span><span class="n">current_row</span><span class="p">,</span> <span class="n">current_col</span><span class="p">]</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="p">[</span><span class="n">current_row</span><span class="p">,</span> <span class="n">current_col</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;predicted: {}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">class_names</span><span class="p">[</span><span class="n">preds</span><span class="p">[</span><span class="n">jdx</span><span class="p">]]))</span>
+        <span class="n">ax</span><span class="p">[</span><span class="n">current_row</span><span class="p">,</span> <span class="n">current_col</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;predicted: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">class_names</span><span class="p">[</span><span class="n">preds</span><span class="p">[</span><span class="n">jdx</span><span class="p">]]))</span>
 
         <span class="n">current_col</span> <span class="o">+=</span> <span class="mi">1</span>
         <span class="k">if</span> <span class="n">current_col</span> <span class="o">&gt;=</span> <span class="n">cols</span><span class="p">:</span>
@@ -566,12 +565,12 @@ <h2>파트 1. 양자화된 특징 추출기(Quantized Feature Extractor)를 기
 학습합니다. 부동 소수점 모델과 다르게 양자화된 모델에는 학습 가능한 매개 변수가 없으므로
 requires_grad = False를 설정할 필요가 없습니다. 자세한 내용은 <a class="reference external" href="https://pytorch.org/docs/stable/quantization.html">설명서</a> 를 참조하세요.</p>
 <p>미리 학습된 모델을 불러옵니다: 이번 예제에서는 <a class="reference external" href="https://pytorch.org/hub/pytorch_vision_resnet/">ResNet-18</a> 을 사용할 것입니다.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models.quantization</span> <span class="kn">as</span> <span class="nn">models</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models.quantization</span> <span class="k">as</span> <span class="nn">models</span>
 
-<span class="c1"># 나중에 사용할 수 있게 `fc`에 필터의 수가 필요합니다.</span>
+<span class="c1"># 나중에 사용할 수 있게 `fc` 에 필터의 수가 필요합니다.</span>
 <span class="c1"># 여기서 각 출력 샘플의 크기는 2로 설정합니다.</span>
 <span class="c1"># 또한, nn.Linear(num_ftrs, len(class_names))로 일반화 할 수 있습니다.</span>
-<span class="n">model_fe</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">quantize</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">model_fe</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quantize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">num_ftrs</span> <span class="o">=</span> <span class="n">model_fe</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">in_features</span>
 </pre></div>
 </div>
@@ -579,7 +578,7 @@ <h2>파트 1. 양자화된 특징 추출기(Quantized Feature Extractor)를 기
 그러나 특징 추출기만 사용하기 때문에 역양자화 계층은 선형 계층(헤드) 바로 전으로 이동시켜야 합니다.
 가장 쉬운 방법은 모델을 <code class="docutils literal notranslate"><span class="pre">nn.Sequential</span></code> 모듈로 감싸는 것입니다.</p>
 <p>첫번째 단계는 ResNet 모델에서 특징 추출기를 분리하는 것입니다.
-이 예제에서는 <a href="#id10"><span class="problematic" id="id11">``</span></a>fc``를 제외한 모든 계층을 특징 추출기로 사용해야 하지만, 실제로는 필요한 만큼 많은 부분을 사용할 수 있습니다.
+이 예제에서는 <code class="docutils literal notranslate"><span class="pre">fc</span></code> 를 제외한 모든 계층을 특징 추출기로 사용해야 하지만, 실제로는 필요한 만큼 많은 부분을 사용할 수 있습니다.
 이것은 합성곱 계층 중 일부를 교체하려는 경우에도 유용합니다.</p>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -624,7 +623,7 @@ <h2>파트 1. 양자화된 특징 추출기(Quantized Feature Extractor)를 기
 <p>현재 양자화된 모델은 CPU에서만 실행할 수 있습니다.
 그러나 모델의 양자화 되지 않은 부분은 GPU로 보낼 수 있습니다.</p>
 </div>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.optim</span> <span class="kn">as</span> <span class="nn">optim</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
 <span class="n">new_model</span> <span class="o">=</span> <span class="n">create_combined_model</span><span class="p">(</span><span class="n">model_fe</span><span class="p">)</span>
 <span class="n">new_model</span> <span class="o">=</span> <span class="n">new_model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s1">&#39;cpu&#39;</span><span class="p">)</span>
 
@@ -637,8 +636,8 @@ <h2>파트 1. 양자화된 특징 추출기(Quantized Feature Extractor)를 기
 <span class="n">exp_lr_scheduler</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">StepLR</span><span class="p">(</span><span class="n">optimizer_ft</span><span class="p">,</span> <span class="n">step_size</span><span class="o">=</span><span class="mi">7</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="mf">0.1</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="section" id="id12">
-<h3>학습과 평가<a class="headerlink" href="#id12" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id10">
+<h3>학습과 평가<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h3>
 <p>이 단계는 CPU에서 약 15 ~ 25분 걸립니다. 양자화된 모델은 CPU에서만 실행되기 때문에
 GPU에서는 훈련을 실행할 수 없습니다.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">new_model</span> <span class="o">=</span> <span class="n">train_model</span><span class="p">(</span><span class="n">new_model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer_ft</span><span class="p">,</span> <span class="n">exp_lr_scheduler</span><span class="p">,</span>
@@ -685,7 +684,7 @@ <h2>파트 2. 양자화 가능한 모델 미세조정(Finetuning)<a class="heade
 <p>랜덤 초기화 때문에 여러분의 결과가 튜토리얼에 표시된 결과와 다를 수 있습니다.</p>
 </div>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># `quantize=False` 를 주목하세요</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">quantize</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">progress</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quantize</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 <span class="n">num_ftrs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fc</span><span class="o">.</span><span class="n">in_features</span>
 
 <span class="c1"># 1 단계</span>
@@ -695,17 +694,17 @@ <h2>파트 2. 양자화 가능한 모델 미세조정(Finetuning)<a class="heade
 <span class="n">model_ft</span> <span class="o">=</span> <span class="n">create_combined_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 <span class="n">model_ft</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">qconfig</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">default_qat_qconfig</span>  <span class="c1"># Use default QAT configuration</span>
 <span class="c1"># 3 단계</span>
-<span class="n">model_ft</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare_qat</span><span class="p">(</span><span class="n">model_ft</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">model_ft</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quantization</span><span class="o">.</span><span class="n">prepare_qat</span><span class="p">(</span><span class="n">model_ft</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
-<div class="section" id="id13">
-<h3>모델 미세조정<a class="headerlink" href="#id13" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id11">
+<h3>모델 미세조정<a class="headerlink" href="#id11" title="Permalink to this headline">¶</a></h3>
 <p>현재 튜토리얼에서는 전체 모델이 미세조정 되었습니다.
 일반적으로 이것은 더 높은 정확도로 이어질 것입니다.
 그러나 여기서는 크기가 작은 학습용 데이터셋을 사용했기 때문에 결국 과적합하게 됩니다.</p>
 <p>4 단계. 모델 미세조정하기</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model_ft</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
-  <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="bp">True</span>
+  <span class="n">param</span><span class="o">.</span><span class="n">requires_grad</span> <span class="o">=</span> <span class="kc">True</span>
 
 <span class="n">model_ft</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>  <span class="c1"># GPU에서 미세조정(Finetuning) 할 수 있습니다.</span>
 
@@ -726,7 +725,7 @@ <h3>모델 미세조정<a class="headerlink" href="#id13" title="Permalink to th
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="kn">import</span> <span class="n">convert</span>
 <span class="n">model_ft_tuned</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
 
-<span class="n">model_quantized_and_trained</span> <span class="o">=</span> <span class="n">convert</span><span class="p">(</span><span class="n">model_ft_tuned</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
+<span class="n">model_quantized_and_trained</span> <span class="o">=</span> <span class="n">convert</span><span class="p">(</span><span class="n">model_ft_tuned</span><span class="p">,</span> <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>양자화된 모델이 일부 이미지에서 어떻게 동작하는지 살펴보겠습니다.</p>
@@ -801,11 +800,11 @@ <h3>모델 미세조정<a class="headerlink" href="#id13" title="Permalink to th
 </ul>
 </li>
 <li><a class="reference internal" href="#quantized-feature-extractor">파트 1. 양자화된 특징 추출기(Quantized Feature Extractor)를 기반으로 사용자 지정 분류기 훈련하기</a><ul>
-<li><a class="reference internal" href="#id12">학습과 평가</a></li>
+<li><a class="reference internal" href="#id10">학습과 평가</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#finetuning">파트 2. 양자화 가능한 모델 미세조정(Finetuning)</a><ul>
-<li><a class="reference internal" href="#id13">모델 미세조정</a></li>
+<li><a class="reference internal" href="#id11">모델 미세조정</a></li>
 </ul>
 </li>
 </ul>
@@ -825,11 +824,9 @@ <h3>모델 미세조정<a class="headerlink" href="#id13" title="Permalink to th
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/reinforcement_q_learning.html b/docs/intermediate/reinforcement_q_learning.html
index dee9daef0..364538253 100644
--- a/docs/intermediate/reinforcement_q_learning.html
+++ b/docs/intermediate/reinforcement_q_learning.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -350,9 +350,9 @@
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">namedtuple</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">count</span>
-<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">namedtuple</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">count</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
@@ -366,7 +366,7 @@
 <span class="c1"># matplotlib 설정</span>
 <span class="n">is_ipython</span> <span class="o">=</span> <span class="s1">&#39;inline&#39;</span> <span class="ow">in</span> <span class="n">matplotlib</span><span class="o">.</span><span class="n">get_backend</span><span class="p">()</span>
 <span class="k">if</span> <span class="n">is_ipython</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">IPython</span> <span class="k">import</span> <span class="n">display</span>
+    <span class="kn">from</span> <span class="nn">IPython</span> <span class="kn">import</span> <span class="n">display</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>
 
@@ -395,7 +395,7 @@ <h2>재현 메모리(Replay Memory)<a class="headerlink" href="#replay-memory" t
 
 <span class="k">class</span> <span class="nc">ReplayMemory</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">capacity</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">capacity</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">capacity</span> <span class="o">=</span> <span class="n">capacity</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">memory</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">position</span> <span class="o">=</span> <span class="mi">0</span>
@@ -410,7 +410,7 @@ <h2>재현 메모리(Replay Memory)<a class="headerlink" href="#replay-memory" t
     <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
         <span class="k">return</span> <span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -467,7 +467,7 @@ <h3>Q-네트워크<a class="headerlink" href="#q" title="Permalink to this headl
 결과적으로 네트워크는 주어진 현재 입력에서 각 행동의 <em>기대값</em> 을 예측하려고 합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DQN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">outputs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">outputs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">DQN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">bn1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">16</span><span class="p">)</span>
@@ -837,11 +837,10 @@ <h3>학습 루프<a class="headerlink" href="#id6" title="Permalink to this head
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
+         <script async="async" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
      
 
   
diff --git a/docs/intermediate/rpc_async_execution.html b/docs/intermediate/rpc_async_execution.html
index 674a1f2bf..002e07f56 100644
--- a/docs/intermediate/rpc_async_execution.html
+++ b/docs/intermediate/rpc_async_execution.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -376,7 +376,7 @@ <h2>Batch-Updating Parameter Server<a class="headerlink" href="#batch-updating-p
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">threading</span>
 <span class="kn">import</span> <span class="nn">torchvision</span>
 <span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="kn">as</span> <span class="nn">rpc</span>
+<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</span>
 
 <span class="n">num_classes</span><span class="p">,</span> <span class="n">batch_update_size</span> <span class="o">=</span> <span class="mi">30</span><span class="p">,</span> <span class="mi">5</span>
@@ -396,7 +396,7 @@ <h2>Batch-Updating Parameter Server<a class="headerlink" href="#batch-updating-p
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span>
 
     <span class="nd">@staticmethod</span>
-    <span class="nd">@rpc.functions.async_execution</span>
+    <span class="nd">@rpc</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">async_execution</span>
     <span class="k">def</span> <span class="nf">update_and_fetch_model</span><span class="p">(</span><span class="n">ps_rref</span><span class="p">,</span> <span class="n">grads</span><span class="p">):</span>
         <span class="c1"># Using the RRef to retrieve the local PS instance</span>
         <span class="bp">self</span> <span class="o">=</span> <span class="n">ps_rref</span><span class="o">.</span><span class="n">local_value</span><span class="p">()</span>
@@ -499,8 +499,8 @@ <h2>Batch-Processing CartPole Solver<a class="headerlink" href="#batch-processin
 function contains states from multiple observers and hence the dimension needs
 to change properly. Everything else stays intact.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="kn">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="kn">as</span> <span class="nn">F</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 
 <span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span><span class="n">description</span><span class="o">=</span><span class="s1">&#39;PyTorch RPC Batch RL example&#39;</span><span class="p">)</span>
 <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span><span class="s1">&#39;--gamma&#39;</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="nb">float</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">metavar</span><span class="o">=</span><span class="s1">&#39;G&#39;</span><span class="p">,</span>
@@ -514,7 +514,7 @@ <h2>Batch-Processing CartPole Solver<a class="headerlink" href="#batch-processin
 <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span>
 
 <span class="k">class</span> <span class="nc">Policy</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Policy</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">affine1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
@@ -535,10 +535,10 @@ <h2>Batch-Processing CartPole Solver<a class="headerlink" href="#batch-processin
 which will be presented shortly, and this function will be decorated with
 <a class="reference external" href="https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.functions.async_execution">&#64;rpc.functions.async_execution</a>.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gym</span>
-<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="kn">as</span> <span class="nn">rpc</span>
+<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
 
 <span class="k">class</span> <span class="nc">Observer</span><span class="p">:</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">rpc</span><span class="o">.</span><span class="n">get_worker_info</span><span class="p">()</span><span class="o">.</span><span class="n">id</span> <span class="o">-</span> <span class="mi">1</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">env</span> <span class="o">=</span> <span class="n">gym</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s1">&#39;CartPole-v1&#39;</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">env</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span>
@@ -606,7 +606,7 @@ <h2>Batch-Processing CartPole Solver<a class="headerlink" href="#batch-processin
 <span class="kn">from</span> <span class="nn">torch.distributed.rpc</span> <span class="kn">import</span> <span class="n">RRef</span>
 
 <span class="k">class</span> <span class="nc">Agent</span><span class="p">:</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">batch</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">ob_rrefs</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">agent_rref</span> <span class="o">=</span> <span class="n">RRef</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">rewards</span> <span class="o">=</span> <span class="p">{}</span>
@@ -658,7 +658,7 @@ <h2>Batch-Processing CartPole Solver<a class="headerlink" href="#batch-processin
     <span class="o">...</span>
 
     <span class="nd">@staticmethod</span>
-    <span class="nd">@rpc.functions.async_execution</span>
+    <span class="nd">@rpc</span><span class="o">.</span><span class="n">functions</span><span class="o">.</span><span class="n">async_execution</span>
     <span class="k">def</span> <span class="nf">select_action_batch</span><span class="p">(</span><span class="n">agent_rref</span><span class="p">,</span> <span class="n">ob_id</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
         <span class="bp">self</span> <span class="o">=</span> <span class="n">agent_rref</span><span class="o">.</span><span class="n">local_value</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">states</span><span class="p">[</span><span class="n">ob_id</span><span class="p">]</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
@@ -730,7 +730,7 @@ <h2>Batch-Processing CartPole Solver<a class="headerlink" href="#batch-processin
 waiting for commands from the agent. Please refer to the
 <a class="reference external" href="https://github.com/pytorch/examples/tree/master/distributed/rpc">examples</a>
 repo for the full implementation.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">run_worker</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">n_episode</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">print_log</span><span class="o">=</span><span class="bp">True</span><span class="p">):</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">run_worker</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">n_episode</span><span class="p">,</span> <span class="n">batch</span><span class="p">,</span> <span class="n">print_log</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
     <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;MASTER_ADDR&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;localhost&#39;</span>
     <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;MASTER_PORT&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;29500&#39;</span>
     <span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
@@ -742,7 +742,7 @@ <h2>Batch-Processing CartPole Solver<a class="headerlink" href="#batch-processin
             <span class="n">last_reward</span><span class="p">,</span> <span class="n">running_reward</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">run_episode</span><span class="p">(</span><span class="n">n_steps</span><span class="o">=</span><span class="n">NUM_STEPS</span><span class="p">)</span>
 
             <span class="k">if</span> <span class="n">print_log</span><span class="p">:</span>
-                <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Episode {}</span><span class="se">\t</span><span class="s1">Last reward: {:.2f}</span><span class="se">\t</span><span class="s1">Average reward: {:.2f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Episode </span><span class="si">{}</span><span class="se">\t</span><span class="s1">Last reward: </span><span class="si">{:.2f}</span><span class="se">\t</span><span class="s1">Average reward: </span><span class="si">{:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                     <span class="n">i_episode</span><span class="p">,</span> <span class="n">last_reward</span><span class="p">,</span> <span class="n">running_reward</span><span class="p">))</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="c1"># other ranks are the observer</span>
@@ -754,18 +754,18 @@ <h2>Batch-Processing CartPole Solver<a class="headerlink" href="#batch-processin
 <span class="k">def</span> <span class="nf">main</span><span class="p">():</span>
     <span class="k">for</span> <span class="n">world_size</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">12</span><span class="p">):</span>
         <span class="n">delays</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="p">[</span><span class="bp">True</span><span class="p">,</span> <span class="bp">False</span><span class="p">]:</span>
+        <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="p">[</span><span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]:</span>
             <span class="n">tik</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
             <span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span>
                 <span class="n">run_worker</span><span class="p">,</span>
                 <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">num_episode</span><span class="p">,</span> <span class="n">batch</span><span class="p">),</span>
                 <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span>
-                <span class="n">join</span><span class="o">=</span><span class="bp">True</span>
+                <span class="n">join</span><span class="o">=</span><span class="kc">True</span>
             <span class="p">)</span>
             <span class="n">tok</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
             <span class="n">delays</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tok</span> <span class="o">-</span> <span class="n">tik</span><span class="p">)</span>
 
-        <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;{world_size}, {delays[0]}, {delays[1]}&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">world_size</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">delays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">delays</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
@@ -865,11 +865,9 @@ <h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/rpc_param_server_tutorial.html b/docs/intermediate/rpc_param_server_tutorial.html
index 58b8ef872..cfb3c133a 100644
--- a/docs/intermediate/rpc_param_server_tutorial.html
+++ b/docs/intermediate/rpc_param_server_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -315,11 +315,11 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
 <span class="kn">from</span> <span class="nn">threading</span> <span class="kn">import</span> <span class="n">Lock</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.distributed.autograd</span> <span class="kn">as</span> <span class="nn">dist_autograd</span>
-<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="kn">as</span> <span class="nn">rpc</span>
-<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="kn">as</span> <span class="nn">mp</span>
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="kn">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="kn">as</span> <span class="nn">F</span>
+<span class="kn">import</span> <span class="nn">torch.distributed.autograd</span> <span class="k">as</span> <span class="nn">dist_autograd</span>
+<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
+<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 <span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</span>
 <span class="kn">from</span> <span class="nn">torch.distributed.optim</span> <span class="kn">import</span> <span class="n">DistributedOptimizer</span>
 <span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
@@ -329,11 +329,11 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
 <span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
     <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_gpus</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Using {num_gpus} GPUs to train&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Using </span><span class="si">{</span><span class="n">num_gpus</span><span class="si">}</span><span class="s2"> GPUs to train&quot;</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">num_gpus</span> <span class="o">=</span> <span class="n">num_gpus</span>
         <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span>
             <span class="s2">&quot;cuda:0&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_gpus</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
-        <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Putting first 2 convs on {str(device)}&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Putting first 2 convs on </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
         <span class="c1"># Put conv layers on the first cuda device, or CPU if no cuda device</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
@@ -341,7 +341,7 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
         <span class="k">if</span> <span class="s2">&quot;cuda&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="ow">and</span> <span class="n">num_gpus</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
             <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:1&quot;</span><span class="p">)</span>
 
-        <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Putting rest of layers on {str(device)}&quot;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Putting rest of layers on </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dropout1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="mf">0.25</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">dropout2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">9216</span><span class="p">,</span> <span class="mi">128</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
@@ -435,7 +435,7 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
 </div>
 <p>Finally, we’ll create methods to initialize our parameter server. Note that there will only be one instance of a parameter server across all processes, and all trainers will talk to the same parameter server and update the same stored model. As seen in <code class="docutils literal notranslate"><span class="pre">run_parameter_server</span></code>, the server itself does not take any independent actions; it waits for requests from trainers (which are yet to be defined) and responds to them by running the requested function.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># The global parameter server instance.</span>
-<span class="n">param_server</span> <span class="o">=</span> <span class="bp">None</span>
+<span class="n">param_server</span> <span class="o">=</span> <span class="kc">None</span>
 <span class="c1"># A lock to ensure we only have one parameter server.</span>
 <span class="n">global_lock</span> <span class="o">=</span> <span class="n">Lock</span><span class="p">()</span>
 
@@ -458,11 +458,11 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
     <span class="c1"># rpc.shutdown() will wait for all workers to complete by default, which</span>
     <span class="c1"># in this case means that the parameter server will wait for all trainers</span>
     <span class="c1"># to complete, and then exit.</span>
-    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;PS master initializing RPC&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;PS master initializing RPC&quot;</span><span class="p">)</span>
     <span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;parameter_server&quot;</span><span class="p">,</span> <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="o">=</span><span class="n">world_size</span><span class="p">)</span>
-    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;RPC initialized! Running parameter server...&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;RPC initialized! Running parameter server...&quot;</span><span class="p">)</span>
     <span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
-    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;RPC shutdown on parameter server.&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;RPC shutdown on parameter server.&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Note that above, <code class="docutils literal notranslate"><span class="pre">rpc.shutdown()</span></code> will not immediately shut down the Parameter Server. Instead, it will wait for all workers (trainers in this case) to also call into <code class="docutils literal notranslate"><span class="pre">rpc.shutdown()</span></code>. This gives us the guarantee that the parameter server will not go offline before all trainers (yet to be define) have completed their training process.</p>
@@ -520,7 +520,7 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
             <span class="n">target</span> <span class="o">=</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">model_output</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
             <span class="n">loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">model_output</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
             <span class="k">if</span> <span class="n">i</span> <span class="o">%</span> <span class="mi">5</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Rank {rank} training batch {i} loss {loss.item()}&quot;</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> training batch </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2"> loss </span><span class="si">{</span><span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
             <span class="n">dist_autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span><span class="n">cid</span><span class="p">,</span> <span class="p">[</span><span class="n">loss</span><span class="p">])</span>
             <span class="c1"># Ensure that dist autograd ran successfully and gradients were</span>
             <span class="c1"># returned.</span>
@@ -530,8 +530,8 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
                 <span class="n">cid</span><span class="p">)</span> <span class="o">!=</span> <span class="p">{}</span>
             <span class="n">opt</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">cid</span><span class="p">)</span>
 
-     <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Training complete!&quot;</span><span class="p">)</span>
-     <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Getting accuracy....&quot;</span><span class="p">)</span>
+     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training complete!&quot;</span><span class="p">)</span>
+     <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Getting accuracy....&quot;</span><span class="p">)</span>
      <span class="n">get_accuracy</span><span class="p">(</span><span class="n">test_loader</span><span class="p">,</span> <span class="n">net</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -545,24 +545,24 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
     <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
         <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">test_loader</span><span class="p">):</span>
             <span class="n">out</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
-            <span class="n">pred</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+            <span class="n">pred</span> <span class="o">=</span> <span class="n">out</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
             <span class="n">pred</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
             <span class="n">correct</span> <span class="o">=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
             <span class="n">correct_sum</span> <span class="o">+=</span> <span class="n">correct</span>
 
-    <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Accuracy {correct_sum / len(test_loader.dataset)}&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Accuracy </span><span class="si">{</span><span class="n">correct_sum</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Next, similar to how we defined <code class="docutils literal notranslate"><span class="pre">run_parameter_server</span></code> as the main loop for our <code class="docutils literal notranslate"><span class="pre">ParameterServer</span></code> that is responsible for initializing RPC, let’s define a similar loop for our trainers. The difference will be that our trainers must run the training loop we defined above:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Main loop for trainers.</span>
 <span class="k">def</span> <span class="nf">run_worker</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">,</span> <span class="n">num_gpus</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">test_loader</span><span class="p">):</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Worker rank {rank} initializing RPC&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Worker rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> initializing RPC&quot;</span><span class="p">)</span>
     <span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
-        <span class="n">name</span><span class="o">=</span><span class="n">f</span><span class="s2">&quot;trainer_{rank}&quot;</span><span class="p">,</span>
+        <span class="n">name</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;trainer_</span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span>
         <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span>
         <span class="n">world_size</span><span class="o">=</span><span class="n">world_size</span><span class="p">)</span>
 
-    <span class="k">print</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Worker {rank} done initializing RPC&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Worker </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> done initializing RPC&quot;</span><span class="p">)</span>
 
     <span class="n">run_training_loop</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">num_gpus</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">test_loader</span><span class="p">)</span>
     <span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
@@ -582,7 +582,7 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
     <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
         <span class="s2">&quot;rank&quot;</span><span class="p">,</span>
         <span class="nb">type</span><span class="o">=</span><span class="nb">int</span><span class="p">,</span>
-        <span class="n">default</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span>
+        <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
         <span class="n">help</span><span class="o">=</span><span class="s2">&quot;Global rank of this process. Pass in 0 for master.&quot;</span><span class="p">)</span>
     <span class="n">parser</span><span class="o">.</span><span class="n">add_argument</span><span class="p">(</span>
         <span class="s2">&quot;num_gpus&quot;</span><span class="p">,</span>
@@ -604,8 +604,8 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
 <span class="s2">        provided. Master must be able to accept network traffic on the host and port.&quot;&quot;&quot;</span><span class="p">)</span>
 
     <span class="n">args</span> <span class="o">=</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse_args</span><span class="p">()</span>
-    <span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">rank</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">,</span> <span class="s2">&quot;must provide rank argument.&quot;</span>
-    <span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">num_gpus</span> <span class="o">&lt;=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">f</span><span class="s2">&quot;Only 0-2 GPUs currently supported (got {args.num_gpus}).&quot;</span>
+    <span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">rank</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;must provide rank argument.&quot;</span>
+    <span class="k">assert</span> <span class="n">args</span><span class="o">.</span><span class="n">num_gpus</span> <span class="o">&lt;=</span> <span class="mi">3</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;Only 0-2 GPUs currently supported (got </span><span class="si">{</span><span class="n">args</span><span class="o">.</span><span class="n">num_gpus</span><span class="si">}</span><span class="s2">).&quot;</span>
     <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s1">&#39;MASTER_ADDR&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">master_addr</span>
     <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;MASTER_PORT&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">args</span><span class="o">.</span><span class="n">master_port</span>
 </pre></div>
@@ -620,22 +620,22 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
 <span class="k">else</span><span class="p">:</span>
     <span class="c1"># Get data to train on</span>
     <span class="n">train_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
-        <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="s1">&#39;../data&#39;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+        <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="s1">&#39;../data&#39;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                        <span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
                            <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                            <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,))</span>
                        <span class="p">])),</span>
-        <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,)</span>
+        <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,)</span>
     <span class="n">test_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
         <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span>
             <span class="s1">&#39;../data&#39;</span><span class="p">,</span>
-            <span class="n">train</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
+            <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
             <span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
                     <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                     <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,))</span>
                         <span class="p">])),</span>
         <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span>
-        <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <span class="p">)</span>
     <span class="c1"># start training worker on this node</span>
     <span class="n">p</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">Process</span><span class="p">(</span>
@@ -722,11 +722,9 @@ <h1>Implementing a Parameter Server Using Distributed RPC Framework<a class="hea
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/rpc_tutorial.html b/docs/intermediate/rpc_tutorial.html
index 1320351cb..0406f077b 100644
--- a/docs/intermediate/rpc_tutorial.html
+++ b/docs/intermediate/rpc_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -353,8 +353,8 @@ <h2>Distributed Reinforcement Learning using RPC and RRef<a class="headerlink" h
 <a class="reference external" href="https://github.com/pytorch/examples/blob/master/reinforcement_learning">example</a>
 as shown below. We will skip details of the <code class="docutils literal notranslate"><span class="pre">Policy</span></code> design, and focus on RPC
 usages.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="kn">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="kn">as</span> <span class="nn">F</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 
 <span class="k">class</span> <span class="nc">Policy</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
 
@@ -410,7 +410,7 @@ <h2>Distributed Reinforcement Learning using RPC and RRef<a class="headerlink" h
 simple and the two steps explicit in this example.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">argparse</span>
 <span class="kn">import</span> <span class="nn">gym</span>
-<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="kn">as</span> <span class="nn">rpc</span>
+<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
 
 <span class="n">parser</span> <span class="o">=</span> <span class="n">argparse</span><span class="o">.</span><span class="n">ArgumentParser</span><span class="p">(</span>
     <span class="n">description</span><span class="o">=</span><span class="s2">&quot;RPC Reinforcement Learning Example&quot;</span><span class="p">,</span>
@@ -460,11 +460,11 @@ <h2>Distributed Reinforcement Learning using RPC and RRef<a class="headerlink" h
 there is any live user of that <code class="docutils literal notranslate"><span class="pre">RRef</span></code>. Please refer to the <code class="docutils literal notranslate"><span class="pre">RRef</span></code>
 <a class="reference external" href="https://pytorch.org/docs/master/notes/rref.html">design doc</a> for details.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gym</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="kn">as</span> <span class="nn">rpc</span>
-<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="kn">as</span> <span class="nn">optim</span>
+<span class="kn">import</span> <span class="nn">torch.distributed.rpc</span> <span class="k">as</span> <span class="nn">rpc</span>
+<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
 <span class="kn">from</span> <span class="nn">torch.distributed.rpc</span> <span class="kn">import</span> <span class="n">RRef</span><span class="p">,</span> <span class="n">rpc_async</span><span class="p">,</span> <span class="n">remote</span>
 <span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">Categorical</span>
 
@@ -583,7 +583,7 @@ <h2>Distributed Reinforcement Learning using RPC and RRef<a class="headerlink" h
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">count</span>
 
-<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="kn">as</span> <span class="nn">mp</span>
+<span class="kn">import</span> <span class="nn">torch.multiprocessing</span> <span class="k">as</span> <span class="nn">mp</span>
 
 <span class="n">AGENT_NAME</span> <span class="o">=</span> <span class="s2">&quot;agent&quot;</span>
 <span class="n">OBSERVER_NAME</span><span class="o">=</span><span class="s2">&quot;obs&quot;</span>
@@ -603,11 +603,11 @@ <h2>Distributed Reinforcement Learning using RPC and RRef<a class="headerlink" h
             <span class="n">last_reward</span> <span class="o">=</span> <span class="n">agent</span><span class="o">.</span><span class="n">finish_episode</span><span class="p">()</span>
 
             <span class="k">if</span> <span class="n">i_episode</span> <span class="o">%</span> <span class="n">args</span><span class="o">.</span><span class="n">log_interval</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Episode {}</span><span class="se">\t</span><span class="s1">Last reward: {:.2f}</span><span class="se">\t</span><span class="s1">Average reward: {:.2f}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Episode </span><span class="si">{}</span><span class="se">\t</span><span class="s1">Last reward: </span><span class="si">{:.2f}</span><span class="se">\t</span><span class="s1">Average reward: </span><span class="si">{:.2f}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
                       <span class="n">i_episode</span><span class="p">,</span> <span class="n">last_reward</span><span class="p">,</span> <span class="n">agent</span><span class="o">.</span><span class="n">running_reward</span><span class="p">))</span>
 
             <span class="k">if</span> <span class="n">agent</span><span class="o">.</span><span class="n">running_reward</span> <span class="o">&gt;</span> <span class="n">agent</span><span class="o">.</span><span class="n">reward_threshold</span><span class="p">:</span>
-                <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Solved! Running reward is now {}!&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">agent</span><span class="o">.</span><span class="n">running_reward</span><span class="p">))</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Solved! Running reward is now </span><span class="si">{}</span><span class="s2">!&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">agent</span><span class="o">.</span><span class="n">running_reward</span><span class="p">))</span>
                 <span class="k">break</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="c1"># other ranks are the observer</span>
@@ -622,7 +622,7 @@ <h2>Distributed Reinforcement Learning using RPC and RRef<a class="headerlink" h
     <span class="n">run_worker</span><span class="p">,</span>
     <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span> <span class="p">),</span>
     <span class="n">nprocs</span><span class="o">=</span><span class="n">args</span><span class="o">.</span><span class="n">world_size</span><span class="p">,</span>
-    <span class="n">join</span><span class="o">=</span><span class="bp">True</span>
+    <span class="n">join</span><span class="o">=</span><span class="kc">True</span>
 <span class="p">)</span>
 </pre></div>
 </div>
@@ -847,7 +847,7 @@ <h2>Distributed RNN using Distributed Autograd and Distributed Optimizer<a class
                 <span class="c1"># not necessary to zero grads since they are</span>
                 <span class="c1"># accumulated into the distributed autograd context</span>
                 <span class="c1"># which is reset every iteration.</span>
-        <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Training epoch {}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Training epoch </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">epoch</span><span class="p">))</span>
 </pre></div>
 </div>
 <p>Finally, let’s add some glue code to launch the parameter server and the trainer
@@ -869,7 +869,7 @@ <h2>Distributed RNN using Distributed Autograd and Distributed Optimizer<a class
 
 <span class="k">if</span> <span class="vm">__name__</span><span class="o">==</span><span class="s2">&quot;__main__&quot;</span><span class="p">:</span>
     <span class="n">world_size</span> <span class="o">=</span> <span class="mi">2</span>
-    <span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">run_worker</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="p">),</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+    <span class="n">mp</span><span class="o">.</span><span class="n">spawn</span><span class="p">(</span><span class="n">run_worker</span><span class="p">,</span> <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">world_size</span><span class="p">,</span> <span class="p">),</span> <span class="n">nprocs</span><span class="o">=</span><span class="n">world_size</span><span class="p">,</span> <span class="n">join</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
@@ -945,11 +945,9 @@ <h2>Distributed RNN using Distributed Autograd and Distributed Optimizer<a class
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/seq2seq_translation_tutorial.html b/docs/intermediate/seq2seq_translation_tutorial.html
index ea0f7a8a0..15039eb13 100644
--- a/docs/intermediate/seq2seq_translation_tutorial.html
+++ b/docs/intermediate/seq2seq_translation_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -374,8 +374,8 @@
 <li><p><a class="reference external" href="https://arxiv.org/abs/1506.05869">A Neural Conversational Model</a></p></li>
 </ul>
 <p><strong>요구 사항</strong></p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">unicode_literals</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
-<span class="kn">from</span> <span class="nn">io</span> <span class="k">import</span> <span class="nb">open</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">unicode_literals</span><span class="p">,</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
+<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="nb">open</span>
 <span class="kn">import</span> <span class="nn">unicodedata</span>
 <span class="kn">import</span> <span class="nn">string</span>
 <span class="kn">import</span> <span class="nn">re</span>
@@ -383,7 +383,7 @@
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
-<span class="kn">from</span> <span class="nn">torch</span> <span class="k">import</span> <span class="n">optim</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">optim</span>
 <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 
 <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
@@ -427,7 +427,7 @@ <h2>데이터 파일 로딩<a class="headerlink" href="#id2" title="Permalink to
 
 
 <span class="k">class</span> <span class="nc">Lang</span><span class="p">:</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">word2index</span> <span class="o">=</span> <span class="p">{}</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">word2count</span> <span class="o">=</span> <span class="p">{}</span>
@@ -555,7 +555,7 @@ <h2>데이터 파일 로딩<a class="headerlink" href="#id2" title="Permalink to
 Counted words:
 fra 4345
 eng 2803
-[&#39;je suis touchee !&#39;, &#39;i m hit !&#39;]
+[&#39;tu es celle qui m a formee .&#39;, &#39;you re the one who trained me .&#39;]
 </pre></div>
 </div>
 </div>
@@ -593,7 +593,7 @@ <h3>인코더<a class="headerlink" href="#id4" title="Permalink to this headline
 <img alt="" src="../_images/encoder-network.png" />
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">EncoderRNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_size</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">EncoderRNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
 
@@ -628,7 +628,7 @@ <h4>간단한 디코더<a class="headerlink" href="#id6" title="Permalink to thi
 <img alt="" src="../_images/decoder-network.png" />
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DecoderRNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">DecoderRNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
 
@@ -675,7 +675,7 @@ <h4>Attention 디코더<a class="headerlink" href="#attention" title="Permalink
 <img alt="" src="../_images/attention-decoder-network.png" />
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">AttnDecoderRNN</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">dropout_p</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">max_length</span><span class="o">=</span><span class="n">MAX_LENGTH</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">hidden_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">,</span> <span class="n">dropout_p</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">max_length</span><span class="o">=</span><span class="n">MAX_LENGTH</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">AttnDecoderRNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">hidden_size</span> <span class="o">=</span> <span class="n">hidden_size</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">output_size</span> <span class="o">=</span> <span class="n">output_size</span>
@@ -989,66 +989,66 @@ <h2>학습과 평가<a class="headerlink" href="#id12" title="Permalink to this
 </li>
 </ul>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>3m 15s (- 45m 42s) (5000 6%) 2.8477
-6m 55s (- 45m 3s) (10000 13%) 2.3066
-10m 35s (- 42m 20s) (15000 20%) 1.9764
-14m 30s (- 39m 53s) (20000 26%) 1.7295
-18m 24s (- 36m 49s) (25000 33%) 1.5273
-22m 18s (- 33m 28s) (30000 40%) 1.3492
-26m 14s (- 29m 59s) (35000 46%) 1.1963
-30m 9s (- 26m 23s) (40000 53%) 1.0962
-33m 50s (- 22m 33s) (45000 60%) 0.9568
-37m 45s (- 18m 52s) (50000 66%) 0.8809
-41m 41s (- 15m 9s) (55000 73%) 0.8069
-45m 35s (- 11m 23s) (60000 80%) 0.7345
-49m 15s (- 7m 34s) (65000 86%) 0.6972
-53m 9s (- 3m 47s) (70000 93%) 0.6247
-57m 5s (- 0m 0s) (75000 100%) 0.5715
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>2m 49s (- 39m 38s) (5000 6%) 2.8527
+5m 35s (- 36m 19s) (10000 13%) 2.3096
+8m 20s (- 33m 21s) (15000 20%) 1.9973
+10m 48s (- 29m 44s) (20000 26%) 1.7392
+13m 29s (- 26m 58s) (25000 33%) 1.5423
+16m 14s (- 24m 21s) (30000 40%) 1.3578
+19m 3s (- 21m 46s) (35000 46%) 1.2045
+21m 51s (- 19m 7s) (40000 53%) 1.0928
+24m 29s (- 16m 19s) (45000 60%) 0.9549
+27m 11s (- 13m 35s) (50000 66%) 0.8779
+30m 0s (- 10m 54s) (55000 73%) 0.8116
+32m 49s (- 8m 12s) (60000 80%) 0.7292
+35m 39s (- 5m 29s) (65000 86%) 0.6974
+38m 28s (- 2m 44s) (70000 93%) 0.6277
+41m 18s (- 0m 0s) (75000 100%) 0.5717
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">evaluateRandomly</span><span class="p">(</span><span class="n">encoder1</span><span class="p">,</span> <span class="n">attn_decoder1</span><span class="p">)</span>
 </pre></div>
 </div>
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&gt; je suis en train de m entrainer .
-= i m exercising .
-&lt; i m talking . &lt;EOS&gt;
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>&gt; je viens de portugal .
+= i am from portugal .
+&lt; i am from portugal . &lt;EOS&gt;
 
-&gt; vous etes fort craintives .
-= you re very timid .
-&lt; you re very timid . &lt;EOS&gt;
+&gt; il commence a perdre ses cheveux .
+= he is beginning to lose his hair .
+&lt; he is beginning to lose his . &lt;EOS&gt;
 
-&gt; elles sont asiatiques .
-= they re asian .
-&lt; they re asian . &lt;EOS&gt;
+&gt; vous etes fatigue n est ce pas ?
+= you are tired aren t you ?
+&lt; you re tired aren t you ? &lt;EOS&gt;
 
-&gt; je me demande si j aime tom .
-= i m wondering if i love tom .
-&lt; i m wondering if i can tom tom . &lt;EOS&gt;
+&gt; je pars aujourd hui .
+= i m leaving today .
+&lt; i m leaving today . &lt;EOS&gt;
 
-&gt; nous mourons de faim .
-= we re starving .
-&lt; we re going . . &lt;EOS&gt;
+&gt; vous etes invitees .
+= you re invited .
+&lt; you re invited . &lt;EOS&gt;
 
-&gt; je ne suis pas votre ennemie .
-= i am not your enemy .
-&lt; i am not your enemy . &lt;EOS&gt;
+&gt; je ne suis pas une menteuse .
+= i m not a liar .
+&lt; i m not a liar . &lt;EOS&gt;
 
-&gt; il est officier de l armee .
-= he is an army officer .
-&lt; he is an army . . &lt;EOS&gt;
+&gt; tu es fort avise .
+= you re very wise .
+&lt; you re very wise . &lt;EOS&gt;
 
-&gt; je suis presque pret .
-= i am almost ready .
-&lt; i am almost ready . &lt;EOS&gt;
+&gt; il est l elu .
+= he is the chosen one .
+&lt; he is the chosen one . &lt;EOS&gt;
 
-&gt; il lui fait du chantage .
-= she is being blackmailed by him .
-&lt; she is fond of the him . &lt;EOS&gt;
+&gt; tu es chiante .
+= you re annoying .
+&lt; you re annoying . &lt;EOS&gt;
 
-&gt; il manque de pratique .
-= he s out of practice .
-&lt; he s out of practice . &lt;EOS&gt;
+&gt; elle a une forte volonte .
+= she s strong willed .
+&lt; she s strong . . &lt;EOS&gt;
 </pre></div>
 </div>
 <div class="section" id="id13">
@@ -1113,13 +1113,13 @@ <h3>Attention 시각화<a class="headerlink" href="#id13" title="Permalink to th
 </ul>
 <p class="sphx-glr-script-out">Out:</p>
 <div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>input = elle a cinq ans de moins que moi .
-output = she s five years younger than me . &lt;EOS&gt;
+output = she s five years younger than i am &lt;EOS&gt;
 input = elle est trop petit .
 output = she s too short . &lt;EOS&gt;
 input = je ne crains pas de mourir .
-output = i m afraid of dying . &lt;EOS&gt;
+output = i m not scared i dying . &lt;EOS&gt;
 input = c est un jeune directeur plein de talent .
-output = he s very clever and . &lt;EOS&gt;
+output = he s a big . &lt;EOS&gt;
 </pre></div>
 </div>
 </div>
@@ -1150,7 +1150,7 @@ <h2>연습<a class="headerlink" href="#id14" title="Permalink to this headline">
 </ul>
 </li>
 </ul>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 57 minutes  11.053 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 41 minutes  24.300 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-seq2seq-translation-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/3baf9960a4be104931872ff3ffda07b7/seq2seq_translation_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">seq2seq_translation_tutorial.py</span></code></a></p>
@@ -1253,11 +1253,9 @@ <h2>연습<a class="headerlink" href="#id14" title="Permalink to this headline">
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/sg_execution_times.html b/docs/intermediate/sg_execution_times.html
index a060aafe1..f55a8be8e 100644
--- a/docs/intermediate/sg_execution_times.html
+++ b/docs/intermediate/sg_execution_times.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -297,18 +297,18 @@
               
   <div class="section" id="computation-times">
 <span id="sphx-glr-intermediate-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>00:02.785</strong> total execution time for <strong>intermediate</strong> files:</p>
+<p><strong>51:04.070</strong> total execution time for <strong>intermediate</strong> files:</p>
 <ul class="simple">
-<li><p><strong>00:02.785</strong>: <a class="reference internal" href="model_parallel_tutorial.html#sphx-glr-intermediate-model-parallel-tutorial-py"><span class="std std-ref">단일 머신을 이용한 모델 병렬화 실습 예제</span></a> (<code class="docutils literal notranslate"><span class="pre">model_parallel_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="char_rnn_classification_tutorial.html#sphx-glr-intermediate-char-rnn-classification-tutorial-py"><span class="std std-ref">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</span></a> (<code class="docutils literal notranslate"><span class="pre">char_rnn_classification_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="char_rnn_generation_tutorial.html#sphx-glr-intermediate-char-rnn-generation-tutorial-py"><span class="std std-ref">기초부터 시작하는 NLP:  문자-단위 RNN으로 이름 생성하기</span></a> (<code class="docutils literal notranslate"><span class="pre">char_rnn_generation_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="flask_rest_api_tutorial.html#sphx-glr-intermediate-flask-rest-api-tutorial-py"><span class="std std-ref">Flask를 이용하여 Python에서 PyTorch를 REST API로 배포하기</span></a> (<code class="docutils literal notranslate"><span class="pre">flask_rest_api_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="memory_format_tutorial.html#sphx-glr-intermediate-memory-format-tutorial-py"><span class="std std-ref">(beta) Channels Last Memory Format in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">memory_format_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="named_tensor_tutorial.html#sphx-glr-intermediate-named-tensor-tutorial-py"><span class="std std-ref">(prototype) Introduction to Named Tensors in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">named_tensor_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="pruning_tutorial.html#sphx-glr-intermediate-pruning-tutorial-py"><span class="std std-ref">가지치기 기법(Pruning) 튜토리얼</span></a> (<code class="docutils literal notranslate"><span class="pre">pruning_tutorial.py</span></code>)</p></li>
+<li><p><strong>41:24.300</strong>: <a class="reference internal" href="seq2seq_translation_tutorial.html#sphx-glr-intermediate-seq2seq-translation-tutorial-py"><span class="std std-ref">기초부터 시작하는 NLP: Sequence to Sequence 네트워크와 Attention을 이용한 번역</span></a> (<code class="docutils literal notranslate"><span class="pre">seq2seq_translation_tutorial.py</span></code>)</p></li>
+<li><p><strong>04:50.128</strong>: <a class="reference internal" href="char_rnn_generation_tutorial.html#sphx-glr-intermediate-char-rnn-generation-tutorial-py"><span class="std std-ref">기초부터 시작하는 NLP:  문자-단위 RNN으로 이름 생성하기</span></a> (<code class="docutils literal notranslate"><span class="pre">char_rnn_generation_tutorial.py</span></code>)</p></li>
+<li><p><strong>02:26.809</strong>: <a class="reference internal" href="spatial_transformer_tutorial.html#sphx-glr-intermediate-spatial-transformer-tutorial-py"><span class="std std-ref">공간 변형 네트워크(Spatial Transformer Networks) 튜토리얼</span></a> (<code class="docutils literal notranslate"><span class="pre">spatial_transformer_tutorial.py</span></code>)</p></li>
+<li><p><strong>02:21.579</strong>: <a class="reference internal" href="char_rnn_classification_tutorial.html#sphx-glr-intermediate-char-rnn-classification-tutorial-py"><span class="std std-ref">기초부터 시작하는 NLP: 문자-단위 RNN으로 이름 분류하기</span></a> (<code class="docutils literal notranslate"><span class="pre">char_rnn_classification_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.767</strong>: <a class="reference internal" href="flask_rest_api_tutorial.html#sphx-glr-intermediate-flask-rest-api-tutorial-py"><span class="std std-ref">Flask를 이용하여 Python에서 PyTorch를 REST API로 배포하기</span></a> (<code class="docutils literal notranslate"><span class="pre">flask_rest_api_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.170</strong>: <a class="reference internal" href="pruning_tutorial.html#sphx-glr-intermediate-pruning-tutorial-py"><span class="std std-ref">가지치기 기법(Pruning) 튜토리얼</span></a> (<code class="docutils literal notranslate"><span class="pre">pruning_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.111</strong>: <a class="reference internal" href="model_parallel_tutorial.html#sphx-glr-intermediate-model-parallel-tutorial-py"><span class="std std-ref">단일 머신을 이용한 모델 병렬화 실습 예제</span></a> (<code class="docutils literal notranslate"><span class="pre">model_parallel_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.105</strong>: <a class="reference internal" href="memory_format_tutorial.html#sphx-glr-intermediate-memory-format-tutorial-py"><span class="std std-ref">(실험용) PyTorch를 사용한 Channels Last Memory Format</span></a> (<code class="docutils literal notranslate"><span class="pre">memory_format_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.101</strong>: <a class="reference internal" href="named_tensor_tutorial.html#sphx-glr-intermediate-named-tensor-tutorial-py"><span class="std std-ref">(prototype) Introduction to Named Tensors in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">named_tensor_tutorial.py</span></code>)</p></li>
 <li><p><strong>00:00.000</strong>: <a class="reference internal" href="reinforcement_q_learning.html#sphx-glr-intermediate-reinforcement-q-learning-py"><span class="std std-ref">강화 학습 (DQN) 튜토리얼</span></a> (<code class="docutils literal notranslate"><span class="pre">reinforcement_q_learning.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="seq2seq_translation_tutorial.html#sphx-glr-intermediate-seq2seq-translation-tutorial-py"><span class="std std-ref">기초부터 시작하는 NLP: Sequence to Sequence 네트워크와 Attention을 이용한 번역</span></a> (<code class="docutils literal notranslate"><span class="pre">seq2seq_translation_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:00.000</strong>: <a class="reference internal" href="spatial_transformer_tutorial.html#sphx-glr-intermediate-spatial-transformer-tutorial-py"><span class="std std-ref">Spatial Transformer Networks Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">spatial_transformer_tutorial.py</span></code>)</p></li>
 </ul>
 </div>
 
@@ -369,11 +369,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/spatial_transformer_tutorial.html b/docs/intermediate/spatial_transformer_tutorial.html
index 7dafffe0b..a1767ddca 100644
--- a/docs/intermediate/spatial_transformer_tutorial.html
+++ b/docs/intermediate/spatial_transformer_tutorial.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>Spatial Transformer Networks Tutorial &mdash; PyTorch Tutorials 1.6.0 documentation</title>
+  <title>공간 변형 네트워크(Spatial Transformer Networks) 튜토리얼 &mdash; PyTorch Tutorials 1.6.0 documentation</title>
   
 
   
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -241,7 +241,7 @@
       </li>
 
         
-      <li>Spatial Transformer Networks Tutorial</li>
+      <li>공간 변형 네트워크(Spatial Transformer Networks) 튜토리얼</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -299,58 +299,55 @@
 <p class="admonition-title">Note</p>
 <p>Click <a class="reference internal" href="#sphx-glr-download-intermediate-spatial-transformer-tutorial-py"><span class="std std-ref">here</span></a> to download the full example code</p>
 </div>
-<div class="sphx-glr-example-title section" id="spatial-transformer-networks-tutorial">
-<span id="sphx-glr-intermediate-spatial-transformer-tutorial-py"></span><h1>Spatial Transformer Networks Tutorial<a class="headerlink" href="#spatial-transformer-networks-tutorial" title="Permalink to this headline">¶</a></h1>
-<p><strong>Author</strong>: <a class="reference external" href="https://github.com/GHamrouni">Ghassen HAMROUNI</a></p>
-<div class="figure align-default">
-<img alt="../_images/FSeq.png" src="../_images/FSeq.png" />
-</div>
-<p>In this tutorial, you will learn how to augment your network using
-a visual attention mechanism called spatial transformer
-networks. You can read more about the spatial transformer
-networks in the <a class="reference external" href="https://arxiv.org/abs/1506.02025">DeepMind paper</a></p>
-<p>Spatial transformer networks are a generalization of differentiable
-attention to any spatial transformation. Spatial transformer networks
-(STN for short) allow a neural network to learn how to perform spatial
-transformations on the input image in order to enhance the geometric
-invariance of the model.
-For example, it can crop a region of interest, scale and correct
-the orientation of an image. It can be a useful mechanism because CNNs
-are not invariant to rotation and scale and more general affine
-transformations.</p>
-<p>One of the best things about STN is the ability to simply plug it into
-any existing CNN with very little modification.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># License: BSD</span>
-<span class="c1"># Author: Ghassen Hamrouni</span>
-
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span>
+<div class="sphx-glr-example-title section" id="spatial-transformer-networks">
+<span id="sphx-glr-intermediate-spatial-transformer-tutorial-py"></span><h1>공간 변형 네트워크(Spatial Transformer Networks) 튜토리얼<a class="headerlink" href="#spatial-transformer-networks" title="Permalink to this headline">¶</a></h1>
+<p><strong>저자</strong>: <a class="reference external" href="https://github.com/GHamrouni">Ghassen HAMROUNI</a>
+<strong>번역</strong>: <a class="reference external" href="https://github.com/adonisues">황성수</a> , <a class="reference external" href="https://github.com/SSinyu">정신유</a>
+.. figure:: /_static/img/stn/FSeq.png
+이 튜토리얼에서는 공간 변형 네트워크(spatial transformer networks, 이하 STN)이라
+불리는 비주얼 어텐션 메커니즘을 이용해 신경망을 증강(augment)시키는 방법에 대해
+학습합니다. 이 방법에 대한 자세한 내용은 <a class="reference external" href="https://arxiv.org/abs/1506.02025">DeepMind paper</a> 에서
+확인할 수 있습니다.
+STN은 어떠한 공간적 변형(spatial transformation)에도 적용할 수 있는 미분 가능한
+어텐션의 일반화입니다. 따라서 STN은 신경망의 기하학적 불변성(geometric invariance)을
+강화하기 위해 입력 이미지를 대상으로 어떠한 공간적 변형을 수행해야 하는지 학습하도록
+합니다.
+예를 들어 이미지의 관심 영역을 잘라내거나, 크기를 조정하거나, 방향(orientation)을
+수정할 수 있습니다. CNN은 이러한 회전, 크기 조정 등의 일반적인 아핀(affine) 변환된
+입력에 대해 결과의 변동이 크기 때문에 (민감하기 때문에), STN은 이를 극복하는데 매우
+유용한 메커니즘이 될 수 있습니다.
+STN이 가진 장점 중 하나는 아주 작은 수정만으로 기존에 사용하던 CNN에 간단하게 연결 시킬
+수 있다는 것입니다.</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># 라이센스: BSD</span>
+<span class="c1"># 저자: Ghassen Hamrouni</span>
+
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
 <span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
 <span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span><span class="p">,</span> <span class="n">transforms</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
-<span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>   <span class="c1"># interactive mode</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>   <span class="c1"># 대화형 모드</span>
 </pre></div>
 </div>
-<div class="section" id="loading-the-data">
-<h2>Loading the data<a class="headerlink" href="#loading-the-data" title="Permalink to this headline">¶</a></h2>
-<p>In this post we experiment with the classic MNIST dataset. Using a
-standard convolutional network augmented with a spatial transformer
-network.</p>
+<div class="section" id="id3">
+<h2>데이터 불러오기<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h2>
+<p>이 튜토리얼에서는 MNIST 데이터셋을 이용해 실험합니다. 실험에는 STN으로
+증강된 일반적인 CNN을 사용합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
 
-<span class="c1"># Training dataset</span>
+<span class="c1"># 학습용 데이터셋</span>
 <span class="n">train_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
     <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                    <span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
                        <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
                        <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.1307</span><span class="p">,),</span> <span class="p">(</span><span class="mf">0.3081</span><span class="p">,))</span>
                    <span class="p">])),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
-<span class="c1"># Test dataset</span>
+<span class="c1"># 테스트용 데이터셋</span>
 <span class="n">test_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
     <span class="n">datasets</span><span class="o">.</span><span class="n">MNIST</span><span class="p">(</span><span class="n">root</span><span class="o">=</span><span class="s1">&#39;.&#39;</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">([</span>
         <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
@@ -358,43 +355,27 @@ <h2>Loading the data<a class="headerlink" href="#loading-the-data" title="Permal
     <span class="p">])),</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz
-Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw
-Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz
-Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw
-Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz
-Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw
-Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz
-Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw
-Processing...
-Done!
-</pre></div>
-</div>
 </div>
-<div class="section" id="depicting-spatial-transformer-networks">
-<h2>Depicting spatial transformer networks<a class="headerlink" href="#depicting-spatial-transformer-networks" title="Permalink to this headline">¶</a></h2>
-<p>Spatial transformer networks boils down to three main components :</p>
+<div class="section" id="spatial-transformer-networks-stn">
+<h2>Spatial Transformer Networks(STN) 구성하기<a class="headerlink" href="#spatial-transformer-networks-stn" title="Permalink to this headline">¶</a></h2>
+<p>STN은 다음의 세 가지 주요 구성 요소로 요약됩니다.</p>
 <ul class="simple">
-<li><p>The localization network is a regular CNN which regresses the
-transformation parameters. The transformation is never learned
-explicitly from this dataset, instead the network learns automatically
-the spatial transformations that enhances the global accuracy.</p></li>
-<li><p>The grid generator generates a grid of coordinates in the input
-image corresponding to each pixel from the output image.</p></li>
-<li><p>The sampler uses the parameters of the transformation and applies
-it to the input image.</p></li>
+<li><p>위치 결정 네트워크(localization network)는 공간 변환 파라미터를 예측(regress)
+하는 일반적인 CNN 입니다. 공간 변환은 데이터 셋으로부터 명시적으로 학습되지 않고,
+신경망이 전체 정확도를 향상 시키도록 공간 변환을 자동으로 학습합니다.</p></li>
+<li><p>그리드 생성기(grid generator)는 출력 이미지로부터 각 픽셀에 대응하는 입력
+이미지 내 좌표 그리드를 생성합니다.</p></li>
+<li><p>샘플러(sampler)는 공간 변환 파라미터를 입력 이미지에 적용합니다.</p></li>
 </ul>
 <div class="figure align-default">
 <img alt="../_images/stn-arch.png" src="../_images/stn-arch.png" />
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
-<p>We need the latest version of PyTorch that contains
-affine_grid and grid_sample modules.</p>
+<p>affine_grid 및 grid_sample 모듈이 포함된 최신 버전의 PyTorch가 필요합니다.</p>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
@@ -402,7 +383,7 @@ <h2>Depicting spatial transformer networks<a class="headerlink" href="#depicting
         <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">320</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
 
-        <span class="c1"># Spatial transformer localization-network</span>
+        <span class="c1"># 공간 변환을 위한 위치 결정 네트워크 (localization-network)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">localization</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
             <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">7</span><span class="p">),</span>
             <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span>
@@ -412,18 +393,18 @@ <h2>Depicting spatial transformer networks<a class="headerlink" href="#depicting
             <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
         <span class="p">)</span>
 
-        <span class="c1"># Regressor for the 3 * 2 affine matrix</span>
+        <span class="c1"># [3 * 2] 크기의 아핀(affine) 행렬에 대해 예측</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fc_loc</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
             <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">10</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">*</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span>
             <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(</span><span class="kc">True</span><span class="p">),</span>
             <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">3</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
         <span class="p">)</span>
 
-        <span class="c1"># Initialize the weights/bias with identity transformation</span>
+        <span class="c1"># 항등 변환(identity transformation)으로 가중치/바이어스 초기화</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fc_loc</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fc_loc</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">bias</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">copy_</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">))</span>
 
-    <span class="c1"># Spatial transformer network forward function</span>
+    <span class="c1"># STN의 forward 함수</span>
     <span class="k">def</span> <span class="nf">stn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
         <span class="n">xs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">localization</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
         <span class="n">xs</span> <span class="o">=</span> <span class="n">xs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">3</span> <span class="o">*</span> <span class="mi">3</span><span class="p">)</span>
@@ -436,10 +417,10 @@ <h2>Depicting spatial transformer networks<a class="headerlink" href="#depicting
         <span class="k">return</span> <span class="n">x</span>
 
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-        <span class="c1"># transform the input</span>
+        <span class="c1"># 입력을 변환</span>
         <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">stn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
 
-        <span class="c1"># Perform the usual forward pass</span>
+        <span class="c1"># 일반적인 forward pass를 수행</span>
         <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
         <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2_drop</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="mi">2</span><span class="p">))</span>
         <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">320</span><span class="p">)</span>
@@ -453,11 +434,11 @@ <h2>Depicting spatial transformer networks<a class="headerlink" href="#depicting
 </pre></div>
 </div>
 </div>
-<div class="section" id="training-the-model">
-<h2>Training the model<a class="headerlink" href="#training-the-model" title="Permalink to this headline">¶</a></h2>
-<p>Now, let’s use the SGD algorithm to train the model. The network is
-learning the classification task in a supervised way. In the same time
-the model is learning STN automatically in an end-to-end fashion.</p>
+<div class="section" id="id4">
+<h2>모델 학습하기<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h2>
+<p>이제 SGD 알고리즘을 이용해 모델을 학습시켜 봅시다. 앞서 구성한 신경망은
+감독 학습 방식(supervised way)으로 분류 문제를 학습합니다. 또한 이 모델은
+end-to-end 방식으로 STN을 자동으로 학습합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">optimizer</span> <span class="o">=</span> <span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
 
 
@@ -476,7 +457,7 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Pe
                 <span class="n">epoch</span><span class="p">,</span> <span class="n">batch_idx</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">),</span>
                 <span class="mf">100.</span> <span class="o">*</span> <span class="n">batch_idx</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">),</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>
 <span class="c1">#</span>
-<span class="c1"># A simple test procedure to measure STN the performances on MNIST.</span>
+<span class="c1"># MNIST 데이터셋에서 STN의 성능을 측정하기 위한 간단한 테스트 절차</span>
 <span class="c1">#</span>
 
 
@@ -489,9 +470,9 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Pe
             <span class="n">data</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">),</span> <span class="n">target</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
             <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
 
-            <span class="c1"># sum up batch loss</span>
+            <span class="c1"># 배치 손실 합하기</span>
             <span class="n">test_loss</span> <span class="o">+=</span> <span class="n">F</span><span class="o">.</span><span class="n">nll_loss</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">size_average</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
-            <span class="c1"># get the index of the max log-probability</span>
+            <span class="c1"># 로그-확률의 최대값에 해당하는 인덱스 가져오기</span>
             <span class="n">pred</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span>
             <span class="n">correct</span> <span class="o">+=</span> <span class="n">pred</span><span class="o">.</span><span class="n">eq</span><span class="p">(</span><span class="n">target</span><span class="o">.</span><span class="n">view_as</span><span class="p">(</span><span class="n">pred</span><span class="p">))</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
 
@@ -502,12 +483,10 @@ <h2>Training the model<a class="headerlink" href="#training-the-model" title="Pe
 </pre></div>
 </div>
 </div>
-<div class="section" id="visualizing-the-stn-results">
-<h2>Visualizing the STN results<a class="headerlink" href="#visualizing-the-stn-results" title="Permalink to this headline">¶</a></h2>
-<p>Now, we will inspect the results of our learned visual attention
-mechanism.</p>
-<p>We define a small helper function in order to visualize the
-transformations while training.</p>
+<div class="section" id="stn">
+<h2>STN 결과 시각화하기<a class="headerlink" href="#stn" title="Permalink to this headline">¶</a></h2>
+<p>이제 학습한 비주얼 어텐션 메커니즘의 결과를 살펴보겠습니다.</p>
+<p>학습하는 동안 변환된 결과를 시각화하기 위해 작은 도움(helper) 함수를 정의합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">convert_image_np</span><span class="p">(</span><span class="n">inp</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Convert a Tensor to numpy image.&quot;&quot;&quot;</span>
     <span class="n">inp</span> <span class="o">=</span> <span class="n">inp</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">transpose</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
@@ -517,14 +496,13 @@ <h2>Visualizing the STN results<a class="headerlink" href="#visualizing-the-stn-
     <span class="n">inp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">inp</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">inp</span>
 
-<span class="c1"># We want to visualize the output of the spatial transformers layer</span>
-<span class="c1"># after the training, we visualize a batch of input images and</span>
-<span class="c1"># the corresponding transformed batch using STN.</span>
+<span class="c1"># 학습 후 공간 변환 계층의 출력을 시각화하고, 입력 이미지 배치 데이터 및</span>
+<span class="c1"># STN을 사용해 변환된 배치 데이터를 시각화 합니다.</span>
 
 
 <span class="k">def</span> <span class="nf">visualize_stn</span><span class="p">():</span>
     <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
-        <span class="c1"># Get a batch of training data</span>
+        <span class="c1"># 학습 데이터의 배치 가져오기</span>
         <span class="n">data</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">test_loader</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
 
         <span class="n">input_tensor</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
@@ -536,7 +514,7 @@ <h2>Visualizing the STN results<a class="headerlink" href="#visualizing-the-stn-
         <span class="n">out_grid</span> <span class="o">=</span> <span class="n">convert_image_np</span><span class="p">(</span>
             <span class="n">torchvision</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">transformed_input_tensor</span><span class="p">))</span>
 
-        <span class="c1"># Plot the results side-by-side</span>
+        <span class="c1"># 결과를 나란히 표시하기</span>
         <span class="n">f</span><span class="p">,</span> <span class="n">axarr</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
         <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">in_grid</span><span class="p">)</span>
         <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Dataset Images&#39;</span><span class="p">)</span>
@@ -548,7 +526,7 @@ <h2>Visualizing the STN results<a class="headerlink" href="#visualizing-the-stn-
     <span class="n">train</span><span class="p">(</span><span class="n">epoch</span><span class="p">)</span>
     <span class="n">test</span><span class="p">()</span>
 
-<span class="c1"># Visualize the STN transformation on some input batch</span>
+<span class="c1"># 일부 입력 배치 데이터에서 STN 변환 결과를 시각화</span>
 <span class="n">visualize_stn</span><span class="p">()</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span>
@@ -557,108 +535,108 @@ <h2>Visualizing the STN results<a class="headerlink" href="#visualizing-the-stn-
 </div>
 <img alt="../_images/sphx_glr_spatial_transformer_tutorial_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_spatial_transformer_tutorial_001.png" />
 <p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Train Epoch: 1 [0/60000 (0%)]   Loss: 2.322509
-Train Epoch: 1 [32000/60000 (53%)]      Loss: 0.931219
+<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Train Epoch: 1 [0/60000 (0%)]   Loss: 2.317141
+Train Epoch: 1 [32000/60000 (53%)]      Loss: 1.169722
 
-Test set: Average loss: 0.1964, Accuracy: 9460/10000 (95%)
+Test set: Average loss: 0.4297, Accuracy: 8680/10000 (87%)
 
-Train Epoch: 2 [0/60000 (0%)]   Loss: 0.739472
-Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.255152
+Train Epoch: 2 [0/60000 (0%)]   Loss: 0.850162
+Train Epoch: 2 [32000/60000 (53%)]      Loss: 0.403260
 
-Test set: Average loss: 0.1154, Accuracy: 9646/10000 (96%)
+Test set: Average loss: 0.1459, Accuracy: 9536/10000 (95%)
 
-Train Epoch: 3 [0/60000 (0%)]   Loss: 0.292564
-Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.197150
+Train Epoch: 3 [0/60000 (0%)]   Loss: 0.331244
+Train Epoch: 3 [32000/60000 (53%)]      Loss: 0.560218
 
-Test set: Average loss: 0.0941, Accuracy: 9709/10000 (97%)
+Test set: Average loss: 0.1030, Accuracy: 9672/10000 (97%)
 
-Train Epoch: 4 [0/60000 (0%)]   Loss: 0.305629
-Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.350042
+Train Epoch: 4 [0/60000 (0%)]   Loss: 0.168686
+Train Epoch: 4 [32000/60000 (53%)]      Loss: 0.195199
 
-Test set: Average loss: 0.0798, Accuracy: 9751/10000 (98%)
+Test set: Average loss: 0.0786, Accuracy: 9745/10000 (97%)
 
-Train Epoch: 5 [0/60000 (0%)]   Loss: 0.161943
-Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.096154
+Train Epoch: 5 [0/60000 (0%)]   Loss: 0.164006
+Train Epoch: 5 [32000/60000 (53%)]      Loss: 0.368153
 
-Test set: Average loss: 0.0800, Accuracy: 9756/10000 (98%)
+Test set: Average loss: 0.0660, Accuracy: 9816/10000 (98%)
 
-Train Epoch: 6 [0/60000 (0%)]   Loss: 0.179021
-Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.064287
+Train Epoch: 6 [0/60000 (0%)]   Loss: 0.293069
+Train Epoch: 6 [32000/60000 (53%)]      Loss: 0.185670
 
-Test set: Average loss: 0.0685, Accuracy: 9786/10000 (98%)
+Test set: Average loss: 0.0693, Accuracy: 9788/10000 (98%)
 
-Train Epoch: 7 [0/60000 (0%)]   Loss: 0.170600
-Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.064743
+Train Epoch: 7 [0/60000 (0%)]   Loss: 0.115762
+Train Epoch: 7 [32000/60000 (53%)]      Loss: 0.151216
 
-Test set: Average loss: 0.0567, Accuracy: 9827/10000 (98%)
+Test set: Average loss: 0.0695, Accuracy: 9795/10000 (98%)
 
-Train Epoch: 8 [0/60000 (0%)]   Loss: 0.150940
-Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.155436
+Train Epoch: 8 [0/60000 (0%)]   Loss: 0.153606
+Train Epoch: 8 [32000/60000 (53%)]      Loss: 0.219680
 
-Test set: Average loss: 0.0651, Accuracy: 9811/10000 (98%)
+Test set: Average loss: 0.0541, Accuracy: 9834/10000 (98%)
 
-Train Epoch: 9 [0/60000 (0%)]   Loss: 0.266612
-Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.180357
+Train Epoch: 9 [0/60000 (0%)]   Loss: 0.090507
+Train Epoch: 9 [32000/60000 (53%)]      Loss: 0.147483
 
-Test set: Average loss: 0.0496, Accuracy: 9848/10000 (98%)
+Test set: Average loss: 0.0512, Accuracy: 9839/10000 (98%)
 
-Train Epoch: 10 [0/60000 (0%)]  Loss: 0.200832
-Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.163813
+Train Epoch: 10 [0/60000 (0%)]  Loss: 0.065643
+Train Epoch: 10 [32000/60000 (53%)]     Loss: 0.101421
 
-Test set: Average loss: 0.0482, Accuracy: 9853/10000 (99%)
+Test set: Average loss: 0.0533, Accuracy: 9853/10000 (99%)
 
-Train Epoch: 11 [0/60000 (0%)]  Loss: 0.042187
-Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.102783
+Train Epoch: 11 [0/60000 (0%)]  Loss: 0.140934
+Train Epoch: 11 [32000/60000 (53%)]     Loss: 0.188626
 
-Test set: Average loss: 0.0504, Accuracy: 9856/10000 (99%)
+Test set: Average loss: 0.0528, Accuracy: 9832/10000 (98%)
 
-Train Epoch: 12 [0/60000 (0%)]  Loss: 0.040896
-Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.096896
+Train Epoch: 12 [0/60000 (0%)]  Loss: 0.115515
+Train Epoch: 12 [32000/60000 (53%)]     Loss: 0.170645
 
-Test set: Average loss: 0.0442, Accuracy: 9875/10000 (99%)
+Test set: Average loss: 0.0582, Accuracy: 9831/10000 (98%)
 
-Train Epoch: 13 [0/60000 (0%)]  Loss: 0.023365
-Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.276145
+Train Epoch: 13 [0/60000 (0%)]  Loss: 0.158255
+Train Epoch: 13 [32000/60000 (53%)]     Loss: 0.117169
 
-Test set: Average loss: 0.0480, Accuracy: 9863/10000 (99%)
+Test set: Average loss: 0.0545, Accuracy: 9845/10000 (98%)
 
-Train Epoch: 14 [0/60000 (0%)]  Loss: 0.045835
-Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.033465
+Train Epoch: 14 [0/60000 (0%)]  Loss: 0.045876
+Train Epoch: 14 [32000/60000 (53%)]     Loss: 0.043410
 
-Test set: Average loss: 0.0577, Accuracy: 9825/10000 (98%)
+Test set: Average loss: 0.0503, Accuracy: 9852/10000 (99%)
 
-Train Epoch: 15 [0/60000 (0%)]  Loss: 0.055503
-Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.022570
+Train Epoch: 15 [0/60000 (0%)]  Loss: 0.416799
+Train Epoch: 15 [32000/60000 (53%)]     Loss: 0.043577
 
-Test set: Average loss: 0.0679, Accuracy: 9802/10000 (98%)
+Test set: Average loss: 0.0466, Accuracy: 9868/10000 (99%)
 
-Train Epoch: 16 [0/60000 (0%)]  Loss: 0.155806
-Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.085502
+Train Epoch: 16 [0/60000 (0%)]  Loss: 0.068028
+Train Epoch: 16 [32000/60000 (53%)]     Loss: 0.052074
 
-Test set: Average loss: 0.0430, Accuracy: 9871/10000 (99%)
+Test set: Average loss: 0.0496, Accuracy: 9864/10000 (99%)
 
-Train Epoch: 17 [0/60000 (0%)]  Loss: 0.045421
-Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.082653
+Train Epoch: 17 [0/60000 (0%)]  Loss: 0.102239
+Train Epoch: 17 [32000/60000 (53%)]     Loss: 0.111362
 
-Test set: Average loss: 0.0427, Accuracy: 9870/10000 (99%)
+Test set: Average loss: 0.0490, Accuracy: 9864/10000 (99%)
 
-Train Epoch: 18 [0/60000 (0%)]  Loss: 0.059736
-Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.138764
+Train Epoch: 18 [0/60000 (0%)]  Loss: 0.144808
+Train Epoch: 18 [32000/60000 (53%)]     Loss: 0.065100
 
-Test set: Average loss: 0.0454, Accuracy: 9865/10000 (99%)
+Test set: Average loss: 0.0438, Accuracy: 9879/10000 (99%)
 
-Train Epoch: 19 [0/60000 (0%)]  Loss: 0.033023
-Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.135506
+Train Epoch: 19 [0/60000 (0%)]  Loss: 0.054307
+Train Epoch: 19 [32000/60000 (53%)]     Loss: 0.059669
 
-Test set: Average loss: 0.0389, Accuracy: 9888/10000 (99%)
+Test set: Average loss: 0.0494, Accuracy: 9856/10000 (99%)
 
-Train Epoch: 20 [0/60000 (0%)]  Loss: 0.193454
-Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.017996
+Train Epoch: 20 [0/60000 (0%)]  Loss: 0.113642
+Train Epoch: 20 [32000/60000 (53%)]     Loss: 0.126756
 
-Test set: Average loss: 0.0406, Accuracy: 9887/10000 (99%)
+Test set: Average loss: 0.0518, Accuracy: 9852/10000 (99%)
 </pre></div>
 </div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  5.559 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 2 minutes  26.809 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-intermediate-spatial-transformer-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/a4f07fecba75b5e84fe9e56cac0c7b71/spatial_transformer_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">spatial_transformer_tutorial.py</span></code></a></p>
@@ -712,11 +690,11 @@ <h2>Visualizing the STN results<a class="headerlink" href="#visualizing-the-stn-
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">Spatial Transformer Networks Tutorial</a><ul>
-<li><a class="reference internal" href="#loading-the-data">Loading the data</a></li>
-<li><a class="reference internal" href="#depicting-spatial-transformer-networks">Depicting spatial transformer networks</a></li>
-<li><a class="reference internal" href="#training-the-model">Training the model</a></li>
-<li><a class="reference internal" href="#visualizing-the-stn-results">Visualizing the STN results</a></li>
+<li><a class="reference internal" href="#">공간 변형 네트워크(Spatial Transformer Networks) 튜토리얼</a><ul>
+<li><a class="reference internal" href="#id3">데이터 불러오기</a></li>
+<li><a class="reference internal" href="#spatial-transformer-networks-stn">Spatial Transformer Networks(STN) 구성하기</a></li>
+<li><a class="reference internal" href="#id4">모델 학습하기</a></li>
+<li><a class="reference internal" href="#stn">STN 결과 시각화하기</a></li>
 </ul>
 </li>
 </ul>
@@ -734,11 +712,9 @@ <h2>Visualizing the STN results<a class="headerlink" href="#visualizing-the-stn-
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/tensorboard_tutorial.html b/docs/intermediate/tensorboard_tutorial.html
index b3f82e737..0aac8cf93 100644
--- a/docs/intermediate/tensorboard_tutorial.html
+++ b/docs/intermediate/tensorboard_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -328,16 +328,16 @@ <h1>TensorBoard로 모델, 데이터, 학습 시각화하기<a class="headerlink
 </div></blockquote>
 <p><a class="reference internal" href="../beginner/blitz/cifar10_tutorial.html"><span class="doc">분류기(Classifier) 학습하기</span></a> 와 비슷한 코드로 시작해보겠습니다:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># imports</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="kn">as</span> <span class="nn">transforms</span>
+<span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
 
-<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="kn">as</span> <span class="nn">nn</span>
-<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="kn">as</span> <span class="nn">F</span>
-<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="kn">as</span> <span class="nn">optim</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
+<span class="kn">import</span> <span class="nn">torch.optim</span> <span class="k">as</span> <span class="nn">optim</span>
 
 <span class="c1"># transforms</span>
 <span class="n">transform</span> <span class="o">=</span> <span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
@@ -346,21 +346,21 @@ <h1>TensorBoard로 모델, 데이터, 학습 시각화하기<a class="headerlink
 
 <span class="c1"># datasets</span>
 <span class="n">trainset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">FashionMNIST</span><span class="p">(</span><span class="s1">&#39;./data&#39;</span><span class="p">,</span>
-    <span class="n">download</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
-    <span class="n">train</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
     <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
 <span class="n">testset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">FashionMNIST</span><span class="p">(</span><span class="s1">&#39;./data&#39;</span><span class="p">,</span>
-    <span class="n">download</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
-    <span class="n">train</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
+    <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
     <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">)</span>
 
 <span class="c1"># dataloaders</span>
 <span class="n">trainloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">trainset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-                                        <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+                                        <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 
 
 <span class="n">testloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span><span class="n">testset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
-                                        <span class="n">shuffle</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+                                        <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
 
 <span class="c1"># 분류 결과를 위한 상수</span>
 <span class="n">classes</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;T-shirt/top&#39;</span><span class="p">,</span> <span class="s1">&#39;Trouser&#39;</span><span class="p">,</span> <span class="s1">&#39;Pullover&#39;</span><span class="p">,</span> <span class="s1">&#39;Dress&#39;</span><span class="p">,</span> <span class="s1">&#39;Coat&#39;</span><span class="p">,</span>
@@ -368,7 +368,7 @@ <h1>TensorBoard로 모델, 데이터, 학습 시각화하기<a class="headerlink
 
 <span class="c1"># 이미지를 보여주기 위한 헬퍼(helper) 함수</span>
 <span class="c1"># (아래 `plot_classes_preds` 함수에서 사용)</span>
-<span class="k">def</span> <span class="nf">matplotlib_imshow</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">one_channel</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">matplotlib_imshow</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">one_channel</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">one_channel</span><span class="p">:</span>
         <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
     <span class="n">img</span> <span class="o">=</span> <span class="n">img</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">+</span> <span class="mf">0.5</span>     <span class="c1"># unnormalize</span>
@@ -435,7 +435,7 @@ <h2>2. TensorBoard에 기록하기<a class="headerlink" href="#id2" title="Perma
 <span class="n">img_grid</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">make_grid</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
 
 <span class="c1"># 이미지를 보여줍니다.</span>
-<span class="n">matplotlib_imshow</span><span class="p">(</span><span class="n">img_grid</span><span class="p">,</span> <span class="n">one_channel</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">matplotlib_imshow</span><span class="p">(</span><span class="n">img_grid</span><span class="p">,</span> <span class="n">one_channel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="c1"># tensorboard에 기록합니다.</span>
 <span class="n">writer</span><span class="o">.</span><span class="n">add_image</span><span class="p">(</span><span class="s1">&#39;four_fashion_mnist_images&#39;</span><span class="p">,</span> <span class="n">img_grid</span><span class="p">)</span>
@@ -447,7 +447,7 @@ <h2>2. TensorBoard에 기록하기<a class="headerlink" href="#id2" title="Perma
 </div>
 <p>를 실행하고, <a class="reference external" href="https://localhost:6006">https://localhost:6006</a> 을 열어보면
 다음과 같은 화면이 나타납니다.</p>
-<img alt="intermediate/../../_static/img/tensorboard_first_view.png" src="intermediate/../../_static/img/tensorboard_first_view.png" />
+<img alt="../_static/img/tensorboard_first_view.png" src="../_static/img/tensorboard_first_view.png" />
 <p>지금까지 TensorBoard를 어떻게 사용하는지를 알아보았습니다! 그러나, 이 예제는
 Jupyter Notebook에서도 가능합니다 - TensorBoard는 대화형 시각화를 만드는데
 특히 뛰어납니다. 다음에 그런 기능들 중 하나를 살펴보고, 튜토리얼의 나머지 부분에서
@@ -462,7 +462,7 @@ <h2>3. TensorBoard를 사용하여 모델 살펴보기(inspect)<a class="headerl
 </pre></div>
 </div>
 <p>TensorBoard를 새로고침(refresh)하면 아래와 같이 “Graphs” 탭을 볼 수 있습니다:</p>
-<img alt="intermediate/../../_static/img/tensorboard_model_viz.png" src="intermediate/../../_static/img/tensorboard_model_viz.png" />
+<img alt="../_static/img/tensorboard_model_viz.png" src="../_static/img/tensorboard_model_viz.png" />
 <p>아래에서 “Net”을 더블클릭하여 펼쳐보면, 모델을 구성하는 개별 연산(operation)들에
 대해 자세히 볼 수 있습니다.</p>
 <p>TensorBoard는 이미지 데이터와 같은 고차원 데이터를 저차원 공간에 시각화하는데
@@ -502,7 +502,7 @@ <h2>4. TensorBoard에 “Projector” 추가하기<a class="headerlink" href="#t
 시각화를 더 편히 볼 수 있는 몇 가지 팁이 있습니다: 좌측 상단에서 “Color by: label”을
 선택하고, “야간모드(night mode)”를 활성화하면 이미지 배경이 흰색이 되어 더 편하게
 볼 수 있습니다:</p>
-<img alt="intermediate/../../_static/img/tensorboard_projector.png" src="intermediate/../../_static/img/tensorboard_projector.png" />
+<img alt="../_static/img/tensorboard_projector.png" src="../_static/img/tensorboard_projector.png" />
 <p>지금까지 데이터를 충분히 살펴보았으므로, 이제 학습 과정부터 시작하여 TensorBoard가
 어떻게 모델 학습과 평가(evaluation)를 더 명확히 추적(track)할 수 있는지 살펴보겠습니다.</p>
 </div>
@@ -536,8 +536,8 @@ <h2>5. TensorBoard로 모델 학습 추적하기<a class="headerlink" href="#id3
     <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">48</span><span class="p">))</span>
     <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
         <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">idx</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">xticks</span><span class="o">=</span><span class="p">[],</span> <span class="n">yticks</span><span class="o">=</span><span class="p">[])</span>
-        <span class="n">matplotlib_imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">one_channel</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;{0}, {1:.1f}%</span><span class="se">\n</span><span class="s2">(label: {2})&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+        <span class="n">matplotlib_imshow</span><span class="p">(</span><span class="n">images</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">one_channel</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">{0}</span><span class="s2">, </span><span class="si">{1:.1f}</span><span class="s2">%</span><span class="se">\n</span><span class="s2">(label: </span><span class="si">{2}</span><span class="s2">)&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
             <span class="n">classes</span><span class="p">[</span><span class="n">preds</span><span class="p">[</span><span class="n">idx</span><span class="p">]],</span>
             <span class="n">probs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">*</span> <span class="mf">100.0</span><span class="p">,</span>
             <span class="n">classes</span><span class="p">[</span><span class="n">labels</span><span class="p">[</span><span class="n">idx</span><span class="p">]]),</span>
@@ -582,16 +582,16 @@ <h2>5. TensorBoard로 모델 학습 추적하기<a class="headerlink" href="#id3
                             <span class="n">plot_classes_preds</span><span class="p">(</span><span class="n">net</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">),</span>
                             <span class="n">global_step</span><span class="o">=</span><span class="n">epoch</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">trainloader</span><span class="p">)</span> <span class="o">+</span> <span class="n">i</span><span class="p">)</span>
             <span class="n">running_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
-<span class="k">print</span><span class="p">(</span><span class="s1">&#39;Finished Training&#39;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Finished Training&#39;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>이제 ‘Scalars’ 탭에서 15,000번 반복 학습할 때의 손실을 확인할 수 있습니다:</p>
-<img alt="intermediate/../../_static/img/tensorboard_scalar_runs.png" src="intermediate/../../_static/img/tensorboard_scalar_runs.png" />
+<img alt="../_static/img/tensorboard_scalar_runs.png" src="../_static/img/tensorboard_scalar_runs.png" />
 <p>또한, 학습 과정 전반에 걸쳐 임의의 배치에 대한 모델의 예측 결과를 확인할 수 있습니다.
 “Images” 탭에서 스크롤을 내려 “예측 vs. 정답(predictions vs. actuals)” 시각화 부분에서
 이 내용을 볼 수 있습니다; 예를 들어 학습을 단지 3000번 반복하기만 해도, 신뢰도는
 높진 않지만, 모델은 셔츠와 운동화(sneakers), 코트와 같은 분류들을 구분할 수 있었습니다:</p>
-<img alt="intermediate/../../_static/img/tensorboard_images.png" src="intermediate/../../_static/img/tensorboard_images.png" />
+<img alt="../_static/img/tensorboard_images.png" src="../_static/img/tensorboard_images.png" />
 <p>이전 튜토리얼에서는 모델이 학습 완료된 후에 각 분류별 정확도(per-class accuracy)를
 살펴봤습니다; 여기서는 TensorBoard를 사용하여 각 분류별 정밀도-재현율(precision-recall)
 곡선( <a class="reference external" href="https://www.scikit-yb.org/en/latest/api/classifier/prcurve.html">여기</a> 에
@@ -640,7 +640,7 @@ <h2>6. TensorBoard로 학습된 모델 평가하기<a class="headerlink" href="#
 <p>이제 “PR Curves” 탭에서 각 분류별 정밀도-재현율 곡선을 볼 수 있습니다.
 내려보면서 살펴보십시오; 일부 분류는 거의 100%의 “영역이 곡선 아래”에 있고,
 다른 분류들은 이 영역이 더 적습니다:</p>
-<img alt="intermediate/../../_static/img/tensorboard_pr_curves.png" src="intermediate/../../_static/img/tensorboard_pr_curves.png" />
+<img alt="../_static/img/tensorboard_pr_curves.png" src="../_static/img/tensorboard_pr_curves.png" />
 <p>여기까지 TensorBoard와 PyTorch의 통합에 대해 소개했습니다. 물론 TensorBoard에서
 제공하는 모든 것들을 Jupyter Notebook에서도 할 수 있지만, TensorBoard를 사용하면
 대화형 시각화가 기본으로 제공됩니다.</p>
@@ -721,11 +721,9 @@ <h2>6. TensorBoard로 학습된 모델 평가하기<a class="headerlink" href="#
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/intermediate/torchvision_tutorial.html b/docs/intermediate/torchvision_tutorial.html
index 64badd4f3..3325dd2b8 100644
--- a/docs/intermediate/torchvision_tutorial.html
+++ b/docs/intermediate/torchvision_tutorial.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -318,24 +318,22 @@ <h2>데이터셋 정의하기<a class="headerlink" href="#id1" title="Permalink
 상속 받아야 하며, <code class="docutils literal notranslate"><span class="pre">__len__</span></code> 와 <code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> 메소드를 구현해 주어야 합니다.</p>
 <p>우리가 데이터셋 클래스에서 필요로 하는 유일한 특성은  <code class="docutils literal notranslate"><span class="pre">__getitem__</span></code> 를
 리턴을 해야 하는 점입니다:</p>
-<ul>
+<ul class="simple">
 <li><p>이미지 : PIL(Python Image Library) 이미지의 크기 <code class="docutils literal notranslate"><span class="pre">(H,</span> <span class="pre">W)</span></code></p></li>
 <li><p>대상: 다음의 필드를 포함하는 사전 타입</p>
-<ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">boxes</span> <span class="pre">(FloatTensor[N,</span> <span class="pre">4])</span></code>:  <code class="docutils literal notranslate"><span class="pre">N</span></code> 개의 바운딩 박스(Bounding box)의 좌표를 <code class="docutils literal notranslate"><span class="pre">[x0,</span> <span class="pre">y0,</span> <span class="pre">x1,</span> <span class="pre">y1]</span></code> 형태로 가집니다.</p></li>
-</ul>
-<p>x와 관련된 값 범위는 <code class="docutils literal notranslate"><span class="pre">0</span></code> 부터 <code class="docutils literal notranslate"><span class="pre">W</span></code> 이고 y와 관련된 값의 범위는 <code class="docutils literal notranslate"><span class="pre">0</span></code> 부터 <code class="docutils literal notranslate"><span class="pre">H</span></code> 까지입니다.
--  <code class="docutils literal notranslate"><span class="pre">labels</span> <span class="pre">(Int64Tensor[N])</span></code>: 바운딩 박스 마다의 라벨 정보입니다. <code class="docutils literal notranslate"><span class="pre">0</span></code> 은 항상 배경의 클래스를 표현합니다.
--  <code class="docutils literal notranslate"><span class="pre">image_id</span> <span class="pre">(Int64Tensor[1])</span></code>: 이미지 구분자입니다. 데이터셋의 모든 이미지 간에 고유한 값이어야 하며 평가 중에도 사용됩니다.
--  <code class="docutils literal notranslate"><span class="pre">area</span> <span class="pre">(Tensor[N])</span></code>: 바운딩 박스의 면적입니다. 면적은 평가 시 작음,중간,큰 박스 간의 점수를 내기 위한 기준이며 COCO 평가를 기준으로 합니다.
--  <code class="docutils literal notranslate"><span class="pre">iscrowd</span> <span class="pre">(UInt8Tensor[N])</span></code>: 이 값이 참일 경우 평가에서 제외합니다.
--  (선택적) <code class="docutils literal notranslate"><span class="pre">masks</span> <span class="pre">(UInt8Tensor[N,</span> <span class="pre">H,</span> <span class="pre">W])</span></code>: <code class="docutils literal notranslate"><span class="pre">N</span></code> 개의 객체 마다의 분할 마스크 정보입니다.
--  (선택적) <code class="docutils literal notranslate"><span class="pre">keypoints</span> <span class="pre">(FloatTensor[N,</span> <span class="pre">K,</span> <span class="pre">3])</span></code>: <code class="docutils literal notranslate"><span class="pre">N</span></code> 개의 객체마다의 키포인트 정보입니다.</p>
-<blockquote>
-<div><p>키포인트는 <code class="docutils literal notranslate"><span class="pre">[x,</span> <span class="pre">y,</span> <span class="pre">visibility]</span></code> 형태의 값입니다. visibility 값이 0인 경우 키포인트는 보이지 않음을 의미합니다.
+<ul>
+<li><p><code class="docutils literal notranslate"><span class="pre">boxes</span> <span class="pre">(FloatTensor[N,</span> <span class="pre">4])</span></code>:  <code class="docutils literal notranslate"><span class="pre">N</span></code> 개의 바운딩 박스(Bounding box)의 좌표를 <code class="docutils literal notranslate"><span class="pre">[x0,</span> <span class="pre">y0,</span> <span class="pre">x1,</span> <span class="pre">y1]</span></code> 형태로 가집니다.
+x와 관련된 값 범위는 <code class="docutils literal notranslate"><span class="pre">0</span></code> 부터 <code class="docutils literal notranslate"><span class="pre">W</span></code> 이고 y와 관련된 값의 범위는 <code class="docutils literal notranslate"><span class="pre">0</span></code> 부터 <code class="docutils literal notranslate"><span class="pre">H</span></code> 까지입니다.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">labels</span> <span class="pre">(Int64Tensor[N])</span></code>: 바운딩 박스 마다의 라벨 정보입니다. <code class="docutils literal notranslate"><span class="pre">0</span></code> 은 항상 배경의 클래스를 표현합니다.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">image_id</span> <span class="pre">(Int64Tensor[1])</span></code>: 이미지 구분자입니다. 데이터셋의 모든 이미지 간에 고유한 값이어야 하며 평가 중에도 사용됩니다.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">area</span> <span class="pre">(Tensor[N])</span></code>: 바운딩 박스의 면적입니다. 면적은 평가 시 작음,중간,큰 박스 간의 점수를 내기 위한 기준이며 COCO 평가를 기준으로 합니다.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">iscrowd</span> <span class="pre">(UInt8Tensor[N])</span></code>: 이 값이 참일 경우 평가에서 제외합니다.</p></li>
+<li><p>(선택적) <code class="docutils literal notranslate"><span class="pre">masks</span> <span class="pre">(UInt8Tensor[N,</span> <span class="pre">H,</span> <span class="pre">W])</span></code>: <code class="docutils literal notranslate"><span class="pre">N</span></code> 개의 객체 마다의 분할 마스크 정보입니다.</p></li>
+<li><p>(선택적) <code class="docutils literal notranslate"><span class="pre">keypoints</span> <span class="pre">(FloatTensor[N,</span> <span class="pre">K,</span> <span class="pre">3])</span></code>: <code class="docutils literal notranslate"><span class="pre">N</span></code> 개의 객체마다의 키포인트 정보입니다.
+키포인트는 <code class="docutils literal notranslate"><span class="pre">[x,</span> <span class="pre">y,</span> <span class="pre">visibility]</span></code> 형태의 값입니다. visibility 값이 0인 경우 키포인트는 보이지 않음을 의미합니다.
 데이터 증강(Data augmentation)의 경우 키포인트 좌우 반전의 개념은 데이터 표현에 따라 달라지며,
-새로운 키포인트 표현에 대해 “references/detection/transforms.py” 코드 부분을 수정 해야 할 수도 있습니다.</p>
-</div></blockquote>
+새로운 키포인트 표현에 대해 “references/detection/transforms.py” 코드 부분을 수정 해야 할 수도 있습니다.</p></li>
+</ul>
 </li>
 </ul>
 <p>모델이 위의 방법대로 리턴을 하면, 학습과 평가 둘 다에 대해서 동작을 할 것이며
@@ -355,7 +353,7 @@ <h2>데이터셋 정의하기<a class="headerlink" href="#id1" title="Permalink
 <div class="section" id="pennfudan">
 <h3>PennFudan를 위한 사용자 정의 데이터셋 작성하기<a class="headerlink" href="#pennfudan" title="Permalink to this headline">¶</a></h3>
 <p>PennFudan 데이터셋을 위한 코드를 작성해 보겠습니다.
-<a href="#id10"><span class="problematic" id="id11">`다운로드 후 압축 파일을 해제하면&lt;https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip&gt;`__</span></a>,
+<a href="#id8"><span class="problematic" id="id9">`다운로드 후 압축 파일을 해제하면&lt;https://www.cis.upenn.edu/~jshi/ped_html/PennFudanPed.zip&gt;`__</span></a>,
 다음의 폴더 구조를 볼 수 있습니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">PennFudanPed</span><span class="o">/</span>
   <span class="n">PedMasks</span><span class="o">/</span>
@@ -372,12 +370,12 @@ <h3>PennFudan를 위한 사용자 정의 데이터셋 작성하기<a class="head
 </pre></div>
 </div>
 <p>한 쌍의 영상과 분할 마스크의 한 가지 예는 다음과 같습니다.</p>
-<img alt="intermediate/../../_static/img/tv_tutorial/tv_image01.png" src="intermediate/../../_static/img/tv_tutorial/tv_image01.png" />
-<img alt="intermediate/../../_static/img/tv_tutorial/tv_image02.png" src="intermediate/../../_static/img/tv_tutorial/tv_image02.png" />
+<img alt="../_static/img/tv_tutorial/tv_image01.png" src="../_static/img/tv_tutorial/tv_image01.png" />
+<img alt="../_static/img/tv_tutorial/tv_image02.png" src="../_static/img/tv_tutorial/tv_image02.png" />
 <p>각 이미지에는 해당하는 분할 마스크가 있으며, 여기서 각각의 색상은 다른 인스턴스에 해당합니다.
 데이터셋에 해당하는 <cite>torch.utils.data.Dataset`</cite> 클래스를 작성합시다.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 
@@ -407,7 +405,7 @@ <h3>PennFudan를 위한 사용자 정의 데이터셋 작성하기<a class="head
         <span class="n">obj_ids</span> <span class="o">=</span> <span class="n">obj_ids</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
 
         <span class="c1"># 컬러 인코딩된 마스크를 바이너리 마스크 세트로 나눕니다</span>
-        <span class="n">masks</span> <span class="o">=</span> <span class="n">mask</span> <span class="o">==</span> <span class="n">obj_ids</span><span class="p">[:,</span> <span class="bp">None</span><span class="p">,</span> <span class="bp">None</span><span class="p">]</span>
+        <span class="n">masks</span> <span class="o">=</span> <span class="n">mask</span> <span class="o">==</span> <span class="n">obj_ids</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">]</span>
 
         <span class="c1"># 각 마스크의 바운딩 박스 좌표를 얻습니다</span>
         <span class="n">num_objs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">obj_ids</span><span class="p">)</span>
@@ -439,7 +437,7 @@ <h3>PennFudan를 위한 사용자 정의 데이터셋 작성하기<a class="head
         <span class="n">target</span><span class="p">[</span><span class="s2">&quot;area&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">area</span>
         <span class="n">target</span><span class="p">[</span><span class="s2">&quot;iscrowd&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">iscrowd</span>
 
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">img</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transforms</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="n">img</span><span class="p">,</span> <span class="n">target</span>
@@ -456,9 +454,9 @@ <h2>모델 정의하기<a class="headerlink" href="#id2" title="Permalink to thi
 <p>이번 튜토리얼에서는 <a class="reference external" href="https://arxiv.org/abs/1506.01497">Faster R-CNN</a>
 에 기반한 <a class="reference external" href="https://arxiv.org/abs/1703.06870">Mask R-CNN</a> 모델을 사용할 예정입니다.
 Faster R-CNN은 이미지에 존재할 수 있는 객체에 대한 바운딩 박스와 클래스 점수를 모두 예측하는 모델입니다.</p>
-<img alt="intermediate/../../_static/img/tv_tutorial/tv_image03.png" src="intermediate/../../_static/img/tv_tutorial/tv_image03.png" />
+<img alt="../_static/img/tv_tutorial/tv_image03.png" src="../_static/img/tv_tutorial/tv_image03.png" />
 <p>Mask R-CNN은 각 인스턴스에 대한 분할 마스크 예측하는 추가 분기(레이어)를 Faster R-CNN에 추가한 모델입니다.</p>
-<img alt="intermediate/../../_static/img/tv_tutorial/tv_image04.png" src="intermediate/../../_static/img/tv_tutorial/tv_image04.png" />
+<img alt="../_static/img/tv_tutorial/tv_image04.png" src="../_static/img/tv_tutorial/tv_image04.png" />
 <p>Torchvision 모델주(model zoo, 역자주:미리 학습된 모델들을 모아 놓은 공간)에서 사용 가능한 모델들 중 하나를 이용해
 모델을 수정하려면 보통 두가지 상황이 있습니다.
 첫 번째 방법은 미리 학습된 모델에서 시작해서 마지막 레이어 수준만 미세 조정하는 것입니다.
@@ -474,7 +472,7 @@ <h3>1 - 미리 학습된 모델로부터 미세 조정<a class="headerlink" href
 <span class="kn">from</span> <span class="nn">torchvision.models.detection.faster_rcnn</span> <span class="kn">import</span> <span class="n">FastRCNNPredictor</span>
 
 <span class="c1"># COCO로 미리 학솝된 모델 읽기</span>
-<span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">detection</span><span class="o">.</span><span class="n">fasterrcnn_resnet50_fpn</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">detection</span><span class="o">.</span><span class="n">fasterrcnn_resnet50_fpn</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
 <span class="c1"># 분류기를 새로운 것으로 교체하는데, num_classes는 사용자가 정의합니다</span>
 <span class="n">num_classes</span> <span class="o">=</span> <span class="mi">2</span>  <span class="c1"># 1 클래스(사람) + 배경</span>
@@ -492,7 +490,7 @@ <h3>2 - 다른 백본을 추가하도록 모델을 수정하기<a class="headerl
 <span class="kn">from</span> <span class="nn">torchvision.models.detection.rpn</span> <span class="kn">import</span> <span class="n">AnchorGenerator</span>
 
 <span class="c1"># 분류 목적으로 미리 학습된 모델을 로드하고 특징들만을 리턴하도록 합니다</span>
-<span class="n">backbone</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span><span class="o">.</span><span class="n">features</span>
+<span class="n">backbone</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">features</span>
 <span class="c1"># Faster RCNN은 백본의 출력 채널 수를 알아야 합니다.</span>
 <span class="c1"># mobilenetV2의 경우 1280이므로 여기에 추가해야 합니다.</span>
 <span class="n">backbone</span><span class="o">.</span><span class="n">out_channels</span> <span class="o">=</span> <span class="mi">1280</span>
@@ -532,7 +530,7 @@ <h3>PennFudan 데이터셋을 위한 인스턴스 분할 모델<a class="headerl
 
 <span class="k">def</span> <span class="nf">get_model_instance_segmentation</span><span class="p">(</span><span class="n">num_classes</span><span class="p">):</span>
     <span class="c1"># COCO 에서 미리 학습된 인스턴스 분할 모델을 읽어옵니다</span>
-    <span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">detection</span><span class="o">.</span><span class="n">maskrcnn_resnet50_fpn</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+    <span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">detection</span><span class="o">.</span><span class="n">maskrcnn_resnet50_fpn</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
     <span class="c1"># 분류를 위한 입력 특징 차원을 얻습니다</span>
     <span class="n">in_features</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">roi_heads</span><span class="o">.</span><span class="n">box_predictor</span><span class="o">.</span><span class="n">cls_score</span><span class="o">.</span><span class="n">in_features</span>
@@ -550,17 +548,17 @@ <h3>PennFudan 데이터셋을 위한 인스턴스 분할 모델<a class="headerl
     <span class="k">return</span> <span class="n">model</span>
 </pre></div>
 </div>
-<p>그렇습니다. 이렇게 하면 <a href="#id6"><span class="problematic" id="id7">``</span></a>모델``을 사용자 정의 데이터셋에서 학습하고 평가할 준비가 될 겁니다.</p>
+<p>그렇습니다. 이렇게 하면 <code class="docutils literal notranslate"><span class="pre">모델</span></code> 을 사용자 정의 데이터셋에서 학습하고 평가할 준비가 될 겁니다.</p>
 </div>
 </div>
-<div class="section" id="id8">
-<h2>모든 것을 하나로 합치기<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id6">
+<h2>모든 것을 하나로 합치기<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">references/detection/</span></code> 폴더내에 검출 모델들의 학습과 평과를 쉽게 하기 위한 도움 함수들이 있습니다.
 여기서 <code class="docutils literal notranslate"><span class="pre">references/detection/engine.py</span></code>, <code class="docutils literal notranslate"><span class="pre">references/detection/utils.py</span></code>,
 <code class="docutils literal notranslate"><span class="pre">references/detection/transforms.py</span></code> 를 사용 할 것입니다.
 위 파일들을 폴더로 복사하고 사용합시다.</p>
 <p>데이터 증강 / 변환을 위한 도움 함수를 작성해 봅시다</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">transforms</span> <span class="kn">as</span> <span class="nn">T</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">transforms</span> <span class="k">as</span> <span class="nn">T</span>
 
 <span class="k">def</span> <span class="nf">get_transform</span><span class="p">(</span><span class="n">train</span><span class="p">):</span>
     <span class="n">transforms</span> <span class="o">=</span> <span class="p">[]</span>
@@ -576,10 +574,10 @@ <h2>모든 것을 하나로 합치기<a class="headerlink" href="#id8" title="Pe
 <h2>(선택) <code class="docutils literal notranslate"><span class="pre">forward()</span></code> 메소드 테스트하기<a class="headerlink" href="#forward" title="Permalink to this headline">¶</a></h2>
 <p>데이터셋을 반복하기 전에, 샘플 데이터로 학습과 추론 시 모델이 예상대로
 동작하는지 살펴보는 것이 좋습니다.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">detection</span><span class="o">.</span><span class="n">fasterrcnn_resnet50_fpn</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
-<span class="n">dataset</span> <span class="o">=</span> <span class="n">PennFudanDataset</span><span class="p">(</span><span class="s1">&#39;PennFudanPed&#39;</span><span class="p">,</span> <span class="n">get_transform</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="bp">True</span><span class="p">))</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">detection</span><span class="o">.</span><span class="n">fasterrcnn_resnet50_fpn</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">dataset</span> <span class="o">=</span> <span class="n">PennFudanDataset</span><span class="p">(</span><span class="s1">&#39;PennFudanPed&#39;</span><span class="p">,</span> <span class="n">get_transform</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
 <span class="n">data_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
- <span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+ <span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
  <span class="n">collate_fn</span><span class="o">=</span><span class="n">utils</span><span class="o">.</span><span class="n">collate_fn</span><span class="p">)</span>
 <span class="c1"># 학습 시</span>
 <span class="n">images</span><span class="p">,</span><span class="n">targets</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">iter</span><span class="p">(</span><span class="n">data_loader</span><span class="p">))</span>
@@ -604,8 +602,8 @@ <h2>(선택) <code class="docutils literal notranslate"><span class="pre">forwar
     <span class="c1"># 우리 데이터셋은 두 개의 클래스만 가집니다 - 배경과 사람</span>
     <span class="n">num_classes</span> <span class="o">=</span> <span class="mi">2</span>
     <span class="c1"># 데이터셋과 정의된 변환들을 사용합니다</span>
-    <span class="n">dataset</span> <span class="o">=</span> <span class="n">PennFudanDataset</span><span class="p">(</span><span class="s1">&#39;PennFudanPed&#39;</span><span class="p">,</span> <span class="n">get_transform</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="bp">True</span><span class="p">))</span>
-    <span class="n">dataset_test</span> <span class="o">=</span> <span class="n">PennFudanDataset</span><span class="p">(</span><span class="s1">&#39;PennFudanPed&#39;</span><span class="p">,</span> <span class="n">get_transform</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="bp">False</span><span class="p">))</span>
+    <span class="n">dataset</span> <span class="o">=</span> <span class="n">PennFudanDataset</span><span class="p">(</span><span class="s1">&#39;PennFudanPed&#39;</span><span class="p">,</span> <span class="n">get_transform</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+    <span class="n">dataset_test</span> <span class="o">=</span> <span class="n">PennFudanDataset</span><span class="p">(</span><span class="s1">&#39;PennFudanPed&#39;</span><span class="p">,</span> <span class="n">get_transform</span><span class="p">(</span><span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">))</span>
 
     <span class="c1"># 데이터셋을 학습용과 테스트용으로 나눕니다(역자주: 여기서는 전체의 50개를 테스트에, 나머지를 학습에 사용합니다)</span>
     <span class="n">indices</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randperm</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">dataset</span><span class="p">))</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
@@ -614,11 +612,11 @@ <h2>(선택) <code class="docutils literal notranslate"><span class="pre">forwar
 
     <span class="c1"># 데이터 로더를 학습용과 검증용으로 정의합니다</span>
     <span class="n">data_loader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
-        <span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+        <span class="n">dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
         <span class="n">collate_fn</span><span class="o">=</span><span class="n">utils</span><span class="o">.</span><span class="n">collate_fn</span><span class="p">)</span>
 
     <span class="n">data_loader_test</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
-        <span class="n">dataset_test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
+        <span class="n">dataset_test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
         <span class="n">collate_fn</span><span class="o">=</span><span class="n">utils</span><span class="o">.</span><span class="n">collate_fn</span><span class="p">)</span>
 
     <span class="c1"># 도움 함수를 이용해 모델을 가져옵니다</span>
@@ -647,7 +645,7 @@ <h2>(선택) <code class="docutils literal notranslate"><span class="pre">forwar
         <span class="c1"># 테스트 데이터셋에서 평가를 합니다</span>
         <span class="n">evaluate</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data_loader_test</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">)</span>
 
-    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;That&#39;s it!&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;That&#39;s it!&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>첫번째 에포크의 출력값은 아래와 같을 것입니다:</p>
@@ -728,15 +726,15 @@ <h2>(선택) <code class="docutils literal notranslate"><span class="pre">forwar
 </pre></div>
 </div>
 <p>하지만 예측들의 결과가 어떻게 나왔을까요? 데이터 셋에 이미지 하나를 가져와서 확인해 봅시다.</p>
-<img alt="intermediate/../../_static/img/tv_tutorial/tv_image05.png" src="intermediate/../../_static/img/tv_tutorial/tv_image05.png" />
+<img alt="../_static/img/tv_tutorial/tv_image05.png" src="../_static/img/tv_tutorial/tv_image05.png" />
 <p>학습된 모델이 이미지에서 9개의 사람 인스턴스를 예측했습니다.
 그 중 두어개를 확인해 봅시다:</p>
-<img alt="intermediate/../../_static/img/tv_tutorial/tv_image06.png" src="intermediate/../../_static/img/tv_tutorial/tv_image06.png" />
-<img alt="intermediate/../../_static/img/tv_tutorial/tv_image07.png" src="intermediate/../../_static/img/tv_tutorial/tv_image07.png" />
+<img alt="../_static/img/tv_tutorial/tv_image06.png" src="../_static/img/tv_tutorial/tv_image06.png" />
+<img alt="../_static/img/tv_tutorial/tv_image07.png" src="../_static/img/tv_tutorial/tv_image07.png" />
 <p>결과가 꽤 좋아 보입니다!</p>
 </div>
-<div class="section" id="id9">
-<h2>요약<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id7">
+<h2>요약<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h2>
 <p>이 튜토리얼에서는 사용자 정의 데이터셋에서 인스턴스 분할 모델을 위한 자체 학습 파이프라인을 생성하는 방법을 배웠습니다.
 이를 위해 영상과 정답 및 분할 마스크를 반환하는 <code class="docutils literal notranslate"><span class="pre">torch.utils.data.Dataset</span></code> 클래스를 작성했습니다.
 또한 이 새로운 데이터 셋에 대한 전송 학습(Transfer learning)을 수행하기 위해
@@ -809,9 +807,9 @@ <h2>요약<a class="headerlink" href="#id9" title="Permalink to this headline">
 <li><a class="reference internal" href="#id5">PennFudan 데이터셋을 위한 인스턴스 분할 모델</a></li>
 </ul>
 </li>
-<li><a class="reference internal" href="#id8">모든 것을 하나로 합치기</a></li>
+<li><a class="reference internal" href="#id6">모든 것을 하나로 합치기</a></li>
 <li><a class="reference internal" href="#forward">(선택) <code class="docutils literal notranslate"><span class="pre">forward()</span></code> 메소드 테스트하기</a></li>
-<li><a class="reference internal" href="#id9">요약</a></li>
+<li><a class="reference internal" href="#id7">요약</a></li>
 </ul>
 </li>
 </ul>
@@ -829,11 +827,9 @@ <h2>요약<a class="headerlink" href="#id9" title="Permalink to this headline">
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/objects.inv b/docs/objects.inv
index b1018ae49..50b7f2208 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/prototype/distributed_rpc_profiling.html b/docs/prototype/distributed_rpc_profiling.html
index 33a7b0f84..3141c7c3d 100644
--- a/docs/prototype/distributed_rpc_profiling.html
+++ b/docs/prototype/distributed_rpc_profiling.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -353,7 +353,7 @@ <h2>How to use the Profiler for RPC-based workloads<a class="headerlink" href="#
 <span class="k">def</span> <span class="nf">worker</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
     <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;MASTER_ADDR&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;localhost&quot;</span>
     <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;MASTER_PORT&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;29500&quot;</span>
-    <span class="n">worker_name</span> <span class="o">=</span> <span class="n">f</span><span class="s2">&quot;worker</span><span class="si">{rank}</span><span class="s2">&quot;</span>
+    <span class="n">worker_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;worker</span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">&quot;</span>
 
     <span class="c1"># Initialize RPC framework.</span>
     <span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
@@ -361,13 +361,13 @@ <h2>How to use the Profiler for RPC-based workloads<a class="headerlink" href="#
         <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span>
         <span class="n">world_size</span><span class="o">=</span><span class="n">world_size</span>
     <span class="p">)</span>
-    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;</span><span class="si">{worker_name}</span><span class="s2"> successfully initialized RPC.&quot;</span><span class="p">)</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">worker_name</span><span class="si">}</span><span class="s2"> successfully initialized RPC.&quot;</span><span class="p">)</span>
 
     <span class="k">pass</span> <span class="c1"># to be continued below</span>
 
-    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Rank </span><span class="si">{rank}</span><span class="s2"> waiting for workers and shutting down RPC&quot;</span><span class="p">)</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> waiting for workers and shutting down RPC&quot;</span><span class="p">)</span>
     <span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
-    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Rank </span><span class="si">{rank}</span><span class="s2"> shutdown RPC&quot;</span><span class="p">)</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> shutdown RPC&quot;</span><span class="p">)</span>
 
 
 <span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
@@ -392,7 +392,7 @@ <h2>How to use the Profiler for RPC-based workloads<a class="headerlink" href="#
     <span class="c1"># Above code omitted...</span>
     <span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
         <span class="n">dst_worker_rank</span> <span class="o">=</span> <span class="p">(</span><span class="n">rank</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">world_size</span>
-        <span class="n">dst_worker_name</span> <span class="o">=</span> <span class="n">f</span><span class="s2">&quot;worker</span><span class="si">{dst_worker_rank}</span><span class="s2">&quot;</span>
+        <span class="n">dst_worker_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;worker</span><span class="si">{</span><span class="n">dst_worker_rank</span><span class="si">}</span><span class="s2">&quot;</span>
         <span class="n">t1</span><span class="p">,</span> <span class="n">t2</span> <span class="o">=</span> <span class="n">random_tensor</span><span class="p">(),</span> <span class="n">random_tensor</span><span class="p">()</span>
         <span class="c1"># Send and wait RPC completion under profiling scope.</span>
         <span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">()</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
@@ -487,7 +487,7 @@ <h2>How to use the Profiler for RPC-based workloads<a class="headerlink" href="#
     <span class="c1"># Will generated trace for above profiling output</span>
     <span class="n">trace_file</span> <span class="o">=</span> <span class="s2">&quot;/tmp/trace.json&quot;</span>
     <span class="n">prof</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="n">trace_file</span><span class="p">)</span>
-    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Wrote trace to </span><span class="si">{trace_file}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Wrote trace to </span><span class="si">{</span><span class="n">trace_file</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Now, we can load the trace file in Chrome (<code class="docutils literal notranslate"><span class="pre">chrome://tracing</span></code>). We should see output similar to
@@ -520,7 +520,7 @@ <h2>How to use the Profiler for RPC-based workloads<a class="headerlink" href="#
 <span class="k">def</span> <span class="nf">worker</span><span class="p">(</span><span class="n">rank</span><span class="p">,</span> <span class="n">world_size</span><span class="p">):</span>
   <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;MASTER_ADDR&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;localhost&quot;</span>
   <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">&quot;MASTER_PORT&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;29500&quot;</span>
-  <span class="n">worker_name</span> <span class="o">=</span> <span class="n">f</span><span class="s2">&quot;worker</span><span class="si">{rank}</span><span class="s2">&quot;</span>
+  <span class="n">worker_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;worker</span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2">&quot;</span>
 
   <span class="c1"># Initialize RPC framework.</span>
   <span class="n">rpc</span><span class="o">.</span><span class="n">init_rpc</span><span class="p">(</span>
@@ -528,11 +528,11 @@ <h2>How to use the Profiler for RPC-based workloads<a class="headerlink" href="#
       <span class="n">rank</span><span class="o">=</span><span class="n">rank</span><span class="p">,</span>
       <span class="n">world_size</span><span class="o">=</span><span class="n">world_size</span>
   <span class="p">)</span>
-  <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;</span><span class="si">{worker_name}</span><span class="s2"> successfully initialized RPC.&quot;</span><span class="p">)</span>
+  <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">worker_name</span><span class="si">}</span><span class="s2"> successfully initialized RPC.&quot;</span><span class="p">)</span>
 
   <span class="k">if</span> <span class="n">rank</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
     <span class="n">dst_worker_rank</span> <span class="o">=</span> <span class="p">(</span><span class="n">rank</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="n">world_size</span>
-    <span class="n">dst_worker_name</span> <span class="o">=</span> <span class="n">f</span><span class="s2">&quot;worker</span><span class="si">{dst_worker_rank}</span><span class="s2">&quot;</span>
+    <span class="n">dst_worker_name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;worker</span><span class="si">{</span><span class="n">dst_worker_rank</span><span class="si">}</span><span class="s2">&quot;</span>
     <span class="n">t1</span><span class="p">,</span> <span class="n">t2</span> <span class="o">=</span> <span class="n">random_tensor</span><span class="p">(),</span> <span class="n">random_tensor</span><span class="p">()</span>
     <span class="c1"># Send and wait RPC completion under profiling scope.</span>
     <span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">()</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
@@ -551,12 +551,12 @@ <h2>How to use the Profiler for RPC-based workloads<a class="headerlink" href="#
 
     <span class="n">trace_file</span> <span class="o">=</span> <span class="s2">&quot;/tmp/trace.json&quot;</span>
     <span class="n">prof</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="n">trace_file</span><span class="p">)</span>
-    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Wrote trace to </span><span class="si">{trace_file}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Wrote trace to </span><span class="si">{</span><span class="n">trace_file</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 
 
-  <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Rank </span><span class="si">{rank}</span><span class="s2"> waiting for workers and shutting down RPC&quot;</span><span class="p">)</span>
+  <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> waiting for workers and shutting down RPC&quot;</span><span class="p">)</span>
   <span class="n">rpc</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>
-  <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="n">f</span><span class="s2">&quot;Rank </span><span class="si">{rank}</span><span class="s2"> shutdown RPC&quot;</span><span class="p">)</span>
+  <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Rank </span><span class="si">{</span><span class="n">rank</span><span class="si">}</span><span class="s2"> shutdown RPC&quot;</span><span class="p">)</span>
 
 
 
@@ -642,11 +642,9 @@ <h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/prototype/graph_mode_dynamic_bert_tutorial.html b/docs/prototype/graph_mode_dynamic_bert_tutorial.html
index 88b4912b7..63b6e1262 100644
--- a/docs/prototype/graph_mode_dynamic_bert_tutorial.html
+++ b/docs/prototype/graph_mode_dynamic_bert_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -328,7 +328,7 @@ <h3>1.1 Setup<a class="headerlink" href="#setup" title="Permalink to this headli
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">absolute_import</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">print_function</span>
 
 <span class="kn">import</span> <span class="nn">logging</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">random</span>
 <span class="kn">import</span> <span class="nn">sys</span>
@@ -349,9 +349,9 @@ <h3>1.1 Setup<a class="headerlink" href="#setup" title="Permalink to this headli
 
 <span class="n">global_rng</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">Random</span><span class="p">()</span>
 
-<span class="k">def</span> <span class="nf">ids_tensor</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">rng</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">ids_tensor</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">vocab_size</span><span class="p">,</span> <span class="n">rng</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
     <span class="c1">#  Creates a random int32 tensor of the shape within the vocab size</span>
-    <span class="k">if</span> <span class="n">rng</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">rng</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
         <span class="n">rng</span> <span class="o">=</span> <span class="n">global_rng</span>
 
     <span class="n">total_dims</span> <span class="o">=</span> <span class="mi">1</span>
@@ -366,17 +366,17 @@ <h3>1.1 Setup<a class="headerlink" href="#setup" title="Permalink to this headli
 
 <span class="c1"># Setup logging</span>
 <span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
-<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="n">format</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> - </span><span class="si">%(levelname)s</span><span class="s1"> - </span><span class="si">%(name)s</span><span class="s1"> -   </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span>
+<span class="n">logging</span><span class="o">.</span><span class="n">basicConfig</span><span class="p">(</span><span class="nb">format</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">%(asctime)s</span><span class="s1"> - </span><span class="si">%(levelname)s</span><span class="s1"> - </span><span class="si">%(name)s</span><span class="s1"> -   </span><span class="si">%(message)s</span><span class="s1">&#39;</span><span class="p">,</span>
                     <span class="n">datefmt</span> <span class="o">=</span> <span class="s1">&#39;%m/</span><span class="si">%d</span><span class="s1">/%Y %H:%M:%S&#39;</span><span class="p">,</span>
                     <span class="n">level</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">WARN</span><span class="p">)</span>
 
 <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;transformers.modeling_utils&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">setLevel</span><span class="p">(</span>
    <span class="n">logging</span><span class="o">.</span><span class="n">WARN</span><span class="p">)</span>  <span class="c1"># Reduce logging</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
 
 <span class="n">torch</span><span class="o">.</span><span class="n">set_num_threads</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__config__</span><span class="o">.</span><span class="n">parallel_info</span><span class="p">())</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__config__</span><span class="o">.</span><span class="n">parallel_info</span><span class="p">())</span>
 </pre></div>
 </div>
 </div>
@@ -409,14 +409,14 @@ <h3>1.3 Set global BERT configurations<a class="headerlink" href="#set-global-be
 <span class="n">configs</span><span class="o">.</span><span class="n">output_mode</span> <span class="o">=</span> <span class="n">output_modes</span><span class="p">[</span><span class="n">configs</span><span class="o">.</span><span class="n">task_name</span><span class="p">]</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">label_list</span> <span class="o">=</span> <span class="n">configs</span><span class="o">.</span><span class="n">processor</span><span class="o">.</span><span class="n">get_labels</span><span class="p">()</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="s2">&quot;bert&quot;</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-<span class="n">configs</span><span class="o">.</span><span class="n">do_lower_case</span> <span class="o">=</span> <span class="bp">True</span>
+<span class="n">configs</span><span class="o">.</span><span class="n">do_lower_case</span> <span class="o">=</span> <span class="kc">True</span>
 
 <span class="c1"># Set the device, batch size, topology, and caching flags.</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">per_gpu_eval_batch_size</span> <span class="o">=</span> <span class="mi">8</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">n_gpu</span> <span class="o">=</span> <span class="mi">0</span>
 <span class="n">configs</span><span class="o">.</span><span class="n">local_rank</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
-<span class="n">configs</span><span class="o">.</span><span class="n">overwrite_cache</span> <span class="o">=</span> <span class="bp">False</span>
+<span class="n">configs</span><span class="o">.</span><span class="n">overwrite_cache</span> <span class="o">=</span> <span class="kc">False</span>
 
 <span class="c1"># Set random seed for reproducibility.</span>
 <span class="k">def</span> <span class="nf">set_seed</span><span class="p">(</span><span class="n">seed</span><span class="p">):</span>
@@ -428,7 +428,7 @@ <h3>1.3 Set global BERT configurations<a class="headerlink" href="#set-global-be
 <span class="n">tokenizer</span> <span class="o">=</span> <span class="n">BertTokenizer</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
     <span class="n">configs</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">do_lower_case</span><span class="o">=</span><span class="n">configs</span><span class="o">.</span><span class="n">do_lower_case</span><span class="p">)</span>
 
-<span class="n">model</span> <span class="o">=</span> <span class="n">BertForSequenceClassification</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">configs</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">torchscript</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">BertForSequenceClassification</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="n">configs</span><span class="o">.</span><span class="n">output_dir</span><span class="p">,</span> <span class="n">torchscript</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">configs</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -489,7 +489,7 @@ <h2>2. Evaluation<a class="headerlink" href="#evaluation" title="Permalink to th
 
     <span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
     <span class="k">for</span> <span class="n">eval_task</span><span class="p">,</span> <span class="n">eval_output_dir</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">eval_task_names</span><span class="p">,</span> <span class="n">eval_outputs_dirs</span><span class="p">):</span>
-        <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">load_and_cache_examples</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">eval_task</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">evaluate</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+        <span class="n">eval_dataset</span> <span class="o">=</span> <span class="n">load_and_cache_examples</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">eval_task</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">evaluate</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
         <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">eval_output_dir</span><span class="p">)</span> <span class="ow">and</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="ow">in</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]:</span>
             <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">eval_output_dir</span><span class="p">)</span>
@@ -504,12 +504,12 @@ <h2>2. Evaluation<a class="headerlink" href="#evaluation" title="Permalink to th
             <span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">DataParallel</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 
         <span class="c1"># Eval!</span>
-        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Running evaluation {} *****&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Running evaluation </span><span class="si">{}</span><span class="s2"> *****&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
         <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;  Num examples = </span><span class="si">%d</span><span class="s2">&quot;</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">eval_dataset</span><span class="p">))</span>
         <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;  Batch size = </span><span class="si">%d</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">eval_batch_size</span><span class="p">)</span>
         <span class="n">nb_eval_steps</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="n">preds</span> <span class="o">=</span> <span class="bp">None</span>
-        <span class="n">out_label_ids</span> <span class="o">=</span> <span class="bp">None</span>
+        <span class="n">preds</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">out_label_ids</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="k">for</span> <span class="n">batch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">eval_dataloader</span><span class="p">,</span> <span class="n">desc</span><span class="o">=</span><span class="s2">&quot;Evaluating&quot;</span><span class="p">):</span>
             <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
             <span class="n">batch</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">t</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">)</span>
@@ -519,11 +519,11 @@ <h2>2. Evaluation<a class="headerlink" href="#evaluation" title="Permalink to th
                           <span class="s1">&#39;attention_mask&#39;</span><span class="p">:</span> <span class="n">batch</span><span class="p">[</span><span class="mi">1</span><span class="p">]}</span>
                 <span class="n">labels</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
                 <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="o">!=</span> <span class="s1">&#39;distilbert&#39;</span><span class="p">:</span>
-                    <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bert&#39;</span><span class="p">,</span> <span class="s1">&#39;xlnet&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="bp">None</span>  <span class="c1"># XLM, DistilBERT and RoBERTa don&#39;t use segment_ids</span>
+                    <span class="n">inputs</span><span class="p">[</span><span class="s1">&#39;input&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bert&#39;</span><span class="p">,</span> <span class="s1">&#39;xlnet&#39;</span><span class="p">]</span> <span class="k">else</span> <span class="kc">None</span>  <span class="c1"># XLM, DistilBERT and RoBERTa don&#39;t use segment_ids</span>
                 <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="o">**</span><span class="n">inputs</span><span class="p">)</span>
                 <span class="n">logits</span> <span class="o">=</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
             <span class="n">nb_eval_steps</span> <span class="o">+=</span> <span class="mi">1</span>
-            <span class="k">if</span> <span class="n">preds</span> <span class="ow">is</span> <span class="bp">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">preds</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
                 <span class="n">preds</span> <span class="o">=</span> <span class="n">logits</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
                 <span class="n">out_label_ids</span> <span class="o">=</span> <span class="n">labels</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
             <span class="k">else</span><span class="p">:</span>
@@ -539,23 +539,23 @@ <h2>2. Evaluation<a class="headerlink" href="#evaluation" title="Permalink to th
 
         <span class="n">output_eval_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">eval_output_dir</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="s2">&quot;eval_results.txt&quot;</span><span class="p">)</span>
         <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">output_eval_file</span><span class="p">,</span> <span class="s2">&quot;w&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">writer</span><span class="p">:</span>
-            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Eval results {} *****&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
+            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;***** Eval results </span><span class="si">{}</span><span class="s2"> *****&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">prefix</span><span class="p">))</span>
             <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
                 <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;  </span><span class="si">%s</span><span class="s2"> = </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]))</span>
                 <span class="n">writer</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="s2">&quot;</span><span class="si">%s</span><span class="s2"> = </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">])))</span>
 
     <span class="k">return</span> <span class="n">results</span>
 
-<span class="k">def</span> <span class="nf">load_and_cache_examples</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">evaluate</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
+<span class="k">def</span> <span class="nf">load_and_cache_examples</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">task</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">evaluate</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
     <span class="k">if</span> <span class="n">args</span><span class="o">.</span><span class="n">local_rank</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">evaluate</span><span class="p">:</span>
         <span class="n">torch</span><span class="o">.</span><span class="n">distributed</span><span class="o">.</span><span class="n">barrier</span><span class="p">()</span>  <span class="c1"># Make sure only the first process in distributed training process the dataset, and the others will use the cache</span>
 
     <span class="n">processor</span> <span class="o">=</span> <span class="n">processors</span><span class="p">[</span><span class="n">task</span><span class="p">]()</span>
     <span class="n">output_mode</span> <span class="o">=</span> <span class="n">output_modes</span><span class="p">[</span><span class="n">task</span><span class="p">]</span>
     <span class="c1"># Load data features from cache or dataset file</span>
-    <span class="n">cached_features_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;cached_{}_{}_{}_{}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+    <span class="n">cached_features_file</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="s1">&#39;cached_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">_</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
         <span class="s1">&#39;dev&#39;</span> <span class="k">if</span> <span class="n">evaluate</span> <span class="k">else</span> <span class="s1">&#39;train&#39;</span><span class="p">,</span>
-        <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="bp">None</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)))</span><span class="o">.</span><span class="n">pop</span><span class="p">(),</span>
+        <span class="nb">list</span><span class="p">(</span><span class="nb">filter</span><span class="p">(</span><span class="kc">None</span><span class="p">,</span> <span class="n">args</span><span class="o">.</span><span class="n">model_name_or_path</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)))</span><span class="o">.</span><span class="n">pop</span><span class="p">(),</span>
         <span class="nb">str</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">max_seq_length</span><span class="p">),</span>
         <span class="nb">str</span><span class="p">(</span><span class="n">task</span><span class="p">)))</span>
     <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">cached_features_file</span><span class="p">)</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">args</span><span class="o">.</span><span class="n">overwrite_cache</span><span class="p">:</span>
@@ -597,8 +597,8 @@ <h2>2. Evaluation<a class="headerlink" href="#evaluation" title="Permalink to th
     <span class="n">result</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">configs</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">tokenizer</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
     <span class="n">eval_end_time</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
     <span class="n">eval_duration_time</span> <span class="o">=</span> <span class="n">eval_end_time</span> <span class="o">-</span> <span class="n">eval_start_time</span>
-    <span class="k">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
-    <span class="k">print</span><span class="p">(</span><span class="s2">&quot;Evaluate total time (seconds): {0:.1f}&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">eval_duration_time</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluate total time (seconds): </span><span class="si">{0:.1f}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">eval_duration_time</span><span class="p">))</span>
 </pre></div>
 </div>
 <div class="section" id="check-model-size">
@@ -609,12 +609,12 @@ <h3>2.1 Check Model Size<a class="headerlink" href="#check-model-size" title="Pe
         <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;temp.p&quot;</span><span class="p">)</span>
     <span class="k">else</span><span class="p">:</span>
         <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">),</span> <span class="s2">&quot;temp.p&quot;</span><span class="p">)</span>
-    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Size (MB):&#39;</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getsize</span><span class="p">(</span><span class="s2">&quot;temp.p&quot;</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Size (MB):&#39;</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">getsize</span><span class="p">(</span><span class="s2">&quot;temp.p&quot;</span><span class="p">)</span><span class="o">/</span><span class="mf">1e6</span><span class="p">)</span>
     <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">&#39;temp.p&#39;</span><span class="p">)</span>
 
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Size of model before quantization&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Size of model before quantization&quot;</span><span class="p">)</span>
 <span class="n">print_size_of_model</span><span class="p">(</span><span class="n">traced_model</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Size of model after quantization&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Size of model after quantization&quot;</span><span class="p">)</span>
 
 <span class="n">print_size_of_model</span><span class="p">(</span><span class="n">quantized_model</span><span class="p">)</span>
 </pre></div>
@@ -655,7 +655,7 @@ <h2>3. Debugging the Quantized Model<a class="headerlink" href="#debugging-the-q
 <li><p>We can access the attributes of the quantized model the same way as in a torchscript model, e.g. model.fc1.weight (might be harder if you use a module list or sequential).</p></li>
 <li><p>The arithmetic operations all occur in floating point with the numerics being identical to the final quantized model, allowing for debugging.</p></li>
 </ul>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">quantized_model_debug</span> <span class="o">=</span> <span class="n">quantize_dynamic_jit</span><span class="p">(</span><span class="n">traced_model</span><span class="p">,</span> <span class="n">qconfig_dict</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">quantized_model_debug</span> <span class="o">=</span> <span class="n">quantize_dynamic_jit</span><span class="p">(</span><span class="n">traced_model</span><span class="p">,</span> <span class="n">qconfig_dict</span><span class="p">,</span> <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>Calling <code class="docutils literal notranslate"><span class="pre">quantize_dynamic_jit</span></code> is equivalent to calling <code class="docutils literal notranslate"><span class="pre">prepare_dynamic_jit</span></code> followed by <code class="docutils literal notranslate"><span class="pre">convert_dynamic_jit</span></code>. Usage of the one-line API is recommended. But if you wish to debug or analyze the model after each step, the multi-line API comes into use.</p>
@@ -673,7 +673,7 @@ <h3>3.1. Evaluate the Debug Model<a class="headerlink" href="#evaluate-the-debug
 </div>
 <p>Note that the accuracy of the debug version is close to, but not exactly the same as the non-debug version as the debug version uses floating point ops to emulate quantized ops and the numerics match is approximate.
 This is the case only for per-channel quantization (we are working on improving this). Per-tensor quantization (using default_dynamic_qconfig) has exact numerics match between debug and non-debug version.</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">quantized_model_debug</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">quantized_model_debug</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>
 </pre></div>
 </div>
 <p>Snippet of the graph printed -</p>
@@ -702,7 +702,7 @@ <h3>3.1. Evaluate the Debug Model<a class="headerlink" href="#evaluate-the-debug
 </div>
 <p>Size of the debug model is the close to the floating point model because all the weights are in float and not yet quantized and frozen, this allows people to inspect the weight.
 You may access the weight attributes directly in the torchscript model. Accessing the weight in the debug model is the same as accessing the weight in a TorchScript model:</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">encoder</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">_c</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;0&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">attention</span><span class="o">.</span><span class="n">self</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">encoder</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">_c</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;0&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">attention</span><span class="o">.</span><span class="n">self</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">tensor</span><span class="p">([[</span><span class="o">-</span><span class="mf">0.0157</span><span class="p">,</span>  <span class="mf">0.0257</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.0269</span><span class="p">,</span>  <span class="o">...</span><span class="p">,</span>  <span class="mf">0.0158</span><span class="p">,</span>  <span class="mf">0.0764</span><span class="p">,</span>  <span class="mf">0.0548</span><span class="p">],</span>
@@ -716,8 +716,8 @@ <h3>3.1. Evaluate the Debug Model<a class="headerlink" href="#evaluate-the-debug
 </pre></div>
 </div>
 <p>Accessing the scale and zero_point for the corresponding weight can be done as follows -</p>
-<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">encoder</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">_c</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;0&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">attention</span><span class="o">.</span><span class="n">self</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;4_scale_0&#39;</span><span class="p">))</span>
-<span class="k">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">encoder</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">_c</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;0&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">attention</span><span class="o">.</span><span class="n">self</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;4_zero_point_0&#39;</span><span class="p">))</span>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">encoder</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">_c</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;0&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">attention</span><span class="o">.</span><span class="n">self</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;4_scale_0&#39;</span><span class="p">))</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="o">.</span><span class="n">bert</span><span class="o">.</span><span class="n">encoder</span><span class="o">.</span><span class="n">layer</span><span class="o">.</span><span class="n">_c</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;0&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">attention</span><span class="o">.</span><span class="n">self</span><span class="o">.</span><span class="n">query</span><span class="o">.</span><span class="n">getattr</span><span class="p">(</span><span class="s1">&#39;4_zero_point_0&#39;</span><span class="p">))</span>
 </pre></div>
 </div>
 <p>Since we use per-channel quantization, we get per-channel scales tensor.</p>
@@ -769,13 +769,13 @@ <h2>5. Benchmarking the Model<a class="headerlink" href="#benchmarking-the-model
         <span class="n">output</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">input_ids</span><span class="p">,</span> <span class="n">token_type_ids</span><span class="p">,</span> <span class="n">attention_mask</span><span class="p">)</span>
         <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
         <span class="n">elapsed</span> <span class="o">=</span> <span class="n">elapsed</span> <span class="o">+</span> <span class="p">(</span><span class="n">end</span> <span class="o">-</span> <span class="n">start</span><span class="p">)</span>
-    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;Elapsed time: &#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">elapsed</span> <span class="o">/</span> <span class="mi">50</span><span class="p">),</span> <span class="s1">&#39; s&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Elapsed time: &#39;</span><span class="p">,</span> <span class="p">(</span><span class="n">elapsed</span> <span class="o">/</span> <span class="mi">50</span><span class="p">),</span> <span class="s1">&#39; s&#39;</span><span class="p">)</span>
     <span class="k">return</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Running benchmark for Float model&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Running benchmark for Float model&quot;</span><span class="p">)</span>
 <span class="n">benchmark</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">jit_model_path_float</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Running benchmark for Eager Mode Quantized model&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Running benchmark for Eager Mode Quantized model&quot;</span><span class="p">)</span>
 <span class="n">benchmark</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">jit_model_path_eager</span><span class="p">)</span>
-<span class="k">print</span><span class="p">(</span><span class="s2">&quot;Running benchmark for Graph Mode Quantized model&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Running benchmark for Graph Mode Quantized model&quot;</span><span class="p">)</span>
 <span class="n">benchmark</span><span class="p">(</span><span class="n">args</span><span class="o">.</span><span class="n">jit_model_path_graph</span><span class="p">)</span>
 </pre></div>
 </div>
@@ -879,11 +879,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/prototype/graph_mode_static_quantization_tutorial.html b/docs/prototype/graph_mode_static_quantization_tutorial.html
index 0d2ad4367..4d1613bca 100644
--- a/docs/prototype/graph_mode_static_quantization_tutorial.html
+++ b/docs/prototype/graph_mode_static_quantization_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -360,8 +360,8 @@ <h2>2. Define Helper Functions and Prepare Dataset<a class="headerlink" href="#d
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">DataLoader</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span>
 <span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">time</span>
@@ -384,12 +384,12 @@ <h2>2. Define Helper Functions and Prepare Dataset<a class="headerlink" href="#d
 <span class="n">_</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">manual_seed</span><span class="p">(</span><span class="mi">191009</span><span class="p">)</span>
 
 
-<span class="kn">from</span> <span class="nn">torchvision.models.resnet</span> <span class="k">import</span> <span class="n">resnet18</span>
-<span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="k">import</span> <span class="n">get_default_qconfig</span><span class="p">,</span> <span class="n">quantize_jit</span>
+<span class="kn">from</span> <span class="nn">torchvision.models.resnet</span> <span class="kn">import</span> <span class="n">resnet18</span>
+<span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="kn">import</span> <span class="n">get_default_qconfig</span><span class="p">,</span> <span class="n">quantize_jit</span>
 
 <span class="k">class</span> <span class="nc">AverageMeter</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Computes and stores the average and current value&quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;:f&#39;</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">fmt</span><span class="o">=</span><span class="s1">&#39;:f&#39;</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">=</span> <span class="n">fmt</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
@@ -406,7 +406,7 @@ <h2>2. Define Helper Functions and Prepare Dataset<a class="headerlink" href="#d
         <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">n</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">avg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span>
 
-    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">fmtstr</span> <span class="o">=</span> <span class="s1">&#39;</span><span class="si">{name}</span><span class="s1"> {val&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="s1">&#39;} ({avg&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">fmt</span> <span class="o">+</span> <span class="s1">&#39;})&#39;</span>
         <span class="k">return</span> <span class="n">fmtstr</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">)</span>
 
@@ -530,7 +530,7 @@ <h2>4. Specify how to quantize the model with <code class="docutils literal notr
       <span class="s1">&#39;&#39;</span> <span class="p">:</span> <span class="n">qconfig_global</span><span class="p">,</span>
      <span class="s1">&#39;sub&#39;</span> <span class="p">:</span> <span class="n">qconfig_sub</span><span class="p">,</span>
       <span class="s1">&#39;sub.fc&#39;</span> <span class="p">:</span> <span class="n">qconfig_fc</span><span class="p">,</span>
-     <span class="s1">&#39;sub.conv&#39;</span><span class="p">:</span> <span class="bp">None</span>
+     <span class="s1">&#39;sub.conv&#39;</span><span class="p">:</span> <span class="kc">None</span>
 <span class="p">}</span>
 </pre></div>
 </div>
@@ -566,8 +566,8 @@ <h2>6. Quantize<a class="headerlink" href="#quantize" title="Permalink to this h
     <span class="p">{</span><span class="s1">&#39;&#39;</span><span class="p">:</span> <span class="n">qconfig</span><span class="p">},</span> <span class="c1"># qconfig dict</span>
     <span class="n">calibrate</span><span class="p">,</span> <span class="c1"># calibration function</span>
     <span class="p">[</span><span class="n">data_loader_test</span><span class="p">],</span> <span class="c1"># positional arguments to calibration function, typically some sample dataset</span>
-    <span class="n">inplace</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span> <span class="c1"># whether to modify the model inplace or not</span>
-    <span class="n">debug</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span> <span class="c1"># whether to prduce a debug friendly model or not</span>
+    <span class="n">inplace</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="c1"># whether to modify the model inplace or not</span>
+    <span class="n">debug</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># whether to prduce a debug friendly model or not</span>
 </pre></div>
 </div>
 <p>There are three things we do in <code class="docutils literal notranslate"><span class="pre">quantize_jit</span></code>:</p>
@@ -598,171 +598,6 @@ <h2>6. Quantize<a class="headerlink" href="#quantize" title="Permalink to this h
 <span class="nb">print</span><span class="p">(</span><span class="n">quantized_model</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self : __torch__.torchvision.models.resnet.___torch_mangle_228.ResNet,
-      %x.2 : Tensor):
-  %639 : QInt8(1000:512, 512:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %637 : QInt8(512:4608, 512:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %635 : QInt8(512:4608, 512:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %633 : QInt8(512:256, 256:1, 1:1, 1:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %631 : QInt8(512:4608, 512:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %629 : QInt8(512:2304, 256:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %627 : QInt8(256:2304, 256:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %625 : QInt8(256:2304, 256:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %623 : QInt8(256:128, 128:1, 1:1, 1:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %621 : QInt8(256:2304, 256:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %619 : QInt8(256:1152, 128:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %617 : QInt8(128:1152, 128:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %615 : QInt8(128:1152, 128:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %613 : QInt8(128:64, 64:1, 1:1, 1:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %611 : QInt8(128:1152, 128:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %609 : QInt8(128:576, 64:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %607 : QInt8(64:576, 64:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %605 : QInt8(64:576, 64:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %603 : QInt8(64:576, 64:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %601 : QInt8(64:576, 64:9, 3:3, 3:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %599 : QInt8(64:147, 3:49, 7:7, 7:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %14 : int = prim::Constant[value=-1]()
-  %13 : int = prim::Constant[value=1]() # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:214:29
-  %12 : int[] = prim::Constant[value=[2, 2]]()
-  %11 : int[] = prim::Constant[value=[3, 3]]()
-  %10 : int[] = prim::Constant[value=[1, 1]]()
-  %9 : int = prim::Constant[value=2]() # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/pooling.py:157:53
-  %8 : bool = prim::Constant[value=0]() # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/pooling.py:158:57
-  %7 : int[] = prim::Constant[value=[0, 0]]()
-  %6 : str = prim::Constant[value=&quot;Exception&quot;]() # &lt;string&gt;:5:2
-  %self.4_zero_point_0 : int = prim::Constant[value=34]()
-  %self.4_scale_0 : float = prim::Constant[value=0.28741154074668884]()
-  %self.fc.bias : Float(1000:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer4.1.out.14_scale_0 : float = prim::Constant[value=0.19620364904403687]()
-  %self.layer4.1.out.8_zero_point_0 : int = prim::Constant[value=41]()
-  %self.layer4.1.out.8_scale_0 : float = prim::Constant[value=0.2577512264251709]()
-  %self.layer4.1.conv2.bias : Float(512:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer4.1.out.6_scale_0 : float = prim::Constant[value=0.014143082313239574]()
-  %self.layer4.1.conv1.bias : Float(512:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer4.0.out.14_scale_0 : float = prim::Constant[value=0.03087528795003891]()
-  %self.layer4.0.identity.2_zero_point_0 : int = prim::Constant[value=64]()
-  %self.layer4.0.identity.2_scale_0 : float = prim::Constant[value=0.037815552204847336]()
-  %self.layer4.0.downsample.0.bias : Float(512:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer4.0.out.8_zero_point_0 : int = prim::Constant[value=65]()
-  %self.layer4.0.out.8_scale_0 : float = prim::Constant[value=0.053702641278505325]()
-  %self.layer4.0.conv2.bias : Float(512:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer4.0.out.6_scale_0 : float = prim::Constant[value=0.013209396041929722]()
-  %self.layer4.0.conv1.bias : Float(512:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer3.1.out.14_scale_0 : float = prim::Constant[value=0.02887437492609024]()
-  %self.layer3.1.out.8_zero_point_0 : int = prim::Constant[value=75]()
-  %self.layer3.1.out.8_scale_0 : float = prim::Constant[value=0.051453046500682831]()
-  %self.layer3.1.conv2.bias : Float(256:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer3.1.out.6_scale_0 : float = prim::Constant[value=0.01376732811331749]()
-  %self.layer3.1.conv1.bias : Float(256:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer3.0.out.14_scale_0 : float = prim::Constant[value=0.026459669694304466]()
-  %self.layer3.0.identity.2_zero_point_0 : int = prim::Constant[value=81]()
-  %self.layer3.0.identity.2_scale_0 : float = prim::Constant[value=0.015993364155292511]()
-  %self.layer3.0.downsample.0.bias : Float(256:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer3.0.out.8_zero_point_0 : int = prim::Constant[value=45]()
-  %self.layer3.0.out.8_scale_0 : float = prim::Constant[value=0.05363830178976059]()
-  %self.layer3.0.conv2.bias : Float(256:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer3.0.out.6_scale_0 : float = prim::Constant[value=0.018105363473296165]()
-  %self.layer3.0.conv1.bias : Float(256:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer2.1.out.14_scale_0 : float = prim::Constant[value=0.031941868364810944]()
-  %self.layer2.1.out.8_zero_point_0 : int = prim::Constant[value=70]()
-  %self.layer2.1.out.8_scale_0 : float = prim::Constant[value=0.046229708939790726]()
-  %self.layer2.1.conv2.bias : Float(128:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer2.1.out.6_scale_0 : float = prim::Constant[value=0.013803998939692974]()
-  %self.layer2.1.conv1.bias : Float(128:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer2.0.out.14_scale_0 : float = prim::Constant[value=0.026813706383109093]()
-  %self.layer2.0.identity.2_zero_point_0 : int = prim::Constant[value=67]()
-  %self.layer2.0.identity.2_scale_0 : float = prim::Constant[value=0.034867286682128906]()
-  %self.layer2.0.downsample.0.bias : Float(128:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer2.0.out.8_scale_0 : float = prim::Constant[value=0.045976739376783371]()
-  %self.layer2.0.conv2.bias : Float(128:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer2.0.out.6_scale_0 : float = prim::Constant[value=0.013110089115798473]()
-  %self.layer2.0.conv1.bias : Float(128:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer1.1.out.14_scale_0 : float = prim::Constant[value=0.036671146750450134]()
-  %self.layer1.1.out.8_zero_point_0 : int = prim::Constant[value=79]()
-  %self.layer1.1.out.8_scale_0 : float = prim::Constant[value=0.063445292413234711]()
-  %self.layer1.1.conv2.bias : Float(64:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer1.1.out.6_scale_0 : float = prim::Constant[value=0.014171220362186432]()
-  %self.layer1.1.conv1.bias : Float(64:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer1.0.out.14_scale_0 : float = prim::Constant[value=0.033903006464242935]()
-  %self.layer1.0.out.8_zero_point_0 : int = prim::Constant[value=73]()
-  %self.layer1.0.out.8_scale_0 : float = prim::Constant[value=0.05241066962480545]()
-  %self.layer1.0.conv2.bias : Float(64:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.layer1.0.out.6_scale_0 : float = prim::Constant[value=0.01314008142799139]()
-  %self.layer1.0.conv1.bias : Float(64:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.x.8_scale_0 : float = prim::Constant[value=0.026803469285368919]()
-  %self.conv1.bias : Float(64:1) = prim::Constant[value=&lt;Tensor&gt;]()
-  %self.conv1.2_axis_0 : int = prim::Constant[value=0]()
-  %self.x.2_scalar_type_0 : int = prim::Constant[value=13]()
-  %self.x.2_zero_point_0 : int = prim::Constant[value=57]()
-  %self.x.2_scale_0 : float = prim::Constant[value=0.037445519119501114]()
-  %x.2.quant : Tensor = aten::quantize_per_tensor(%x.2, %self.x.2_scale_0, %self.x.2_zero_point_0, %self.x.2_scalar_type_0)
-  %641 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_0&quot;](%self)
-  %27 : Tensor = quantized::conv2d_relu(%x.2.quant, %641, %self.x.8_scale_0, %self.conv1.2_axis_0)
-  %x.9 : Tensor = aten::max_pool2d(%27, %11, %12, %10, %10, %8) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:575:11
-  %642 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_1&quot;](%self)
-  %48 : Tensor = quantized::conv2d_relu(%x.9, %642, %self.layer1.0.out.6_scale_0, %self.conv1.2_axis_0)
-  %643 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_2&quot;](%self)
-  %61 : Tensor = quantized::conv2d(%48, %643, %self.layer1.0.out.8_scale_0, %self.layer1.0.out.8_zero_point_0)
-  %65 : Tensor = quantized::add_relu(%61, %x.9, %self.layer1.0.out.14_scale_0, %self.conv1.2_axis_0)
-  %644 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_3&quot;](%self)
-  %78 : Tensor = quantized::conv2d_relu(%65, %644, %self.layer1.1.out.6_scale_0, %self.conv1.2_axis_0)
-  %645 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_4&quot;](%self)
-  %91 : Tensor = quantized::conv2d(%78, %645, %self.layer1.1.out.8_scale_0, %self.layer1.1.out.8_zero_point_0)
-  %95 : Tensor = quantized::add_relu(%91, %65, %self.layer1.1.out.14_scale_0, %self.conv1.2_axis_0)
-  %646 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_5&quot;](%self)
-  %111 : Tensor = quantized::conv2d_relu(%95, %646, %self.layer2.0.out.6_scale_0, %self.conv1.2_axis_0)
-  %647 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_6&quot;](%self)
-  %124 : Tensor = quantized::conv2d(%111, %647, %self.layer2.0.out.8_scale_0, %self.x.2_zero_point_0)
-  %648 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_7&quot;](%self)
-  %138 : Tensor = quantized::conv2d(%95, %648, %self.layer2.0.identity.2_scale_0, %self.layer2.0.identity.2_zero_point_0)
-  %142 : Tensor = quantized::add_relu(%124, %138, %self.layer2.0.out.14_scale_0, %self.conv1.2_axis_0)
-  %649 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_8&quot;](%self)
-  %155 : Tensor = quantized::conv2d_relu(%142, %649, %self.layer2.1.out.6_scale_0, %self.conv1.2_axis_0)
-  %650 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_9&quot;](%self)
-  %168 : Tensor = quantized::conv2d(%155, %650, %self.layer2.1.out.8_scale_0, %self.layer2.1.out.8_zero_point_0)
-  %172 : Tensor = quantized::add_relu(%168, %142, %self.layer2.1.out.14_scale_0, %self.conv1.2_axis_0)
-  %651 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_10&quot;](%self)
-  %188 : Tensor = quantized::conv2d_relu(%172, %651, %self.layer3.0.out.6_scale_0, %self.conv1.2_axis_0)
-  %652 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_11&quot;](%self)
-  %201 : Tensor = quantized::conv2d(%188, %652, %self.layer3.0.out.8_scale_0, %self.layer3.0.out.8_zero_point_0)
-  %653 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_12&quot;](%self)
-  %215 : Tensor = quantized::conv2d(%172, %653, %self.layer3.0.identity.2_scale_0, %self.layer3.0.identity.2_zero_point_0)
-  %219 : Tensor = quantized::add_relu(%201, %215, %self.layer3.0.out.14_scale_0, %self.conv1.2_axis_0)
-  %654 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_13&quot;](%self)
-  %232 : Tensor = quantized::conv2d_relu(%219, %654, %self.layer3.1.out.6_scale_0, %self.conv1.2_axis_0)
-  %655 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_14&quot;](%self)
-  %245 : Tensor = quantized::conv2d(%232, %655, %self.layer3.1.out.8_scale_0, %self.layer3.1.out.8_zero_point_0)
-  %249 : Tensor = quantized::add_relu(%245, %219, %self.layer3.1.out.14_scale_0, %self.conv1.2_axis_0)
-  %656 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_15&quot;](%self)
-  %265 : Tensor = quantized::conv2d_relu(%249, %656, %self.layer4.0.out.6_scale_0, %self.conv1.2_axis_0)
-  %657 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_16&quot;](%self)
-  %278 : Tensor = quantized::conv2d(%265, %657, %self.layer4.0.out.8_scale_0, %self.layer4.0.out.8_zero_point_0)
-  %658 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_17&quot;](%self)
-  %292 : Tensor = quantized::conv2d(%249, %658, %self.layer4.0.identity.2_scale_0, %self.layer4.0.identity.2_zero_point_0)
-  %296 : Tensor = quantized::add_relu(%278, %292, %self.layer4.0.out.14_scale_0, %self.conv1.2_axis_0)
-  %659 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_18&quot;](%self)
-  %309 : Tensor = quantized::conv2d_relu(%296, %659, %self.layer4.1.out.6_scale_0, %self.conv1.2_axis_0)
-  %660 : __torch__.torch.classes.quantized.Conv2dPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_19&quot;](%self)
-  %322 : Tensor = quantized::conv2d(%309, %660, %self.layer4.1.out.8_scale_0, %self.layer4.1.out.8_zero_point_0)
-  %326 : Tensor = quantized::add_relu(%322, %296, %self.layer4.1.out.14_scale_0, %self.conv1.2_axis_0)
-  %328 : int[] = aten::size(%326) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:925:51
-  %329 : int = aten::len(%328) # &lt;string&gt;:5:9
-  %330 : bool = aten::gt(%329, %9) # &lt;string&gt;:5:9
-   = prim::If(%330) # &lt;string&gt;:5:2
-    block0():
-      -&gt; ()
-    block1():
-       = prim::RaiseException(%6) # &lt;string&gt;:5:2
-      -&gt; ()
-  %331 : Tensor = aten::adaptive_avg_pool2d(%326, %10)
-  %x.21 : Tensor = aten::flatten(%331, %13, %14) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:214:12
-  %661 : __torch__.torch.classes.quantized.LinearPackedParamsBase = prim::GetAttr[name=&quot;quantized._jit_pass_packed_weight_20&quot;](%self)
-  %345 : Tensor = quantized::linear(%x.21, %661, %self.4_scale_0, %self.4_zero_point_0)
-  %4.dequant.0 : Tensor = aten::dequantize(%345)
-  return (%4.dequant.0)
-</pre></div>
-</div>
 <p>As we can see <code class="docutils literal notranslate"><span class="pre">aten::conv2d</span></code> is changed to <code class="docutils literal notranslate"><span class="pre">quantized::conv2d</span></code> and the floating point weight has been quantized
 and packed into an attribute (<code class="docutils literal notranslate"><span class="pre">quantized._jit_pass_packed_weight_30</span></code>), so we don’t need to quantize/pack in runtime.
 Also we can’t access the weight attributes anymore after the debug option since they are frozen.</p>
@@ -784,17 +619,6 @@ <h2>7. Evaluation<a class="headerlink" href="#evaluation" title="Permalink to th
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;[after serialization/deserialization] Evaluation accuracy on test dataset: </span><span class="si">%2.2f</span><span class="s1">, </span><span class="si">%2.2f</span><span class="s1">&#39;</span><span class="o">%</span><span class="p">(</span><span class="n">top1</span><span class="o">.</span><span class="n">avg</span><span class="p">,</span> <span class="n">top5</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size of model before quantization
-Size (MB): 46.871565
-Size of model after quantization
-Size (MB): 11.849675
-
-[before serilaization] Evaluation accuracy on test dataset: 68.60, 89.80
-
-[after serialization/deserialization] Evaluation accuracy on test dataset: 68.60, 89.80
-</pre></div>
-</div>
 <p>If you want to get better accuracy or performance,  try changing the <cite>qconfig_dict</cite>.
 We plan to add support for graph mode in the Numerical Suite so that you can
 easily determine the sensitivity towards quantization of different modules in a model: <a class="reference external" href="https://pytorch.org/tutorials/prototype/numeric_suite_tutorial.html">PyTorch Numeric Suite Tutorial</a></p>
@@ -813,470 +637,17 @@ <h2>8. Debugging Quantized Model<a class="headerlink" href="#debugging-quantized
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;[debug=True] quantized model Evaluation accuracy on test dataset: </span><span class="si">%2.2f</span><span class="s1">, </span><span class="si">%2.2f</span><span class="s1">&#39;</span><span class="o">%</span><span class="p">(</span><span class="n">top1</span><span class="o">.</span><span class="n">avg</span><span class="p">,</span> <span class="n">top5</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>[debug=True] quantized model Evaluation accuracy on test dataset: 69.20, 90.40
-</pre></div>
-</div>
 <p>Note that the accuracy of the debug version is close to, but not exactly the same as the non-debug
 version as the debug version uses floating point ops to emulate quantized ops and the numerics match
 is approximate. We are working on making this even more exact.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">quantized_debug_model</span><span class="o">.</span><span class="n">graph</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self : __torch__.torchvision.models.resnet.___torch_mangle_330.ResNet,
-      %x.2 : Tensor):
-  %7 : float = prim::GetAttr[name=&quot;x.2_scale_0&quot;](%self)
-  %8 : int = prim::GetAttr[name=&quot;x.2_zero_point_0&quot;](%self)
-  %9 : int = prim::GetAttr[name=&quot;x.2_scalar_type_0&quot;](%self)
-  %x.2.quant : Tensor = aten::quantize_per_tensor(%x.2, %7, %8, %9)
-  %x.2.dequant.0 : Tensor = aten::dequantize(%x.2.quant)
-  %17 : str = prim::Constant[value=&quot;Exception&quot;]() # &lt;string&gt;:5:2
-  %18 : int[] = prim::Constant[value=[0, 0]]()
-  %19 : bool = prim::Constant[value=0]() # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/pooling.py:158:57
-  %20 : int = prim::Constant[value=2]() # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/pooling.py:157:53
-  %22 : int[] = prim::Constant[value=[1, 1]]()
-  %23 : int[] = prim::Constant[value=[3, 3]]()
-  %24 : int[] = prim::Constant[value=[2, 2]]()
-  %25 : int = prim::Constant[value=1]() # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:214:29
-  %26 : int = prim::Constant[value=-1]()
-  %27 : __torch__.torch.nn.modules.conv.___torch_mangle_331.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%self)
-  %28 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%27)
-  %29 : Float(64:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%27)
-  %30 : Int(64:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%27)
-  %31 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%27)
-  %32 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%27)
-  %2.quant.7 : Tensor = aten::quantize_per_channel(%28, %29, %30, %31, %32)
-  %2.dequant.7 : Tensor = aten::dequantize(%2.quant.7)
-  %35 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%27)
-  %x.3 : Tensor = aten::conv2d(%x.2.dequant.0, %2.dequant.7, %35, %24, %23, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %x.8 : Tensor = aten::relu_(%x.3) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %38 : float = prim::GetAttr[name=&quot;x.8_scale_0&quot;](%self)
-  %39 : int = prim::GetAttr[name=&quot;x.8_zero_point_0&quot;](%self)
-  %40 : int = prim::GetAttr[name=&quot;x.8_scalar_type_0&quot;](%self)
-  %x.8.quant : Tensor = aten::quantize_per_tensor(%x.8, %38, %39, %40)
-  %428 : int[] = prim::Constant[value=[3, 3]]()
-  %429 : int[] = prim::Constant[value=[2, 2]]()
-  %430 : int[] = prim::Constant[value=[1, 1]]()
-  %431 : int[] = prim::Constant[value=[1, 1]]()
-  %x.9 : Tensor = aten::max_pool2d(%x.8.quant, %428, %429, %430, %431, %19) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:575:11
-  %48 : __torch__.torch.nn.modules.container.___torch_mangle_336.Sequential = prim::GetAttr[name=&quot;layer1&quot;](%self)
-  %49 : __torch__.torchvision.models.resnet.___torch_mangle_337.BasicBlock = prim::GetAttr[name=&quot;0&quot;](%48)
-  %50 : __torch__.torchvision.models.resnet.___torch_mangle_337.BasicBlock = prim::GetAttr[name=&quot;1&quot;](%48)
-  %51 : __torch__.torch.nn.modules.conv.___torch_mangle_338.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%49)
-  %52 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%51)
-  %53 : Float(64:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%51)
-  %54 : Int(64:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%51)
-  %55 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%51)
-  %56 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%51)
-  %2.quant.6 : Tensor = aten::quantize_per_channel(%52, %53, %54, %55, %56)
-  %2.dequant.6 : Tensor = aten::dequantize(%2.quant.6)
-  %59 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%51)
-  %x.9.dequant.0 : Tensor = aten::dequantize(%x.9)
-  %out.3 : Tensor = aten::conv2d(%x.9.dequant.0, %2.dequant.6, %59, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %out.10 : Tensor = aten::relu_(%out.3) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %62 : float = prim::GetAttr[name=&quot;out.6_scale_0&quot;](%49)
-  %63 : int = prim::GetAttr[name=&quot;out.6_zero_point_0&quot;](%49)
-  %64 : int = prim::GetAttr[name=&quot;out.6_scalar_type_0&quot;](%49)
-  %out.6.quant.2 : Tensor = aten::quantize_per_tensor(%out.10, %62, %63, %64)
-  %out.6.dequant.2 : Tensor = aten::dequantize(%out.6.quant.2)
-  %67 : __torch__.torch.nn.modules.conv.___torch_mangle_338.Conv2d = prim::GetAttr[name=&quot;conv2&quot;](%49)
-  %68 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%67)
-  %69 : Float(64:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%67)
-  %70 : Int(64:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%67)
-  %71 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%67)
-  %72 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%67)
-  %2.quant.5 : Tensor = aten::quantize_per_channel(%68, %69, %70, %71, %72)
-  %2.dequant.5 : Tensor = aten::dequantize(%2.quant.5)
-  %75 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%67)
-  %out.13 : Tensor = aten::conv2d(%out.6.dequant.2, %2.dequant.5, %75, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %77 : float = prim::GetAttr[name=&quot;out.8_scale_0&quot;](%49)
-  %78 : int = prim::GetAttr[name=&quot;out.8_zero_point_0&quot;](%49)
-  %79 : int = prim::GetAttr[name=&quot;out.8_scalar_type_0&quot;](%49)
-  %out.8.quant.2 : Tensor = aten::quantize_per_tensor(%out.13, %77, %78, %79)
-  %out.8.dequant.2 : Tensor = aten::dequantize(%out.8.quant.2)
-  %x.9.dequant.1 : Tensor = aten::dequantize(%x.9)
-  %out.16 : Tensor = aten::add_(%out.8.dequant.2, %x.9.dequant.1, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:69:8
-  %out.17 : Tensor = aten::relu_(%out.16) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %84 : float = prim::GetAttr[name=&quot;out.14_scale_0&quot;](%49)
-  %85 : int = prim::GetAttr[name=&quot;out.14_zero_point_0&quot;](%49)
-  %86 : int = prim::GetAttr[name=&quot;out.14_scalar_type_0&quot;](%49)
-  %out.14.quant.2 : Tensor = aten::quantize_per_tensor(%out.17, %84, %85, %86)
-  %89 : __torch__.torch.nn.modules.conv.___torch_mangle_338.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%50)
-  %90 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%89)
-  %91 : Float(64:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%89)
-  %92 : Int(64:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%89)
-  %93 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%89)
-  %94 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%89)
-  %2.quant.8 : Tensor = aten::quantize_per_channel(%90, %91, %92, %93, %94)
-  %2.dequant.8 : Tensor = aten::dequantize(%2.quant.8)
-  %97 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%89)
-  %input.4.dequant.0 : Tensor = aten::dequantize(%out.14.quant.2)
-  %out.4 : Tensor = aten::conv2d(%input.4.dequant.0, %2.dequant.8, %97, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %out.18 : Tensor = aten::relu_(%out.4) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %100 : float = prim::GetAttr[name=&quot;out.6_scale_0&quot;](%50)
-  %101 : int = prim::GetAttr[name=&quot;out.6_zero_point_0&quot;](%50)
-  %102 : int = prim::GetAttr[name=&quot;out.6_scalar_type_0&quot;](%50)
-  %out.6.quant.3 : Tensor = aten::quantize_per_tensor(%out.18, %100, %101, %102)
-  %out.6.dequant.3 : Tensor = aten::dequantize(%out.6.quant.3)
-  %105 : __torch__.torch.nn.modules.conv.___torch_mangle_338.Conv2d = prim::GetAttr[name=&quot;conv2&quot;](%50)
-  %106 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%105)
-  %107 : Float(64:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%105)
-  %108 : Int(64:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%105)
-  %109 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%105)
-  %110 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%105)
-  %2.quant.9 : Tensor = aten::quantize_per_channel(%106, %107, %108, %109, %110)
-  %2.dequant.9 : Tensor = aten::dequantize(%2.quant.9)
-  %113 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%105)
-  %out.19 : Tensor = aten::conv2d(%out.6.dequant.3, %2.dequant.9, %113, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %115 : float = prim::GetAttr[name=&quot;out.8_scale_0&quot;](%50)
-  %116 : int = prim::GetAttr[name=&quot;out.8_zero_point_0&quot;](%50)
-  %117 : int = prim::GetAttr[name=&quot;out.8_scalar_type_0&quot;](%50)
-  %out.8.quant.3 : Tensor = aten::quantize_per_tensor(%out.19, %115, %116, %117)
-  %out.8.dequant.3 : Tensor = aten::dequantize(%out.8.quant.3)
-  %input.4.dequant.1 : Tensor = aten::dequantize(%out.14.quant.2)
-  %out.20 : Tensor = aten::add_(%out.8.dequant.3, %input.4.dequant.1, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:69:8
-  %out.21 : Tensor = aten::relu_(%out.20) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %122 : float = prim::GetAttr[name=&quot;out.14_scale_0&quot;](%50)
-  %123 : int = prim::GetAttr[name=&quot;out.14_zero_point_0&quot;](%50)
-  %124 : int = prim::GetAttr[name=&quot;out.14_scalar_type_0&quot;](%50)
-  %out.14.quant.3 : Tensor = aten::quantize_per_tensor(%out.21, %122, %123, %124)
-  %127 : __torch__.torch.nn.modules.container.___torch_mangle_340.Sequential = prim::GetAttr[name=&quot;layer2&quot;](%self)
-  %128 : __torch__.torchvision.models.resnet.___torch_mangle_341.BasicBlock = prim::GetAttr[name=&quot;0&quot;](%127)
-  %129 : __torch__.torchvision.models.resnet.___torch_mangle_347.BasicBlock = prim::GetAttr[name=&quot;1&quot;](%127)
-  %130 : __torch__.torch.nn.modules.conv.___torch_mangle_342.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%128)
-  %131 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%130)
-  %132 : Float(128:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%130)
-  %133 : Int(128:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%130)
-  %134 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%130)
-  %135 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%130)
-  %2.quant.10 : Tensor = aten::quantize_per_channel(%131, %132, %133, %134, %135)
-  %2.dequant.10 : Tensor = aten::dequantize(%2.quant.10)
-  %138 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%130)
-  %x.11.dequant.0 : Tensor = aten::dequantize(%out.14.quant.3)
-  %out.5 : Tensor = aten::conv2d(%x.11.dequant.0, %2.dequant.10, %138, %24, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %out.22 : Tensor = aten::relu_(%out.5) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %141 : float = prim::GetAttr[name=&quot;out.6_scale_0&quot;](%128)
-  %142 : int = prim::GetAttr[name=&quot;out.6_zero_point_0&quot;](%128)
-  %143 : int = prim::GetAttr[name=&quot;out.6_scalar_type_0&quot;](%128)
-  %out.6.quant.4 : Tensor = aten::quantize_per_tensor(%out.22, %141, %142, %143)
-  %out.6.dequant.4 : Tensor = aten::dequantize(%out.6.quant.4)
-  %146 : __torch__.torch.nn.modules.conv.___torch_mangle_344.Conv2d = prim::GetAttr[name=&quot;conv2&quot;](%128)
-  %147 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%146)
-  %148 : Float(128:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%146)
-  %149 : Int(128:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%146)
-  %150 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%146)
-  %151 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%146)
-  %2.quant.11 : Tensor = aten::quantize_per_channel(%147, %148, %149, %150, %151)
-  %2.dequant.11 : Tensor = aten::dequantize(%2.quant.11)
-  %154 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%146)
-  %out.23 : Tensor = aten::conv2d(%out.6.dequant.4, %2.dequant.11, %154, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %156 : float = prim::GetAttr[name=&quot;out.8_scale_0&quot;](%128)
-  %157 : int = prim::GetAttr[name=&quot;out.8_zero_point_0&quot;](%128)
-  %158 : int = prim::GetAttr[name=&quot;out.8_scalar_type_0&quot;](%128)
-  %out.8.quant.4 : Tensor = aten::quantize_per_tensor(%out.23, %156, %157, %158)
-  %out.8.dequant.4 : Tensor = aten::dequantize(%out.8.quant.4)
-  %161 : __torch__.torch.nn.modules.container.___torch_mangle_345.Sequential = prim::GetAttr[name=&quot;downsample&quot;](%128)
-  %162 : __torch__.torch.nn.modules.conv.___torch_mangle_346.Conv2d = prim::GetAttr[name=&quot;0&quot;](%161)
-  %163 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%162)
-  %164 : Float(128:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%162)
-  %165 : Int(128:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%162)
-  %166 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%162)
-  %167 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%162)
-  %2.quant.12 : Tensor = aten::quantize_per_channel(%163, %164, %165, %166, %167)
-  %2.dequant.12 : Tensor = aten::dequantize(%2.quant.12)
-  %170 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%162)
-  %x.11.dequant.1 : Tensor = aten::dequantize(%out.14.quant.3)
-  %identity.3 : Tensor = aten::conv2d(%x.11.dequant.1, %2.dequant.12, %170, %24, %18, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %172 : float = prim::GetAttr[name=&quot;identity.2_scale_0&quot;](%128)
-  %173 : int = prim::GetAttr[name=&quot;identity.2_zero_point_0&quot;](%128)
-  %174 : int = prim::GetAttr[name=&quot;identity.2_scalar_type_0&quot;](%128)
-  %identity.2.quant.1 : Tensor = aten::quantize_per_tensor(%identity.3, %172, %173, %174)
-  %identity.2.dequant.1 : Tensor = aten::dequantize(%identity.2.quant.1)
-  %out.24 : Tensor = aten::add_(%out.8.dequant.4, %identity.2.dequant.1, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:69:8
-  %out.25 : Tensor = aten::relu_(%out.24) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %179 : float = prim::GetAttr[name=&quot;out.14_scale_0&quot;](%128)
-  %180 : int = prim::GetAttr[name=&quot;out.14_zero_point_0&quot;](%128)
-  %181 : int = prim::GetAttr[name=&quot;out.14_scalar_type_0&quot;](%128)
-  %out.14.quant.4 : Tensor = aten::quantize_per_tensor(%out.25, %179, %180, %181)
-  %184 : __torch__.torch.nn.modules.conv.___torch_mangle_344.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%129)
-  %185 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%184)
-  %186 : Float(128:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%184)
-  %187 : Int(128:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%184)
-  %188 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%184)
-  %189 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%184)
-  %2.quant.13 : Tensor = aten::quantize_per_channel(%185, %186, %187, %188, %189)
-  %2.dequant.13 : Tensor = aten::dequantize(%2.quant.13)
-  %192 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%184)
-  %input.6.dequant.0 : Tensor = aten::dequantize(%out.14.quant.4)
-  %out.26 : Tensor = aten::conv2d(%input.6.dequant.0, %2.dequant.13, %192, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %out.27 : Tensor = aten::relu_(%out.26) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %195 : float = prim::GetAttr[name=&quot;out.6_scale_0&quot;](%129)
-  %196 : int = prim::GetAttr[name=&quot;out.6_zero_point_0&quot;](%129)
-  %197 : int = prim::GetAttr[name=&quot;out.6_scalar_type_0&quot;](%129)
-  %out.6.quant.5 : Tensor = aten::quantize_per_tensor(%out.27, %195, %196, %197)
-  %out.6.dequant.5 : Tensor = aten::dequantize(%out.6.quant.5)
-  %200 : __torch__.torch.nn.modules.conv.___torch_mangle_344.Conv2d = prim::GetAttr[name=&quot;conv2&quot;](%129)
-  %201 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%200)
-  %202 : Float(128:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%200)
-  %203 : Int(128:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%200)
-  %204 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%200)
-  %205 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%200)
-  %2.quant.14 : Tensor = aten::quantize_per_channel(%201, %202, %203, %204, %205)
-  %2.dequant.14 : Tensor = aten::dequantize(%2.quant.14)
-  %208 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%200)
-  %out.28 : Tensor = aten::conv2d(%out.6.dequant.5, %2.dequant.14, %208, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %210 : float = prim::GetAttr[name=&quot;out.8_scale_0&quot;](%129)
-  %211 : int = prim::GetAttr[name=&quot;out.8_zero_point_0&quot;](%129)
-  %212 : int = prim::GetAttr[name=&quot;out.8_scalar_type_0&quot;](%129)
-  %out.8.quant.5 : Tensor = aten::quantize_per_tensor(%out.28, %210, %211, %212)
-  %out.8.dequant.5 : Tensor = aten::dequantize(%out.8.quant.5)
-  %input.6.dequant.1 : Tensor = aten::dequantize(%out.14.quant.4)
-  %out.29 : Tensor = aten::add_(%out.8.dequant.5, %input.6.dequant.1, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:69:8
-  %out.30 : Tensor = aten::relu_(%out.29) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %217 : float = prim::GetAttr[name=&quot;out.14_scale_0&quot;](%129)
-  %218 : int = prim::GetAttr[name=&quot;out.14_zero_point_0&quot;](%129)
-  %219 : int = prim::GetAttr[name=&quot;out.14_scalar_type_0&quot;](%129)
-  %out.14.quant.5 : Tensor = aten::quantize_per_tensor(%out.30, %217, %218, %219)
-  %222 : __torch__.torch.nn.modules.container.___torch_mangle_348.Sequential = prim::GetAttr[name=&quot;layer3&quot;](%self)
-  %223 : __torch__.torchvision.models.resnet.___torch_mangle_349.BasicBlock = prim::GetAttr[name=&quot;0&quot;](%222)
-  %224 : __torch__.torchvision.models.resnet.___torch_mangle_355.BasicBlock = prim::GetAttr[name=&quot;1&quot;](%222)
-  %225 : __torch__.torch.nn.modules.conv.___torch_mangle_350.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%223)
-  %226 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%225)
-  %227 : Float(256:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%225)
-  %228 : Int(256:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%225)
-  %229 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%225)
-  %230 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%225)
-  %2.quant.15 : Tensor = aten::quantize_per_channel(%226, %227, %228, %229, %230)
-  %2.dequant.15 : Tensor = aten::dequantize(%2.quant.15)
-  %233 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%225)
-  %x.13.dequant.0 : Tensor = aten::dequantize(%out.14.quant.5)
-  %out.31 : Tensor = aten::conv2d(%x.13.dequant.0, %2.dequant.15, %233, %24, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %out.32 : Tensor = aten::relu_(%out.31) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %236 : float = prim::GetAttr[name=&quot;out.6_scale_0&quot;](%223)
-  %237 : int = prim::GetAttr[name=&quot;out.6_zero_point_0&quot;](%223)
-  %238 : int = prim::GetAttr[name=&quot;out.6_scalar_type_0&quot;](%223)
-  %out.6.quant.6 : Tensor = aten::quantize_per_tensor(%out.32, %236, %237, %238)
-  %out.6.dequant.6 : Tensor = aten::dequantize(%out.6.quant.6)
-  %241 : __torch__.torch.nn.modules.conv.___torch_mangle_352.Conv2d = prim::GetAttr[name=&quot;conv2&quot;](%223)
-  %242 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%241)
-  %243 : Float(256:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%241)
-  %244 : Int(256:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%241)
-  %245 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%241)
-  %246 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%241)
-  %2.quant.16 : Tensor = aten::quantize_per_channel(%242, %243, %244, %245, %246)
-  %2.dequant.16 : Tensor = aten::dequantize(%2.quant.16)
-  %249 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%241)
-  %out.33 : Tensor = aten::conv2d(%out.6.dequant.6, %2.dequant.16, %249, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %251 : float = prim::GetAttr[name=&quot;out.8_scale_0&quot;](%223)
-  %252 : int = prim::GetAttr[name=&quot;out.8_zero_point_0&quot;](%223)
-  %253 : int = prim::GetAttr[name=&quot;out.8_scalar_type_0&quot;](%223)
-  %out.8.quant.6 : Tensor = aten::quantize_per_tensor(%out.33, %251, %252, %253)
-  %out.8.dequant.6 : Tensor = aten::dequantize(%out.8.quant.6)
-  %256 : __torch__.torch.nn.modules.container.___torch_mangle_353.Sequential = prim::GetAttr[name=&quot;downsample&quot;](%223)
-  %257 : __torch__.torch.nn.modules.conv.___torch_mangle_354.Conv2d = prim::GetAttr[name=&quot;0&quot;](%256)
-  %258 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%257)
-  %259 : Float(256:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%257)
-  %260 : Int(256:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%257)
-  %261 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%257)
-  %262 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%257)
-  %2.quant.17 : Tensor = aten::quantize_per_channel(%258, %259, %260, %261, %262)
-  %2.dequant.17 : Tensor = aten::dequantize(%2.quant.17)
-  %265 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%257)
-  %x.13.dequant.1 : Tensor = aten::dequantize(%out.14.quant.5)
-  %identity.4 : Tensor = aten::conv2d(%x.13.dequant.1, %2.dequant.17, %265, %24, %18, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %267 : float = prim::GetAttr[name=&quot;identity.2_scale_0&quot;](%223)
-  %268 : int = prim::GetAttr[name=&quot;identity.2_zero_point_0&quot;](%223)
-  %269 : int = prim::GetAttr[name=&quot;identity.2_scalar_type_0&quot;](%223)
-  %identity.2.quant.2 : Tensor = aten::quantize_per_tensor(%identity.4, %267, %268, %269)
-  %identity.2.dequant.2 : Tensor = aten::dequantize(%identity.2.quant.2)
-  %out.34 : Tensor = aten::add_(%out.8.dequant.6, %identity.2.dequant.2, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:69:8
-  %out.35 : Tensor = aten::relu_(%out.34) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %274 : float = prim::GetAttr[name=&quot;out.14_scale_0&quot;](%223)
-  %275 : int = prim::GetAttr[name=&quot;out.14_zero_point_0&quot;](%223)
-  %276 : int = prim::GetAttr[name=&quot;out.14_scalar_type_0&quot;](%223)
-  %out.14.quant.6 : Tensor = aten::quantize_per_tensor(%out.35, %274, %275, %276)
-  %279 : __torch__.torch.nn.modules.conv.___torch_mangle_352.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%224)
-  %280 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%279)
-  %281 : Float(256:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%279)
-  %282 : Int(256:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%279)
-  %283 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%279)
-  %284 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%279)
-  %2.quant.18 : Tensor = aten::quantize_per_channel(%280, %281, %282, %283, %284)
-  %2.dequant.18 : Tensor = aten::dequantize(%2.quant.18)
-  %287 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%279)
-  %input.7.dequant.0 : Tensor = aten::dequantize(%out.14.quant.6)
-  %out.36 : Tensor = aten::conv2d(%input.7.dequant.0, %2.dequant.18, %287, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %out.37 : Tensor = aten::relu_(%out.36) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %290 : float = prim::GetAttr[name=&quot;out.6_scale_0&quot;](%224)
-  %291 : int = prim::GetAttr[name=&quot;out.6_zero_point_0&quot;](%224)
-  %292 : int = prim::GetAttr[name=&quot;out.6_scalar_type_0&quot;](%224)
-  %out.6.quant.7 : Tensor = aten::quantize_per_tensor(%out.37, %290, %291, %292)
-  %out.6.dequant.7 : Tensor = aten::dequantize(%out.6.quant.7)
-  %295 : __torch__.torch.nn.modules.conv.___torch_mangle_352.Conv2d = prim::GetAttr[name=&quot;conv2&quot;](%224)
-  %296 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%295)
-  %297 : Float(256:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%295)
-  %298 : Int(256:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%295)
-  %299 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%295)
-  %300 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%295)
-  %2.quant.19 : Tensor = aten::quantize_per_channel(%296, %297, %298, %299, %300)
-  %2.dequant.19 : Tensor = aten::dequantize(%2.quant.19)
-  %303 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%295)
-  %out.38 : Tensor = aten::conv2d(%out.6.dequant.7, %2.dequant.19, %303, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %305 : float = prim::GetAttr[name=&quot;out.8_scale_0&quot;](%224)
-  %306 : int = prim::GetAttr[name=&quot;out.8_zero_point_0&quot;](%224)
-  %307 : int = prim::GetAttr[name=&quot;out.8_scalar_type_0&quot;](%224)
-  %out.8.quant.7 : Tensor = aten::quantize_per_tensor(%out.38, %305, %306, %307)
-  %out.8.dequant.7 : Tensor = aten::dequantize(%out.8.quant.7)
-  %input.7.dequant.1 : Tensor = aten::dequantize(%out.14.quant.6)
-  %out.39 : Tensor = aten::add_(%out.8.dequant.7, %input.7.dequant.1, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:69:8
-  %out.40 : Tensor = aten::relu_(%out.39) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %312 : float = prim::GetAttr[name=&quot;out.14_scale_0&quot;](%224)
-  %313 : int = prim::GetAttr[name=&quot;out.14_zero_point_0&quot;](%224)
-  %314 : int = prim::GetAttr[name=&quot;out.14_scalar_type_0&quot;](%224)
-  %out.14.quant.7 : Tensor = aten::quantize_per_tensor(%out.40, %312, %313, %314)
-  %317 : __torch__.torch.nn.modules.container.___torch_mangle_356.Sequential = prim::GetAttr[name=&quot;layer4&quot;](%self)
-  %318 : __torch__.torchvision.models.resnet.___torch_mangle_357.BasicBlock = prim::GetAttr[name=&quot;0&quot;](%317)
-  %319 : __torch__.torchvision.models.resnet.___torch_mangle_363.BasicBlock = prim::GetAttr[name=&quot;1&quot;](%317)
-  %320 : __torch__.torch.nn.modules.conv.___torch_mangle_358.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%318)
-  %321 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%320)
-  %322 : Float(512:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%320)
-  %323 : Int(512:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%320)
-  %324 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%320)
-  %325 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%320)
-  %2.quant.3 : Tensor = aten::quantize_per_channel(%321, %322, %323, %324, %325)
-  %2.dequant.3 : Tensor = aten::dequantize(%2.quant.3)
-  %328 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%320)
-  %x.15.dequant.0 : Tensor = aten::dequantize(%out.14.quant.7)
-  %out.2 : Tensor = aten::conv2d(%x.15.dequant.0, %2.dequant.3, %328, %24, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %out.7 : Tensor = aten::relu_(%out.2) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %331 : float = prim::GetAttr[name=&quot;out.6_scale_0&quot;](%318)
-  %332 : int = prim::GetAttr[name=&quot;out.6_zero_point_0&quot;](%318)
-  %333 : int = prim::GetAttr[name=&quot;out.6_scalar_type_0&quot;](%318)
-  %out.6.quant.1 : Tensor = aten::quantize_per_tensor(%out.7, %331, %332, %333)
-  %out.6.dequant.1 : Tensor = aten::dequantize(%out.6.quant.1)
-  %336 : __torch__.torch.nn.modules.conv.___torch_mangle_360.Conv2d = prim::GetAttr[name=&quot;conv2&quot;](%318)
-  %337 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%336)
-  %338 : Float(512:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%336)
-  %339 : Int(512:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%336)
-  %340 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%336)
-  %341 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%336)
-  %2.quant.2 : Tensor = aten::quantize_per_channel(%337, %338, %339, %340, %341)
-  %2.dequant.2 : Tensor = aten::dequantize(%2.quant.2)
-  %344 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%336)
-  %out.9 : Tensor = aten::conv2d(%out.6.dequant.1, %2.dequant.2, %344, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %346 : float = prim::GetAttr[name=&quot;out.8_scale_0&quot;](%318)
-  %347 : int = prim::GetAttr[name=&quot;out.8_zero_point_0&quot;](%318)
-  %348 : int = prim::GetAttr[name=&quot;out.8_scalar_type_0&quot;](%318)
-  %out.8.quant.1 : Tensor = aten::quantize_per_tensor(%out.9, %346, %347, %348)
-  %out.8.dequant.1 : Tensor = aten::dequantize(%out.8.quant.1)
-  %351 : __torch__.torch.nn.modules.container.___torch_mangle_361.Sequential = prim::GetAttr[name=&quot;downsample&quot;](%318)
-  %352 : __torch__.torch.nn.modules.conv.___torch_mangle_362.Conv2d = prim::GetAttr[name=&quot;0&quot;](%351)
-  %353 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%352)
-  %354 : Float(512:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%352)
-  %355 : Int(512:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%352)
-  %356 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%352)
-  %357 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%352)
-  %2.quant.4 : Tensor = aten::quantize_per_channel(%353, %354, %355, %356, %357)
-  %2.dequant.4 : Tensor = aten::dequantize(%2.quant.4)
-  %360 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%352)
-  %x.15.dequant.1 : Tensor = aten::dequantize(%out.14.quant.7)
-  %identity.2 : Tensor = aten::conv2d(%x.15.dequant.1, %2.dequant.4, %360, %24, %18, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %362 : float = prim::GetAttr[name=&quot;identity.2_scale_0&quot;](%318)
-  %363 : int = prim::GetAttr[name=&quot;identity.2_zero_point_0&quot;](%318)
-  %364 : int = prim::GetAttr[name=&quot;identity.2_scalar_type_0&quot;](%318)
-  %identity.2.quant : Tensor = aten::quantize_per_tensor(%identity.2, %362, %363, %364)
-  %identity.2.dequant.0 : Tensor = aten::dequantize(%identity.2.quant)
-  %out.12 : Tensor = aten::add_(%out.8.dequant.1, %identity.2.dequant.0, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:69:8
-  %out.15 : Tensor = aten::relu_(%out.12) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %369 : float = prim::GetAttr[name=&quot;out.14_scale_0&quot;](%318)
-  %370 : int = prim::GetAttr[name=&quot;out.14_zero_point_0&quot;](%318)
-  %371 : int = prim::GetAttr[name=&quot;out.14_scalar_type_0&quot;](%318)
-  %out.14.quant.1 : Tensor = aten::quantize_per_tensor(%out.15, %369, %370, %371)
-  %374 : __torch__.torch.nn.modules.conv.___torch_mangle_360.Conv2d = prim::GetAttr[name=&quot;conv1&quot;](%319)
-  %375 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%374)
-  %376 : Float(512:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%374)
-  %377 : Int(512:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%374)
-  %378 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%374)
-  %379 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%374)
-  %2.quant.1 : Tensor = aten::quantize_per_channel(%375, %376, %377, %378, %379)
-  %2.dequant.1 : Tensor = aten::dequantize(%2.quant.1)
-  %382 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%374)
-  %input.3.dequant.0 : Tensor = aten::dequantize(%out.14.quant.1)
-  %out.1 : Tensor = aten::conv2d(%input.3.dequant.0, %2.dequant.1, %382, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %out.6 : Tensor = aten::relu_(%out.1) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %385 : float = prim::GetAttr[name=&quot;out.6_scale_0&quot;](%319)
-  %386 : int = prim::GetAttr[name=&quot;out.6_zero_point_0&quot;](%319)
-  %387 : int = prim::GetAttr[name=&quot;out.6_scalar_type_0&quot;](%319)
-  %out.6.quant : Tensor = aten::quantize_per_tensor(%out.6, %385, %386, %387)
-  %out.6.dequant.0 : Tensor = aten::dequantize(%out.6.quant)
-  %390 : __torch__.torch.nn.modules.conv.___torch_mangle_360.Conv2d = prim::GetAttr[name=&quot;conv2&quot;](%319)
-  %391 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%390)
-  %392 : Float(512:1) = prim::GetAttr[name=&quot;2_scale_0&quot;](%390)
-  %393 : Int(512:1) = prim::GetAttr[name=&quot;2_zero_point_0&quot;](%390)
-  %394 : int = prim::GetAttr[name=&quot;2_axis_0&quot;](%390)
-  %395 : int = prim::GetAttr[name=&quot;2_scalar_type_0&quot;](%390)
-  %2.quant : Tensor = aten::quantize_per_channel(%391, %392, %393, %394, %395)
-  %2.dequant.0 : Tensor = aten::dequantize(%2.quant)
-  %398 : Tensor? = prim::GetAttr[name=&quot;bias&quot;](%390)
-  %out.8 : Tensor = aten::conv2d(%out.6.dequant.0, %2.dequant.0, %398, %22, %22, %22, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/conv.py:415:15
-  %400 : float = prim::GetAttr[name=&quot;out.8_scale_0&quot;](%319)
-  %401 : int = prim::GetAttr[name=&quot;out.8_zero_point_0&quot;](%319)
-  %402 : int = prim::GetAttr[name=&quot;out.8_scalar_type_0&quot;](%319)
-  %out.8.quant : Tensor = aten::quantize_per_tensor(%out.8, %400, %401, %402)
-  %out.8.dequant.0 : Tensor = aten::dequantize(%out.8.quant)
-  %input.3.dequant.1 : Tensor = aten::dequantize(%out.14.quant.1)
-  %out.11 : Tensor = aten::add_(%out.8.dequant.0, %input.3.dequant.1, %25) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:69:8
-  %out.14 : Tensor = aten::relu_(%out.11) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:1117:17
-  %407 : float = prim::GetAttr[name=&quot;out.14_scale_0&quot;](%319)
-  %408 : int = prim::GetAttr[name=&quot;out.14_zero_point_0&quot;](%319)
-  %409 : int = prim::GetAttr[name=&quot;out.14_scalar_type_0&quot;](%319)
-  %out.14.quant : Tensor = aten::quantize_per_tensor(%out.14, %407, %408, %409)
-  %432 : int[] = prim::Constant[value=[1, 1]]()
-  %413 : int[] = aten::size(%out.14.quant) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:925:51
-  %414 : int = aten::len(%413) # &lt;string&gt;:5:9
-  %415 : bool = aten::gt(%414, %20) # &lt;string&gt;:5:9
-   = prim::If(%415) # &lt;string&gt;:5:2
-    block0():
-      -&gt; ()
-    block1():
-       = prim::RaiseException(%17) # &lt;string&gt;:5:2
-      -&gt; ()
-  %x.17.dequant.1 : Tensor = aten::dequantize(%out.14.quant)
-  %x.19 : Tensor = aten::adaptive_avg_pool2d(%x.17.dequant.1, %432) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torch/nn/functional.py:926:11
-  %out.14.quant.q_scale : float = aten::q_scale(%out.14.quant)
-  %out.14.quant.q_zero_point : int = aten::q_zero_point(%out.14.quant)
-  %out.14.quant.dtype : int = prim::dtype(%out.14.quant)
-  %x.19.quant : Tensor = aten::quantize_per_tensor(%x.19, %out.14.quant.q_scale, %out.14.quant.q_zero_point, %out.14.quant.dtype)
-  %x.21 : Tensor = aten::flatten(%x.19.quant, %25, %26) # /home/reserve/.pyenv/versions/3.6.7/envs/pytorch/lib/python3.6/site-packages/torchvision/models/resnet.py:214:12
-  %418 : __torch__.torch.nn.modules.linear.___torch_mangle_365.Linear = prim::GetAttr[name=&quot;fc&quot;](%self)
-  %419 : Tensor = prim::GetAttr[name=&quot;weight&quot;](%418)
-  %420 : Float(1000:1) = prim::GetAttr[name=&quot;3_scale_0&quot;](%418)
-  %421 : Int(1000:1) = prim::GetAttr[name=&quot;3_zero_point_0&quot;](%418)
-  %422 : int = prim::GetAttr[name=&quot;3_axis_0&quot;](%418)
-  %423 : int = prim::GetAttr[name=&quot;3_scalar_type_0&quot;](%418)
-  %3.quant : Tensor = aten::quantize_per_channel(%419, %420, %421, %422, %423)
-  %3.dequant.0 : Tensor = aten::dequantize(%3.quant)
-  %426 : Tensor = prim::GetAttr[name=&quot;bias&quot;](%418)
-  %x.21.dequant.0 : Tensor = aten::dequantize(%x.21)
-  %x.23 : Tensor = aten::linear(%x.21.dequant.0, %3.dequant.0, %426)
-  %12 : float = prim::GetAttr[name=&quot;4_scale_0&quot;](%self)
-  %13 : int = prim::GetAttr[name=&quot;4_zero_point_0&quot;](%self)
-  %14 : int = prim::GetAttr[name=&quot;4_scalar_type_0&quot;](%self)
-  %4.quant : Tensor = aten::quantize_per_tensor(%x.23, %12, %13, %14)
-  %4.dequant.0 : Tensor = aten::dequantize(%4.quant)
-  return (%4.dequant.0)
-</pre></div>
-</div>
 <p>We can see that there is no <code class="docutils literal notranslate"><span class="pre">quantized::conv2d</span></code> in the model, but the numerically equivalent pattern
 of <code class="docutils literal notranslate"><span class="pre">aten::dequnatize</span> <span class="pre">-</span> <span class="pre">aten::conv2d</span> <span class="pre">-</span> <span class="pre">aten::quantize_per_tensor</span></code>.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">print_size_of_model</span><span class="p">(</span><span class="n">quantized_debug_model</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size (MB): 46.956979
-</pre></div>
-</div>
 <p>Size of the debug model is the close to the floating point model because all the weights are
 in float and not yet quantized and frozen, this allows people to inspect the weight.
 You may access the weight attributes directly in the torchscript model, except for batch norm as
@@ -1291,18 +662,6 @@ <h2>8. Debugging Quantized Model<a class="headerlink" href="#debugging-quantized
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;first conv weight for quantized model:&#39;</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">w2</span><span class="p">)[:</span><span class="mi">200</span><span class="p">])</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>first conv weight for input model: Parameter containing:
-tensor([[[[-1.0419e-02, -6.1356e-03, -1.8098e-03,  ...,  5.6615e-02,
-            1.7083e-02, -1.2694e-02],
-          [ 1.1083e-02,  9.5276e-03, -1.0993e-01,  ..., -2.7124e-01,
-
-first conv weight for quantized model: tensor([[[[-2.4267e-03, -1.4290e-03, -4.2151e-04,  ...,  1.3186e-02,
-            3.9788e-03, -2.9565e-03],
-          [ 2.5812e-03,  2.2190e-03, -2.5603e-02,  ..., -6.3173e-02,
-           -3.0062e-02,
-</pre></div>
-</div>
 <p>The weights are different because we fold the weights of BatchNorm to the previous conv before we quantize the model.
 More instructions on how to debug TorchScript model can be found <a class="reference external" href="https://pytorch.org/docs/stable/jit.html#debugging">here</a>.</p>
 <p>As we can see, this is not as straightforward as eager mode, that’s why we also plan to support graph mode <code class="docutils literal notranslate"><span class="pre">Numeric</span> <span class="pre">Suite</span></code>,
@@ -1320,13 +679,6 @@ <h2>9. Comparison with Baseline Float Model and Eager Mode Quantization<a class=
 <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">float_model</span><span class="p">),</span> <span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">scripted_float_model_file</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size of baseline model
-Size (MB): 46.871565
-
-Baseline Float Model Evaluation accuracy: 69.30, 89.60
-</pre></div>
-</div>
 <p>In this section we compare the model quantized with graph mode quantization with the model
 quantized in eager mode. Graph mode and eager mode produce very similar quantized models,
 so the expectation is that the accuracy and speedup are similar as well.</p>
@@ -1335,7 +687,7 @@ <h2>9. Comparison with Baseline Float Model and Eager Mode Quantization<a class=
 <span class="n">top1</span><span class="p">,</span> <span class="n">top5</span> <span class="o">=</span> <span class="n">evaluate</span><span class="p">(</span><span class="n">quantized_model</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">data_loader_test</span><span class="p">)</span>
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;graph mode quantized model Evaluation accuracy on test dataset: </span><span class="si">%2.2f</span><span class="s1">, </span><span class="si">%2.2f</span><span class="s1">&#39;</span><span class="o">%</span><span class="p">(</span><span class="n">top1</span><span class="o">.</span><span class="n">avg</span><span class="p">,</span> <span class="n">top5</span><span class="o">.</span><span class="n">avg</span><span class="p">))</span>
 
-<span class="kn">from</span> <span class="nn">torchvision.models.quantization.resnet</span> <span class="k">import</span> <span class="n">resnet18</span>
+<span class="kn">from</span> <span class="nn">torchvision.models.quantization.resnet</span> <span class="kn">import</span> <span class="n">resnet18</span>
 <span class="n">eager_quantized_model</span> <span class="o">=</span> <span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">quantize</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Size of eager mode quantized model&#39;</span><span class="p">)</span>
 <span class="n">eager_quantized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">eager_quantized_model</span><span class="p">)</span>
@@ -1346,17 +698,6 @@ <h2>9. Comparison with Baseline Float Model and Eager Mode Quantization<a class=
 <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">eager_quantized_model</span><span class="p">,</span> <span class="n">saved_model_dir</span> <span class="o">+</span> <span class="n">eager_mode_model_file</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Size of graph mode quantized model
-Size (MB): 11.849803
-
-graph mode quantized model Evaluation accuracy on test dataset: 68.60, 89.80
-Size of eager mode quantized model
-Size (MB): 11.835043
-
-eager mode quantized model Evaluation accuracy on test dataset: 68.70, 89.70
-</pre></div>
-</div>
 <p>We can see that the model size and accuracy of graph mode and eager mode quantized model are pretty similar.</p>
 <p>Running the model in AIBench (with single threading) gives the following result:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">Scripted</span> <span class="n">Float</span> <span class="n">Model</span><span class="p">:</span>
@@ -1372,7 +713,7 @@ <h2>9. Comparison with Baseline Float Model and Eager Mode Quantization<a class=
 <p>As we can see for resnet18 both graph mode and eager mode quantized model get similar speed up over the floating point model,
 which is around 2-3x faster than the floating point model. But the actual speedup over floating point model may vary
 depending on model, device, build, input batch sizes, threading etc.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 3 minutes  47.121 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-prototype-graph-mode-static-quantization-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/22c959e82dde8cfb5f1b0cf5cb84f293/graph_mode_static_quantization_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">graph_mode_static_quantization_tutorial.py</span></code></a></p>
@@ -1453,11 +794,9 @@ <h2>9. Comparison with Baseline Float Model and Eager Mode Quantization<a class=
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/prototype/numeric_suite_tutorial.html b/docs/prototype/numeric_suite_tutorial.html
index 9f5ddac0f..0e0039c0f 100644
--- a/docs/prototype/numeric_suite_tutorial.html
+++ b/docs/prototype/numeric_suite_tutorial.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -313,17 +313,17 @@ <h2>Numeric Suite for Static Quantization<a class="headerlink" href="#numeric-su
 <div class="section" id="setup">
 <h3>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">¶</a></h3>
 <p>We’ll start by doing the necessary imports:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">absolute_import</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span><span class="p">,</span> <span class="n">absolute_import</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
 <span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">datasets</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">datasets</span>
 <span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">torch.quantization</span>
 <span class="kn">import</span> <span class="nn">torch.quantization._numeric_suite</span> <span class="k">as</span> <span class="nn">ns</span>
-<span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="k">import</span> <span class="p">(</span>
+<span class="kn">from</span> <span class="nn">torch.quantization</span> <span class="kn">import</span> <span class="p">(</span>
     <span class="n">default_eval_fn</span><span class="p">,</span>
     <span class="n">default_qconfig</span><span class="p">,</span>
     <span class="n">quantize</span><span class="p">,</span>
@@ -358,16 +358,6 @@ <h3>1. Compare the weights of float and quantized models<a class="headerlink" hr
 <span class="nb">print</span><span class="p">(</span><span class="n">wt_compare_dict</span><span class="p">[</span><span class="s1">&#39;conv1.weight&#39;</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>keys of wt_compare_dict:
-dict_keys([&#39;conv1.weight&#39;, &#39;layer1.0.conv1.weight&#39;, &#39;layer1.0.conv2.weight&#39;, &#39;layer1.1.conv1.weight&#39;, &#39;layer1.1.conv2.weight&#39;, &#39;layer2.0.conv1.weight&#39;, &#39;layer2.0.conv2.weight&#39;, &#39;layer2.0.downsample.0.weight&#39;, &#39;layer2.1.conv1.weight&#39;, &#39;layer2.1.conv2.weight&#39;, &#39;layer3.0.conv1.weight&#39;, &#39;layer3.0.conv2.weight&#39;, &#39;layer3.0.downsample.0.weight&#39;, &#39;layer3.1.conv1.weight&#39;, &#39;layer3.1.conv2.weight&#39;, &#39;layer4.0.conv1.weight&#39;, &#39;layer4.0.conv2.weight&#39;, &#39;layer4.0.downsample.0.weight&#39;, &#39;layer4.1.conv1.weight&#39;, &#39;layer4.1.conv2.weight&#39;, &#39;fc._packed_params._packed_params&#39;])
-
-keys of wt_compare_dict entry for conv1&#39;s weight:
-dict_keys([&#39;float&#39;, &#39;quantized&#39;])
-torch.Size([64, 3, 7, 7])
-torch.Size([64, 3, 7, 7])
-</pre></div>
-</div>
 <p>Once get <code class="docutils literal notranslate"><span class="pre">wt_compare_dict</span></code>, users can process this dictionary in whatever way they want. Here as an example we compute the quantization error of the weights of float and quantized models as following.
 Compute the Signal-to-Quantization-Noise Ratio (SQNR) of the quantized tensor <code class="docutils literal notranslate"><span class="pre">y</span></code>. The SQNR reflects the
 relationship between the maximum nominal signal strength and the quantization error introduced in the
@@ -381,30 +371,6 @@ <h3>1. Compare the weights of float and quantized models<a class="headerlink" hr
     <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">compute_error</span><span class="p">(</span><span class="n">wt_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">],</span> <span class="n">wt_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dequantize</span><span class="p">()))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv1.weight tensor(31.6638)
-layer1.0.conv1.weight tensor(30.6450)
-layer1.0.conv2.weight tensor(31.1528)
-layer1.1.conv1.weight tensor(32.1438)
-layer1.1.conv2.weight tensor(31.2477)
-layer2.0.conv1.weight tensor(30.9890)
-layer2.0.conv2.weight tensor(28.8233)
-layer2.0.downsample.0.weight tensor(31.5558)
-layer2.1.conv1.weight tensor(30.7668)
-layer2.1.conv2.weight tensor(28.4516)
-layer3.0.conv1.weight tensor(30.9247)
-layer3.0.conv2.weight tensor(26.6840)
-layer3.0.downsample.0.weight tensor(28.7825)
-layer3.1.conv1.weight tensor(28.9706)
-layer3.1.conv2.weight tensor(25.6784)
-layer4.0.conv1.weight tensor(26.8494)
-layer4.0.conv2.weight tensor(25.8391)
-layer4.0.downsample.0.weight tensor(28.6355)
-layer4.1.conv1.weight tensor(26.8755)
-layer4.1.conv2.weight tensor(28.4316)
-fc._packed_params._packed_params tensor(32.6506)
-</pre></div>
-</div>
 <p>As another example <code class="docutils literal notranslate"><span class="pre">wt_compare_dict</span></code> can also be used to plot the histogram of the weights of floating point and quantized models.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 
@@ -419,7 +385,6 @@ <h3>1. Compare the weights of float and quantized models<a class="headerlink" hr
 <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
-<img alt="../_images/sphx_glr_numeric_suite_tutorial_001.png" class="sphx-glr-single-img" src="../_images/sphx_glr_numeric_suite_tutorial_001.png" />
 </div>
 <div class="section" id="compare-float-point-and-quantized-models-at-corresponding-locations">
 <h3>2. Compare float point and quantized models at corresponding locations<a class="headerlink" href="#compare-float-point-and-quantized-models-at-corresponding-locations" title="Permalink to this headline">¶</a></h3>
@@ -444,54 +409,11 @@ <h3>2. Compare float point and quantized models at corresponding locations<a cla
 <span class="nb">print</span><span class="p">(</span><span class="n">act_compare_dict</span><span class="p">[</span><span class="s1">&#39;conv1.stats&#39;</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>keys of act_compare_dict:
-dict_keys([&#39;conv1.stats&#39;, &#39;layer1.0.conv1.stats&#39;, &#39;layer1.0.conv2.stats&#39;, &#39;layer1.0.add_relu.stats&#39;, &#39;layer1.1.conv1.stats&#39;, &#39;layer1.1.conv2.stats&#39;, &#39;layer1.1.add_relu.stats&#39;, &#39;layer2.0.conv1.stats&#39;, &#39;layer2.0.conv2.stats&#39;, &#39;layer2.0.downsample.0.stats&#39;, &#39;layer2.0.add_relu.stats&#39;, &#39;layer2.1.conv1.stats&#39;, &#39;layer2.1.conv2.stats&#39;, &#39;layer2.1.add_relu.stats&#39;, &#39;layer3.0.conv1.stats&#39;, &#39;layer3.0.conv2.stats&#39;, &#39;layer3.0.downsample.0.stats&#39;, &#39;layer3.0.add_relu.stats&#39;, &#39;layer3.1.conv1.stats&#39;, &#39;layer3.1.conv2.stats&#39;, &#39;layer3.1.add_relu.stats&#39;, &#39;layer4.0.conv1.stats&#39;, &#39;layer4.0.conv2.stats&#39;, &#39;layer4.0.downsample.0.stats&#39;, &#39;layer4.0.add_relu.stats&#39;, &#39;layer4.1.conv1.stats&#39;, &#39;layer4.1.conv2.stats&#39;, &#39;layer4.1.add_relu.stats&#39;, &#39;fc.stats&#39;, &#39;quant.stats&#39;])
-
-keys of act_compare_dict entry for conv1&#39;s output:
-dict_keys([&#39;float&#39;, &#39;quantized&#39;])
-torch.Size([2, 64, 5, 5])
-torch.Size([2, 64, 5, 5])
-</pre></div>
-</div>
 <p>This dict can be used to compare and compute the quantization error of the activations of float and quantized models as following.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">act_compare_dict</span><span class="p">:</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">compute_error</span><span class="p">(</span><span class="n">act_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">],</span> <span class="n">act_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dequantize</span><span class="p">()))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>conv1.stats tensor(37.4652, grad_fn=&lt;MulBackward0&gt;)
-layer1.0.conv1.stats tensor(29.7700, grad_fn=&lt;MulBackward0&gt;)
-layer1.0.conv2.stats tensor(28.3451, grad_fn=&lt;MulBackward0&gt;)
-layer1.0.add_relu.stats tensor(32.2504, grad_fn=&lt;MulBackward0&gt;)
-layer1.1.conv1.stats tensor(29.6623, grad_fn=&lt;MulBackward0&gt;)
-layer1.1.conv2.stats tensor(25.7083, grad_fn=&lt;MulBackward0&gt;)
-layer1.1.add_relu.stats tensor(29.4557, grad_fn=&lt;MulBackward0&gt;)
-layer2.0.conv1.stats tensor(26.0570, grad_fn=&lt;MulBackward0&gt;)
-layer2.0.conv2.stats tensor(26.1098, grad_fn=&lt;MulBackward0&gt;)
-layer2.0.downsample.0.stats tensor(21.4495, grad_fn=&lt;MulBackward0&gt;)
-layer2.0.add_relu.stats tensor(25.6908, grad_fn=&lt;MulBackward0&gt;)
-layer2.1.conv1.stats tensor(25.2258, grad_fn=&lt;MulBackward0&gt;)
-layer2.1.conv2.stats tensor(24.4197, grad_fn=&lt;MulBackward0&gt;)
-layer2.1.add_relu.stats tensor(26.0970, grad_fn=&lt;MulBackward0&gt;)
-layer3.0.conv1.stats tensor(27.4878, grad_fn=&lt;MulBackward0&gt;)
-layer3.0.conv2.stats tensor(27.4627, grad_fn=&lt;MulBackward0&gt;)
-layer3.0.downsample.0.stats tensor(25.0780, grad_fn=&lt;MulBackward0&gt;)
-layer3.0.add_relu.stats tensor(25.5823, grad_fn=&lt;MulBackward0&gt;)
-layer3.1.conv1.stats tensor(31.2421, grad_fn=&lt;MulBackward0&gt;)
-layer3.1.conv2.stats tensor(26.2208, grad_fn=&lt;MulBackward0&gt;)
-layer3.1.add_relu.stats tensor(25.5204, grad_fn=&lt;MulBackward0&gt;)
-layer4.0.conv1.stats tensor(26.5500, grad_fn=&lt;MulBackward0&gt;)
-layer4.0.conv2.stats tensor(27.0827, grad_fn=&lt;MulBackward0&gt;)
-layer4.0.downsample.0.stats tensor(22.3420, grad_fn=&lt;MulBackward0&gt;)
-layer4.0.add_relu.stats tensor(21.2533, grad_fn=&lt;MulBackward0&gt;)
-layer4.1.conv1.stats tensor(26.6726, grad_fn=&lt;MulBackward0&gt;)
-layer4.1.conv2.stats tensor(18.2529, grad_fn=&lt;MulBackward0&gt;)
-layer4.1.add_relu.stats tensor(18.3587, grad_fn=&lt;MulBackward0&gt;)
-fc.stats tensor(20.0616, grad_fn=&lt;MulBackward0&gt;)
-quant.stats tensor(48.0445)
-</pre></div>
-</div>
 <p>If we want to do the comparison for more than one input data, we can do the following.
 Prepare the model by attaching the logger to both floating point module and quantized
 module if they are in the <code class="docutils literal notranslate"><span class="pre">white_list</span></code>. Default logger is <code class="docutils literal notranslate"><span class="pre">OutputLogger</span></code>, and default white_list
@@ -513,7 +435,7 @@ <h3>2. Compare float point and quantized models at corresponding locations<a cla
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Customized logger class</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyOutputLogger</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
@@ -576,32 +498,11 @@ <h3>3. Compare a module in a quantized model with its float point equivalent, wi
 <span class="nb">print</span><span class="p">(</span><span class="n">ob_dict</span><span class="p">[</span><span class="s1">&#39;layer1.0.stats&#39;</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>keys of ob_dict:
-dict_keys([&#39;layer1.0.stats&#39;, &#39;layer1.1.stats&#39;, &#39;layer2.0.stats&#39;, &#39;layer2.1.stats&#39;, &#39;layer3.0.stats&#39;, &#39;layer3.1.stats&#39;, &#39;layer4.0.stats&#39;, &#39;layer4.1.stats&#39;])
-
-keys of ob_dict entry for layer1.0&#39;s output:
-dict_keys([&#39;float&#39;, &#39;quantized&#39;])
-torch.Size([64, 3, 3])
-torch.Size([64, 3, 3])
-</pre></div>
-</div>
 <p>This dict can be then used to compare and compute the module level quantization error.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">ob_dict</span><span class="p">:</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">compute_error</span><span class="p">(</span><span class="n">ob_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">],</span> <span class="n">ob_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">dequantize</span><span class="p">()))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>layer1.0.stats tensor(32.8602)
-layer1.1.stats tensor(34.1511)
-layer2.0.stats tensor(28.7263)
-layer2.1.stats tensor(31.0849)
-layer3.0.stats tensor(29.0064)
-layer3.1.stats tensor(31.8086)
-layer4.0.stats tensor(26.3882)
-layer4.1.stats tensor(23.1786)
-</pre></div>
-</div>
 <p>If we want to do the comparison for more than one input data, we can do the following.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">ns</span><span class="o">.</span><span class="n">prepare_model_with_stubs</span><span class="p">(</span><span class="n">float_model</span><span class="p">,</span> <span class="n">qmodel</span><span class="p">,</span> <span class="n">module_swap_list</span><span class="p">,</span> <span class="n">ns</span><span class="o">.</span><span class="n">ShadowLogger</span><span class="p">)</span>
 <span class="k">for</span> <span class="n">data</span> <span class="ow">in</span> <span class="n">img_data</span><span class="p">:</span>
@@ -614,7 +515,7 @@ <h3>3. Compare a module in a quantized model with its float point equivalent, wi
     <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Customized logger class</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MyShadowLogger</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
     <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
@@ -646,7 +547,7 @@ <h3>Setup<a class="headerlink" href="#id1" title="Permalink to this headline">¶
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">LSTMModel</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot;Container module with an encoder, a recurrent module, and a decoder.&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">LSTMModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">encoder</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Embedding</span><span class="p">(</span><span class="n">ntoken</span><span class="p">,</span> <span class="n">ninp</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">rnn</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="n">ninp</span><span class="p">,</span> <span class="n">nhid</span><span class="p">,</span> <span class="n">nlayers</span><span class="p">,</span> <span class="n">dropout</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
@@ -699,19 +600,6 @@ <h3>1. Compare the weights of float and quantized models<a class="headerlink" hr
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">wt_compare_dict</span> <span class="o">=</span> <span class="n">ns</span><span class="o">.</span><span class="n">compare_weights</span><span class="p">(</span><span class="n">float_model</span><span class="o">.</span><span class="n">state_dict</span><span class="p">(),</span> <span class="n">qmodel</span><span class="o">.</span><span class="n">state_dict</span><span class="p">())</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>lstm float ih key is:  rnn.weight_ih_l0
-lstm float hh key is:  rnn.weight_hh_l0
-lstm float ih key is:  rnn.weight_ih_l1
-lstm float hh key is:  rnn.weight_hh_l1
-lstm float ih key is:  rnn.weight_ih_l2
-lstm float hh key is:  rnn.weight_hh_l2
-lstm float ih key is:  rnn.weight_ih_l3
-lstm float hh key is:  rnn.weight_hh_l3
-lstm float ih key is:  rnn.weight_ih_l4
-lstm float hh key is:  rnn.weight_hh_l4
-</pre></div>
-</div>
 <p>Once we get <code class="docutils literal notranslate"><span class="pre">wt_compare_dict</span></code>, it can be used to compare and compute the quantization error of the weights of float and quantized models as following.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">wt_compare_dict</span><span class="p">:</span>
     <span class="k">if</span> <span class="n">wt_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">is_quantized</span><span class="p">:</span>
@@ -720,16 +608,6 @@ <h3>1. Compare the weights of float and quantized models<a class="headerlink" hr
         <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">compute_error</span><span class="p">(</span><span class="n">wt_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">],</span> <span class="n">wt_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>encoder.weight tensor(inf)
-rnn._all_weight_values.0.param tensor(48.1309)
-rnn._all_weight_values.1.param tensor(48.1245)
-rnn._all_weight_values.2.param tensor(48.1303)
-rnn._all_weight_values.3.param tensor(48.1265)
-rnn._all_weight_values.4.param tensor(48.1148)
-decoder._packed_params._packed_params tensor(48.1688)
-</pre></div>
-</div>
 <p>The Inf value in <code class="docutils literal notranslate"><span class="pre">encoder.weight</span></code> entry above is because encoder module is not quantized and the weights are the same in both floating point and quantized models.</p>
 </div>
 <div class="section" id="id3">
@@ -742,20 +620,11 @@ <h3>2. Compare float point and quantized models at corresponding locations<a cla
 <span class="nb">print</span><span class="p">(</span><span class="n">act_compare_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>dict_keys([&#39;rnn.stats&#39;, &#39;decoder.stats&#39;])
-</pre></div>
-</div>
 <p>This dict can be used to compare and compute the quantization error of the activations of float and quantized models as following. The LSTM module in this model has two outputs, in this example we compute the error of the first output.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">act_compare_dict</span><span class="p">:</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">compute_error</span><span class="p">(</span><span class="n">act_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">act_compare_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>rnn.stats tensor(56.1608, grad_fn=&lt;MulBackward0&gt;)
-decoder.stats tensor(40.8233, grad_fn=&lt;MulBackward0&gt;)
-</pre></div>
-</div>
 </div>
 <div class="section" id="id4">
 <h3>3. Compare a module in a quantized model with its float point equivalent, with the same input data<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
@@ -780,20 +649,11 @@ <h3>3. Compare a module in a quantized model with its float point equivalent, wi
 <span class="nb">print</span><span class="p">(</span><span class="n">ob_dict</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>dict_keys([&#39;rnn.stats&#39;, &#39;decoder.stats&#39;])
-</pre></div>
-</div>
 <p>This dict can be then used to compare and compute the module level quantization error.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">ob_dict</span><span class="p">:</span>
     <span class="nb">print</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">compute_error</span><span class="p">(</span><span class="n">ob_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;float&#39;</span><span class="p">],</span> <span class="n">ob_dict</span><span class="p">[</span><span class="n">key</span><span class="p">][</span><span class="s1">&#39;quantized&#39;</span><span class="p">]))</span>
 </pre></div>
 </div>
-<p class="sphx-glr-script-out">Out:</p>
-<div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>rnn.stats tensor(54.7989)
-decoder.stats tensor(38.1035)
-</pre></div>
-</div>
 <p>SQNR of 40 dB is high and this is a situation where we have very good numerical alignment between the floating point and quantized model.</p>
 </div>
 </div>
@@ -805,7 +665,7 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 <div class="section" id="references">
 <h2>References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
 <p>[1] <a class="reference external" href="https://pytorch.org/tutorials/advanced/dynamic_quantization_tutorial.html">DYNAMIC QUANTIZATION ON AN LSTM WORD LANGUAGE MODEL</a>.</p>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  1.753 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-prototype-numeric-suite-tutorial-py">
 <div class="sphx-glr-download docutils container">
 <p><a class="reference download internal" download="" href="../_downloads/6aefea42cfe1bb0d4cb1b244c3feba1b/numeric_suite_tutorial.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">numeric_suite_tutorial.py</span></code></a></p>
@@ -894,11 +754,9 @@ <h2>References<a class="headerlink" href="#references" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/prototype/sg_execution_times.html b/docs/prototype/sg_execution_times.html
index 70245b09a..572d82d6a 100644
--- a/docs/prototype/sg_execution_times.html
+++ b/docs/prototype/sg_execution_times.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -297,10 +297,10 @@
               
   <div class="section" id="computation-times">
 <span id="sphx-glr-prototype-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this headline">¶</a></h1>
-<p><strong>03:48.874</strong> total execution time for <strong>prototype</strong> files:</p>
+<p><strong>00:00.895</strong> total execution time for <strong>prototype</strong> files:</p>
 <ul class="simple">
-<li><p><strong>03:47.121</strong>: <a class="reference internal" href="graph_mode_static_quantization_tutorial.html#sphx-glr-prototype-graph-mode-static-quantization-tutorial-py"><span class="std std-ref">(prototype) Graph Mode Post Training Static Quantization in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">graph_mode_static_quantization_tutorial.py</span></code>)</p></li>
-<li><p><strong>00:01.753</strong>: <a class="reference internal" href="numeric_suite_tutorial.html#sphx-glr-prototype-numeric-suite-tutorial-py"><span class="std std-ref">PyTorch Numeric Suite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">numeric_suite_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.542</strong>: <a class="reference internal" href="numeric_suite_tutorial.html#sphx-glr-prototype-numeric-suite-tutorial-py"><span class="std std-ref">PyTorch Numeric Suite Tutorial</span></a> (<code class="docutils literal notranslate"><span class="pre">numeric_suite_tutorial.py</span></code>)</p></li>
+<li><p><strong>00:00.353</strong>: <a class="reference internal" href="graph_mode_static_quantization_tutorial.html#sphx-glr-prototype-graph-mode-static-quantization-tutorial-py"><span class="std std-ref">(prototype) Graph Mode Post Training Static Quantization in PyTorch</span></a> (<code class="docutils literal notranslate"><span class="pre">graph_mode_static_quantization_tutorial.py</span></code>)</p></li>
 <li><p><strong>00:00.000</strong>: <a class="reference internal" href="torchscript_freezing.html#sphx-glr-prototype-torchscript-freezing-py"><span class="std std-ref">Model Freezing in TorchScript</span></a> (<code class="docutils literal notranslate"><span class="pre">torchscript_freezing.py</span></code>)</p></li>
 </ul>
 </div>
@@ -362,11 +362,9 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/prototype/torchscript_freezing.html b/docs/prototype/torchscript_freezing.html
index 946f4f766..d8b384645 100644
--- a/docs/prototype/torchscript_freezing.html
+++ b/docs/prototype/torchscript_freezing.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -318,7 +318,7 @@ <h2>Basic Syntax<a class="headerlink" href="#basic-syntax" title="Permalink to t
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span><span class="o">,</span> <span class="nn">time</span>
 
 <span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -498,11 +498,9 @@ <h2>Conclusion<a class="headerlink" href="#conclusion" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/android_native_app_with_custom_op.html b/docs/recipes/android_native_app_with_custom_op.html
index c21fe47fa..816b1bdcf 100644
--- a/docs/recipes/android_native_app_with_custom_op.html
+++ b/docs/recipes/android_native_app_with_custom_op.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -358,7 +358,7 @@ <h2>Preparing TorchScript Model With Custom C++ Operator<a class="headerlink" hr
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torch.utils.cpp_extension</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">version</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
 <span class="n">op_source</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
 <span class="s2">#include &lt;opencv2/opencv.hpp&gt;</span>
 <span class="s2">#include &lt;torch/script.h&gt;</span>
@@ -389,14 +389,14 @@ <h2>Preparing TorchScript Model With Custom C++ Operator<a class="headerlink" hr
     <span class="n">name</span><span class="o">=</span><span class="s2">&quot;warp_perspective&quot;</span><span class="p">,</span>
     <span class="n">cpp_sources</span><span class="o">=</span><span class="n">op_source</span><span class="p">,</span>
     <span class="n">extra_ldflags</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;-lopencv_core&quot;</span><span class="p">,</span> <span class="s2">&quot;-lopencv_imgproc&quot;</span><span class="p">],</span>
-    <span class="n">is_python_module</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
-    <span class="n">verbose</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
+    <span class="n">is_python_module</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
-<span class="k">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">my_ops</span><span class="o">.</span><span class="n">warp_perspective</span><span class="p">)</span>
 
 
-<span class="nd">@torch.jit.script</span>
+<span class="nd">@torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span>
 <span class="k">def</span> <span class="nf">compute</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
     <span class="k">if</span> <span class="nb">bool</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">42</span><span class="p">):</span>
         <span class="n">z</span> <span class="o">=</span> <span class="mi">5</span>
@@ -692,52 +692,52 @@ <h3>Sources<a class="headerlink" href="#sources" title="Permalink to this headli
 <h3>Java Code<a class="headerlink" href="#java-code" title="Permalink to this headline">¶</a></h3>
 <p>Now we are ready to implement our MainActivity in</p>
 <p><code class="docutils literal notranslate"><span class="pre">NativeApp/app/src/main/java/org/pytorch/nativeapp/MainActivity.java</span></code>:</p>
-<div class="highlight-java notranslate"><div class="highlight"><pre><span></span><span class="kn">package</span> <span class="nn">org.pytorch.nativeapp</span><span class="o">;</span>
-
-<span class="kn">import</span> <span class="nn">android.content.Context</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">android.os.Bundle</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">android.util.Log</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">androidx.appcompat.app.AppCompatActivity</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">java.io.File</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">java.io.FileOutputStream</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">java.io.IOException</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">java.io.InputStream</span><span class="o">;</span>
-<span class="kn">import</span> <span class="nn">java.io.OutputStream</span><span class="o">;</span>
-
-<span class="kd">public</span> <span class="kd">class</span> <span class="nc">MainActivity</span> <span class="kd">extends</span> <span class="n">AppCompatActivity</span> <span class="o">{</span>
-
-  <span class="kd">private</span> <span class="kd">static</span> <span class="kd">final</span> <span class="n">String</span> <span class="n">TAG</span> <span class="o">=</span> <span class="s">&quot;PyTorchNativeApp&quot;</span><span class="o">;</span>
-
-  <span class="kd">public</span> <span class="kd">static</span> <span class="n">String</span> <span class="nf">assetFilePath</span><span class="o">(</span><span class="n">Context</span> <span class="n">context</span><span class="o">,</span> <span class="n">String</span> <span class="n">assetName</span><span class="o">)</span> <span class="o">{</span>
-    <span class="n">File</span> <span class="n">file</span> <span class="o">=</span> <span class="k">new</span> <span class="n">File</span><span class="o">(</span><span class="n">context</span><span class="o">.</span><span class="na">getFilesDir</span><span class="o">(),</span> <span class="n">assetName</span><span class="o">);</span>
-    <span class="k">if</span> <span class="o">(</span><span class="n">file</span><span class="o">.</span><span class="na">exists</span><span class="o">()</span> <span class="o">&amp;&amp;</span> <span class="n">file</span><span class="o">.</span><span class="na">length</span><span class="o">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="o">)</span> <span class="o">{</span>
-      <span class="k">return</span> <span class="n">file</span><span class="o">.</span><span class="na">getAbsolutePath</span><span class="o">();</span>
-    <span class="o">}</span>
-
-    <span class="k">try</span> <span class="o">(</span><span class="n">InputStream</span> <span class="n">is</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="na">getAssets</span><span class="o">().</span><span class="na">open</span><span class="o">(</span><span class="n">assetName</span><span class="o">))</span> <span class="o">{</span>
-      <span class="k">try</span> <span class="o">(</span><span class="n">OutputStream</span> <span class="n">os</span> <span class="o">=</span> <span class="k">new</span> <span class="n">FileOutputStream</span><span class="o">(</span><span class="n">file</span><span class="o">))</span> <span class="o">{</span>
-        <span class="kt">byte</span><span class="o">[]</span> <span class="n">buffer</span> <span class="o">=</span> <span class="k">new</span> <span class="kt">byte</span><span class="o">[</span><span class="mi">4</span> <span class="o">*</span> <span class="mi">1024</span><span class="o">];</span>
-        <span class="kt">int</span> <span class="n">read</span><span class="o">;</span>
-        <span class="k">while</span> <span class="o">((</span><span class="n">read</span> <span class="o">=</span> <span class="n">is</span><span class="o">.</span><span class="na">read</span><span class="o">(</span><span class="n">buffer</span><span class="o">))</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="o">)</span> <span class="o">{</span>
-          <span class="n">os</span><span class="o">.</span><span class="na">write</span><span class="o">(</span><span class="n">buffer</span><span class="o">,</span> <span class="mi">0</span><span class="o">,</span> <span class="n">read</span><span class="o">);</span>
-        <span class="o">}</span>
-        <span class="n">os</span><span class="o">.</span><span class="na">flush</span><span class="o">();</span>
-      <span class="o">}</span>
-      <span class="k">return</span> <span class="n">file</span><span class="o">.</span><span class="na">getAbsolutePath</span><span class="o">();</span>
-    <span class="o">}</span> <span class="k">catch</span> <span class="o">(</span><span class="n">IOException</span> <span class="n">e</span><span class="o">)</span> <span class="o">{</span>
-      <span class="n">Log</span><span class="o">.</span><span class="na">e</span><span class="o">(</span><span class="n">TAG</span><span class="o">,</span> <span class="s">&quot;Error process asset &quot;</span> <span class="o">+</span> <span class="n">assetName</span> <span class="o">+</span> <span class="s">&quot; to file path&quot;</span><span class="o">);</span>
-    <span class="o">}</span>
-    <span class="k">return</span> <span class="kc">null</span><span class="o">;</span>
-  <span class="o">}</span>
+<div class="highlight-java notranslate"><div class="highlight"><pre><span></span><span class="kn">package</span> <span class="nn">org.pytorch.nativeapp</span><span class="p">;</span>
+
+<span class="kn">import</span> <span class="nn">android.content.Context</span><span class="p">;</span>
+<span class="kn">import</span> <span class="nn">android.os.Bundle</span><span class="p">;</span>
+<span class="kn">import</span> <span class="nn">android.util.Log</span><span class="p">;</span>
+<span class="kn">import</span> <span class="nn">androidx.appcompat.app.AppCompatActivity</span><span class="p">;</span>
+<span class="kn">import</span> <span class="nn">java.io.File</span><span class="p">;</span>
+<span class="kn">import</span> <span class="nn">java.io.FileOutputStream</span><span class="p">;</span>
+<span class="kn">import</span> <span class="nn">java.io.IOException</span><span class="p">;</span>
+<span class="kn">import</span> <span class="nn">java.io.InputStream</span><span class="p">;</span>
+<span class="kn">import</span> <span class="nn">java.io.OutputStream</span><span class="p">;</span>
+
+<span class="kd">public</span> <span class="kd">class</span> <span class="nc">MainActivity</span> <span class="kd">extends</span> <span class="n">AppCompatActivity</span> <span class="p">{</span>
+
+  <span class="kd">private</span> <span class="kd">static</span> <span class="kd">final</span> <span class="n">String</span> <span class="n">TAG</span> <span class="o">=</span> <span class="s">&quot;PyTorchNativeApp&quot;</span><span class="p">;</span>
+
+  <span class="kd">public</span> <span class="kd">static</span> <span class="n">String</span> <span class="nf">assetFilePath</span><span class="p">(</span><span class="n">Context</span> <span class="n">context</span><span class="p">,</span> <span class="n">String</span> <span class="n">assetName</span><span class="p">)</span> <span class="p">{</span>
+    <span class="n">File</span> <span class="n">file</span> <span class="o">=</span> <span class="k">new</span> <span class="n">File</span><span class="p">(</span><span class="n">context</span><span class="p">.</span><span class="na">getFilesDir</span><span class="p">(),</span> <span class="n">assetName</span><span class="p">);</span>
+    <span class="k">if</span> <span class="p">(</span><span class="n">file</span><span class="p">.</span><span class="na">exists</span><span class="p">()</span> <span class="o">&amp;&amp;</span> <span class="n">file</span><span class="p">.</span><span class="na">length</span><span class="p">()</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="p">{</span>
+      <span class="k">return</span> <span class="n">file</span><span class="p">.</span><span class="na">getAbsolutePath</span><span class="p">();</span>
+    <span class="p">}</span>
+
+    <span class="k">try</span> <span class="p">(</span><span class="n">InputStream</span> <span class="n">is</span> <span class="o">=</span> <span class="n">context</span><span class="p">.</span><span class="na">getAssets</span><span class="p">().</span><span class="na">open</span><span class="p">(</span><span class="n">assetName</span><span class="p">))</span> <span class="p">{</span>
+      <span class="k">try</span> <span class="p">(</span><span class="n">OutputStream</span> <span class="n">os</span> <span class="o">=</span> <span class="k">new</span> <span class="n">FileOutputStream</span><span class="p">(</span><span class="n">file</span><span class="p">))</span> <span class="p">{</span>
+        <span class="kt">byte</span><span class="o">[]</span> <span class="n">buffer</span> <span class="o">=</span> <span class="k">new</span> <span class="kt">byte</span><span class="o">[</span><span class="mi">4</span> <span class="o">*</span> <span class="mi">1024</span><span class="o">]</span><span class="p">;</span>
+        <span class="kt">int</span> <span class="n">read</span><span class="p">;</span>
+        <span class="k">while</span> <span class="p">((</span><span class="n">read</span> <span class="o">=</span> <span class="n">is</span><span class="p">.</span><span class="na">read</span><span class="p">(</span><span class="n">buffer</span><span class="p">))</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="p">{</span>
+          <span class="n">os</span><span class="p">.</span><span class="na">write</span><span class="p">(</span><span class="n">buffer</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">read</span><span class="p">);</span>
+        <span class="p">}</span>
+        <span class="n">os</span><span class="p">.</span><span class="na">flush</span><span class="p">();</span>
+      <span class="p">}</span>
+      <span class="k">return</span> <span class="n">file</span><span class="p">.</span><span class="na">getAbsolutePath</span><span class="p">();</span>
+    <span class="p">}</span> <span class="k">catch</span> <span class="p">(</span><span class="n">IOException</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
+      <span class="n">Log</span><span class="p">.</span><span class="na">e</span><span class="p">(</span><span class="n">TAG</span><span class="p">,</span> <span class="s">&quot;Error process asset &quot;</span> <span class="o">+</span> <span class="n">assetName</span> <span class="o">+</span> <span class="s">&quot; to file path&quot;</span><span class="p">);</span>
+    <span class="p">}</span>
+    <span class="k">return</span> <span class="kc">null</span><span class="p">;</span>
+  <span class="p">}</span>
 
   <span class="nd">@Override</span>
-  <span class="kd">protected</span> <span class="kt">void</span> <span class="nf">onCreate</span><span class="o">(</span><span class="n">Bundle</span> <span class="n">savedInstanceState</span><span class="o">)</span> <span class="o">{</span>
-    <span class="kd">super</span><span class="o">.</span><span class="na">onCreate</span><span class="o">(</span><span class="n">savedInstanceState</span><span class="o">);</span>
+  <span class="kd">protected</span> <span class="kt">void</span> <span class="nf">onCreate</span><span class="p">(</span><span class="n">Bundle</span> <span class="n">savedInstanceState</span><span class="p">)</span> <span class="p">{</span>
+    <span class="kd">super</span><span class="p">.</span><span class="na">onCreate</span><span class="p">(</span><span class="n">savedInstanceState</span><span class="p">);</span>
     <span class="kd">final</span> <span class="n">String</span> <span class="n">modelFileAbsoluteFilePath</span> <span class="o">=</span>
-        <span class="k">new</span> <span class="n">File</span><span class="o">(</span><span class="n">assetFilePath</span><span class="o">(</span><span class="k">this</span><span class="o">,</span> <span class="s">&quot;compute.pt&quot;</span><span class="o">)).</span><span class="na">getAbsolutePath</span><span class="o">();</span>
-    <span class="n">NativeClient</span><span class="o">.</span><span class="na">loadAndForwardModel</span><span class="o">(</span><span class="n">modelFileAbsoluteFilePath</span><span class="o">);</span>
-  <span class="o">}</span>
-<span class="o">}</span>
+        <span class="k">new</span> <span class="n">File</span><span class="p">(</span><span class="n">assetFilePath</span><span class="p">(</span><span class="k">this</span><span class="p">,</span> <span class="s">&quot;compute.pt&quot;</span><span class="p">)).</span><span class="na">getAbsolutePath</span><span class="p">();</span>
+    <span class="n">NativeClient</span><span class="p">.</span><span class="na">loadAndForwardModel</span><span class="p">(</span><span class="n">modelFileAbsoluteFilePath</span><span class="p">);</span>
+  <span class="p">}</span>
+<span class="p">}</span>
 </pre></div>
 </div>
 <p>In the previous step, when we copied our <code class="docutils literal notranslate"><span class="pre">compute.pt</span></code> to <code class="docutils literal notranslate"><span class="pre">NativeApp/app/src/main/assets</span></code> that file became an Android application asset, which will be packed in application. Android system provides only stream access to it.
@@ -745,22 +745,22 @@ <h3>Java Code<a class="headerlink" href="#java-code" title="Permalink to this he
 <p><code class="docutils literal notranslate"><span class="pre">OnCreate</span></code> method of Activity is called just after Activity creation. In this method, we call <code class="docutils literal notranslate"><span class="pre">assertFilePath</span></code> and call <code class="docutils literal notranslate"><span class="pre">NativeClient</span></code> class that will dispatch it to native code through JNI call.</p>
 <p><code class="docutils literal notranslate"><span class="pre">NativeClient</span></code> is a helper class with an internal private class <code class="docutils literal notranslate"><span class="pre">NativePeer</span></code>, which is responsible for working with the native part of our application. It has a static block that will load <code class="docutils literal notranslate"><span class="pre">libpytorch_nativeapp.so</span></code>, that is build with <code class="docutils literal notranslate"><span class="pre">CMakeLists.txt</span></code> that we added on the previous step. The static block will be executed with the first reference of <code class="docutils literal notranslate"><span class="pre">NativePeer</span></code> class. It happens in <code class="docutils literal notranslate"><span class="pre">NativeClient#loadAndForwardModel</span></code>.</p>
 <p><code class="docutils literal notranslate"><span class="pre">NativeApp/app/src/main/java/org/pytorch/nativeapp/NativeClient.java</span></code>:</p>
-<div class="highlight-java notranslate"><div class="highlight"><pre><span></span><span class="kn">package</span> <span class="nn">org.pytorch.nativeapp</span><span class="o">;</span>
+<div class="highlight-java notranslate"><div class="highlight"><pre><span></span><span class="kn">package</span> <span class="nn">org.pytorch.nativeapp</span><span class="p">;</span>
 
-<span class="kd">public</span> <span class="kd">final</span> <span class="kd">class</span> <span class="nc">NativeClient</span> <span class="o">{</span>
+<span class="kd">public</span> <span class="kd">final</span> <span class="kd">class</span> <span class="nc">NativeClient</span> <span class="p">{</span>
 
-  <span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">loadAndForwardModel</span><span class="o">(</span><span class="kd">final</span> <span class="n">String</span> <span class="n">modelPath</span><span class="o">)</span> <span class="o">{</span>
-    <span class="n">NativePeer</span><span class="o">.</span><span class="na">loadAndForwardModel</span><span class="o">(</span><span class="n">modelPath</span><span class="o">);</span>
-  <span class="o">}</span>
+  <span class="kd">public</span> <span class="kd">static</span> <span class="kt">void</span> <span class="nf">loadAndForwardModel</span><span class="p">(</span><span class="kd">final</span> <span class="n">String</span> <span class="n">modelPath</span><span class="p">)</span> <span class="p">{</span>
+    <span class="n">NativePeer</span><span class="p">.</span><span class="na">loadAndForwardModel</span><span class="p">(</span><span class="n">modelPath</span><span class="p">);</span>
+  <span class="p">}</span>
 
-  <span class="kd">private</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">NativePeer</span> <span class="o">{</span>
-    <span class="kd">static</span> <span class="o">{</span>
-      <span class="n">System</span><span class="o">.</span><span class="na">loadLibrary</span><span class="o">(</span><span class="s">&quot;pytorch_nativeapp&quot;</span><span class="o">);</span>
-    <span class="o">}</span>
+  <span class="kd">private</span> <span class="kd">static</span> <span class="kd">class</span> <span class="nc">NativePeer</span> <span class="p">{</span>
+    <span class="kd">static</span> <span class="p">{</span>
+      <span class="n">System</span><span class="p">.</span><span class="na">loadLibrary</span><span class="p">(</span><span class="s">&quot;pytorch_nativeapp&quot;</span><span class="p">);</span>
+    <span class="p">}</span>
 
-    <span class="kd">private</span> <span class="kd">static</span> <span class="kd">native</span> <span class="kt">void</span> <span class="nf">loadAndForwardModel</span><span class="o">(</span><span class="kd">final</span> <span class="n">String</span> <span class="n">modelPath</span><span class="o">);</span>
-  <span class="o">}</span>
-<span class="o">}</span>
+    <span class="kd">private</span> <span class="kd">static</span> <span class="kd">native</span> <span class="kt">void</span> <span class="nf">loadAndForwardModel</span><span class="p">(</span><span class="kd">final</span> <span class="n">String</span> <span class="n">modelPath</span><span class="p">);</span>
+  <span class="p">}</span>
+<span class="p">}</span>
 </pre></div>
 </div>
 <p><code class="docutils literal notranslate"><span class="pre">NativePeer#loadAndForwardModel</span></code> is declared as <code class="docutils literal notranslate"><span class="pre">native</span></code>, it does not have definition in Java. Call to this method will be re-dispatched through JNI to C++ method in our <code class="docutils literal notranslate"><span class="pre">libpytorch_nativeapp.so</span></code>, in <code class="docutils literal notranslate"><span class="pre">NativeApp/app/src/main/cpp/pytorch_nativeapp.cpp</span></code>.</p>
@@ -808,13 +808,13 @@ <h3>Native code<a class="headerlink" href="#native-code" title="Permalink to thi
 <span class="k">static</span> <span class="k">auto</span> <span class="n">registry</span> <span class="o">=</span>
     <span class="n">torch</span><span class="o">::</span><span class="n">RegisterOperators</span><span class="p">(</span><span class="s">&quot;my_ops::warp_perspective&quot;</span><span class="p">,</span> <span class="o">&amp;</span><span class="n">warp_perspective</span><span class="p">);</span>
 
-<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="n">T</span><span class="o">&gt;</span> <span class="kt">void</span> <span class="n">log</span><span class="p">(</span><span class="k">const</span> <span class="kt">char</span> <span class="o">*</span><span class="n">m</span><span class="p">,</span> <span class="n">T</span> <span class="n">t</span><span class="p">)</span> <span class="p">{</span>
+<span class="k">template</span> <span class="o">&lt;</span><span class="k">typename</span> <span class="nc">T</span><span class="o">&gt;</span> <span class="kt">void</span> <span class="n">log</span><span class="p">(</span><span class="k">const</span> <span class="kt">char</span> <span class="o">*</span><span class="n">m</span><span class="p">,</span> <span class="n">T</span> <span class="n">t</span><span class="p">)</span> <span class="p">{</span>
   <span class="n">std</span><span class="o">::</span><span class="n">ostringstream</span> <span class="n">os</span><span class="p">;</span>
   <span class="n">os</span> <span class="o">&lt;&lt;</span> <span class="n">t</span> <span class="o">&lt;&lt;</span> <span class="n">std</span><span class="o">::</span><span class="n">endl</span><span class="p">;</span>
   <span class="n">ALOGI</span><span class="p">(</span><span class="s">&quot;%s %s&quot;</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">os</span><span class="p">.</span><span class="n">str</span><span class="p">().</span><span class="n">c_str</span><span class="p">());</span>
 <span class="p">}</span>
 
-<span class="k">struct</span> <span class="n">JITCallGuard</span> <span class="p">{</span>
+<span class="k">struct</span> <span class="nc">JITCallGuard</span> <span class="p">{</span>
   <span class="n">torch</span><span class="o">::</span><span class="n">autograd</span><span class="o">::</span><span class="n">AutoGradMode</span> <span class="n">no_autograd_guard</span><span class="p">{</span><span class="nb">false</span><span class="p">};</span>
   <span class="n">torch</span><span class="o">::</span><span class="n">AutoNonVariableTypeMode</span> <span class="n">non_var_guard</span><span class="p">{</span><span class="nb">true</span><span class="p">};</span>
   <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">GraphOptimizerEnabledGuard</span> <span class="n">no_optimizer_guard</span><span class="p">{</span><span class="nb">false</span><span class="p">};</span>
@@ -825,13 +825,13 @@ <h3>Native code<a class="headerlink" href="#native-code" title="Permalink to thi
   <span class="k">const</span> <span class="kt">char</span> <span class="o">*</span><span class="n">modelPath</span> <span class="o">=</span> <span class="n">env</span><span class="o">-&gt;</span><span class="n">GetStringUTFChars</span><span class="p">(</span><span class="n">jModelPath</span><span class="p">,</span> <span class="mi">0</span><span class="p">);</span>
   <span class="n">assert</span><span class="p">(</span><span class="n">modelPath</span><span class="p">);</span>
   <span class="n">JITCallGuard</span> <span class="n">guard</span><span class="p">;</span>
-  <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="n">modelPath</span><span class="p">);</span>
-  <span class="n">module</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+  <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="n">modelPath</span><span class="p">);</span>
+  <span class="k">module</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
   <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">4</span><span class="p">,</span> <span class="mi">8</span><span class="p">});</span>
   <span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">y</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">8</span><span class="p">,</span> <span class="mi">5</span><span class="p">});</span>
   <span class="n">log</span><span class="p">(</span><span class="s">&quot;x:&quot;</span><span class="p">,</span> <span class="n">x</span><span class="p">);</span>
   <span class="n">log</span><span class="p">(</span><span class="s">&quot;y:&quot;</span><span class="p">,</span> <span class="n">y</span><span class="p">);</span>
-  <span class="n">c10</span><span class="o">::</span><span class="n">IValue</span> <span class="n">t_out</span> <span class="o">=</span> <span class="n">module</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">});</span>
+  <span class="n">c10</span><span class="o">::</span><span class="n">IValue</span> <span class="n">t_out</span> <span class="o">=</span> <span class="k">module</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">});</span>
   <span class="n">log</span><span class="p">(</span><span class="s">&quot;result:&quot;</span><span class="p">,</span> <span class="n">t_out</span><span class="p">);</span>
   <span class="n">env</span><span class="o">-&gt;</span><span class="n">ReleaseStringUTFChars</span><span class="p">(</span><span class="n">jModelPath</span><span class="p">,</span> <span class="n">modelPath</span><span class="p">);</span>
 <span class="p">}</span>
@@ -1006,11 +1006,9 @@ <h3>Building the app<a class="headerlink" href="#building-the-app" title="Permal
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/deployment_with_flask.html b/docs/recipes/deployment_with_flask.html
index 4c8602472..e897761a5 100644
--- a/docs/recipes/deployment_with_flask.html
+++ b/docs/recipes/deployment_with_flask.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -358,8 +358,8 @@ <h2>Flask 서비스 구축하기<a class="headerlink" href="#id6" title="Permali
 <h3>Import<a class="headerlink" href="#import" title="Permalink to this headline">¶</a></h3>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
 <span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
-<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
-<span class="kn">from</span> <span class="nn">flask</span> <span class="k">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span><span class="p">,</span> <span class="n">request</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
+<span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span><span class="p">,</span> <span class="n">request</span>
 </pre></div>
 </div>
 <p>순서대로:</p>
@@ -439,8 +439,8 @@ <h2>전체 Flask App 실행<a class="headerlink" href="#flask-app" title="Permal
 
 <span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
 <span class="kn">import</span> <span class="nn">torchvision.transforms</span> <span class="k">as</span> <span class="nn">transforms</span>
-<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
-<span class="kn">from</span> <span class="nn">flask</span> <span class="k">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span><span class="p">,</span> <span class="n">request</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
+<span class="kn">from</span> <span class="nn">flask</span> <span class="kn">import</span> <span class="n">Flask</span><span class="p">,</span> <span class="n">jsonify</span><span class="p">,</span> <span class="n">request</span>
 
 
 <span class="n">app</span> <span class="o">=</span> <span class="n">Flask</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
@@ -616,11 +616,9 @@ <h2>중요한 자료<a class="headerlink" href="#id7" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/mobile_perf.html b/docs/recipes/mobile_perf.html
index 38c861b4c..fa541ae1b 100644
--- a/docs/recipes/mobile_perf.html
+++ b/docs/recipes/mobile_perf.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -325,10 +325,10 @@ <h3>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">
 </div>
 <p>Code your model:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch.utils.mobile_optimizer</span> <span class="k">import</span> <span class="n">optimize_for_mobile</span>
+<span class="kn">from</span> <span class="nn">torch.utils.mobile_optimizer</span> <span class="kn">import</span> <span class="n">optimize_for_mobile</span>
 
 <span class="k">class</span> <span class="nc">AnnotatedConvBnReLUModel</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">AnnotatedConvBnReLUModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">bn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">BatchNorm2d</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
@@ -491,7 +491,7 @@ <h3>iOS - Benchmarking Setup<a class="headerlink" href="#ios-benchmarking-setup"
 <p>To begin with, let’s apply the <code class="docutils literal notranslate"><span class="pre">optimize_for_mobile</span></code> method to our python script located at <a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/ios/TestApp/benchmark/trace_model.py">TestApp/benchmark/trace_mode.py</a>. Simply modify the code as below.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">from</span> <span class="nn">torch.utils.mobile_optimizer</span> <span class="k">import</span> <span class="n">optimize_for_mobile</span>
+<span class="kn">from</span> <span class="nn">torch.utils.mobile_optimizer</span> <span class="kn">import</span> <span class="n">optimize_for_mobile</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
@@ -595,11 +595,9 @@ <h3>iOS - Benchmarking Setup<a class="headerlink" href="#ios-benchmarking-setup"
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/Captum_Recipe.html b/docs/recipes/recipes/Captum_Recipe.html
index ea4166646..4223199be 100644
--- a/docs/recipes/recipes/Captum_Recipe.html
+++ b/docs/recipes/recipes/Captum_Recipe.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -305,8 +305,8 @@
 </div>
 <div class="sphx-glr-example-title section" id="captum">
 <span id="sphx-glr-recipes-recipes-captum-recipe-py"></span><h1>Captum을 사용하여 모델 해석하기<a class="headerlink" href="#captum" title="Permalink to this headline">¶</a></h1>
-<p><strong>번역</strong>: <a class="reference external" href="https://github.com/jjeamin">정재민</a></p>
-<p>Captum을 사용하면 데이터 특징(features)이 모델의 예측 또는 뉴런 활성화에
+<p><strong>번역</strong>: <a class="reference external" href="https://github.com/jjeamin">정재민</a>
+Captum을 사용하면 데이터 특징(features)이 모델의 예측 또는 뉴런 활성화에
 미치는 영향을 이해하고, 모델의 동작 방식을 알 수 있습니다.</p>
 <p>그리고 <code class="docutils literal notranslate"><span class="pre">Integrated</span> <span class="pre">Gradients</span></code>와 <code class="docutils literal notranslate"><span class="pre">Guided</span> <span class="pre">GradCam</span></code>과 같은
 최첨단의 feature attribution 알고리즘을 적용할 수 있습니다.</p>
@@ -322,10 +322,10 @@ <h2>시작하기 전에<a class="headerlink" href="#id2" title="Permalink to thi
 Captum은 샘플 이미지의 어떤 부분이 모델에 의해 만들어진
 특정한 예측에 도움을 주는지 보여줍니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">transforms</span>
-<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span>
+<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
 <span class="kn">import</span> <span class="nn">requests</span>
-<span class="kn">from</span> <span class="nn">io</span> <span class="k">import</span> <span class="n">BytesIO</span>
+<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="n">BytesIO</span>
 
 <span class="n">model</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
 
@@ -352,7 +352,7 @@ <h2>시작하기 전에<a class="headerlink" href="#id2" title="Permalink to thi
 <h2>속성(attribution) 계산하기<a class="headerlink" href="#attribution" title="Permalink to this headline">¶</a></h2>
 <p>모델의 top-3 예측 중에는 개와 고양이에 해당하는 클래스 208과 283이 있습니다.</p>
 <p>Captum의 <code class="docutils literal notranslate"><span class="pre">Occlusion</span></code>알고리즘을 사용하여 각 예측을 입력의 해당 부분에 표시합니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">captum.attr</span> <span class="k">import</span> <span class="n">Occlusion</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">captum.attr</span> <span class="kn">import</span> <span class="n">Occlusion</span>
 
 <span class="n">occlusion</span> <span class="o">=</span> <span class="n">Occlusion</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 
@@ -389,7 +389,7 @@ <h2>결과 시각화하기<a class="headerlink" href="#id3" title="Permalink to
 <p>Captum의 <code class="docutils literal notranslate"><span class="pre">visualization</span></code>유틸리티는 그림과 텍스트 입력 모두에 대한
 속성(attribution) 결과를 시각화 할 수 있는 즉시 사용가능한 방법을 제공합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">captum.attr</span> <span class="k">import</span> <span class="n">visualization</span> <span class="k">as</span> <span class="n">viz</span>
+<span class="kn">from</span> <span class="nn">captum.attr</span> <span class="kn">import</span> <span class="n">visualization</span> <span class="k">as</span> <span class="n">viz</span>
 
 <span class="c1"># 계산 속성 Tensor를 이미지 같은 numpy 배열로 변환합니다.</span>
 <span class="n">attribution_dog</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">attribution_dog</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">(),</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
@@ -518,11 +518,9 @@ <h2>마지막 노트<a class="headerlink" href="#id4" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/custom_dataset_transforms_loader.html b/docs/recipes/recipes/custom_dataset_transforms_loader.html
index 8950b958b..c50abad9d 100644
--- a/docs/recipes/recipes/custom_dataset_transforms_loader.html
+++ b/docs/recipes/recipes/custom_dataset_transforms_loader.html
@@ -31,7 +31,7 @@
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
     <link rel="next" title="Captum을 사용하여 모델 해석하기" href="Captum_Recipe.html" />
-    <link rel="prev" title="PyTorch Profiler" href="profiler.html" /> 
+    <link rel="prev" title="PyTorch 프로파일러(Profiler)" href="profiler.html" /> 
 
   
   <script src="../../_static/js/modernizr.min.js"></script>
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -323,19 +323,19 @@
 </dl>
 <p>작성되고 있는 이 시점에서, 이 레시피는 <a class="reference external" href="https://chsasank.github.io">Sasank Chilamkurthy</a> 의 오리지널 튜토리얼을 바탕으로 하며
 나중에는 <a class="reference external" href="https://github.com/jspisak">Joe Spisak</a> 에 의해 수정되었습니다.
-한국어로 <a class="reference external" href="https://https://github.com/JaeLee18">Jae Joong Lee</a> 에 의해 번역되었습니다.</p>
-<div class="section" id="id1">
-<h2>설정<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+한국어로 <a class="reference external" href="https://https://github.com/JaeLee18">Jae Joong Lee</a> 에 의해 번역되었습니다.
+설정
+———————-</p>
 <p>먼저 이 레시피에 필요한 모든 라이브러리들을 불러오도록 하겠습니다.</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="k">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">skimage</span> <span class="k">import</span> <span class="n">io</span><span class="p">,</span> <span class="n">transform</span>
+<span class="kn">from</span> <span class="nn">skimage</span> <span class="kn">import</span> <span class="n">io</span><span class="p">,</span> <span class="n">transform</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">DataLoader</span>
-<span class="kn">from</span> <span class="nn">torchvision</span> <span class="k">import</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">utils</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">torchvision</span> <span class="kn">import</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">utils</span>
 
 <span class="c1"># 경고 메시지 무시하기</span>
 <span class="kn">import</span> <span class="nn">warnings</span>
@@ -344,9 +344,8 @@ <h2>설정<a class="headerlink" href="#id1" title="Permalink to this headline">
 <span class="n">plt</span><span class="o">.</span><span class="n">ion</span><span class="p">()</span>   <span class="c1"># 반응형 모드 설정</span>
 </pre></div>
 </div>
-</div>
-<div class="section" id="id2">
-<h2>첫 번째: 데이터셋<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id1">
+<h2>첫 번째: 데이터셋<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
 <p>우리가 다룰 데이터셋은 얼굴 포즈입니다.
 전반적으로, 한 얼굴에는 68개의 랜드마크들이 표시되어 있습니다.</p>
 <p>다음 단계로는, <a class="reference external" href="https://download.pytorch.org/tutorial/faces.zip">여기</a> 에서
@@ -382,8 +381,8 @@ <h2>첫 번째: 데이터셋<a class="headerlink" href="#id2" title="Permalink t
 <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;First 4 Landmarks: </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">landmarks</span><span class="p">[:</span><span class="mi">4</span><span class="p">]))</span>
 </pre></div>
 </div>
-<div class="section" id="id4">
-<h3>1.1 이미지를 표시하기 위해 간단한 도움 함수 작성하기<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id3">
+<h3>1.1 이미지를 표시하기 위해 간단한 도움 함수 작성하기<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
 <p>다음으로는 이미지를 보여주기 위해 간단한 도움 함수를 작성하여 이미지가 가지고 있는 랜드마크들과
 이미지 샘플을 보여주도록 하겠습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">show_landmarks</span><span class="p">(</span><span class="n">image</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">):</span>
@@ -399,8 +398,8 @@ <h3>1.1 이미지를 표시하기 위해 간단한 도움 함수 작성하기<a
 </pre></div>
 </div>
 </div>
-<div class="section" id="id5">
-<h3>1.2 데이터셋 클래스 만들기<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id4">
+<h3>1.2 데이터셋 클래스 만들기<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
 <p>이제 PyTorch 데이터셋 클래스에 대해 알아봅시다.</p>
 <p><code class="docutils literal notranslate"><span class="pre">torch.utils.data.Dataset</span></code> 은 추상 클래스로서 데이터셋을 맡고 있습니다
 <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> 을 상속받아야 하며 다음의 메소드들을 오버라이드 해야합니다.</p>
@@ -423,7 +422,7 @@ <h3>1.2 데이터셋 클래스 만들기<a class="headerlink" href="#id5" title=
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">FaceLandmarksDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot; 얼굴 랜드마크 데이터셋. &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">csv_file</span><span class="p">,</span> <span class="n">root_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">csv_file</span><span class="p">,</span> <span class="n">root_dir</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        매개변수 :</span>
 <span class="sd">            csv_file (문자열): 설명이 포함된 csv 파일 경로.</span>
@@ -434,10 +433,10 @@ <h3>1.2 데이터셋 클래스 만들기<a class="headerlink" href="#id5" title=
         <span class="bp">self</span><span class="o">.</span><span class="n">root_dir</span> <span class="o">=</span> <span class="n">root_dir</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
 
-    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_frame</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">):</span>
         <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_tensor</span><span class="p">(</span><span class="n">idx</span><span class="p">):</span>
             <span class="n">idx</span> <span class="o">=</span> <span class="n">idx</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
 
@@ -456,8 +455,8 @@ <h3>1.2 데이터셋 클래스 만들기<a class="headerlink" href="#id5" title=
 </pre></div>
 </div>
 </div>
-<div class="section" id="id6">
-<h3>1.3 반복문을 통한 데이터 샘플 사용<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id5">
+<h3>1.3 반복문을 통한 데이터 샘플 사용<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
 <p>다음으로는 이 클래스를 인스턴스화하고 데이터 샘플을 반복문을 이용하여 사용해봅시다.
 우리는 첫 4개의 샘플들만 출력하고 그 4개 샘플들의 랜드마크를 보여주겠습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">face_dataset</span> <span class="o">=</span> <span class="n">FaceLandmarksDataset</span><span class="p">(</span><span class="n">csv_file</span><span class="o">=</span><span class="s1">&#39;faces/face_landmarks.csv&#39;</span><span class="p">,</span>
@@ -483,13 +482,13 @@ <h3>1.3 반복문을 통한 데이터 샘플 사용<a class="headerlink" href="#
 </div>
 </div>
 </div>
-<div class="section" id="id7">
-<h2>두 번째: 데이터 변형<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h2>
+<div class="section" id="id6">
+<h2>두 번째: 데이터 변형<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h2>
 <p>우리는 지금까지 어느정도 사용자 정의 데이터셋을 만들어 보았는데 이제는 사용자 정의 변형을 만들 차례 입니다.
 컴퓨터 비전에서는 사용자 정의 변형은 알고리즘을 일반화시키고 정확도를 올리는데 도움을 줍니다.
 변형들은 훈련시에 사용이 되며 주로 데이터 증강으로 참조되며 최근의 모델 개발에선 흔히 사용됩니다.</p>
 <p>데이터셋을 다룰때 자주 일어나는 문제중 하나는 모든 샘플들이 같은 크기를 가지고 있지 않을 경우입니다.
-대부분의 신경망들은 미리 정해진 크기의 이미지들을 받아드립니다.
+대부분의 신경망들은 미리 정해진 크기의 이미지들을 받아들입니다.
 그렇기 때문에 우리는 전처리 코드를 작성해야할 필요가 있습니다.
 이제 세개의 변형을 만들어 봅시다.</p>
 <ul class="simple">
@@ -505,8 +504,8 @@ <h2>두 번째: 데이터 변형<a class="headerlink" href="#id7" title="Permali
 </pre></div>
 </div>
 <p>어떻게 이런 변형들이 이미지와 랜드마크에 적용이 되었는지 아래를 봐주시길 바랍니다.</p>
-<div class="section" id="id8">
-<h3>2.1 호출 가능한 클래스들 작성하기<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id7">
+<h3>2.1 호출 가능한 클래스들 작성하기<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h3>
 <p>각각의 변형에 맞는 호출 가능한 클래스 작성을 시작해 봅시다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Rescale</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot; 주어진 크기로 샘플안에 있는 이미지를 재변환 합니다.</span>
@@ -517,11 +516,11 @@ <h3>2.1 호출 가능한 클래스들 작성하기<a class="headerlink" href="#i
 <span class="sd">        int일때는 설정된 값보다 작은 이미지들의 가로와 세로는 output_size 에 적절한 비율로 변환됩니다.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">output_size</span> <span class="o">=</span> <span class="n">output_size</span>
 
-    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
         <span class="n">image</span><span class="p">,</span> <span class="n">landmarks</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">],</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;landmarks&#39;</span><span class="p">]</span>
 
         <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
@@ -552,7 +551,7 @@ <h3>2.1 호출 가능한 클래스들 작성하기<a class="headerlink" href="#i
 <span class="sd">        int로 설정하시면 정사각형 형태로 자르게 됩니다.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_size</span><span class="p">):</span>
         <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
         <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">output_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">output_size</span><span class="p">,</span> <span class="n">output_size</span><span class="p">)</span>
@@ -560,7 +559,7 @@ <h3>2.1 호출 가능한 클래스들 작성하기<a class="headerlink" href="#i
             <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">output_size</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">output_size</span> <span class="o">=</span> <span class="n">output_size</span>
 
-    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
         <span class="n">image</span><span class="p">,</span> <span class="n">landmarks</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">],</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;landmarks&#39;</span><span class="p">]</span>
 
         <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
@@ -580,7 +579,7 @@ <h3>2.1 호출 가능한 클래스들 작성하기<a class="headerlink" href="#i
 <span class="k">class</span> <span class="nc">ToTensor</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
     <span class="sd">&quot;&quot;&quot; 샘플 안에 있는 n차원 배열을 Tensor로 변홥힙니다. &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
         <span class="n">image</span><span class="p">,</span> <span class="n">landmarks</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;image&#39;</span><span class="p">],</span> <span class="n">sample</span><span class="p">[</span><span class="s1">&#39;landmarks&#39;</span><span class="p">]</span>
 
         <span class="c1"># 색깔 축들을 바꿔치기해야하는데 그 이유는 numpy와 torch의 이미지 표현방식이 다르기 때문입니다.</span>
@@ -592,8 +591,8 @@ <h3>2.1 호출 가능한 클래스들 작성하기<a class="headerlink" href="#i
 </pre></div>
 </div>
 </div>
-<div class="section" id="id9">
-<h3>2.2 변환들을 구성하고 샘플에 적용해보기.<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id8">
+<h3>2.2 변환들을 구성하고 샘플에 적용해보기.<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h3>
 <p>다음에는 작성해왔던 변환들을 구성하고 샘플에 적용해봅시다.</p>
 <p>우리가 한 이미지의 가로나 세로중에서 더작은 쪽을 256으로 크기를 바꾸고싶고
 바뀐 이미지에서 무작위하게 가로 세로 전부 224로 자르고 싶다고 상황을 가정해봅시다.
@@ -619,8 +618,8 @@ <h3>2.2 변환들을 구성하고 샘플에 적용해보기.<a class="headerlink
 </pre></div>
 </div>
 </div>
-<div class="section" id="id10">
-<h3>2.3 데이터셋을 반복문을 통해 사용하기<a class="headerlink" href="#id10" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="id9">
+<h3>2.3 데이터셋을 반복문을 통해 사용하기<a class="headerlink" href="#id9" title="Permalink to this headline">¶</a></h3>
 <p>다음으로 우리는 데이터셋을 반복문을 통해 사용해보도록 하겠습니다.</p>
 <p>이제 이 모든것을 다 꺼내어서 변환을 구성하고 데이터셋을 만들어봅시다.
 요약하자면 항상 이 데이터셋을 다음과 같이 불러와집니다.</p>
@@ -725,7 +724,7 @@ <h2>세번째: Dataloader<a class="headerlink" href="#dataloader" title="Permali
         <a href="Captum_Recipe.html" class="btn btn-neutral float-right" title="Captum을 사용하여 모델 해석하기" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
-        <a href="profiler.html" class="btn btn-neutral" title="PyTorch Profiler" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
+        <a href="profiler.html" class="btn btn-neutral" title="PyTorch 프로파일러(Profiler)" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
       
     </div>
   
@@ -765,17 +764,16 @@ <h2>세번째: Dataloader<a class="headerlink" href="#dataloader" title="Permali
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
 <li><a class="reference internal" href="#">사용자 정의 PyTorch Dataloader 작성하기</a><ul>
-<li><a class="reference internal" href="#id1">설정</a></li>
-<li><a class="reference internal" href="#id2">첫 번째: 데이터셋</a><ul>
-<li><a class="reference internal" href="#id4">1.1 이미지를 표시하기 위해 간단한 도움 함수 작성하기</a></li>
-<li><a class="reference internal" href="#id5">1.2 데이터셋 클래스 만들기</a></li>
-<li><a class="reference internal" href="#id6">1.3 반복문을 통한 데이터 샘플 사용</a></li>
+<li><a class="reference internal" href="#id1">첫 번째: 데이터셋</a><ul>
+<li><a class="reference internal" href="#id3">1.1 이미지를 표시하기 위해 간단한 도움 함수 작성하기</a></li>
+<li><a class="reference internal" href="#id4">1.2 데이터셋 클래스 만들기</a></li>
+<li><a class="reference internal" href="#id5">1.3 반복문을 통한 데이터 샘플 사용</a></li>
 </ul>
 </li>
-<li><a class="reference internal" href="#id7">두 번째: 데이터 변형</a><ul>
-<li><a class="reference internal" href="#id8">2.1 호출 가능한 클래스들 작성하기</a></li>
-<li><a class="reference internal" href="#id9">2.2 변환들을 구성하고 샘플에 적용해보기.</a></li>
-<li><a class="reference internal" href="#id10">2.3 데이터셋을 반복문을 통해 사용하기</a></li>
+<li><a class="reference internal" href="#id6">두 번째: 데이터 변형</a><ul>
+<li><a class="reference internal" href="#id7">2.1 호출 가능한 클래스들 작성하기</a></li>
+<li><a class="reference internal" href="#id8">2.2 변환들을 구성하고 샘플에 적용해보기.</a></li>
+<li><a class="reference internal" href="#id9">2.3 데이터셋을 반복문을 통해 사용하기</a></li>
 </ul>
 </li>
 <li><a class="reference internal" href="#dataloader">세번째: Dataloader</a></li>
@@ -796,11 +794,9 @@ <h2>세번째: Dataloader<a class="headerlink" href="#dataloader" title="Permali
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/defining_a_neural_network.html b/docs/recipes/recipes/defining_a_neural_network.html
index 7b4e7391f..6c9c8b533 100644
--- a/docs/recipes/recipes/defining_a_neural_network.html
+++ b/docs/recipes/recipes/defining_a_neural_network.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -350,7 +350,7 @@ <h3>2. 신경망을 정의하고 초기화하기<a class="headerlink" href="#id5
 목표인 0부터 9까지 숫자를 대표하는 10개의 라벨과 되응되 값을 출력하는 모델을 정의하겠습니다.
 이 알고리즘은 만드는 사람에 달렸지만, 기본적인 MNIST 알고리즘을 따르도록 하겠습니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
       <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
       <span class="c1"># 첫번째 2D 컨볼루션 계층</span>
@@ -381,7 +381,7 @@ <h3>3. 데이터가 모델을 어떻게 지나갈 지 구체화하기<a class="h
 이것은 feed-forward 알고리즘을 나타냅니다.</p>
 <p><code class="docutils literal notranslate"><span class="pre">forward</span></code> 함수에서 어떠한 Tensor 연산자도 사용 가능합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
       <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
       <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
       <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
@@ -531,11 +531,9 @@ <h2>더 알아보기<a class="headerlink" href="#id8" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/dynamic_quantization.html b/docs/recipes/recipes/dynamic_quantization.html
index 007007ece..79d9b5522 100644
--- a/docs/recipes/recipes/dynamic_quantization.html
+++ b/docs/recipes/recipes/dynamic_quantization.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -414,7 +414,7 @@ <h3>1: Set Up<a class="headerlink" href="#set-up" title="Permalink to this headl
   <span class="sd">&quot;&quot;&quot;Elementary Long Short Term Memory style model which simply wraps nn.LSTM</span>
 <span class="sd">     Not to be used for anything other than demonstration.</span>
 <span class="sd">  &quot;&quot;&quot;</span>
-  <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">in_dim</span><span class="p">,</span><span class="n">out_dim</span><span class="p">,</span><span class="n">depth</span><span class="p">):</span>
+  <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">in_dim</span><span class="p">,</span><span class="n">out_dim</span><span class="p">,</span><span class="n">depth</span><span class="p">):</span>
      <span class="nb">super</span><span class="p">(</span><span class="n">lstm_for_demonstration</span><span class="p">,</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
      <span class="bp">self</span><span class="o">.</span><span class="n">lstm</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">LSTM</span><span class="p">(</span><span class="n">in_dim</span><span class="p">,</span><span class="n">out_dim</span><span class="p">,</span><span class="n">depth</span><span class="p">)</span>
 
@@ -651,11 +651,9 @@ <h1>Additional Resources<a class="headerlink" href="#additional-resources" title
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/loading_data_recipe.html b/docs/recipes/recipes/loading_data_recipe.html
index 965316c7b..d8bacf652 100644
--- a/docs/recipes/recipes/loading_data_recipe.html
+++ b/docs/recipes/recipes/loading_data_recipe.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -525,11 +525,9 @@ <h2>더 알아보기<a class="headerlink" href="#id7" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/profiler.html b/docs/recipes/recipes/profiler.html
index 799af33eb..7b2628b28 100644
--- a/docs/recipes/recipes/profiler.html
+++ b/docs/recipes/recipes/profiler.html
@@ -9,7 +9,7 @@
   
   <meta name="viewport" content="width=device-width, initial-scale=1.0">
   
-  <title>PyTorch Profiler &mdash; PyTorch Tutorials 1.6.0 documentation</title>
+  <title>PyTorch 프로파일러(Profiler) &mdash; PyTorch Tutorials 1.6.0 documentation</title>
   
 
   
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -245,7 +245,7 @@
         
           <li><a href="../recipes_index.html">PyTorch Recipes</a> &gt;</li>
         
-      <li>PyTorch Profiler</li>
+      <li>PyTorch 프로파일러(Profiler)</li>
     
     
       <li class="pytorch-breadcrumbs-aside">
@@ -304,83 +304,79 @@
 <p>Click <a class="reference internal" href="#sphx-glr-download-recipes-recipes-profiler-py"><span class="std std-ref">here</span></a> to download the full example code</p>
 </div>
 <div class="sphx-glr-example-title section" id="pytorch-profiler">
-<span id="sphx-glr-recipes-recipes-profiler-py"></span><h1>PyTorch Profiler<a class="headerlink" href="#pytorch-profiler" title="Permalink to this headline">¶</a></h1>
-<p>This recipe explains how to use PyTorch profiler and measure the time and
-memory consumption of the model’s operators.</p>
-<div class="section" id="introduction">
-<h2>Introduction<a class="headerlink" href="#introduction" title="Permalink to this headline">¶</a></h2>
-<p>PyTorch includes a simple profiler API that is useful when user needs
-to determine the most expensive operators in the model.</p>
-<p>In this recipe, we will use a simple Resnet model to demonstrate how to
-use profiler to analyze model performance.</p>
+<span id="sphx-glr-recipes-recipes-profiler-py"></span><h1>PyTorch 프로파일러(Profiler)<a class="headerlink" href="#pytorch-profiler" title="Permalink to this headline">¶</a></h1>
+<p>이 레시피에서는 어떻게 PyTorch 프로파일러를 사용하는지, 그리고 모델의 연산자들이 소비하는 메모리와 시간을 측정하는 방법을 살펴보겠습니다.</p>
+<div class="section" id="id1">
+<h2>개요<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>PyTorch는 사용자가 모델 내의 연산 비용이 큰(expensive) 연산자들이 무엇인지 알고싶을 때 유용하게 사용할 수 있는 간단한 프로파일러 API를 포함하고 있습니다.</p>
+<p>이 레시피에서는 모델의 성능(performance)을 분석하려고 할 때 어떻게 프로파일러를 사용해야 하는지를 보여주기 위해 간단한 ResNet 모델을 사용하겠습니다.</p>
 </div>
 <div class="section" id="setup">
-<h2>Setup<a class="headerlink" href="#setup" title="Permalink to this headline">¶</a></h2>
-<p>To install <code class="docutils literal notranslate"><span class="pre">torch</span></code> and <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> use the following command:</p>
+<h2>설정(Setup)<a class="headerlink" href="#setup" title="Permalink to this headline">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">torch</span></code> 와 <code class="docutils literal notranslate"><span class="pre">torchvision</span></code> 을 설치하기 위해서 아래의 커맨드를 입력합니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">torch</span> <span class="n">torchvision</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="steps">
-<h2>Steps<a class="headerlink" href="#steps" title="Permalink to this headline">¶</a></h2>
+<h2>단계(Steps)<a class="headerlink" href="#steps" title="Permalink to this headline">¶</a></h2>
 <ol class="arabic simple">
-<li><p>Import all necessary libraries</p></li>
-<li><p>Instantiate a simple Resnet model</p></li>
-<li><p>Use profiler to analyze execution time</p></li>
-<li><p>Use profiler to analyze memory consumption</p></li>
-<li><p>Using tracing functionality</p></li>
+<li><p>필요한 라이브러리들 불러오기</p></li>
+<li><p>간단한 ResNet 모델 인스턴스화 하기</p></li>
+<li><p>프로파일러를 사용하여 실행시간 분석하기</p></li>
+<li><p>프로파일러를 사용하여 메모리 소비 분석하기</p></li>
+<li><p>추적기능 사용하기</p></li>
 </ol>
-<div class="section" id="import-all-necessary-libraries">
-<h3>1. Import all necessary libraries<a class="headerlink" href="#import-all-necessary-libraries" title="Permalink to this headline">¶</a></h3>
-<p>In this recipe we will use <code class="docutils literal notranslate"><span class="pre">torch</span></code>, <code class="docutils literal notranslate"><span class="pre">torchvision.models</span></code>
-and <code class="docutils literal notranslate"><span class="pre">profiler</span></code> modules:</p>
+<div class="section" id="id2">
+<h3>1. 필요한 라이브러리들 불러오기<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
+<p>이 레시피에서는 <code class="docutils literal notranslate"><span class="pre">torch</span></code> 와 <code class="docutils literal notranslate"><span class="pre">torchvision.models</span></code>,
+그리고 <code class="docutils literal notranslate"><span class="pre">profiler</span></code> 모듈을 사용합니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
 <span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
 <span class="kn">import</span> <span class="nn">torch.autograd.profiler</span> <span class="k">as</span> <span class="nn">profiler</span>
 </pre></div>
 </div>
 </div>
-<div class="section" id="instantiate-a-simple-resnet-model">
-<h3>2. Instantiate a simple Resnet model<a class="headerlink" href="#instantiate-a-simple-resnet-model" title="Permalink to this headline">¶</a></h3>
-<p>Let’s create an instance of a Resnet model and prepare an input
-for it:</p>
+<div class="section" id="resnet">
+<h3>2. 간단한 ResNet 모델 인스턴스화 하기<a class="headerlink" href="#resnet" title="Permalink to this headline">¶</a></h3>
+<p>ResNet 모델 인스턴스를 만들고 입력값을
+준비합니다 :</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">()</span>
 <span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">)</span>
 </pre></div>
 </div>
 </div>
-<div class="section" id="use-profiler-to-analyze-execution-time">
-<h3>3. Use profiler to analyze execution time<a class="headerlink" href="#use-profiler-to-analyze-execution-time" title="Permalink to this headline">¶</a></h3>
-<p>PyTorch profiler is enabled through the context manager and accepts
-a number of parameters, some of the most useful are:</p>
+<div class="section" id="id3">
+<h3>3. 프로파일러를 사용하여 실행시간 분석하기<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
+<p>PyTorch 프로파일러는 컨텍스트 메니저(context manager)를 통해 활성화되고,
+여러 매개변수를 받을 수 있습니다. 유용한 몇 가지 매개변수는 다음과 같습니다:</p>
 <ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">record_shapes</span></code> - whether to record shapes of the operator inputs;</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">profile_memory</span></code> - whether to report amount of memory consumed by
-model’s Tensors;</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">use_cuda</span></code> - whether to measure execution time of CUDA kernels.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">record_shapes</span></code> - 연사자 입력(input)의 shape을 기록할지 여부;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">profile_memory</span></code> - 모델의 텐서(Tensor)들이 소비하는 메모리 양을 보고(report)할지 여부;</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">use_cuda</span></code> - CUDA 커널의 실행시간을 측정할지 여부;</p></li>
 </ul>
-<p>Let’s see how we can use profiler to analyze the execution time:</p>
+<p>프로파일러를 사용하여 어떻게 실행시간을 분석하는지 보겠습니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span><span class="n">record_shapes</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
     <span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;model_inference&quot;</span><span class="p">):</span>
         <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>Note that we can use <code class="docutils literal notranslate"><span class="pre">record_function</span></code> context manager to label
-arbitrary code ranges with user provided names
-(<code class="docutils literal notranslate"><span class="pre">model_inference</span></code> is used as a label in the example above).
-Profiler allows one to check which operators were called during the
-execution of a code range wrapped with a profiler context manager.
-If multiple profiler ranges are active at the same time (e.g. in
-parallel PyTorch threads), each profiling context manager tracks only
-the operators of its corresponding range.
-Profiler also automatically profiles the async tasks launched
-with <code class="docutils literal notranslate"><span class="pre">torch.jit._fork</span></code> and (in case of a backward pass)
-the backward pass operators launched with <code class="docutils literal notranslate"><span class="pre">backward()</span></code> call.</p>
-<p>Let’s print out the stats for the execution above:</p>
+<p><code class="docutils literal notranslate"><span class="pre">record_function</span></code> 컨텍스트 관리자를 사용하여 임의의 코드 범위에
+사용자가 지정한 이름으로 레이블(label)을 표시할 수 있습니다.
+(위 예제에서는 <code class="docutils literal notranslate"><span class="pre">model_inference</span></code> 를 레이블로 사용했습니다.)
+프로파일러를 사용하면 프로파일러 컨텍스트 관리자로 감싸진(wrap) 코드 범위를
+실행하는 동안 어떤 연산자들이 호출되었는지 확인할 수 있습니다.</p>
+<p>만약 여러 프로파일러의 범위가 동시에 활성화된 경우(예. PyTorch 쓰레드가 병렬로
+실행 중인 경우), 각 프로파일링 컨텍스트 관리자는 각각의 범위 내의 연산자들만
+추적(track)합니다.
+프로파일러는 또한 <code class="docutils literal notranslate"><span class="pre">torch.jit._fork</span></code> 로 실행된 비동기 작업과
+(역전파 단계의 경우) <code class="docutils literal notranslate"><span class="pre">backward()</span></code> 의 호출로 실행된 역전파 연산자들도
+자동으로 프로파일링합니다.</p>
+<p>위 코드를 실행한 통계를 출력해보겠습니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">&quot;cpu_time_total&quot;</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
 </pre></div>
 </div>
-<p>The output will look like (omitting some columns):</p>
+<p>(몇몇 열을 제외하고) 출력값이 이렇게 보일 것입니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="c1"># -------------------------  --------------  ----------  ------------  ---------</span>
 <span class="c1"># Name                       Self CPU total   CPU total  CPU time avg  # Calls</span>
 <span class="c1"># -------------------------  --------------  ----------  ------------  ---------</span>
@@ -397,14 +393,16 @@ <h3>3. Use profiler to analyze execution time<a class="headerlink" href="#use-pr
 <span class="c1"># -------------------------  --------------  ----------  ------------  ---------</span>
 </pre></div>
 </div>
-<p>Here we see that, as expected, most of the time is spent in convolution (and specifically in <code class="docutils literal notranslate"><span class="pre">mkldnn_convolution</span></code>
-for PyTorch compiled with MKL-DNN support).
-Note the difference between self cpu time and cpu time - operators can call other operators, self cpu time exludes time
-spent in children operator calls, while total cpu time includes it.</p>
-<p>To get a finer granularity of results and include operator input shapes, pass <code class="docutils literal notranslate"><span class="pre">group_by_input_shape=True</span></code>:</p>
+<p>예상했던 대로, 대부분의 시간이 합성곱(convolution) 연산(특히 MKL-DNN을 지원하도록
+컴파일된 PyTorch의 경우에는 <code class="docutils literal notranslate"><span class="pre">mkldnn_convolution</span></code> )에서 소요되는 것을 확인할 수 있습니다.
+(결과 열들 중) Self CPU time과 CPU time의 차이에 유의해야 합니다 -
+연산자는 다른 연산자들을 호출할 수 있으며, Self CPU time에는 하위(child) 연산자 호출에서 발생한
+시간을 제외해서, Totacl CPU time에는 포함해서 표시합니다.</p>
+<p>보다 세부적인 결과 정보 및 연산자의 입력 shape을 함께 보려면 <code class="docutils literal notranslate"><span class="pre">group_by_input_shape=True</span></code> 를
+인자로 전달하면 됩니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">(</span><span class="n">group_by_input_shape</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">&quot;cpu_time_total&quot;</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
 
-<span class="c1"># (omitting some columns)</span>
+<span class="c1"># (몇몇 열은 제외하였습니다)</span>
 <span class="c1"># -------------------------  -----------  --------  -------------------------------------</span>
 <span class="c1"># Name                       CPU total    # Calls         Input Shapes</span>
 <span class="c1"># -------------------------  -----------  --------  -------------------------------------</span>
@@ -422,19 +420,19 @@ <h3>3. Use profiler to analyze execution time<a class="headerlink" href="#use-pr
 </pre></div>
 </div>
 </div>
-<div class="section" id="use-profiler-to-analyze-memory-consumption">
-<h3>4. Use profiler to analyze memory consumption<a class="headerlink" href="#use-profiler-to-analyze-memory-consumption" title="Permalink to this headline">¶</a></h3>
-<p>PyTorch profiler can also show the amount of memory (used by the model’s tensors)
-that was allocated (or released) during the execution of the model’s operators.
-In the output below, ‘self’ memory corresponds to the memory allocated (released)
-by the operator, excluding the children calls to the other operators.
-To enable memory profiling functionality pass <code class="docutils literal notranslate"><span class="pre">profile_memory=True</span></code>.</p>
+<div class="section" id="id4">
+<h3>4. 프로파일러를 사용하여 메모리 소비 분석하기<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
+<p>PyTorch 프로파일러는 모델의 연산자들을 실행하며 (모델의 텐서들이 사용하며) 할당(또는 해제)한
+메모리의 양도 표시할 수 있습니다.
+아래 출력 결과에서 ‘Self’ memory는 해당 연산자에 의해 호출된 하위(child) 연산자들을 제외한,
+연산자 자체에 할당(해제)된 메모리에 해당합니다.
+메모리 프로파일링 기능을 활성화하려면 <code class="docutils literal notranslate"><span class="pre">profile_memory=True</span></code> 를 인자로 전달하면 됩니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">(</span><span class="n">profile_memory</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">record_shapes</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
     <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
 
 <span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">&quot;self_cpu_memory_usage&quot;</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
 
-<span class="c1"># (omitting some columns)</span>
+<span class="c1"># (몇몇 열은 제외하였습니다)</span>
 <span class="c1"># ---------------------------  ---------------  ---------------  ---------------</span>
 <span class="c1"># Name                         CPU Mem          Self CPU Mem     Number of Calls</span>
 <span class="c1"># ---------------------------  ---------------  ---------------  ---------------</span>
@@ -447,7 +445,7 @@ <h3>4. Use profiler to analyze memory consumption<a class="headerlink" href="#us
 
 <span class="nb">print</span><span class="p">(</span><span class="n">prof</span><span class="o">.</span><span class="n">key_averages</span><span class="p">()</span><span class="o">.</span><span class="n">table</span><span class="p">(</span><span class="n">sort_by</span><span class="o">=</span><span class="s2">&quot;cpu_memory_usage&quot;</span><span class="p">,</span> <span class="n">row_limit</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
 
-<span class="c1"># (omitting some columns)</span>
+<span class="c1"># (몇몇 열은 제외하였습니다)</span>
 <span class="c1"># ---------------------------  ---------------  ---------------  ---------------</span>
 <span class="c1"># Name                         CPU Mem          Self CPU Mem     Number of Calls</span>
 <span class="c1"># ---------------------------  ---------------  ---------------  ---------------</span>
@@ -470,9 +468,9 @@ <h3>4. Use profiler to analyze memory consumption<a class="headerlink" href="#us
 </pre></div>
 </div>
 </div>
-<div class="section" id="using-tracing-functionality">
-<h3>5. Using tracing functionality<a class="headerlink" href="#using-tracing-functionality" title="Permalink to this headline">¶</a></h3>
-<p>Profiling results can be outputted as a .json trace file:</p>
+<div class="section" id="id5">
+<h3>5. 추적기능 사용하기<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
+<p>프로파일링 결과는 .json 형태의 추적 파일(trace file)로 출력할 수 있습니다:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">profile</span><span class="p">()</span> <span class="k">as</span> <span class="n">prof</span><span class="p">:</span>
     <span class="k">with</span> <span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;model_inference&quot;</span><span class="p">):</span>
         <span class="n">model</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
@@ -480,16 +478,16 @@ <h3>5. Using tracing functionality<a class="headerlink" href="#using-tracing-fun
 <span class="n">prof</span><span class="o">.</span><span class="n">export_chrome_trace</span><span class="p">(</span><span class="s2">&quot;trace.json&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
-<p>User can examine the sequence of profiled operators after loading the trace file
-in Chrome (<code class="docutils literal notranslate"><span class="pre">chrome://tracing</span></code>):</p>
+<p>사용자는 Chrome 브라우저( <code class="docutils literal notranslate"><span class="pre">chrome://tracing</span></code> )에서 추적 파일을 불러와
+프로파일된 일련의 연산자들을 검토해볼 수 있습니다:</p>
 <a class="reference internal image-reference" href="../../_images/trace_img.png"><img alt="../../_images/trace_img.png" src="../../_images/trace_img.png" style="width: 823.5px; height: 78.5px;" /></a>
 </div>
 </div>
-<div class="section" id="learn-more">
-<h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this headline">¶</a></h2>
-<p>Take a look at the following tutorial to learn how to visualize your model with TensorBoard:</p>
+<div class="section" id="id6">
+<h2>더 알아보기<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h2>
+<p>다음의 튜토리얼을 통해 Tensorboard를 사용하여 모델 시각화하는 방법을 살펴보세요:</p>
 <ul class="simple">
-<li><p><a class="reference external" href="https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html">Visualizing models, data, and training with TensorBoard</a> tutorial</p></li>
+<li><p><a class="reference internal" href="../../intermediate/tensorboard_tutorial.html"><span class="doc">TensorBoard로 모델, 데이터, 학습 시각화하기</span></a></p></li>
 </ul>
 <p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
 <div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-recipes-recipes-profiler-py">
@@ -554,18 +552,18 @@ <h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this
           <div class="pytorch-right-menu" id="pytorch-right-menu">
             <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
               <ul>
-<li><a class="reference internal" href="#">PyTorch Profiler</a><ul>
-<li><a class="reference internal" href="#introduction">Introduction</a></li>
-<li><a class="reference internal" href="#setup">Setup</a></li>
-<li><a class="reference internal" href="#steps">Steps</a><ul>
-<li><a class="reference internal" href="#import-all-necessary-libraries">1. Import all necessary libraries</a></li>
-<li><a class="reference internal" href="#instantiate-a-simple-resnet-model">2. Instantiate a simple Resnet model</a></li>
-<li><a class="reference internal" href="#use-profiler-to-analyze-execution-time">3. Use profiler to analyze execution time</a></li>
-<li><a class="reference internal" href="#use-profiler-to-analyze-memory-consumption">4. Use profiler to analyze memory consumption</a></li>
-<li><a class="reference internal" href="#using-tracing-functionality">5. Using tracing functionality</a></li>
+<li><a class="reference internal" href="#">PyTorch 프로파일러(Profiler)</a><ul>
+<li><a class="reference internal" href="#id1">개요</a></li>
+<li><a class="reference internal" href="#setup">설정(Setup)</a></li>
+<li><a class="reference internal" href="#steps">단계(Steps)</a><ul>
+<li><a class="reference internal" href="#id2">1. 필요한 라이브러리들 불러오기</a></li>
+<li><a class="reference internal" href="#resnet">2. 간단한 ResNet 모델 인스턴스화 하기</a></li>
+<li><a class="reference internal" href="#id3">3. 프로파일러를 사용하여 실행시간 분석하기</a></li>
+<li><a class="reference internal" href="#id4">4. 프로파일러를 사용하여 메모리 소비 분석하기</a></li>
+<li><a class="reference internal" href="#id5">5. 추적기능 사용하기</a></li>
 </ul>
 </li>
-<li><a class="reference internal" href="#learn-more">Learn More</a></li>
+<li><a class="reference internal" href="#id6">더 알아보기</a></li>
 </ul>
 </li>
 </ul>
@@ -583,11 +581,9 @@ <h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/save_load_across_devices.html b/docs/recipes/recipes/save_load_across_devices.html
index ffb1dc67a..a19b59cb4 100644
--- a/docs/recipes/recipes/save_load_across_devices.html
+++ b/docs/recipes/recipes/save_load_across_devices.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -346,7 +346,7 @@ <h3>2. 신경망을 구성하고 초기화하기<a class="headerlink" href="#id1
 <p>예로, 이미지 트레이닝을 위한 신경망을 생성해보겠습니다.
 자세한 내용은 신경망 정의 레시피를 참조하세요.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -545,11 +545,9 @@ <h2>더 알아보기<a class="headerlink" href="#id25" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/saving_and_loading_a_general_checkpoint.html b/docs/recipes/recipes/saving_and_loading_a_general_checkpoint.html
index 12e26a8f4..61dfc7afa 100644
--- a/docs/recipes/recipes/saving_and_loading_a_general_checkpoint.html
+++ b/docs/recipes/recipes/saving_and_loading_a_general_checkpoint.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -353,7 +353,7 @@ <h3>2. 신경망을 구성하고 초기화하기<a class="headerlink" href="#id6
 <p>예를 들어, 이미지를 학습하는 신경망을 만들어보겠습니다. 더 자세한 내용은
 신경망 구성하기 레시피를 참고해주세요.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -525,11 +525,9 @@ <h2>더 알아보기<a class="headerlink" href="#id10" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/saving_and_loading_models_for_inference.html b/docs/recipes/recipes/saving_and_loading_models_for_inference.html
index eb7a428e5..199463510 100644
--- a/docs/recipes/recipes/saving_and_loading_models_for_inference.html
+++ b/docs/recipes/recipes/saving_and_loading_models_for_inference.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -353,7 +353,7 @@ <h3>2. 신경망을 구성하고 초기화하기<a class="headerlink" href="#id6
 <p>예를 들기 위해, 이미지를 학습하는 신경망을 만들어보겠습니다. 더 자세한 내용은
 신경망 구성하기 레시피를 참고해주세요.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -527,11 +527,9 @@ <h2>더 알아보기<a class="headerlink" href="#id9" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/saving_multiple_models_in_one_file.html b/docs/recipes/recipes/saving_multiple_models_in_one_file.html
index 19d5dd46b..a8519bd51 100644
--- a/docs/recipes/recipes/saving_multiple_models_in_one_file.html
+++ b/docs/recipes/recipes/saving_multiple_models_in_one_file.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -348,7 +348,7 @@ <h3>2. 신경망을 구성하고 초기화하기<a class="headerlink" href="#id6
 <p>예를 들어, 이미지를 학습하는 신경망을 만들어보겠습니다. 더 자세한 내용은
 신경망 구성하기 레시피를 참고해주세요. 모델을 저장할 2개의 변수들을 만듭니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -525,11 +525,9 @@ <h2>더 알아보기<a class="headerlink" href="#id10" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/tensorboard_with_pytorch.html b/docs/recipes/recipes/tensorboard_with_pytorch.html
index bd9f7a6ee..8a2945f77 100644
--- a/docs/recipes/recipes/tensorboard_with_pytorch.html
+++ b/docs/recipes/recipes/tensorboard_with_pytorch.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -327,7 +327,7 @@ <h2>PyTorch로 TensorBoard 사용하기<a class="headerlink" href="#id2" title="
 <p>이제 PyTorch로 TensorBoard를 사용해봅시다!
 먼저 <code class="docutils literal notranslate"><span class="pre">SummaryWriter</span></code> 인스턴스를 생성해야 합니다.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
-<span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="k">import</span> <span class="n">SummaryWriter</span>
+<span class="kn">from</span> <span class="nn">torch.utils.tensorboard</span> <span class="kn">import</span> <span class="n">SummaryWriter</span>
 <span class="n">writer</span> <span class="o">=</span> <span class="n">SummaryWriter</span><span class="p">()</span>
 </pre></div>
 </div>
@@ -508,11 +508,9 @@ <h2>더 알아보기<a class="headerlink" href="#id4" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html b/docs/recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html
index da845cc50..bb01e6a5d 100644
--- a/docs/recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html
+++ b/docs/recipes/recipes/warmstarting_model_using_parameters_from_a_different_model.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -352,7 +352,7 @@ <h3>2. Define and intialize the neural network A and B<a class="headerlink" href
 create two neural networks for sake of loading one parameter of type A
 into type B.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">NetA</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">NetA</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -373,7 +373,7 @@ <h3>2. Define and intialize the neural network A and B<a class="headerlink" href
 <span class="n">netA</span> <span class="o">=</span> <span class="n">NetA</span><span class="p">()</span>
 
 <span class="k">class</span> <span class="nc">NetB</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">NetB</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -516,11 +516,9 @@ <h2>Learn More<a class="headerlink" href="#learn-more" title="Permalink to this
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/what_is_state_dict.html b/docs/recipes/recipes/what_is_state_dict.html
index 320a317e0..1964d070b 100644
--- a/docs/recipes/recipes/what_is_state_dict.html
+++ b/docs/recipes/recipes/what_is_state_dict.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -358,7 +358,7 @@ <h3>2. 신경망을 구성하고 초기화하기<a class="headerlink" href="#id4
 <p>예시를 보이기 위해, 이미지를 학습하는 신경망을 만들어보겠습니다.
 더 자세한 내용은 신경망 구성하기 레시피를 참고해주세요.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -508,11 +508,9 @@ <h2>더 알아보기<a class="headerlink" href="#id6" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes/zeroing_out_gradients.html b/docs/recipes/recipes/zeroing_out_gradients.html
index 2724e85f7..4e327bdb2 100644
--- a/docs/recipes/recipes/zeroing_out_gradients.html
+++ b/docs/recipes/recipes/zeroing_out_gradients.html
@@ -30,7 +30,7 @@
   <link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
     <link rel="index" title="Index" href="../../genindex.html" />
     <link rel="search" title="Search" href="../../search.html" />
-    <link rel="next" title="PyTorch Profiler" href="profiler.html" />
+    <link rel="next" title="PyTorch 프로파일러(Profiler)" href="profiler.html" />
     <link rel="prev" title="PyTorch에서 다양한 장치 간 모델을 저장하고 불러오기" href="save_load_across_devices.html" /> 
 
   
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -383,7 +383,7 @@ <h3>3. 신경망 구축하기<a class="headerlink" href="#id4" title="Permalink
 <p>컨볼루션 신경망을 정의하겠습니다. 자세한 내용은 신경망 정의하기 레시피를
 참조해주세요.</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">Net</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
-    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">Net</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
@@ -479,7 +479,7 @@ <h2>더 알아보기<a class="headerlink" href="#id7" title="Permalink to this h
   
     <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
       
-        <a href="profiler.html" class="btn btn-neutral float-right" title="PyTorch Profiler" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
+        <a href="profiler.html" class="btn btn-neutral float-right" title="PyTorch 프로파일러(Profiler)" accesskey="n" rel="next">Next <img src="../../_static/images/chevron-right-orange.svg" class="next-page"></a>
       
       
         <a href="save_load_across_devices.html" class="btn btn-neutral" title="PyTorch에서 다양한 장치 간 모델을 저장하고 불러오기" accesskey="p" rel="prev"><img src="../../_static/images/chevron-right-orange.svg" class="previous-page"> Previous</a>
@@ -550,11 +550,9 @@ <h2>더 알아보기<a class="headerlink" href="#id7" title="Permalink to this h
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../../_static/jquery.js"></script>
-         <script type="text/javascript" src="../../_static/underscore.js"></script>
-         <script type="text/javascript" src="../../_static/doctools.js"></script>
-         <script type="text/javascript" src="../../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../../_static/jquery.js"></script>
+         <script src="../../_static/underscore.js"></script>
+         <script src="../../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/recipes_index.html b/docs/recipes/recipes_index.html
index 9409f6e35..dd506252d 100644
--- a/docs/recipes/recipes_index.html
+++ b/docs/recipes/recipes_index.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -516,10 +516,10 @@ <h4>PyTorch에서 변화도를 0으로 만들기</h4>
 <div class="card-body">
 
 <div class="card-title-container">
-    <h4>PyTorch Profiler</h4>
+    <h4>Pytorch 프로파일러</h4>
 </div>
 
-<p class="card-summary">Learn how to use PyTorch's profiler to measure operators time and memory consumption</p>
+<p class="card-summary">PyTorch의 프로파일러를 사용하여 운영자 시간과 메모리 소비량을 측정하는 방법을 알아봅니다.</p>
 
 <p class="tags">Basics</p>
 
@@ -774,11 +774,9 @@ <h4>Making Android Native Application That Uses PyTorch Android Prebuilt Librari
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/recipes/torchscript_inference.html b/docs/recipes/torchscript_inference.html
index 639804189..c8131b9cc 100644
--- a/docs/recipes/torchscript_inference.html
+++ b/docs/recipes/torchscript_inference.html
@@ -174,7 +174,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -186,7 +186,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -369,32 +369,32 @@ <h2>C++로 TorchScript 모델 불러오기<a class="headerlink" href="#c-torchsc
 <span class="kt">int</span> <span class="nf">main</span><span class="p">(</span><span class="kt">int</span> <span class="n">argc</span><span class="p">,</span> <span class="k">const</span> <span class="kt">char</span><span class="o">*</span> <span class="n">argv</span><span class="p">[])</span> <span class="p">{</span>
     <span class="k">if</span> <span class="p">(</span><span class="n">argc</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">)</span> <span class="p">{</span>
         <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;usage: ts-infer &lt;path-to-exported-model&gt;</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
-        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+        <span class="k">return</span> <span class="mi">-1</span><span class="p">;</span>
     <span class="p">}</span>
 
     <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;Loading model...</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
 
     <span class="c1">// ScriptModule을 역직렬화(deserialize) 합니다.</span>
-    <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">script</span><span class="o">::</span><span class="n">Module</span> <span class="n">module</span><span class="p">;</span>
+    <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">script</span><span class="o">::</span><span class="n">Module</span> <span class="k">module</span><span class="p">;</span>
     <span class="k">try</span> <span class="p">{</span>
-        <span class="n">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
+        <span class="k">module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="n">argv</span><span class="p">[</span><span class="mi">1</span><span class="p">]);</span>
     <span class="p">}</span> <span class="k">catch</span> <span class="p">(</span><span class="k">const</span> <span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span> <span class="n">e</span><span class="p">)</span> <span class="p">{</span>
         <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;Error loading model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
         <span class="n">std</span><span class="o">::</span><span class="n">cerr</span> <span class="o">&lt;&lt;</span> <span class="n">e</span><span class="p">.</span><span class="n">msg_without_backtrace</span><span class="p">();</span>
-        <span class="k">return</span> <span class="o">-</span><span class="mi">1</span><span class="p">;</span>
+        <span class="k">return</span> <span class="mi">-1</span><span class="p">;</span>
     <span class="p">}</span>
 
     <span class="n">std</span><span class="o">::</span><span class="n">cout</span> <span class="o">&lt;&lt;</span> <span class="s">&quot;Model loaded successfully</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
 
     <span class="n">torch</span><span class="o">::</span><span class="n">NoGradGuard</span> <span class="n">no_grad</span><span class="p">;</span> <span class="c1">// autograd가 꺼져있는지 확인합니다.</span>
-    <span class="n">module</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span> <span class="c1">// dropout과 학습 단의 레이어 및 함수들을 끕니다.</span>
+    <span class="k">module</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span> <span class="c1">// dropout과 학습 단의 레이어 및 함수들을 끕니다.</span>
 
     <span class="c1">// 입력 &quot;이미지&quot;를 생성합니다.</span>
     <span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">IValue</span><span class="o">&gt;</span> <span class="n">inputs</span><span class="p">;</span>
     <span class="n">inputs</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">}));</span>
 
     <span class="c1">// 모델을 실행하고 출력 값을 tensor로 뽑아냅니다.</span>
-    <span class="n">at</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">output</span> <span class="o">=</span> <span class="n">module</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">).</span><span class="n">toTensor</span><span class="p">();</span>
+    <span class="n">at</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">output</span> <span class="o">=</span> <span class="k">module</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">inputs</span><span class="p">).</span><span class="n">toTensor</span><span class="p">();</span>
 
     <span class="k">namespace</span> <span class="n">F</span> <span class="o">=</span> <span class="n">torch</span><span class="o">::</span><span class="n">nn</span><span class="o">::</span><span class="n">functional</span><span class="p">;</span>
     <span class="n">at</span><span class="o">::</span><span class="n">Tensor</span> <span class="n">output_sm</span> <span class="o">=</span> <span class="n">F</span><span class="o">::</span><span class="n">softmax</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">F</span><span class="o">::</span><span class="n">SoftmaxFuncOptions</span><span class="p">(</span><span class="mi">1</span><span class="p">));</span>
@@ -533,11 +533,9 @@ <h2>중요 참고자료<a class="headerlink" href="#id6" title="Permalink to thi
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
-         <script type="text/javascript" src="../_static/jquery.js"></script>
-         <script type="text/javascript" src="../_static/underscore.js"></script>
-         <script type="text/javascript" src="../_static/doctools.js"></script>
-         <script type="text/javascript" src="../_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
+         <script src="../_static/jquery.js"></script>
+         <script src="../_static/underscore.js"></script>
+         <script src="../_static/doctools.js"></script>
      
 
   
diff --git a/docs/search.html b/docs/search.html
index 63b7f2769..ca8df710b 100644
--- a/docs/search.html
+++ b/docs/search.html
@@ -172,7 +172,7 @@
 <p class="caption"><span class="caption-text">프론트엔드 API</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/named_tensor_tutorial.html">(prototype) Introduction to Named Tensors in PyTorch</a></li>
-<li class="toctree-l1"><a class="reference internal" href="intermediate/memory_format_tutorial.html">(beta) Channels Last Memory Format in PyTorch</a></li>
+<li class="toctree-l1"><a class="reference internal" href="intermediate/memory_format_tutorial.html">(실험용) PyTorch를 사용한 Channels Last Memory Format</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_frontend.html">Using the PyTorch C++ Frontend</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/cpp_extension.html">Custom C++ and CUDA Extensions</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/torch_script_custom_ops.html">Extending TorchScript with Custom C++ Operators</a></li>
@@ -184,7 +184,7 @@
 <p class="caption"><span class="caption-text">모델 최적화</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/pruning_tutorial.html">가지치기 기법(Pruning) 튜토리얼</a></li>
-<li class="toctree-l1"><a class="reference internal" href="advanced/dynamic_quantization_tutorial.html">(beta) Dynamic Quantization on an LSTM Word Language Model</a></li>
+<li class="toctree-l1"><a class="reference internal" href="advanced/dynamic_quantization_tutorial.html">(베타) LSTM 기반 단어 단위 언어 모델의 동적 양자화</a></li>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/dynamic_quantization_bert_tutorial.html">(베타) BERT 모델 동적 양자화하기</a></li>
 <li class="toctree-l1"><a class="reference internal" href="advanced/static_quantization_tutorial.html">(beta) Static Quantization with Eager Mode in PyTorch</a></li>
 <li class="toctree-l1"><a class="reference internal" href="intermediate/quantized_transfer_learning_tutorial.html">(beta) 컴퓨터 비전(Vision) 튜토리얼을 위한 양자화된 전이학습(Quantized Transfer Learning)</a></li>
@@ -360,12 +360,10 @@
 
      
        <script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
-         <script type="text/javascript" src="_static/jquery.js"></script>
-         <script type="text/javascript" src="_static/underscore.js"></script>
-         <script type="text/javascript" src="_static/doctools.js"></script>
-         <script type="text/javascript" src="_static/language_data.js"></script>
-         <script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
-         <script type="text/javascript" src="_static/searchtools.js"></script>
+         <script src="_static/jquery.js"></script>
+         <script src="_static/underscore.js"></script>
+         <script src="_static/doctools.js"></script>
+         <script src="_static/searchtools.js"></script>
      
 
   
diff --git a/docs/searchindex.js b/docs/searchindex.js
index cbe168acb..f0adbd76c 100644
--- a/docs/searchindex.js
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