Merge branch 'main' into add-last-reviewed

Author: svekars

advanced_source/cpp_export.rst

@@ -1,7 +1,7 @@
 Loading a TorchScript Model in C++
 =====================================
 
-.. note:: TorchScript is no longer in active development.
+.. warning:: TorchScript is no longer in active development.
 
 As its name suggests, the primary interface to PyTorch is the Python
 programming language. While Python is a suitable and preferred language for

advanced_source/torch-script-parallelism.rst

@@ -1,7 +1,7 @@
 Dynamic Parallelism in TorchScript
 ==================================
 
-.. note:: TorchScript is no longer in active development.
+.. warning:: TorchScript is no longer in active development.
 
 In this tutorial, we introduce the syntax for doing *dynamic inter-op parallelism*
 in TorchScript. This parallelism has the following properties:

advanced_source/torch_script_custom_classes.rst

@@ -1,7 +1,7 @@
 Extending TorchScript with Custom C++ Classes
 ===============================================
 
-.. note:: TorchScript is no longer in active development.
+.. warning:: TorchScript is no longer in active development.
 
 This tutorial is a follow-on to the
 :doc:`custom operator <torch_script_custom_ops>`

beginner_source/Intro_to_TorchScript_tutorial.py

@@ -4,7 +4,7 @@
 
 **Authors:** James Reed (jamesreed@fb.com), Michael Suo (suo@fb.com), rev2
 
-.. note:: TorchScript is no longer in active development.
+.. warning:: TorchScript is no longer in active development.
 
 This tutorial is an introduction to TorchScript, an intermediate
 representation of a PyTorch model (subclass of ``nn.Module``) that

beginner_source/chatbot_tutorial.py

@@ -1128,7 +1128,7 @@ def forward(self, input_seq, input_length, max_length):
         # Forward input through encoder model
         encoder_outputs, encoder_hidden = self.encoder(input_seq, input_length)
         # Prepare encoder's final hidden layer to be first hidden input to the decoder
-        decoder_hidden = encoder_hidden[:decoder.n_layers]
+        decoder_hidden = encoder_hidden[:self.decoder.n_layers]
         # Initialize decoder input with SOS_token
         decoder_input = torch.ones(1, 1, device=device, dtype=torch.long) * SOS_token
         # Initialize tensors to append decoded words to

beginner_source/deploy_seq2seq_hybrid_frontend_tutorial.py

@@ -4,7 +4,7 @@
 ==================================================
 **Author:** `Matthew Inkawhich <https://github.com/MatthewInkawhich>`_
 
-.. note:: TorchScript is no longer in active development.
+.. warning:: TorchScript is no longer in active development.
 """
 
 

beginner_source/nn_tutorial.py

@@ -31,7 +31,7 @@
 # MNIST data setup
 # ----------------
 #
-# We will use the classic `MNIST <http://deeplearning.net/data/mnist/>`_ dataset,
+# We will use the classic `MNIST <https://yann.lecun.com/exdb/mnist/index.html>`_ dataset,
 # which consists of black-and-white images of hand-drawn digits (between 0 and 9).
 #
 # We will use `pathlib <https://docs.python.org/3/library/pathlib.html>`_

prototype_source/torchscript_freezing.py

@@ -2,7 +2,7 @@
 Model Freezing in TorchScript
 =============================
 
-.. note:: TorchScript is no longer in active development.
+.. warning:: TorchScript is no longer in active development.
 
 In this tutorial, we introduce the syntax for *model freezing* in TorchScript.
 Freezing is the process of inlining Pytorch module parameters and attributes

recipes_source/distributed_optim_torchscript.rst

@@ -1,7 +1,7 @@
 Distributed Optimizer with TorchScript support
 ==============================================================
 
-.. note:: TorchScript is no longer in active development.
+.. warning:: TorchScript is no longer in active development.
 
 In this recipe, you will learn:
 

recipes_source/torchscript_inference.rst

@@ -1,7 +1,7 @@
 TorchScript for Deployment
 ==========================
 
-.. note:: TorchScript is no longer in active development.
+.. warning:: TorchScript is no longer in active development.
 
 In this recipe, you will learn: