Add Backends section to Tutorials left menu

This PR adds a `Backends` section to the left menu which initially will
initially contain a placeholder for the series of ONNX export tutorials
based on Torch Dynamo backend

A table of content tree page will have static URL
https://pytorch.org/tutorials/beginner/onnx/intro_onnx.html that will be
referenced on PyTorch user document for torch.onnx module

Author: Thiago Crepaldi

.jenkins/validate_tutorials_built.py

@@ -10,6 +10,7 @@
 
 NOT_RUN = [
     "beginner_source/basics/intro",  # no code
+    "beginner_source/onnx/intro_onnx",
     "beginner_source/translation_transformer",
     "beginner_source/profiler",
     "beginner_source/saving_loading_models",

_templates/layout.html

@@ -107,7 +107,7 @@
 </noscript>
 
 <script type="text/javascript">
-  var collapsedSections = ['PyTorch Recipes', 'Learning PyTorch', 'Image and Video', 'Audio', 'Text', 'Reinforcement Learning', 'Deploying PyTorch Models in Production', 'Code Transforms with FX', 'Frontend APIs', 'Extending PyTorch', 'Model Optimization', 'Parallel and Distributed Training', 'Mobile'];
+  var collapsedSections = ['PyTorch Recipes', 'Learning PyTorch', 'Image and Video', 'Audio', 'Text', 'Backends', 'Reinforcement Learning', 'Deploying PyTorch Models in Production', 'Code Transforms with FX', 'Frontend APIs', 'Extending PyTorch', 'Model Optimization', 'Parallel and Distributed Training', 'Mobile'];
 </script>
 
 <img height="1" width="1" style="border-style:none;" alt="" src="https://www.googleadservices.com/pagead/conversion/795629140/?label=txkmCPmdtosBENSssfsC&amp;guid=ON&amp;script=0"/>

beginner_source/onnx/README.txt

@@ -0,0 +1,7 @@
+ONNX
+----
+
+1. intro_onnx.py
+    Introduction to ONNX
+    https://pytorch.org/tutorials/onnx/intro_onnx.html
+

beginner_source/onnx/intro_onnx.py

@@ -0,0 +1,50 @@
+"""
+**Introduction to ONNX**
+
+Introduction to ONNX
+====================
+
+Authors:
+`Thiago Crepaldi <https://github.com/thiagocrepaldi>`_,
+
+`Open Neural Network eXchange (ONNX) <https://onnx.ai/>`_ is an open standard
+format for representing machine learning models. The ``torch.onnx`` module provides APIs to
+capture the computation graph from a native PyTorch :class:`torch.nn.Module` model and convert
+it into an `ONNX graph <https://github.com/onnx/onnx/blob/main/docs/IR.md>`_.
+
+The exported model can be consumed by any of the many
+`runtimes that support ONNX <https://onnx.ai/supported-tools.html#deployModel>`_,
+including Microsoft's `ONNX Runtime <https://www.onnxruntime.ai>`_.
+
+.. note::
+    Currently, there are two flavors of ONNX exporter APIs,
+    but this tutorial will focus on the ``torch.onnx.dynamo_export``.
+
+The TorchDynamo engine is leveraged to hook into Python's frame evaluation API and dynamically rewrite its
+bytecode into an  `FX graph <https://pytorch.org/docs/stable/fx.html>`_.
+The resulting FX Graph is polished before it is finally translated into an
+`ONNX graph <https://github.com/onnx/onnx/blob/main/docs/IR.md>`_.
+
+The main advantage of this approach is that the `FX graph <https://pytorch.org/docs/stable/fx.html>`_ is captured using
+bytecode analysis that preserves the dynamic nature of the model instead of using traditional static tracing techniques.
+
+Dependencies
+------------
+
+The ONNX exporter depends on extra Python packages:
+
+  - `ONNX <https://onnx.ai>`_
+  - `ONNX Script <https://onnxscript.ai>`_
+
+They can be installed through `pip <https://pypi.org/project/pip/>`_:
+
+.. code-block:: bash
+
+  pip install --upgrade onnx onnxscript
+
+.. include:: /beginner_source/basics/onnx_toc.txt
+
+.. toctree::
+   :hidden:
+
+"""

beginner_source/onnx/onnx_toc.txt

@@ -272,6 +272,16 @@ What's new in PyTorch tutorials?
    :tags: Text
 
 
+.. ONNX
+
+.. customcarditem::
+   :header: (optional) Exporting a PyTorch Model to ONNX using TorchScript backend and Running it using ONNX Runtime
+   :card_description:  Convert a model defined in PyTorch into the ONNX format and then run it with ONNX Runtime.
+   :image: _static/img/thumbnails/cropped/optional-Exporting-a-Model-from-PyTorch-to-ONNX-and-Running-it-using-ONNX-Runtime.png
+   :link: advanced/super_resolution_with_onnxruntime.html
+   :tags: ONNX,Production
+
+
 .. Reinforcement Learning
 
 .. customcarditem::
@@ -328,13 +338,6 @@ What's new in PyTorch tutorials?
    :link: advanced/cpp_export.html
    :tags: Production,TorchScript
 
-.. customcarditem::
-   :header: (optional) Exporting a Model from PyTorch to ONNX and Running it using ONNX Runtime
-   :card_description:  Convert a model defined in PyTorch into the ONNX format and then run it with ONNX Runtime.
-   :image: _static/img/thumbnails/cropped/optional-Exporting-a-Model-from-PyTorch-to-ONNX-and-Running-it-using-ONNX-Runtime.png
-   :link: advanced/super_resolution_with_onnxruntime.html
-   :tags: Production
-
 .. Code Transformations with FX
 
 .. customcarditem::
@@ -902,6 +905,14 @@ Additional Resources
    beginner/torchtext_custom_dataset_tutorial
 
 
+.. toctree::
+   :maxdepth: 2
+   :includehidden:
+   :hidden:
+   :caption: Backends
+
+   beginner/onnx/intro_onnx
+
 .. toctree::
    :maxdepth: 2
    :includehidden:
@@ -918,6 +929,7 @@ Additional Resources
    :hidden:
    :caption: Deploying PyTorch Models in Production
 
+   beginner/onnx/intro_onnx
    intermediate/flask_rest_api_tutorial
    beginner/Intro_to_TorchScript_tutorial
    advanced/cpp_export