.. customcarditem::

:header: (beta) Channels Last Memory Format in PyTorch

:card_description: Get an overview of Channels Last memory format and understand how it is used to order NCHW tensors in memory preserving dimensions.

:card_description: Create a neural network layer with no parameters using numpy. Then use scipy to create a neural network layer that has learnable weights.

:image: _static/img/thumbnails/cropped/Custom-Cpp-and-CUDA-Extensions.png

:link: advanced/cpp_extension.html

.. customcarditem::

:header: Extending TorchScript with Custom C++ Operators

:card_description: Implement a custom TorchScript operator in C++, how to build it into a shared library, how to use it in Python to define TorchScript models and lastly how to load it into a C++ application for inference workloads.

:image: _static/img/thumbnails/cropped/Extending-TorchScript-with-Custom-Cpp-Operators.png

:link: advanced/torch_script_custom_ops.html

.. customcarditem::

:header: Extending TorchScript with Custom C++ Classes

:card_description: This is a continuation of the custom operator tutorial, and introduces the API we’ve built for binding C++ classes into TorchScript and Python simultaneously.

:image: _static/img/thumbnails/cropped/Extending-TorchScript-with-Custom-Cpp-Classes.png

:link: advanced/torch_script_custom_classes.html

.. customcarditem::

:header: Dynamic Parallelism in TorchScript

:hidden:

:caption: Frontend APIs

intermediate/memory_format_tutorial

advanced/cpp_frontend

advanced/cpp_extension

advanced/torch_script_custom_ops

advanced/torch_script_custom_classes

advanced/dispatcher

advanced/extend_dispatcher