Remove TorchScript part from the Saving and Loading tutorial

beginner_source/saving_loading_models.py

@@ -227,42 +227,6 @@
 # normalization layers to evaluation mode before running inference.
 # Failing to do this will yield inconsistent inference results.
 #
-# Export/Load Model in TorchScript Format
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-#
-# One common way to do inference with a trained model is to use
-# `TorchScript <https://pytorch.org/docs/stable/jit.html>`__, an intermediate
-# representation of a PyTorch model that can be run in Python as well as in a
-# high performance environment like C++. TorchScript is actually the recommended model format
-# for scaled inference and deployment.
-#
-# .. note::
-#    Using the TorchScript format, you will be able to load the exported model and
-#    run inference without defining the model class.
-#
-# **Export:**
-#
-# .. code:: python
-#
-#    model_scripted = torch.jit.script(model) # Export to TorchScript
-#    model_scripted.save('model_scripted.pt') # Save
-#
-# **Load:**
-#
-# .. code:: python
-#
-#    model = torch.jit.load('model_scripted.pt')
-#    model.eval()
-#
-# Remember that you must call ``model.eval()`` to set dropout and batch
-# normalization layers to evaluation mode before running inference.
-# Failing to do this will yield inconsistent inference results.
-#
-# For more information on TorchScript, feel free to visit the dedicated
-# `tutorials <https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html>`__.
-# You will get familiar with the tracing conversion and learn how to
-# run a TorchScript module in a `C++ environment <https://pytorch.org/tutorials/advanced/cpp_export.html>`__.
-
 
 
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