Fix broken links to torch.export documentation (#3747)

## Summary
- Fixes broken 404 links to `pytorch.org/docs/main/export.html` which
have moved to
`docs.pytorch.org/docs/stable/user_guide/torch_compiler/export.html`

## Files Updated
- `recipes_source/torch_export_challenges_solutions.rst` - Fixed link to
existing-frameworks section
- `unstable_source/openvino_quantizer.rst` - Fixed link to torch export
mechanism

Fixes #3712

Co-authored-by: Svetlana Karslioglu <svekars@meta.com>

Author: rtj1

recipes_source/torch_export_challenges_solutions.rst

@@ -33,7 +33,7 @@ handle the unsupported code, and then resumes capturing the graph. This break in
 One of the key differences between ``torch.export`` and ``torch.compile`` is that ``torch.export`` doesn’t support graph breaks
 which means that the entire model or part of the model that you are exporting needs to be a single graph. This is because handling graph breaks
 involves interpreting the unsupported operation with default Python evaluation, which is incompatible with what ``torch.export`` is
-designed for. You can read details about the differences between the various PyTorch frameworks in this `link <https://pytorch.org/docs/main/export.html#existing-frameworks>`__
+designed for. You can read details about the differences between the various PyTorch frameworks in this `link <https://docs.pytorch.org/docs/stable/user_guide/torch_compiler/export.html#existing-frameworks>`__
 
 You can identify graph breaks in your program by using the following command:
 

unstable_source/openvino_quantizer.rst

@@ -24,7 +24,7 @@ OpenVINO backend compiles the FX Graph generated by TorchDynamo into an optimize
 
 The quantization flow mainly includes four steps:
 
-- Step 1: Capture the FX Graph from the eager Model based on the `torch export mechanism <https://pytorch.org/docs/main/export.html>`_.
+- Step 1: Capture the FX Graph from the eager Model based on the `torch export mechanism <https://docs.pytorch.org/docs/stable/user_guide/torch_compiler/export.html>`_.
 - Step 2: Apply the PyTorch 2 Export Quantization flow with OpenVINOQuantizer based on the captured FX Graph.
 - Step 3: Lower the quantized model into OpenVINO representation with the `torch.compile <https://docs.openvino.ai/2024/openvino-workflow/torch-compile.html>`_ API.
 - Optional step 4: : Improve quantized model metrics via `quantize_pt2e <https://openvinotoolkit.github.io/nncf/autoapi/nncf/experimental/torch/fx/index.html#nncf.experimental.torch.fx.quantize_pt2e>`_ method.