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Update broken links tia index 3372 (#3376)
* Fix broken links in intermediate_source/tiatoolbox_tutorial.rst * Update broken links in index.rst * Fix index.rst ios executorch demo link
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index.rst

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@@ -806,21 +806,21 @@ Welcome to PyTorch Tutorials
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:header: Using the ExecuTorch SDK to Profile a Model
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:card_description: Explore how to use the ExecuTorch SDK to profile, debug, and visualize ExecuTorch models
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:image: _static/img/ExecuTorch-Logo-cropped.svg
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:link: https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html
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:link: https://docs.pytorch.org/executorch/main/tutorials/devtools-integration-tutorial.html
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:tags: Edge
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.. customcarditem::
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:header: Building an ExecuTorch iOS Demo App
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:card_description: Explore how to set up the ExecuTorch iOS Demo App, which uses the MobileNet v3 model to process live camera images leveraging three different backends: XNNPACK, Core ML, and Metal Performance Shaders (MPS).
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:image: _static/img/ExecuTorch-Logo-cropped.svg
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:link: https://pytorch.org/executorch/stable/demo-apps-ios.html
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:link: https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo
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:tags: Edge
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.. customcarditem::
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:header: Building an ExecuTorch Android Demo App
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:card_description: Learn how to set up the ExecuTorch Android Demo App for image segmentation tasks using the DeepLab v3 model and XNNPACK FP32 backend.
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:image: _static/img/ExecuTorch-Logo-cropped.svg
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:link: https://pytorch.org/executorch/stable/demo-apps-android.html
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:link: https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app
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:tags: Edge
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.. customcarditem::
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Exporting to ExecuTorch Tutorial <https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html>
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Running an ExecuTorch Model in C++ Tutorial < https://pytorch.org/executorch/stable/running-a-model-cpp-tutorial.html>
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Using the ExecuTorch SDK to Profile a Model <https://pytorch.org/executorch/stable/tutorials/sdk-integration-tutorial.html>
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Building an ExecuTorch iOS Demo App <https://pytorch.org/executorch/stable/demo-apps-ios.html>
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Building an ExecuTorch Android Demo App <https://pytorch.org/executorch/stable/demo-apps-android.html>
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Using the ExecuTorch SDK to Profile a Model <https://docs.pytorch.org/executorch/main/tutorials/devtools-integration-tutorial.html>
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Building an ExecuTorch iOS Demo App <https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo>
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Building an ExecuTorch Android Demo App <https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app>
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Lowering a Model as a Delegate <https://pytorch.org/executorch/stable/examples-end-to-end-to-lower-model-to-delegate.html>
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.. toctree::

intermediate_source/tiatoolbox_tutorial.rst

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@@ -348,7 +348,7 @@ The PatchPredictor class runs a CNN-based classifier written in PyTorch.
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- Alternatively, you can pass ``pretrained_model`` as a string
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argument. This specifies the CNN model that performs the prediction,
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and it must be one of the models listed
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`here <https://tia-toolbox.readthedocs.io/en/latest/usage.html?highlight=pretrained%20models#tiatoolbox.models.architecture.get_pretrained_model>`__.
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`here <https://tia-toolbox.readthedocs.io/en/stable/_autosummary/tiatoolbox.models.architecture.get_pretrained_model.html#tiatoolbox.models.architecture.get_pretrained_model>`__.
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The command will look like this:
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``predictor = PatchPredictor(pretrained_model='resnet18-kather100k', pretrained_weights=weights_path, batch_size=32)``.
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- ``pretrained_weights``: When using a ``pretrained_model``, the
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which is equivalent to level 0. In general, this is the level of
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greatest resolution. In this particular case, the image has only one
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level. More information can be found in the
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`documentation <https://tia-toolbox.readthedocs.io/en/latest/usage.html?highlight=WSIReader.read_rect#tiatoolbox.wsicore.wsireader.WSIReader.read_rect>`__.
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`documentation <https://tia-toolbox.readthedocs.io/en/stable/_autosummary/tiatoolbox.wsicore.wsireader.WSIReader.html#tiatoolbox.wsicore.wsireader.WSIReader.read_rect>`__.
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- ``masks``: A list of paths corresponding to the masks of WSIs in the
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``imgs`` list. These masks specify the regions in the original WSIs
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from which we want to extract patches. If the mask of a particular

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