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CONTRIBUTING.md

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│ └── <a href="devtools/visualization">visualization</a> - Visualization tools for representing model structure and performance metrics.
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├── <a href="docs">docs</a> - Static docs tooling and documentation source files.
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├── <a href="examples">examples</a> - Examples of various user flows, such as model export, delegates, and runtime execution.
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├── <a href="exir">exir</a> - Ahead-of-time library: model capture and lowering APIs. EXport Intermediate Representation (EXIR) is a format for representing the result of <a href="https://pytorch.org/docs/stable/export.html">torch.export</a>. This directory contains utilities and passes for lowering the EXIR graphs into different <a href="/docs/source/ir-exir.md">dialects</a> and eventually suitable to run on target hardware.
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├── <a href="exir">exir</a> - Ahead-of-time library: model capture and lowering APIs. EXport Intermediate Representation (EXIR) is a format for representing the result of <a href="https://pytorch.org/docs/stable/export.html">torch.export</a>. This directory contains utilities and passes for lowering the EXIR graphs into different <a href="docs/source/ir-exir.md">dialects</a> and eventually suitable to run on target hardware.
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│ ├── <a href="exir/_serialize">_serialize</a> - Serialize final export artifact.
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│ ├── <a href="exir/backend">backend</a> - Backend delegate ahead of time APIs.
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│ ├── <a href="exir/capture">capture</a> - Program capture.
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│ ├── <a href="exir/dialects">dialects</a> - Op sets for various dialects in the export process. Please refer to the <a href="/docs/source/ir-exir.md">EXIR spec</a> and the <a href="/docs/source/compiler-backend-dialect.md">backend dialect</a> doc for more details.
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│ ├── <a href="exir/dialects">dialects</a> - Op sets for various dialects in the export process. Please refer to the <a href="docs/source/ir-exir.md">EXIR spec</a> and the <a href="docs/source/compiler-backend-dialect.md">backend dialect</a> doc for more details.
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│ ├── <a href="exir/emit">emit</a> - Conversion from ExportedProgram to ExecuTorch execution instructions.
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│ ├── <a href="exir/operator">operator</a> - Operator node manipulation utilities.
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│ ├── <a href="exir/passes">passes</a> - Built-in compiler passes.
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│ ├── <a href="extension/memory_allocator">memory_allocator</a> - 1st party memory allocator implementations.
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│ ├── <a href="extension/module">module</a> - A simplified C++ wrapper for the runtime. An abstraction that deserializes and executes an ExecuTorch artifact (.pte file). Refer to the <a href="docs/source/extension-module.md">module documentation</a> for more information.
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│ ├── <a href="extension/parallel">parallel</a> - C++ threadpool integration.
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│ ├── <a href="extension/pybindings">pybindings</a> - Python API for executorch runtime. This is powering up the <a href="docs/source/runtime-python-api-reference.md">runtime Python API</a> for ExecuTorch.
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│ ├── <a href="extension/pybindings">pybindings</a> - Python API for executorch runtime. This is powering up the <a href="docs/source/runtime-python-api-reference.rst">runtime Python API</a> for ExecuTorch.
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│ ├── <a href="extension/pytree">pytree</a> - C++ and Python flattening and unflattening lib for pytrees.
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│ ├── <a href="extension/runner_util">runner_util</a> - Helpers for writing C++ PTE-execution tools.
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│ ├── <a href="extension/tensor">tensor</a> - Tensor maker and <code>TensorPtr</code>, details in <a href="docs/source/extension-tensor.md">this documentation</a>. For how to use <code>TensorPtr</code> and <code>Module</code>, please refer to the <a href="docs/source/using-executorch-cpp.md">"Using ExecuTorch with C++"</a> doc.
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1. If you've changed APIs or added a new tool or feature, [update the
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documentation](#updating-documentation).
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1. If you added an experimental API or deprecated an existing API, follow the
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[API Life Cycle and Deprecation Policy](/docs/source/api-life-cycle.md).
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[API Life Cycle and Deprecation Policy](docs/source/api-life-cycle.md).
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1. Make sure your code follows the [style guides](#coding-style) and passes the
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[lint checks](#lintrunner).
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1. If you haven't already, complete the [Contributor License Agreement ("CLA")](#contributor-license-agreement-cla).

README-wheel.md

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* [Exporting to ExecuTorch](https://pytorch.org/executorch/main/tutorials/export-to-executorch-tutorial)
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* Learn the fundamentals of exporting a PyTorch `nn.Module` to ExecuTorch, and
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optimizing its performance using quantization and hardware delegation.
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* Running LLaMA on [iOS](docs/source/llm/llama-demo-ios) and [Android](docs/source/llm/llama-demo-android) devices.
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* Running LLaMA on [iOS](docs/source/llm/llama-demo-ios.md) and [Android](docs/source/llm/llama-demo-android.md) devices.
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* Build and run LLaMA in a demo mobile app, and learn how to integrate models
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with your own apps.

backends/apple/coreml/runtime/test/setup.md

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## Running tests
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1. Follow the instructions described in [Setting Up ExecuTorch](/docs/source/getting-started-setup.md) to set up ExecuTorch environment.
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1. Follow the instructions described in [Setting Up ExecuTorch](../../../../../docs/source/getting-started-setup.rst) to set up ExecuTorch environment.
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2. Run `install_requirements.sh` to install dependencies required by the **Core ML** backend.
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```bash
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cd executorch
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sh backends/apple/coreml/scripts/install_requirements.sh
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sh backends/apple/coreml/scripts/install_requirements.sh
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```
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```
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3. Follow the instructions described in [Building with CMake](/docs/source/runtime-build-and-cross-compilation.md#building-with-cmake) to set up CMake build system.
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3. Follow the instructions described in [Building with CMake](../../../../../docs/source/using-executorch-cpp.md#building-with-cmake) to set up CMake build system.
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4. Install [Xcode](https://developer.apple.com/xcode/).
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```bash
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# Builds macOS universal test bundle.
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# Builds macOS universal test bundle.
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sh backends/apple/coreml/srcipts/build_tests.sh
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sh backends/apple/coreml/srcipts/run_tests.sh
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```
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## Updating tests
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1. Open the Xcode workspace.
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```bash
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cd executorch
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# Builds macOS universal test bundle.
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# Builds macOS universal test bundle.
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open backends/apple/coreml/runtime/workspace/executorchcoreml.xcworkspace
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# There is no need to build the tests.
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sh backends/apple/coreml/srcipts/run_tests.sh
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```
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```

backends/apple/coreml/setup.md

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## AOT Setup
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1. Follow the instructions described in [Setting Up ExecuTorch](/docs/source/getting-started-setup.md) to set up ExecuTorch environment.
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1. Follow the instructions described in [Setting Up ExecuTorch](../../../docs/source/getting-started-setup.rst) to set up ExecuTorch environment.
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2. Run the example script to validate that the **Core ML** backend is set up correctly.
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## Integrating Core ML delegate into runtime.
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1. Follow the instructions described in [Building with CMake](/docs/source/runtime-build-and-cross-compilation.md#building-with-cmake) to set up CMake build system.
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1. Follow the instructions described in [Building with CMake](../../../docs/source/using-executorch-cpp.md#building-with-cmake) to set up CMake build system.
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2. Install [Xcode](https://developer.apple.com/xcode/).
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backends/apple/mps/setup.md

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:::
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:::{grid-item-card} Tutorials we recommend you complete before this:
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:class-card: card-prerequisites
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* [Introduction to ExecuTorch](intro-how-it-works.md)
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* [Setting up ExecuTorch](getting-started-setup.md)
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* [Building ExecuTorch with CMake](runtime-build-and-cross-compilation.md)
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* [ExecuTorch iOS Demo App](demo-apps-ios.md)
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* [ExecuTorch iOS LLaMA Demo App](llm/llama-demo-ios.md)
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* [Introduction to ExecuTorch](../../../docs/source/intro-how-it-works.md)
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* [Setting up ExecuTorch](../../../docs/source/getting-started-setup.rst)
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* [Building ExecuTorch with CMake](../../../docs/source/using-executorch-cpp.md#building-with-cmake)
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* [ExecuTorch iOS Demo App](../../../docs/source/demo-apps-ios.md)
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* [ExecuTorch iOS LLaMA Demo App](../../../docs/source/llm/llama-demo-ios.md)
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:::
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::::
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```
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### Profiling:
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1. [Optional] Generate an [ETRecord](./etrecord.rst) while you're exporting your model.
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1. [Optional] Generate an [ETRecord](../../../docs/source/etrecord.rst) while you're exporting your model.
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```bash
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cd executorch
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python3 -m examples.apple.mps.scripts.mps_example --model_name="mv3" --generate_etrecord -b
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2. Run your Program on the ExecuTorch runtime and generate an [ETDump](./etdump.md).
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2. Run your Program on the ExecuTorch runtime and generate an [ETDump](../../../docs/source/etdump.md).
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```
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./cmake-out/examples/apple/mps/mps_executor_runner --model_path mv3_mps_bundled_fp16.pte --bundled_program --dump-outputs
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backends/example/README.md

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![](./diagrams/quantize_delegate.png)
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![](diagrams/quantize_delegate.png)
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### Partitioner and Backend
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The way partitioner and backend is, partitioner will tag the nodes to lower to the backend and backend will will receive all tagged nodes and preprocess them as a delegate.
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![](./diagrams/delegate.png)
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![](diagrams/delegate.png)
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### Memory format permute
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Some operators may have better performance in the memory format other than contiguous. One way to do that is to insert `to_dim_op` to describe memory format permutation and merge if there two opposite one next to each other.
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![](./diagrams/memory_permute.png)
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![](diagrams/memory_permute.png)

backends/qualcomm/README.md

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[utils.py](./utils/utils.py) for supported SoC and inference type.
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[utils.py](utils/utils.py) for supported SoC and inference type.
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### Supported Chipset
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backends/vulkan/docs/android_demo.md

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# Building and Running ExecuTorch with the Vulkan Backend
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The [ExecuTorch Vulkan Delegate](./native-delegates-executorch-vulkan-delegate.md)
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The [ExecuTorch Vulkan Delegate](../../../docs/source/native-delegates-executorch-vulkan-delegate.md)
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<!----This will show a grid card on the page----->
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* Follow [**Setting up ExecuTorch**](../../../docs/source/getting-started-setup.rst)
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* It is also recommended that you read through [**ExecuTorch Vulkan Delegate**](../../../docs/source/native-delegates-executorch-vulkan-delegate.md) and follow the example in that page
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--model "llama3_2" \
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docs/README.md

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<img src="./source/_static/img/s_custom_variables_extension.png" width="300">
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<img src="source/_static/img/s_custom_variables_extension.png" width="300">
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Right now we only support PyTorch version as custom variable, but will support others in the future.
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[index.rst](./source/index.rst) file in the following places:
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[index.md](source/index.md) file in the following places:
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- In the
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[tutorials torctree](https://github.com/pytorch/executorch/blob/main/docs/source/index.md?plain=1#L185)
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[customcard section](https://github.com/pytorch/executorch/blob/main/docs/source/index.md?plain=1#L201)
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**NOTE:** Your tutorial source file needs to follow the tutorial template.
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# Auto-generated API documentation
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### C++ APIs
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build and referenced in `index.md`.

docs/source/_templates/layout.html

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{% block extrahead %}
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<link rel="stylesheet" href="../_static/css/progress-bar.css">
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<script src="../_static/js/progress-bar.js" defer></script>
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{% elif 'compiler-delegate-and-partitioner' in pagename%}
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<!-- END OF LOCAL OVERRIDE -->
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<!-- END OF LOCAL OVERRIDE -->
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{% block footer %}
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