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docs/source/using-executorch-cpp.md

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For more information on the Module class, see [Running an ExecuTorch Model Using the Module Extension in C++](extension-module.md). For information on high-level tensor APIs, see [Managing Tensor Memory in C++](extension-tensor.md).
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For an example of how to use the Module class with ExecuTorch as a dependency, see the [mv2 example](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/cpp) in the [executorch-examples](https://github.com/pytorch-labs/executorch-examples) repository.
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## Low-Level APIs
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Running a model using the low-level runtime APIs allows for a high-degree of control over memory allocation, placement, and loading. This allows for advanced use cases, such as placing allocations in specific memory banks or loading a model without a file system. For an end to end example using the low-level runtime APIs, see [Running an ExecuTorch Model in C++ Tutorial](running-a-model-cpp-tutorial.md).

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