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<li class="toctree-l1"><a class="reference internal" href="user_guide/torch_tensorrt_explained.html">Torch-TensorRT Explained</a></li>
<li class="toctree-l1"><a class="reference internal" href="user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
<li class="toctree-l1"><a class="reference internal" href="user_guide/using_dla.html">DLA</a></li>
<li class="toctree-l1"><a class="reference internal" href="user_guide/mixed_precision.html">Compile Mixed Precision models with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/vgg16_ptq.html">Deploy Quantized Models using Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/engine_caching_example.html">Engine Caching</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html">Engine Caching (BERT)</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/refit_engine_example.html">Refitting Torch-TensorRT Programs with New Weights</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html">Torch Export with Cudagraphs</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/converter_overloading.html">Overloading Torch-TensorRT Converters with Custom Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/auto_generate_converters.html">Automatically Generate a Converter for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/auto_generate_plugins.html">Automatically Generate a Plugin for a Custom Kernel</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html">Mutable Torch TensorRT Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/weight_streaming_example.html">Weight Streaming</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html">Pre-allocated output buffer</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="dynamo/torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="ts/creating_torchscript_module_in_python.html#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="ts/creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
<li class="toctree-l1"><a class="reference internal" href="ts/getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
<li class="toctree-l1"><a class="reference internal" href="ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in C++</a></li>
<li class="toctree-l1"><a class="reference internal" href="ts/ptq.html">Post Training Quantization (PTQ)</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Model Zoo</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet with dynamic shapes using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling BERT using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Compiling Stable Diffusion model using the <cite>torch.compile</cite> backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html">Compiling GPT2 using the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> frontend</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_gpt2.html">Compiling GPT2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_llama2.html">Compiling Llama2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_sam2.html">Compiling SAM2 using the dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="tutorials/notebooks.html">Legacy notebooks</a></li>
</ul>
<p class="caption" role="heading"><span class="caption-text">Python API Documentation</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="py_api/torch_tensorrt.html">torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="py_api/dynamo.html">torch_tensorrt.dynamo</a></li>
<li class="toctree-l1"><a class="reference internal" href="py_api/logging.html">torch_tensorrt.logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="py_api/fx.html">torch_tensorrt.fx</a></li>
<li class="toctree-l1"><a class="reference internal" href="py_api/ts.html">torch_tensorrt.ts</a></li>
<li class="toctree-l1"><a class="reference internal" href="py_api/ptq.html">torch_tensorrt.ts.ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">C++ API Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
<li class="toctree-l1"><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
<li class="toctree-l1"><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a></li>
<li class="toctree-l1"><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
<li class="toctree-l1"><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
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<p class="caption" role="heading"><span class="caption-text">CLI Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="cli/torchtrtc.html">torchtrtc</a></li>
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<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="contributors/system_overview.html">System Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
<li class="toctree-l1"><a class="reference internal" href="contributors/ts_converters.html">Writing TorchScript Converters</a></li>
<li class="toctree-l1"><a class="reference internal" href="contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
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<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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<li class="toctree-l1"><a class="reference internal" href="indices/supported_ops.html">Operators Supported</a></li>
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<section id="torch-tensorrt">
<h1>Torch-TensorRT<a class="headerlink" href="#torch-tensorrt" title="Permalink to this heading">¶</a></h1>
<section id="in-framework-compilation-of-pytorch-inference-code-for-nvidia-gpus">
<h2>In-framework compilation of PyTorch inference code for NVIDIA GPUs<a class="headerlink" href="#in-framework-compilation-of-pytorch-inference-code-for-nvidia-gpus" title="Permalink to this heading">¶</a></h2>
<p>Torch-TensorRT is a inference compiler for PyTorch, targeting NVIDIA GPUs via NVIDIA’s TensorRT Deep Learning Optimizer and Runtime.
It supports both just-in-time (JIT) compilation workflows via the <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> interface as well as ahead-of-time (AOT) workflows.
Torch-TensorRT integrates seamlessly into the PyTorch ecosystem supporting hybrid execution of optimized TensorRT code with standard PyTorch code.</p>
<p>More Information / System Architecture:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51714/">Torch-TensorRT 2.0</a></p></li>
</ul>
</section>
<section id="getting-started">
<h2>Getting Started<a class="headerlink" href="#getting-started" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="getting_started/installation.html#installation"><span class="std std-ref">Installation</span></a></p></li>
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<section id="user-guide">
<h2>User Guide<a class="headerlink" href="#user-guide" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="user_guide/torch_tensorrt_explained.html#torch-tensorrt-explained"><span class="std std-ref">Torch-TensorRT Explained</span></a></p></li>
<li><p><a class="reference internal" href="user_guide/dynamic_shapes.html#dynamic-shapes"><span class="std std-ref">Dynamic shapes with Torch-TensorRT</span></a></p></li>
<li><p><a class="reference internal" href="ts/ptq.html#ptq"><span class="std std-ref">Post Training Quantization (PTQ)</span></a></p></li>
<li><p><a class="reference internal" href="user_guide/saving_models.html#saving-models"><span class="std std-ref">Saving models compiled with Torch-TensorRT</span></a></p></li>
<li><p><a class="reference internal" href="user_guide/runtime.html#runtime"><span class="std std-ref">Deploying Torch-TensorRT Programs</span></a></p></li>
<li><p><a class="reference internal" href="user_guide/using_dla.html#using-dla"><span class="std std-ref">DLA</span></a></p></li>
<li><p><a class="reference internal" href="user_guide/mixed_precision.html#mixed-precision"><span class="std std-ref">Compile Mixed Precision models with Torch-TensorRT</span></a></p></li>
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</section>
<section id="tutorials">
<h2>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html#torch-compile-advanced-usage"><span class="std std-ref">Torch Compile Advanced Usage</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/vgg16_ptq.html#vgg16-ptq"><span class="std std-ref">Deploy Quantized Models using Torch-TensorRT</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/engine_caching_example.html#engine-caching-example"><span class="std std-ref">Engine Caching</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/engine_caching_bert_example.html#engine-caching-bert-example"><span class="std std-ref">Engine Caching (BERT)</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/refit_engine_example.html#refit-engine-example"><span class="std std-ref">Refitting Torch-TensorRT Programs with New Weights</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/serving_torch_tensorrt_with_triton.html#serving-torch-tensorrt-with-triton"><span class="std std-ref">Serving a Torch-TensorRT model with Triton</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_cudagraphs.html#torch-export-cudagraphs"><span class="std std-ref">Torch Export with Cudagraphs</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/converter_overloading.html#converter-overloading"><span class="std std-ref">Overloading Torch-TensorRT Converters with Custom Converters</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/custom_kernel_plugins.html#custom-kernel-plugins"><span class="std std-ref">Using Custom Kernels within TensorRT Engines with Torch-TensorRT</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/auto_generate_converters.html#auto-generate-converters"><span class="std std-ref">Automatically Generate a Converter for a Custom Kernel</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/auto_generate_plugins.html#auto-generate-plugins"><span class="std std-ref">Automatically Generate a Plugin for a Custom Kernel</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/mutable_torchtrt_module_example.html#mutable-torchtrt-module-example"><span class="std std-ref">Mutable Torch TensorRT Module</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/weight_streaming_example.html#weight-streaming-example"><span class="std std-ref">Weight Streaming</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/pre_allocated_output_example.html#pre-allocated-output-example"><span class="std std-ref">Pre-allocated output buffer</span></a></p></li>
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</section>
<section id="dynamo-frontend">
<h2>Dynamo Frontend<a class="headerlink" href="#dynamo-frontend" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="dynamo/torch_compile.html#torch-compile"><span class="std std-ref">TensorRT Backend for torch.compile</span></a></p></li>
<li><p><a class="reference internal" href="dynamo/dynamo_export.html#dynamo-export"><span class="std std-ref">Compiling Exported Programs with Torch-TensorRT</span></a></p></li>
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</section>
<section id="torchscript-frontend">
<h2>TorchScript Frontend<a class="headerlink" href="#torchscript-frontend" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="ts/creating_torchscript_module_in_python.html#creating-a-ts-mod"><span class="std std-ref">Creating a TorchScript Module</span></a></p></li>
<li><p><a class="reference internal" href="ts/getting_started_with_python_api.html#getting-started-with-python-api"><span class="std std-ref">Using Torch-TensorRT in Python</span></a></p></li>
<li><p><a class="reference internal" href="ts/getting_started_with_cpp_api.html#getting-started-cpp"><span class="std std-ref">Using Torch-TensorRT in C++</span></a></p></li>
<li><p><a class="reference internal" href="ts/torchscript_frontend_from_pytorch.html#use-from-pytorch"><span class="std std-ref">Using Torch-TensorRT TorchScript Frontend Directly From PyTorch</span></a></p></li>
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<div class="toctree-wrapper compound">
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</section>
<section id="fx-frontend">
<h2>FX Frontend<a class="headerlink" href="#fx-frontend" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="fx/getting_started_with_fx_path.html#getting-started-with-fx"><span class="std std-ref">Torch-TensorRT (FX Frontend) User Guide</span></a></p></li>
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</section>
<section id="model-zoo">
<h2>Model Zoo<a class="headerlink" href="#model-zoo" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html#torch-compile-resnet"><span class="std std-ref">Compiling ResNet with dynamic shapes using the torch.compile backend</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html#torch-compile-transformer"><span class="std std-ref">Compiling BERT using the torch.compile backend</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html#torch-compile-stable-diffusion"><span class="std std-ref">Compiling Stable Diffusion model using the torch.compile backend</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_gpt2.html#torch-compile-gpt2"><span class="std std-ref">Compiling GPT2 using the Torch-TensorRT torch.compile frontend</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_gpt2.html#torch-export-gpt2"><span class="std std-ref">Compiling GPT2 using the dynamo backend</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_llama2.html#torch-export-llama2"><span class="std std-ref">Compiling Llama2 using the dynamo backend</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_sam2.html#torch-export-sam2"><span class="std std-ref">Compiling SAM2 using the dynamo backend</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_export_flux_dev.html#torch-export-flux-dev"><span class="std std-ref">Compiling FLUX.1-dev model using the Torch-TensorRT dynamo backend</span></a></p></li>
<li><p><a class="reference internal" href="tutorials/notebooks.html#notebooks"><span class="std std-ref">Legacy notebooks</span></a></p></li>
</ul>
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</div>
</section>
<section id="python-api-documentation">
<h2>Python API Documentation<a class="headerlink" href="#python-api-documentation" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="py_api/torch_tensorrt.html#torch-tensorrt-py"><span class="std std-ref">torch_tensorrt</span></a></p></li>
<li><p><a class="reference internal" href="py_api/dynamo.html#torch-tensorrt-dynamo-py"><span class="std std-ref">torch_tensorrt.dynamo</span></a></p></li>
<li><p><a class="reference internal" href="py_api/logging.html#torch-tensorrt-logging-py"><span class="std std-ref">torch_tensorrt.logging</span></a></p></li>
<li><p><a class="reference internal" href="py_api/fx.html#torch-tensorrt-fx-py"><span class="std std-ref">torch_tensorrt.fx</span></a></p></li>
<li><p><a class="reference internal" href="py_api/ts.html#torch-tensorrt-ts-py"><span class="std std-ref">torch_tensorrt.ts</span></a></p></li>
<li><p><a class="reference internal" href="py_api/ptq.html#torch-tensorrt-ptq-py"><span class="std std-ref">torch_tensorrt.ts.ptq</span></a></p></li>
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<section id="c-api-documentation">
<h2>C++ API Documentation<a class="headerlink" href="#c-api-documentation" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt.html#namespace-torch-tensorrt"><span class="std std-ref">Namespace torch_tensorrt</span></a></p></li>
<li><p><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__logging.html#namespace-torch-tensorrt-logging"><span class="std std-ref">Namespace torch_tensorrt::logging</span></a></p></li>
<li><p><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__ptq.html#namespace-torch-tensorrt-ptq"><span class="std std-ref">Namespace torch_tensorrt::ptq</span></a></p></li>
<li><p><a class="reference internal" href="_cpp_api/namespace_torch_tensorrt__torchscript.html#namespace-torch-tensorrt-torchscript"><span class="std std-ref">Namespace torch_tensorrt::torchscript</span></a></p></li>
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<section id="cli-documentation">
<h2>CLI Documentation<a class="headerlink" href="#cli-documentation" title="Permalink to this heading">¶</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="cli/torchtrtc.html#torchtrtc"><span class="std std-ref">torchtrtc</span></a></p></li>
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<section id="contributor-documentation">
<h2>Contributor Documentation<a class="headerlink" href="#contributor-documentation" title="Permalink to this heading">¶</a></h2>
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<li><p><a class="reference internal" href="contributors/system_overview.html#system-overview"><span class="std std-ref">System Overview</span></a></p></li>
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