From 4c3d026ea64bd646a840bf174ef9d1818f10c20f Mon Sep 17 00:00:00 2001
From: Naren Dasan <naren@narendasan.com>
Date: Tue, 13 Feb 2024 17:42:49 -0800
Subject: [PATCH] chore: documentation for release

Signed-off-by: Naren Dasan <naren@narendasan.com>
Signed-off-by: Naren Dasan <narens@nvidia.com>
---
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+              
+  <section id="class-datatype">
+<span id="exhale-class-classtorch-tensorrt-1-1datatype"></span><h1>Class DataType<a class="headerlink" href="#class-datatype" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="class-documentation">
+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataTypeE">
+<span class="target" id="classtorch__tensorrt_1_1DataType"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">DataType</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Supported Data Types that can be used with TensorRT engines</p>
+<p>This class is compatable with c10::DataTypes (but will check for TRT support) so there should not be a reason that you need to use this type explictly. </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Value</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Underlying enum class to support the <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> Class</p>
+<p>In the case that you need to use the <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> class itself, interface using this enum vs. normal instatination</p>
+<p>ex. <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">torch_tensorrt::DataType</span></a> type = <a class="reference internal" href="#classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a45ceda04c1ab50695a4a6aeaeae99817"><span class="std std-ref">DataType::kFloat</span></a>; </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value5kLongE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a2f2ce4f82dad683122c21a788fca097b"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kLong</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value5kLongE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>INT64. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value7kDoubleE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684aacf5b40b44995643185a977d2d1ce1bf"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kDouble</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value7kDoubleE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>FP64. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value6kFloatE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a45ceda04c1ab50695a4a6aeaeae99817"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kFloat</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value6kFloatE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>FP32. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value5kHalfE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a3733a4b256b56596c5c52de99a196179"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kHalf</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value5kHalfE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>FP16. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value5kCharE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684ab97bba9140703786c7aa08baea3ab276"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kChar</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value5kCharE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>INT8. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value4kIntE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684aa331051c24898e3701b925251d85c7c1"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kInt</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value4kIntE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>INT. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value5kBoolE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a67f7e5fb797f4da732c5e217cbda4353"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kBool</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value5kBoolE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Bool. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType5Value8kUnknownE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a6335c0e206340d85a1382a5df17bf684a140934e6b67dfe841b32eb73bea55358"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kUnknown</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType5Value8kUnknownE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sentinel value. </p>
+</dd></dl>
+
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType8DataTypeEv">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1aeaf580c5f2d949bfe358e47b3824de85"></span><span class="sig-name descname"><span class="n"><span class="pre">DataType</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType8DataTypeEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Data Type object. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType8DataTypeE5Value">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a2804fe4287453fd5b5e35f44a382dcdd"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataType</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="torch_tensorrt::DataType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType8DataTypeE5Value" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p><a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> constructor from enum. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataType8DataTypeEN3c1010ScalarTypeE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a7cb2fc58fcea525b8c07388134cb1b37"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataType</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ScalarType</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataType8DataTypeEN3c1010ScalarTypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Data Type object from torch type enums. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypecv5ValueEv">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a09bc3c1870d9c469de12095a98e8caa4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="torch_tensorrt::DataType::Value"><span class="n"><span class="pre">Value</span></span></a></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypecv5ValueEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the enum value of the <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Value </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt8DataTypecvbEv">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1aba0ed97193a870cbddf50c7bc02dd9fb"></span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt8DataTypecvbEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypeeqE8DataType">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1ae8b45ac54760deb077d6dfc4acbd2e2b"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypeeqE8DataType" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypeeqEN8DataType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a1232a15ad160b309db10a5ea8dd4e26c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="torch_tensorrt::DataType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypeeqEN8DataType5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypeneE8DataType">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a3fb2cbdb14408da52fa9bb0e0840d7e4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypeneE8DataType" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt8DataTypeneEN8DataType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1DataType_1a25dd020fd88ec93b421c990045c1aba4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N14torch_tensorrt8DataType5ValueE" title="torch_tensorrt::DataType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt8DataTypeneEN8DataType5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
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diff --git a/docs/v2.2.0/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html b/docs/v2.2.0/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html
new file mode 100644
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--- /dev/null
+++ b/docs/v2.2.0/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html
@@ -0,0 +1,879 @@
+
+
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+<!DOCTYPE html>
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+<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</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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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="class-device-devicetype">
+<span id="exhale-class-classtorch-tensorrt-1-1device-1-1devicetype"></span><h1>Class Device::DeviceType<a class="headerlink" href="#class-device-devicetype" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="nested-relationships">
+<h2>Nested Relationships<a class="headerlink" href="#nested-relationships" title="Permalink to this headline">¶</a></h2>
+<p>This class is a nested type of <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#exhale-struct-structtorch-tensorrt-1-1device"><span class="std std-ref">Struct Device</span></a>.</p>
+</section>
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+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceTypeE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6DeviceE" title="torch_tensorrt::Device"><span class="n"><span class="pre">Device</span></span></a><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Supported <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Types that can be used with TensorRT engines</p>
+<p>This class is compatable with c10::DeviceTypes (but will check for TRT support) but the only applicable value is at::kCUDA, which maps to <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a></p>
+<p>To use the <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> class itself, interface using the enum vs. normal instatination</p>
+<p>ex. torch_tensorrt::DeviceType type = <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a>; </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Value</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Underlying enum class to support the <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a> Class</p>
+<p>In the case that you need to use the <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a> class itself, interface using this enum vs. normal instatination</p>
+<p>ex. torch_tensorrt::DeviceType type = <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a>; </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kGPU</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Target GPU to run engine. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350aa13fb3ec20096f0d0c205b67ca05c98e"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kDLA</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Target DLA to run engine. </p>
+</dd></dl>
+
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1afe573ee91b95b81be8a0f1503a00ca45"></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a5a7e6c4eb98321bd53869a8ec08e2a2c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="torch_tensorrt::Device::DeviceType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object from internal enum. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a499573dd56df8fa1479dcec95dfda711"></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">DeviceType</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object from torch device enums Note: The only valid value is torch::kCUDA (torch::kCPU is not supported) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a4afa0b396b7a076e37054754a367f2f4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="torch_tensorrt::Device::DeviceType::Value"><span class="n"><span class="pre">Value</span></span></a></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the internal value from the <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Value </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a1e78ae4780df8bd258ee8703a22d9a4d"></span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a0177ee9788edabb89a8cb64234d2a60c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparison operator for <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a0d8973b436226f17597d94f4e8f63336"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparison operator for <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
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diff --git a/docs/v2.2.0/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html b/docs/v2.2.0/_cpp_api/classtorch__tensorrt_1_1TensorFormat.html
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+      <li>Class TensorFormat</li>
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+              
+  <section id="class-tensorformat">
+<span id="exhale-class-classtorch-tensorrt-1-1tensorformat"></span><h1>Class TensorFormat<a class="headerlink" href="#class-tensorformat" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="class-documentation">
+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormatE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">TensorFormat</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p><a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> is an enum class which defines the memeory layout used to store Tensor Data. </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Value</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Underlying enum class to support the <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> Class</p>
+<p>In the case that you need to use the <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> class itself, interface using this enum vs. normal instatination</p>
+<p>ex. <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">torch_tensorrt::TensorFormat</span></a> type = <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5abe49335174da5bd374439929a7313a8e"><span class="std std-ref">TensorFormat::kContiguous</span></a>; </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5abe49335174da5bd374439929a7313a8e"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kContiguous</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Contiguous / NCHW / Linear. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat5Value13kChannelsLastE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5a4a290b531327a2bdd79cd8a220764bc5"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kChannelsLast</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat5Value13kChannelsLastE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Channel Last / NHWC. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat5Value8kUnknownE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a5f9c2efc6450a9a6a536fb597762c1d5a39873c22f833d49326284997a7354886"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kUnknown</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat5Value8kUnknownE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sentinel value. </p>
+</dd></dl>
+
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEv">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1ae6c5fe5a84ce30238e7b34b11f747194"></span><span class="sig-name descname"><span class="n"><span class="pre">TensorFormat</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> object. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatE5Value">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a6c68a24cbf07126e9a7232ec101f6173"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">TensorFormat</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="torch_tensorrt::TensorFormat::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatE5Value" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p><a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> constructor from enum. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEN2at12MemoryFormatE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1aec707cf4f1cc30ef51959b598fdeb4ef"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">TensorFormat</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">at</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">MemoryFormat</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEN2at12MemoryFormatE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> object from torch type enums. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormatcv5ValueEv">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a7a6bc4e6c832af328b038486e7c8edd4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="torch_tensorrt::TensorFormat::Value"><span class="n"><span class="pre">Value</span></span></a></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormatcv5ValueEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the enum value of the <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a> object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Value </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt12TensorFormatcvbEv">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a2acd96cd0b987a998eac9d6dbcd64513"></span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt12TensorFormatcvbEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormateqE12TensorFormat">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a0bf675d9440ba073bdd2e0fb4dd4ee02"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormateqE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormateqEN12TensorFormat5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a37a19654f18bdc6f1277b8d3403e4142"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="torch_tensorrt::TensorFormat::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormateqEN12TensorFormat5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormatneE12TensorFormat">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1ad2e6f3e072ffc909bb3020f6ad4c373e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormatneE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt12TensorFormatneEN12TensorFormat5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1TensorFormat_1a7d87b14aa1585bb442e40e547677862d"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="#_CPPv4N14torch_tensorrt12TensorFormat5ValueE" title="torch_tensorrt::TensorFormat::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt12TensorFormatneEN12TensorFormat5ValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Comparision operator for <a class="reference internal" href="#classtorch__tensorrt_1_1TensorFormat"><span class="std std-ref">TensorFormat</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
+</section>
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+              
+  <section id="template-class-int8cachecalibrator">
+<span id="exhale-class-classtorch-tensorrt-1-1ptq-1-1int8cachecalibrator"></span><h1>Template Class Int8CacheCalibrator<a class="headerlink" href="#template-class-int8cachecalibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="inheritance-relationships">
+<h2>Inheritance Relationships<a class="headerlink" href="#inheritance-relationships" title="Permalink to this headline">¶</a></h2>
+<section id="base-type">
+<h3>Base Type<a class="headerlink" href="#base-type" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">private</span> <span class="pre">Algorithm</span></code></p></li>
+</ul>
+</section>
+</section>
+<section id="class-documentation">
+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">private</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="torch_tensorrt::ptq::Int8CacheCalibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><a class="headerlink" href="#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Generic <a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#classtorch__tensorrt_1_1ptq_1_1Int8Calibrator"><span class="std std-ref">Int8Calibrator</span></a> implementation based on a specified TensorRT calibration algorithm that only reads from a calibration file. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p>
+</dd>
+</dl>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator19Int8CacheCalibratorERKNSt6stringE">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1acdad3ba79f02daaf858fce843f80d504"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator19Int8CacheCalibratorERKNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Int 8 Cache Calibrator object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>cache_file_path</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt3ptq19Int8CacheCalibrator12getBatchSizeEv">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1ab118606e734d860a0689fabb8e08447c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getBatchSize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt3ptq19Int8CacheCalibrator12getBatchSizeEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the Batch Size for the next batch (always 1 due to issues with TRT and explicit batch) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>int </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator8getBatchEA_PvA_PKci">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1a63ffe6457b752cf3405990158f2fac96"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getBatch</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">bindings</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">char</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">names</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">nbBindings</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator8getBatchEA_PvA_PKci" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the next Batch. </p>
+<p>Not used always returns false</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>bindings</strong> – void*[] - An array of binding pointers (fed in from TensorRT calibrator), these buffers should be filed with batch data for each input </p></li>
+<li><p><strong>names</strong> – const char*[] - Names of bindings </p></li>
+<li><p><strong>nbBindings</strong> – int - Number of bindings </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator20readCalibrationCacheER6size_t">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1ad4b4808923d96f3cfe2e7616c1825cf0"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">readCalibrationCache</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">length</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator20readCalibrationCacheER6size_t" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Read calibration cache. </p>
+<p>How to read from the calibration cache, only enabled if use_cache is set</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>length</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>const void* - Pointer to cache data </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator21writeCalibrationCacheEPKv6size_t">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1a1a31513eea8017a31852afc9e49eddd9"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">writeCalibrationCache</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">cache</span></span>, <span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">length</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator21writeCalibrationCacheEPKv6size_t" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Write calibration cache. </p>
+<p>Write a the calibration cache provided by TensorRT to a specified file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>cache</strong> – const void* - cache data </p></li>
+<li><p><strong>length</strong> – size_t - length of cache </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibratorcvPN8nvinfer115IInt8CalibratorEEv">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator_1ae51df6619de0f02dc6dc877ba147700a"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8Calibrator</span></span><span class="p"><span class="pre">*</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibratorcvPN8nvinfer115IInt8CalibratorEEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>operator to cast to nvinfer1::IInt8Calibrator* </p>
+<p>Convience function to convert to a IInt8Calibrator* to easily be assigned to the ptq_calibrator field in CompileSpec</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>nvinfer1::IInt8Calibrator* </p>
+</dd>
+</dl>
+</dd></dl>
+
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diff --git a/docs/v2.2.0/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html b/docs/v2.2.0/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html
new file mode 100644
index 0000000000..7a9c4ac5ea
--- /dev/null
+++ b/docs/v2.2.0/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html
@@ -0,0 +1,885 @@
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+              
+  <section id="template-class-int8calibrator">
+<span id="exhale-class-classtorch-tensorrt-1-1ptq-1-1int8calibrator"></span><h1>Template Class Int8Calibrator<a class="headerlink" href="#template-class-int8calibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="inheritance-relationships">
+<h2>Inheritance Relationships<a class="headerlink" href="#inheritance-relationships" title="Permalink to this headline">¶</a></h2>
+<section id="base-type">
+<h3>Base Type<a class="headerlink" href="#base-type" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">private</span> <span class="pre">Algorithm</span></code></p></li>
+</ul>
+</section>
+</section>
+<section id="class-documentation">
+<h2>Class Documentation<a class="headerlink" href="#class-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataLoaderUniquePtr</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Int8Calibrator</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">private</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><a class="headerlink" href="#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Generic <a class="reference internal" href="#classtorch__tensorrt_1_1ptq_1_1Int8Calibrator"><span class="std std-ref">Int8Calibrator</span></a> implementation based on a specified TensorRT calibration algorithm and a LibTorch DataLoader. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p></li>
+<li><p><strong>DataLoaderUniquePtr</strong> – std::unique_ptr&lt;torch::data::DataLoader&gt; - DataLoader type </p></li>
+</ul>
+</dd>
+</dl>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8Calibrator14Int8CalibratorE19DataLoaderUniquePtrRKNSt6stringEb">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1a7cd5d19c509ef27870a6d52e4932884e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Int8Calibrator</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator::DataLoaderUniquePtr"><span class="n"><span class="pre">DataLoaderUniquePtr</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dataloader</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">use_cache</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator14Int8CalibratorE19DataLoaderUniquePtrRKNSt6stringEb" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#classtorch__tensorrt_1_1ptq_1_1Int8Calibrator"><span class="std std-ref">Int8Calibrator</span></a> object. </p>
+<p>Using the provided DataLoader, construct a calibrator that can be used for PTQ with Torch-TensorRT</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dataloader</strong> – std::unqiue_ptr&lt;torch::data::DataLoader&gt; - A unique pointer to the DataLoader, should be what is returned from the make_data_loader factory </p></li>
+<li><p><strong>cache_file_path</strong> – const std::string&amp; - A path to store / find the calibration cache </p></li>
+<li><p><strong>use_cache</strong> – : bool - Whether to use the cache (if it exists) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt3ptq14Int8Calibrator12getBatchSizeEv">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1a2fb5b5bb33808075d54adfdbc5727573"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getBatchSize</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4NK14torch_tensorrt3ptq14Int8Calibrator12getBatchSizeEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the Batch Size for the next batch (always 1 due to issues with TRT and explicit batch) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>int </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8Calibrator8getBatchEA_PvA_PKci">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1a52b0ffc4230d610181c7c47fc0faaf23"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">getBatch</span></span></span><span class="sig-paren">(</span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">bindings</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">char</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">names</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">]</span></span>, <span class="kt"><span class="pre">int</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">nbBindings</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator8getBatchEA_PvA_PKci" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the next Batch. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>bindings</strong> – void*[] - An array of binding pointers (fed in from TensorRT calibrator), these buffers should be filed with batch data for each input </p></li>
+<li><p><strong>names</strong> – const char*[] - Names of bindings </p></li>
+<li><p><strong>nbBindings</strong> – int - Number of bindings </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true - There is a new batch for the calibrator to consume </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false - There is not a new batch for the calibrator to consume </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8Calibrator20readCalibrationCacheER6size_t">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1adb666718fcc006b8e4057625469de6a8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">readCalibrationCache</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">length</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator20readCalibrationCacheER6size_t" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Read calibration cache. </p>
+<p>How to read from the calibration cache, only enabled if use_cache is set</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>length</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>const void* - Pointer to cache data </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8Calibrator21writeCalibrationCacheEPKv6size_t">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1ae8756c27ffa527d05ba32da03561a417"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">writeCalibrationCache</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="n sig-param"><span class="pre">cache</span></span>, <span class="n"><span class="pre">size_t</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">length</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">noexcept</span></span><span class="w"> </span><span class="k"><span class="pre">override</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator21writeCalibrationCacheEPKv6size_t" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Write calibration cache. </p>
+<p>Write a the calibration cache provided by TensorRT to a specified file</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>cache</strong> – const void* - cache data </p></li>
+<li><p><strong>length</strong> – size_t - length of cache </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt3ptq14Int8CalibratorcvPN8nvinfer115IInt8CalibratorEEv">
+<span class="target" id="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator_1aaf70be340fa11c8cc95295d2692c913d"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8Calibrator</span></span><span class="p"><span class="pre">*</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt3ptq14Int8CalibratorcvPN8nvinfer115IInt8CalibratorEEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>operator to cast to nvinfer1::IInt8Calibrator* </p>
+<p>Convience function to convert to a IInt8Calibrator* to easily be assigned to the ptq_calibrator field in CompileSpec</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>nvinfer1::IInt8Calibrator* </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
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diff --git a/docs/v2.2.0/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html b/docs/v2.2.0/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html
new file mode 100644
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diff --git a/docs/v2.2.0/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html b/docs/v2.2.0/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html
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+  <section id="define-torch-tensorrt-major-version">
+<span id="exhale-define-macros-8h-1a31398a6d4d27e28817afb0f0139e909e"></span><h1>Define TORCH_TENSORRT_MAJOR_VERSION<a class="headerlink" href="#define-torch-tensorrt-major-version" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a></p></li>
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+<h2>Define Documentation<a class="headerlink" href="#define-documentation" title="Permalink to this headline">¶</a></h2>
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+<dt class="sig sig-object cpp" id="c.TORCH_TENSORRT_MAJOR_VERSION">
+<span class="target" id="macros_8h_1a31398a6d4d27e28817afb0f0139e909e"></span><span class="sig-name descname"><span class="n"><span class="pre">TORCH_TENSORRT_MAJOR_VERSION</span></span></span><a class="headerlink" href="#c.TORCH_TENSORRT_MAJOR_VERSION" title="Permalink to this definition">¶</a><br /></dt>
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diff --git a/docs/v2.2.0/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html b/docs/v2.2.0/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html
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+<span id="exhale-define-macros-8h-1abe87b341f562fd1cf40b7672e4d759da"></span><h1>Define XSTR<a class="headerlink" href="#define-xstr" title="Permalink to this headline">¶</a></h1>
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+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a></p></li>
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diff --git a/docs/v2.2.0/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html b/docs/v2.2.0/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../user_guide/runtime.html">Deploying Torch-TensorRT Programs</a></li>
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diff --git a/docs/v2.2.0/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html b/docs/v2.2.0/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
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diff --git a/docs/v2.2.0/_cpp_api/dir_cpp_include.html b/docs/v2.2.0/_cpp_api/dir_cpp_include.html
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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/logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</a></li>
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diff --git a/docs/v2.2.0/_cpp_api/dir_cpp_include_torch_tensorrt.html b/docs/v2.2.0/_cpp_api/dir_cpp_include_torch_tensorrt.html
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diff --git a/docs/v2.2.0/_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html b/docs/v2.2.0/_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+  <section id="enum-level">
+<span id="exhale-enum-logging-8h-1a130f65408ad8cbaee060f05e8db69558"></span><h1>Enum Level<a class="headerlink" href="#enum-level" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
+</ul>
+<section id="enum-documentation">
+<h2>Enum Documentation<a class="headerlink" href="#enum-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5LevelE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Level</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5LevelE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Emum for setting message severity </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level15kINTERNAL_ERRORE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a9cb66846a4b6da3a54a00dc1471f49db"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kINTERNAL_ERROR</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level15kINTERNAL_ERRORE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Only print messages for internal errors. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level6kERRORE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a4e69ac7a513b3ec45e04be00b614bb1c"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kERROR</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level6kERRORE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print all internal errors and errors (default) </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level8kWARNINGE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a6cfdce2a1b702d1c93b210dfa2e510c8"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kWARNING</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level8kWARNINGE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print warnings and errors. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level5kINFOE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a762a21c4c39c9214fdc85008587096bc"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kINFO</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level5kINFOE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print all info, warnings and errors. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level6kDEBUGE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a9c330d1bdbebdb7506897806426959d1"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kDEBUG</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level6kDEBUGE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print all debug info, info, warnings and errors. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging5Level6kGRAPHE">
+<span class="target" id="logging_8h_1a130f65408ad8cbaee060f05e8db69558a97098a2a97f4840a7d50b285703ff929"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kGRAPH</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging5Level6kGRAPHE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Print everything including the intermediate graphs of the lowering phase. </p>
+</dd></dl>
+
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diff --git a/docs/v2.2.0/_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html b/docs/v2.2.0/_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html
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diff --git a/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.html b/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.html
new file mode 100644
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@@ -0,0 +1,809 @@
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<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>
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+</ul>
+<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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+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="file-logging-h">
+<span id="file-cpp-include-torch-tensorrt-logging-h"></span><h1>File logging.h<a class="headerlink" href="#file-logging-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt"><span class="std std-ref">Parent directory</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt</span></code>)</p>
+<div class="contents local topic" id="contents">
+<p class="topic-title">Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#definition-cpp-include-torch-tensorrt-logging-h" id="id1">Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/logging.h</span></code>)</a></p></li>
+<li><p><a class="reference internal" href="#includes" id="id2">Includes</a></p></li>
+<li><p><a class="reference internal" href="#included-by" id="id3">Included By</a></p></li>
+<li><p><a class="reference internal" href="#namespaces" id="id4">Namespaces</a></p></li>
+<li><p><a class="reference internal" href="#enums" id="id5">Enums</a></p></li>
+<li><p><a class="reference internal" href="#functions" id="id6">Functions</a></p></li>
+</ul>
+</div>
+<section id="definition-cpp-include-torch-tensorrt-logging-h">
+<h2>Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/logging.h</span></code>)<a class="headerlink" href="#definition-cpp-include-torch-tensorrt-logging-h" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="program_listing_file_cpp_include_torch_tensorrt_logging.h.html">Program Listing for File logging.h</a></li>
+</ul>
+</div>
+</section>
+<section id="includes">
+<h2>Includes<a class="headerlink" href="#includes" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">string</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt/macros.h</span></code> (<a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a>)</p></li>
+</ul>
+</section>
+<section id="included-by">
+<h2>Included By<a class="headerlink" href="#included-by" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+</section>
+<section id="namespaces">
+<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="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="namespace_torch_tensorrt__logging.html#namespace-torch-tensorrt-logging"><span class="std std-ref">Namespace torch_tensorrt::logging</span></a></p></li>
+</ul>
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#exhale-enum-logging-8h-1a130f65408ad8cbaee060f05e8db69558"><span class="std std-ref">Enum Level</span></a></p></li>
+</ul>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#exhale-function-logging-8h-1a56e110feaaba2c3fd44bd201fd21a76a"><span class="std std-ref">Function torch_tensorrt::logging::get_is_colored_output_on</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#exhale-function-logging-8h-1a0593f776f469c20469e2f729fc7861a3"><span class="std std-ref">Function torch_tensorrt::logging::get_logging_prefix</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#exhale-function-logging-8h-1a0c012cb374addd90eb1f42eaec570650"><span class="std std-ref">Function torch_tensorrt::logging::get_reportable_log_level</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#exhale-function-logging-8h-1ac46ac0901cb97e3ae6e93b45f24e90b8"><span class="std std-ref">Function torch_tensorrt::logging::log</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#exhale-function-logging-8h-1ad2efd47b6c3689e58ccc595680579ae5"><span class="std std-ref">Function torch_tensorrt::logging::set_is_colored_output_on</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#exhale-function-logging-8h-1af8f3443813315af7901903d25dd495cc"><span class="std std-ref">Function torch_tensorrt::logging::set_logging_prefix</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#exhale-function-logging-8h-1a7cb50492421ea9de4e3db895819df6f2"><span class="std std-ref">Function torch_tensorrt::logging::set_reportable_log_level</span></a></p></li>
+</ul>
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diff --git a/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html b/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html
new file mode 100644
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+++ b/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.html
@@ -0,0 +1,795 @@
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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>
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+<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/ptq.html">Post Training Quantization (PTQ)</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>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<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="file-macros-h">
+<span id="file-cpp-include-torch-tensorrt-macros-h"></span><h1>File macros.h<a class="headerlink" href="#file-macros-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt"><span class="std std-ref">Parent directory</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt</span></code>)</p>
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+<ul class="simple">
+<li><p><a class="reference internal" href="#definition-cpp-include-torch-tensorrt-macros-h" id="id1">Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/macros.h</span></code>)</a></p></li>
+<li><p><a class="reference internal" href="#included-by" id="id2">Included By</a></p></li>
+<li><p><a class="reference internal" href="#namespaces" id="id3">Namespaces</a></p></li>
+<li><p><a class="reference internal" href="#defines" id="id4">Defines</a></p></li>
+</ul>
+</div>
+<section id="definition-cpp-include-torch-tensorrt-macros-h">
+<h2>Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/macros.h</span></code>)<a class="headerlink" href="#definition-cpp-include-torch-tensorrt-macros-h" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="program_listing_file_cpp_include_torch_tensorrt_macros.h.html">Program Listing for File macros.h</a></li>
+</ul>
+</div>
+</section>
+<section id="included-by">
+<h2>Included By<a class="headerlink" href="#included-by" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
+<li><p><a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+<li><p><a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+</section>
+<section id="namespaces">
+<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="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="namespace_torch_tensorrt__torchscript.html#namespace-torch-tensorrt-torchscript"><span class="std std-ref">Namespace torch_tensorrt::torchscript</span></a></p></li>
+</ul>
+</section>
+<section id="defines">
+<h2>Defines<a class="headerlink" href="#defines" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#exhale-define-macros-8h-1a18d295a837ac71add5578860b55e5502"><span class="std std-ref">Define STR</span></a></p></li>
+<li><p><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html#exhale-define-macros-8h-1a31398a6d4d27e28817afb0f0139e909e"><span class="std std-ref">Define TORCH_TENSORRT_MAJOR_VERSION</span></a></p></li>
+<li><p><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html#exhale-define-macros-8h-1a35703561b26b1a9d2738ad7d58b27827"><span class="std std-ref">Define TORCH_TENSORRT_MINOR_VERSION</span></a></p></li>
+<li><p><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html#exhale-define-macros-8h-1a282fd3c0b1c3a215148ae372070e1268"><span class="std std-ref">Define TORCH_TENSORRT_PATCH_VERSION</span></a></p></li>
+<li><p><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html#exhale-define-macros-8h-1adad592a7b1b7eed529cdf6acd584c883"><span class="std std-ref">Define TORCH_TENSORRT_VERSION</span></a></p></li>
+<li><p><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html#exhale-define-macros-8h-1abd1465eb38256d3f22cc1426b23d516b"><span class="std std-ref">Define TORCHTRT_API</span></a></p></li>
+<li><p><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html#exhale-define-macros-8h-1ad19939408f7be171a74a89928b36eb59"><span class="std std-ref">Define TORCHTRT_HIDDEN</span></a></p></li>
+<li><p><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html#exhale-define-macros-8h-1abe87b341f562fd1cf40b7672e4d759da"><span class="std std-ref">Define XSTR</span></a></p></li>
+</ul>
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diff --git a/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.html b/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.html
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diff --git a/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.html b/docs/v2.2.0/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.html
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+<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>
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+  <section id="file-torch-tensorrt-h">
+<span id="file-cpp-include-torch-tensorrt-torch-tensorrt-h"></span><h1>File torch_tensorrt.h<a class="headerlink" href="#file-torch-tensorrt-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt"><span class="std std-ref">Parent directory</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt</span></code>)</p>
+<div class="contents local topic" id="contents">
+<p class="topic-title">Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#definition-cpp-include-torch-tensorrt-torch-tensorrt-h" id="id1">Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/torch_tensorrt.h</span></code>)</a></p></li>
+<li><p><a class="reference internal" href="#includes" id="id2">Includes</a></p></li>
+<li><p><a class="reference internal" href="#namespaces" id="id3">Namespaces</a></p></li>
+<li><p><a class="reference internal" href="#classes" id="id4">Classes</a></p></li>
+<li><p><a class="reference internal" href="#enums" id="id5">Enums</a></p></li>
+<li><p><a class="reference internal" href="#functions" id="id6">Functions</a></p></li>
+</ul>
+</div>
+<section id="definition-cpp-include-torch-tensorrt-torch-tensorrt-h">
+<h2>Definition (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/torch_tensorrt.h</span></code>)<a class="headerlink" href="#definition-cpp-include-torch-tensorrt-torch-tensorrt-h" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.html">Program Listing for File torch_tensorrt.h</a></li>
+</ul>
+</div>
+</section>
+<section id="includes">
+<h2>Includes<a class="headerlink" href="#includes" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">cuda_runtime.h</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">iostream</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">memory</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">set</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">string</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch/custom_class.h</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt/macros.h</span></code> (<a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">File macros.h</span></a>)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">vector</span></code></p></li>
+</ul>
+</section>
+<section id="namespaces">
+<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="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="namespace_torch_tensorrt__torchscript.html#namespace-torch-tensorrt-torchscript"><span class="std std-ref">Namespace torch_tensorrt::torchscript</span></a></p></li>
+</ul>
+</section>
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#exhale-struct-structtorch-tensorrt-1-1device"><span class="std std-ref">Struct Device</span></a></p></li>
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html#exhale-struct-structtorch-tensorrt-1-1graphinputs"><span class="std std-ref">Struct GraphInputs</span></a></p></li>
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#exhale-struct-structtorch-tensorrt-1-1input"><span class="std std-ref">Struct Input</span></a></p></li>
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#exhale-struct-structtorch-tensorrt-1-1torchscript-1-1compilespec"><span class="std std-ref">Struct CompileSpec</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#exhale-class-classtorch-tensorrt-1-1datatype"><span class="std std-ref">Class DataType</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#exhale-class-classtorch-tensorrt-1-1device-1-1devicetype"><span class="std std-ref">Class Device::DeviceType</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#exhale-class-classtorch-tensorrt-1-1tensorformat"><span class="std std-ref">Class TensorFormat</span></a></p></li>
+</ul>
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#exhale-enum-torch-tensorrt-8h-1a3fbe5d72e4fc624dbd038853079620eb"><span class="std std-ref">Enum EngineCapability</span></a></p></li>
+</ul>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#exhale-function-torch-tensorrt-8h-1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1"><span class="std std-ref">Function torch_tensorrt::dump_build_info</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#exhale-function-torch-tensorrt-8h-1ac4ab8313ae72c2c899ea31548b528528"><span class="std std-ref">Function torch_tensorrt::get_build_info</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#exhale-function-torch-tensorrt-8h-1ad1acd06eaeaffbbcf6e7ebf426891384"><span class="std std-ref">Function torch_tensorrt::set_device</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#exhale-function-torch-tensorrt-8h-1a5b405fd3bf3c8fc2e2a54cbbab979797"><span class="std std-ref">Function torch_tensorrt::torchscript::check_method_operator_support</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#exhale-function-torch-tensorrt-8h-1a6e19490a08fb1553c9dd347a5ae79db9"><span class="std std-ref">Function torch_tensorrt::torchscript::compile</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#exhale-function-torch-tensorrt-8h-1ae8d56472106eeef37fbe51ff7f40c9b2"><span class="std std-ref">Function torch_tensorrt::torchscript::convert_method_to_trt_engine</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.html#exhale-function-torch-tensorrt-8h-1a81f9783517335dda877d8cfcf38987c9"><span class="std std-ref">Function torch_tensorrt::torchscript::embed_engine_in_new_module</span></a></p></li>
+</ul>
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+</section>
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+  <section id="function-torch-tensorrt-logging-get-logging-prefix">
+<span id="exhale-function-logging-8h-1a0593f776f469c20469e2f729fc7861a3"></span><h1>Function torch_tensorrt::logging::get_logging_prefix<a class="headerlink" href="#function-torch-tensorrt-logging-get-logging-prefix" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
+</ul>
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+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging18get_logging_prefixEv">
+<span class="target" id="logging_8h_1a0593f776f469c20469e2f729fc7861a3"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">get_logging_prefix</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging18get_logging_prefixEv" title="Permalink to this definition">¶</a><br /></dt>
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diff --git a/docs/v2.2.0/_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html b/docs/v2.2.0/_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html
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+<span id="exhale-function-logging-8h-1a0c012cb374addd90eb1f42eaec570650"></span><h1>Function torch_tensorrt::logging::get_reportable_log_level<a class="headerlink" href="#function-torch-tensorrt-logging-get-reportable-log-level" title="Permalink to this headline">¶</a></h1>
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+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging24get_reportable_log_levelEv">
+<span class="target" id="logging_8h_1a0c012cb374addd90eb1f42eaec570650"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5LevelE" title="torch_tensorrt::logging::Level"><span class="n"><span class="pre">Level</span></span></a><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">get_reportable_log_level</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging24get_reportable_log_levelEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Get the current reportable log level. </p>
+<dl class="field-list simple">
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diff --git a/docs/v2.2.0/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html b/docs/v2.2.0/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html
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diff --git a/docs/v2.2.0/_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html b/docs/v2.2.0/_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html
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+<span id="exhale-function-logging-8h-1a7cb50492421ea9de4e3db895819df6f2"></span><h1>Function torch_tensorrt::logging::set_reportable_log_level<a class="headerlink" href="#function-torch-tensorrt-logging-set-reportable-log-level" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging24set_reportable_log_levelE5Level">
+<span class="target" id="logging_8h_1a7cb50492421ea9de4e3db895819df6f2"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">set_reportable_log_level</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5LevelE" title="torch_tensorrt::logging::Level"><span class="n"><span class="pre">Level</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">lvl</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging24set_reportable_log_levelE5Level" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sets the level that logging information needs to be to be added to the log. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>lvl</strong> – torch_tensorrt::logging::Level - Level that messages need to be at or above to be added to the log </p>
+</dd>
+</dl>
+</dd></dl>
+
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+<span id="exhale-function-logging-8h-1ac46ac0901cb97e3ae6e93b45f24e90b8"></span><h1>Function torch_tensorrt::logging::log<a class="headerlink" href="#function-torch-tensorrt-logging-log" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
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+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging3logE5LevelNSt6stringE">
+<span class="target" id="logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">log</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5LevelE" title="torch_tensorrt::logging::Level"><span class="n"><span class="pre">Level</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">lvl</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">msg</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging3logE5LevelNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Adds a message to the global log. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>lvl</strong> – torch_tensorrt::logging::Level - Severity of the message </p></li>
+<li><p><strong>msg</strong> – std::string - Message to be logged </p></li>
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diff --git a/docs/v2.2.0/_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html b/docs/v2.2.0/_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html
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diff --git a/docs/v2.2.0/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.html b/docs/v2.2.0/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.html
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+<span id="exhale-function-logging-8h-1af8f3443813315af7901903d25dd495cc"></span><h1>Function torch_tensorrt::logging::set_logging_prefix<a class="headerlink" href="#function-torch-tensorrt-logging-set-logging-prefix" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">File logging.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt7logging18set_logging_prefixENSt6stringE">
+<span class="target" id="logging_8h_1af8f3443813315af7901903d25dd495cc"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">logging</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">set_logging_prefix</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">prefix</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt7logging18set_logging_prefixENSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
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+  <section id="template-function-torch-tensorrt-ptq-make-int8-cache-calibrator">
+<span id="exhale-function-ptq-8h-1a226e3c83379d1012cde8578c1c86b16c"></span><h1>Template Function torch_tensorrt::ptq::make_int8_cache_calibrator<a class="headerlink" href="#template-function-torch-tensorrt-ptq-make-int8-cache-calibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a226e3c83379d1012cde8578c1c86b16c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE" title="torch_tensorrt::ptq::Int8CacheCalibrator"><span class="n"><span class="pre">Int8CacheCalibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE" title="torch_tensorrt::ptq::make_int8_cache_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_cache_calibrator</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>A factory to build a post training quantization calibrator from a torch dataloader that only uses the calibration cache. </p>
+<p>Creates a calibrator to use for post training quantization which reads from a previously created calibration cache, therefore you can have a calibration cache generating program that requires a dataloader and a dataset, then save the cache to use later in a different program that needs to calibrate from scratch and not have the dataset dependency. However, the network should also be recalibrated if its structure changes, or the input data set changes, and it is the responsibility of the application to ensure this.</p>
+<p>By default the returned calibrator uses TensorRT Entropy v2 algorithm to perform calibration. This is recommended for feed forward networks You can override the algorithm selection (such as to use the MinMax Calibrator recomended for NLP tasks) by calling make_int8_calibrator with the calibrator class as a template parameter.</p>
+<p>e.g. torch_tensorrt::ptq::make_int8_cache_calibrator&lt;nvinfer1::IInt8MinMaxCalibrator&gt;(calibration_cache_file); </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p>
+</dd>
+<dt class="field-even">Parameters</dt>
+<dd class="field-even"><p><strong>cache_file_path</strong> – const std::string&amp; - Path to read/write calibration cache </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Int8CacheCalibrator&lt;Algorithm&gt; </p>
+</dd>
+</dl>
+</dd></dl>
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diff --git a/docs/v2.2.0/_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html b/docs/v2.2.0/_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html
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+  <section id="template-function-torch-tensorrt-ptq-make-int8-calibrator">
+<span id="exhale-function-ptq-8h-1a6186e305f47c1d94b6130ef6c7f7e178"></span><h1>Template Function torch_tensorrt::ptq::make_int8_calibrator<a class="headerlink" href="#template-function-torch-tensorrt-ptq-make-int8-calibrator" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">File ptq.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb">
+<span class="k"><span class="pre">template</span></span><span class="p"><span class="pre">&lt;</span></span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Algorithm</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8EntropyCalibrator2</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="k"><span class="pre">typename</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DataLoader</span></span></span><span class="p"><span class="pre">&gt;</span></span><br /><span class="target" id="ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE" title="torch_tensorrt::ptq::Int8Calibrator"><span class="n"><span class="pre">Int8Calibrator</span></span></a><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::Algorithm"><span class="n"><span class="pre">Algorithm</span></span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ptq</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">make_int8_calibrator</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="torch_tensorrt::ptq::make_int8_calibrator::DataLoader"><span class="n"><span class="pre">DataLoader</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dataloader</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">cache_file_path</span></span>, <span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">use_cache</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>A factory to build a post training quantization calibrator from a torch dataloader. </p>
+<p>Creates a calibrator to use for post training quantization. By default the returned calibrator uses TensorRT Entropy v2 algorithm to perform calibration. This is recommended for feed forward networks. You can override the algorithm selection (such as to use the MinMax Calibrator recomended for NLP tasks) by calling make_int8_calibrator with the calibrator class as a template parameter.</p>
+<p>e.g. <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::ptq::make_int8_calibrator&lt;nvinfer1::IInt8MinMaxCalibrator&gt;(std::move(calibration_dataloader),</span> <span class="pre">calibration_cache_file,</span> <span class="pre">use_cache);</span></code> </p>
+<dl class="field-list simple">
+<dt class="field-odd">Template Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>Algorithm</strong> – class nvinfer1::IInt8Calibrator (Default: nvinfer1::IInt8EntropyCalibrator2) - Algorithm to use </p></li>
+<li><p><strong>DataLoader</strong> – std::unique_ptr&lt;torch::data::DataLoader&gt; - DataLoader type </p></li>
+</ul>
+</dd>
+<dt class="field-even">Parameters</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>dataloader</strong> – std::unique_ptr&lt;torch::data::DataLoader&gt; - DataLoader containing data </p></li>
+<li><p><strong>cache_file_path</strong> – const std::string&amp; - Path to read/write calibration cache </p></li>
+<li><p><strong>use_cache</strong> – bool - use calibration cache </p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Int8Calibrator&lt;Algorithm, DataLoader&gt; </p>
+</dd>
+</dl>
+</dd></dl>
+
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diff --git a/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html b/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+  <section id="function-torch-tensorrt-torchscript-check-method-operator-support">
+<span id="exhale-function-torch-tensorrt-8h-1a5b405fd3bf3c8fc2e2a54cbbab979797"></span><h1>Function torch_tensorrt::torchscript::check_method_operator_support<a class="headerlink" href="#function-torch-tensorrt-torchscript-check-method-operator-support" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript29check_method_operator_supportERKN5torch3jit6ModuleENSt6stringE">
+<span class="target" id="torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">check_method_operator_support</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">module</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">method_name</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript29check_method_operator_supportERKN5torch3jit6ModuleENSt6stringE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Check to see if a module is fully supported by the compiler. </p>
+<p>
+Takes a module and a method name and checks if the method graph contains purely convertable operators</p>
+<p>Will print out a list of unsupported operators if the graph is unsupported</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> – torch::jit::script::Module - Existing TorchScript module </p></li>
+<li><p><strong>method_name</strong> – std::string - Name of method to compile</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>bool: Method is supported by Torch-TensorRT.TorchScript </p>
+</dd>
+</dl>
+</dd></dl>
+
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+  <section id="function-torch-tensorrt-torchscript-compile">
+<span id="exhale-function-torch-tensorrt-8h-1a6e19490a08fb1553c9dd347a5ae79db9"></span><h1>Function torch_tensorrt::torchscript::compile<a class="headerlink" href="#function-torch-tensorrt-torchscript-compile" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript7compileERKN5torch3jit6ModuleE11CompileSpec">
+<span class="target" id="torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">compile</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">module</span></span>, <a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpecE" title="torch_tensorrt::torchscript::CompileSpec"><span class="n"><span class="pre">CompileSpec</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">info</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript7compileERKN5torch3jit6ModuleE11CompileSpec" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT. </p>
+<p>
+Takes a existing TorchScript module and a set of settings to configure the compiler and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
+<p>Converts specifically the forward method of a TorchScript Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> – torch::jit::Module - Existing TorchScript module </p></li>
+<li><p><strong>info</strong> – torch_tensorrt::CompileSpec - Compilation settings</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>: A new module trageting a TensorRT engine </p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
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diff --git a/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.html b/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.html
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+<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>
+</ul>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+  <section id="function-torch-tensorrt-torchscript-embed-engine-in-new-module">
+<span id="exhale-function-torch-tensorrt-8h-1a81f9783517335dda877d8cfcf38987c9"></span><h1>Function torch_tensorrt::torchscript::embed_engine_in_new_module<a class="headerlink" href="#function-torch-tensorrt-torchscript-embed-engine-in-new-module" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript26embed_engine_in_new_moduleERKNSt6stringE6DeviceRKNSt6vectorINSt6stringEEERKNSt6vectorINSt6stringEEE">
+<span class="target" id="torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">embed_engine_in_new_module</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">engine</span></span>, <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6DeviceE" title="torch_tensorrt::Device"><span class="n"><span class="pre">Device</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">device</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">input_binding_names</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">(</span></span><span class="p"><span class="pre">)</span></span>, <span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">output_binding_names</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">(</span></span><span class="p"><span class="pre">)</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript26embed_engine_in_new_moduleERKNSt6stringE6DeviceRKNSt6vectorINSt6stringEEERKNSt6vectorINSt6stringEEE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Take a previously created TensorRT engine and embed it in in a TorchScript module. </p>
+<p>
+Takes a pre-built serialized TensorRT engine and embeds it in a TorchScript module. Registers execution of the engine as the forward method of the module Forward is defined as: forward(Tensor[]) -&gt; Tensor[]</p>
+<p>If binding names not specified TensorRT bindings must have names with the following format:<ul class="simple">
+<li><p>[symbol].[index in input / output array] ex.</p></li>
+<li><p>[x.0, x.1, x.2] -&gt; [y.0]</p></li>
+</ul>
+</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>engine</strong> – std::string - Pre-built serialized TensorRT engine </p></li>
+<li><p><strong>device</strong> – CompileSepc::Device - <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> information </p></li>
+<li><p><strong>input_binding_names</strong> – std::vector&lt;std::string&gt; - Name of TensorRT bindings in order passed in by original PyTorch function (defaults to assuming convention below) </p></li>
+<li><p><strong>output_binding_names</strong> – std::vector&lt;std::string&gt; - Name of TensorRT bindings in order returned by original PyTorch function (defaults to assuming convention below)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>: A new module targeting a TensorRT engine </p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+
+
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diff --git a/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html b/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html
new file mode 100644
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+<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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diff --git a/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html b/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html
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+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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diff --git a/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html b/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html
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@@ -0,0 +1,771 @@
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+<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<span id="exhale-function-torch-tensorrt-8h-1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1"></span><h1>Function torch_tensorrt::dump_build_info<a class="headerlink" href="#function-torch-tensorrt-dump-build-info" title="Permalink to this headline">¶</a></h1>
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+<span class="target" id="torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="kt"><span class="pre">void</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">dump_build_info</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt15dump_build_infoEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Dump the version information for Torch-TensorRT including base libtorch and TensorRT versions to stdout. </p>
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diff --git a/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html b/docs/v2.2.0/_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
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+<ul class="current">
+<li class="toctree-l1 current"><a class="reference internal" href="torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</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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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+<span id="exhale-function-torch-tensorrt-8h-1ae8d56472106eeef37fbe51ff7f40c9b2"></span><h1>Function torch_tensorrt::torchscript::convert_method_to_trt_engine<a class="headerlink" href="#function-torch-tensorrt-torchscript-convert-method-to-trt-engine" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="function-documentation">
+<h2>Function Documentation<a class="headerlink" href="#function-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript28convert_method_to_trt_engineERKN5torch3jit6ModuleENSt6stringE11CompileSpec">
+<span class="target" id="torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">convert_method_to_trt_engine</span></span></span><span class="sig-paren">(</span><span class="k"><span class="pre">const</span></span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Module</span></span><span class="w"> </span><span class="p"><span class="pre">&amp;</span></span><span class="n sig-param"><span class="pre">module</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">method_name</span></span>, <a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpecE" title="torch_tensorrt::torchscript::CompileSpec"><span class="n"><span class="pre">CompileSpec</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">info</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript28convert_method_to_trt_engineERKN5torch3jit6ModuleENSt6stringE11CompileSpec" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Compile a TorchScript method for NVIDIA GPUs using TensorRT. </p>
+<p>
+Takes a existing TorchScript module and a set of settings to configure the compiler and will convert selected method to a serialized TensorRT engine which can be run with TensorRT</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> – torch::jit::Module - Existing TorchScript module </p></li>
+<li><p><strong>method_name</strong> – std::string - Name of method to compile </p></li>
+<li><p><strong>info</strong> – torch_tensorrt::CompileSpec - Compilation settings</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>: std::string: Serialized TensorRT engine equivilant to the method graph </p>
+</dd>
+</dl>
+</dd></dl>
+
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../user_guide/ptq.html">Post Training Quantization (PTQ)</a></li>
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+<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>
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+  <section id="namespace-torch-tensorrt">
+<span id="id1"></span><h1>Namespace torch_tensorrt<a class="headerlink" href="#namespace-torch-tensorrt" title="Permalink to this headline">¶</a></h1>
+<div class="contents local topic" id="contents">
+<p class="topic-title">Contents</p>
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+</div>
+<section id="namespaces">
+<h2>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="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="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="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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+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#exhale-struct-structtorch-tensorrt-1-1device"><span class="std std-ref">Struct Device</span></a></p></li>
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html#exhale-struct-structtorch-tensorrt-1-1graphinputs"><span class="std std-ref">Struct GraphInputs</span></a></p></li>
+<li><p><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#exhale-struct-structtorch-tensorrt-1-1input"><span class="std std-ref">Struct Input</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#exhale-class-classtorch-tensorrt-1-1datatype"><span class="std std-ref">Class DataType</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#exhale-class-classtorch-tensorrt-1-1device-1-1devicetype"><span class="std std-ref">Class Device::DeviceType</span></a></p></li>
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#exhale-class-classtorch-tensorrt-1-1tensorformat"><span class="std std-ref">Class TensorFormat</span></a></p></li>
+</ul>
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#exhale-enum-torch-tensorrt-8h-1a3fbe5d72e4fc624dbd038853079620eb"><span class="std std-ref">Enum EngineCapability</span></a></p></li>
+</ul>
+</section>
+<section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#exhale-function-torch-tensorrt-8h-1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1"><span class="std std-ref">Function torch_tensorrt::dump_build_info</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#exhale-function-torch-tensorrt-8h-1ac4ab8313ae72c2c899ea31548b528528"><span class="std std-ref">Function torch_tensorrt::get_build_info</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#exhale-function-torch-tensorrt-8h-1ad1acd06eaeaffbbcf6e7ebf426891384"><span class="std std-ref">Function torch_tensorrt::set_device</span></a></p></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
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+<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>
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+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../user_guide/ptq.html">Post Training Quantization (PTQ)</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>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling a Transformer using torch.compile and TensorRT</a></li>
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+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
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+<li><p><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#exhale-function-logging-8h-1a56e110feaaba2c3fd44bd201fd21a76a"><span class="std std-ref">Function torch_tensorrt::logging::get_is_colored_output_on</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#exhale-function-logging-8h-1a0593f776f469c20469e2f729fc7861a3"><span class="std std-ref">Function torch_tensorrt::logging::get_logging_prefix</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#exhale-function-logging-8h-1a0c012cb374addd90eb1f42eaec570650"><span class="std std-ref">Function torch_tensorrt::logging::get_reportable_log_level</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#exhale-function-logging-8h-1ac46ac0901cb97e3ae6e93b45f24e90b8"><span class="std std-ref">Function torch_tensorrt::logging::log</span></a></p></li>
+<li><p><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#exhale-function-logging-8h-1ad2efd47b6c3689e58ccc595680579ae5"><span class="std std-ref">Function torch_tensorrt::logging::set_is_colored_output_on</span></a></p></li>
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+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1int8calibrator"><span class="std std-ref">Template Class Int8Calibrator</span></a></p></li>
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diff --git a/docs/v2.2.0/_cpp_api/namespace_torch_tensorrt__torchscript.html b/docs/v2.2.0/_cpp_api/namespace_torch_tensorrt__torchscript.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
+</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">User Guide</span></p>
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+<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/ptq.html">Post Training Quantization (PTQ)</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>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling a Transformer using torch.compile and TensorRT</a></li>
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+<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>
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+<span id="id1"></span><h1>Namespace torch_tensorrt::torchscript<a class="headerlink" href="#namespace-torch-tensorrt-torchscript" title="Permalink to this headline">¶</a></h1>
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+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#exhale-function-torch-tensorrt-8h-1a5b405fd3bf3c8fc2e2a54cbbab979797"><span class="std std-ref">Function torch_tensorrt::torchscript::check_method_operator_support</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#exhale-function-torch-tensorrt-8h-1a6e19490a08fb1553c9dd347a5ae79db9"><span class="std std-ref">Function torch_tensorrt::torchscript::compile</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#exhale-function-torch-tensorrt-8h-1ae8d56472106eeef37fbe51ff7f40c9b2"><span class="std std-ref">Function torch_tensorrt::torchscript::convert_method_to_trt_engine</span></a></p></li>
+<li><p><a class="reference internal" href="function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.html#exhale-function-torch-tensorrt-8h-1a81f9783517335dda877d8cfcf38987c9"><span class="std std-ref">Function torch_tensorrt::torchscript::embed_engine_in_new_module</span></a></p></li>
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diff --git a/docs/v2.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.html b/docs/v2.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.html
new file mode 100644
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+              
+  <section id="program-listing-for-file-logging-h">
+<span id="program-listing-file-cpp-include-torch-tensorrt-logging-h"></span><h1>Program Listing for File logging.h<a class="headerlink" href="#program-listing-for-file-logging-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging-h"><span class="std std-ref">Return to documentation for file</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/logging.h</span></code>)</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cm">/*</span>
+<span class="cm"> * Copyright (c) NVIDIA Corporation.</span>
+<span class="cm"> * All rights reserved.</span>
+<span class="cm"> *</span>
+<span class="cm"> * This library is licensed under the BSD-style license found in the</span>
+<span class="cm"> * LICENSE file in the root directory of this source tree.</span>
+<span class="cm"> */</span>
+<span class="cp">#pragma once</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;string&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/macros.h&quot;</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">logging</span><span class="w"> </span><span class="p">{</span>
+<span class="k">enum</span><span class="w"> </span><span class="nc">Level</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="n">kINTERNAL_ERROR</span><span class="p">,</span>
+<span class="w">  </span><span class="n">kERROR</span><span class="p">,</span>
+<span class="w">  </span><span class="n">kWARNING</span><span class="p">,</span>
+<span class="w">  </span><span class="n">kINFO</span><span class="p">,</span>
+<span class="w">  </span><span class="n">kDEBUG</span><span class="p">,</span>
+<span class="w">  </span><span class="n">kGRAPH</span><span class="p">,</span>
+<span class="p">};</span>
+
+<span class="c1">// Are these ones necessary for the user?</span>
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">get_logging_prefix</span><span class="p">();</span>
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">set_logging_prefix</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">prefix</span><span class="p">);</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="w"> </span><span class="n">lvl</span><span class="p">);</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">set_is_colored_output_on</span><span class="p">(</span><span class="kt">bool</span><span class="w"> </span><span class="n">colored_output_on</span><span class="p">);</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">Level</span><span class="w"> </span><span class="n">get_reportable_log_level</span><span class="p">();</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">get_is_colored_output_on</span><span class="p">();</span>
+
+<span class="c1">// Dont know if we want this?</span>
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="w"> </span><span class="n">lvl</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">msg</span><span class="p">);</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace logging</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+</pre></div>
+</div>
+</section>
+
+
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diff --git a/docs/v2.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html b/docs/v2.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.html
new file mode 100644
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@@ -0,0 +1,784 @@
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+              
+  <section id="program-listing-for-file-macros-h">
+<span id="program-listing-file-cpp-include-torch-tensorrt-macros-h"></span><h1>Program Listing for File macros.h<a class="headerlink" href="#program-listing-for-file-macros-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros-h"><span class="std std-ref">Return to documentation for file</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/macros.h</span></code>)</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cm">/*</span>
+<span class="cm"> * Copyright (c) NVIDIA Corporation.</span>
+<span class="cm"> * All rights reserved.</span>
+<span class="cm"> *</span>
+<span class="cm"> * This library is licensed under the BSD-style license found in the</span>
+<span class="cm"> * LICENSE file in the root directory of this source tree.</span>
+<span class="cm"> */</span>
+<span class="cp">#pragma once</span>
+
+<span class="cp">#if defined(USE_CMAKE_GENERATED_EXPORT_HEADER)</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;torch_tensorrt_export.h&gt;</span>
+<span class="cp">#else</span>
+<span class="cp">#if defined(__GNUC__)</span>
+<span class="cp">#define TORCHTRT_API __attribute__((__visibility__(&quot;default&quot;)))</span>
+<span class="cp">#define TORCHTRT_HIDDEN __attribute__((__visibility__(&quot;hidden&quot;)))</span>
+<span class="cp">#else</span>
+<span class="cp">#define TORCHTRT_API</span>
+<span class="cp">#define TORCHTRT_HIDDEN</span>
+<span class="cp">#endif </span><span class="c1">// defined(__GNUC__)</span>
+<span class="cp">#endif </span><span class="c1">// defined(USE_CMAKE_GENERATED_EXPORT_HEADER)</span>
+
+<span class="c1">// Does this need to be gaurded or something?</span>
+<span class="cp">#define XSTR(x) #x</span>
+<span class="cp">#define STR(x) XSTR(x)</span>
+
+<span class="cp">#define TORCH_TENSORRT_MAJOR_VERSION 2</span>
+<span class="cp">#define TORCH_TENSORRT_MINOR_VERSION 2</span>
+<span class="cp">#define TORCH_TENSORRT_PATCH_VERSION 0</span>
+<span class="cp">#define TORCH_TENSORRT_VERSION      \</span>
+<span class="cp">  STR(TORCH_TENSORRT_MAJOR_VERSION) \</span>
+<span class="cp">  &quot;.&quot; STR(TORCH_TENSORRT_MINOR_VERSION) &quot;.&quot; STR(TORCH_TENSORRT_PATCH_VERSION)</span>
+
+<span class="c1">// Setup namespace aliases for ease of use</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torchscript</span><span class="w"> </span><span class="p">{}</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">ts</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nn">torchscript</span><span class="p">;</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torchtrt</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="p">;</span>
+</pre></div>
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diff --git a/docs/v2.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html b/docs/v2.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html
new file mode 100644
index 0000000000..0a6153558f
--- /dev/null
+++ b/docs/v2.2.0/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.html
@@ -0,0 +1,924 @@
+
+
+
+<!DOCTYPE html>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="program-listing-for-file-ptq-h">
+<span id="program-listing-file-cpp-include-torch-tensorrt-ptq-h"></span><h1>Program Listing for File ptq.h<a class="headerlink" href="#program-listing-for-file-ptq-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq-h"><span class="std std-ref">Return to documentation for file</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/ptq.h</span></code>)</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cm">/*</span>
+<span class="cm"> * Copyright (c) NVIDIA Corporation.</span>
+<span class="cm"> * All rights reserved.</span>
+<span class="cm"> *</span>
+<span class="cm"> * This library is licensed under the BSD-style license found in the</span>
+<span class="cm"> * LICENSE file in the root directory of this source tree.</span>
+<span class="cm"> */</span>
+<span class="cp">#pragma once</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;fstream&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iterator&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;memory&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;sstream&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;string&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;vector&gt;</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;NvInfer.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/torch.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/logging.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/macros.h&quot;</span>
+
+<span class="cp">#ifndef DOXYGEN_SHOULD_SKIP_THIS</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">nvinfer1</span><span class="w"> </span><span class="p">{</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">IInt8Calibrator</span><span class="p">;</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">IInt8EntropyCalibrator2</span><span class="p">;</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace nvinfer1</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">ptq</span><span class="w"> </span><span class="p">{</span>
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">get_batch_impl</span><span class="p">(</span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">bindings</span><span class="p">[],</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">names</span><span class="p">[],</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">nbBindings</span><span class="p">,</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span><span class="w"> </span><span class="n">data</span><span class="p">);</span>
+<span class="p">}</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+<span class="cp">#endif </span><span class="c1">// DOXYGEN_SHOULD_SKIP_THIS</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">ptq</span><span class="w"> </span><span class="p">{</span>
+
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Algorithm</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">DataLoaderUniquePtr</span><span class="o">&gt;</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">Int8Calibrator</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Algorithm</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="n">DataLoader</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">DataLoaderUniquePtr</span><span class="o">::</span><span class="n">element_type</span><span class="p">;</span>
+<span class="w">  </span><span class="k">using</span><span class="w"> </span><span class="n">Batch</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">DataLoader</span><span class="o">::</span><span class="n">super</span><span class="o">::</span><span class="n">BatchType</span><span class="p">;</span>
+
+<span class="w"> </span><span class="k">public</span><span class="o">:</span>
+<span class="w">  </span><span class="n">Int8Calibrator</span><span class="p">(</span><span class="n">DataLoaderUniquePtr</span><span class="w"> </span><span class="n">dataloader</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">,</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">use_cache</span><span class="p">)</span>
+<span class="w">      </span><span class="o">:</span><span class="w"> </span><span class="n">dataloader_</span><span class="p">(</span><span class="n">dataloader</span><span class="p">.</span><span class="n">get</span><span class="p">()),</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">(</span><span class="n">cache_file_path</span><span class="p">),</span><span class="w"> </span><span class="n">use_cache_</span><span class="p">(</span><span class="n">use_cache</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">for</span><span class="w"> </span><span class="p">(</span><span class="k">auto</span><span class="w"> </span><span class="n">batch</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="o">*</span><span class="n">dataloader_</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="n">batched_data_</span><span class="p">.</span><span class="n">push_back</span><span class="p">(</span><span class="n">batch</span><span class="p">.</span><span class="n">data</span><span class="p">);</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="w">    </span><span class="n">it_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">batched_data_</span><span class="p">.</span><span class="n">begin</span><span class="p">();</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">getBatchSize</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="c1">// HACK: Torch-TensorRT only uses explict batch sizing, INT8 Calibrator does not</span>
+<span class="w">    </span><span class="c1">// work when reporting the batch size here and having explicity batching.</span>
+<span class="w">    </span><span class="c1">// So we just report batch size 1 (warnings will still be printed out).</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
+<span class="w">    </span><span class="c1">// return static_cast&lt;int&gt;(dataloader_-&gt;options().batch_size);</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">getBatch</span><span class="p">(</span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">bindings</span><span class="p">[],</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">names</span><span class="p">[],</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">nbBindings</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">it_</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">batched_data_</span><span class="p">.</span><span class="n">end</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="k">auto</span><span class="w"> </span><span class="n">status</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">get_batch_impl</span><span class="p">(</span><span class="n">bindings</span><span class="p">,</span><span class="w"> </span><span class="n">names</span><span class="p">,</span><span class="w"> </span><span class="n">nbBindings</span><span class="p">,</span><span class="w"> </span><span class="o">*</span><span class="n">it_</span><span class="p">);</span>
+<span class="w">      </span><span class="n">it_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">++</span><span class="n">it_</span><span class="p">;</span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">status</span><span class="p">;</span>
+<span class="w">    </span><span class="p">}</span><span class="w"> </span><span class="k">else</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="c1">// Reset iterator if incase calibrator is going to be used again</span>
+<span class="w">      </span><span class="n">it_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">batched_data_</span><span class="p">.</span><span class="n">begin</span><span class="p">();</span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">readCalibrationCache</span><span class="p">(</span><span class="kt">size_t</span><span class="o">&amp;</span><span class="w"> </span><span class="n">length</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">use_cache_</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">stringstream</span><span class="w"> </span><span class="n">ss</span><span class="p">;</span>
+<span class="w">      </span><span class="n">ss</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Reading Calibration Cache from &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span>
+<span class="w">      </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kINFO</span><span class="p">,</span><span class="w"> </span><span class="n">ss</span><span class="p">.</span><span class="n">str</span><span class="p">());</span>
+
+<span class="w">      </span><span class="n">cache_</span><span class="p">.</span><span class="n">clear</span><span class="p">();</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="nf">input</span><span class="p">(</span><span class="n">cache_file_path_</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ios</span><span class="o">::</span><span class="n">binary</span><span class="p">);</span>
+<span class="w">      </span><span class="n">input</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">noskipws</span><span class="p">;</span>
+<span class="w">      </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">input</span><span class="p">.</span><span class="n">good</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
+<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">copy</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">istream_iterator</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="p">(</span><span class="n">input</span><span class="p">),</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">istream_iterator</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="p">(),</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">back_inserter</span><span class="p">(</span><span class="n">cache_</span><span class="p">));</span>
+<span class="w">        </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kDEBUG</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;Cache read&quot;</span><span class="p">);</span>
+<span class="w">      </span><span class="p">}</span>
+<span class="w">      </span><span class="n">length</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cache_</span><span class="p">.</span><span class="n">size</span><span class="p">();</span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">length</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="n">cache_</span><span class="p">.</span><span class="n">data</span><span class="p">()</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">nullptr</span><span class="p">;</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="k">nullptr</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="n">writeCalibrationCache</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">cache</span><span class="p">,</span><span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">length</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="nf">cache_file</span><span class="p">(</span><span class="n">cache_file_path_</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ios</span><span class="o">::</span><span class="n">binary</span><span class="p">);</span>
+<span class="w">    </span><span class="n">cache_file</span><span class="p">.</span><span class="n">write</span><span class="p">(</span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*&gt;</span><span class="p">(</span><span class="n">cache</span><span class="p">),</span><span class="w"> </span><span class="n">length</span><span class="p">);</span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">stringstream</span><span class="w"> </span><span class="n">ss</span><span class="p">;</span>
+<span class="w">    </span><span class="n">ss</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Saved Calibration Cache to &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span>
+<span class="w">    </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kINFO</span><span class="p">,</span><span class="w"> </span><span class="n">ss</span><span class="p">.</span><span class="n">str</span><span class="p">());</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="k">operator</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*&gt;</span><span class="p">(</span><span class="k">this</span><span class="p">);</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span>
+<span class="w">  </span><span class="n">DataLoader</span><span class="o">*</span><span class="w"> </span><span class="n">dataloader_</span><span class="p">;</span>
+<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span>
+<span class="w">  </span><span class="kt">size_t</span><span class="w"> </span><span class="n">cache_size_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">use_cache_</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="w"> </span><span class="n">cache_</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&gt;</span><span class="w"> </span><span class="n">batched_data_</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&gt;::</span><span class="n">iterator</span><span class="w"> </span><span class="n">it_</span><span class="p">;</span>
+<span class="p">};</span>
+
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Algorithm</span><span class="o">&gt;</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">Int8CacheCalibrator</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Algorithm</span><span class="w"> </span><span class="p">{</span>
+<span class="w"> </span><span class="k">public</span><span class="o">:</span>
+<span class="w">  </span><span class="n">Int8CacheCalibrator</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">(</span><span class="n">cache_file_path</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span>
+
+<span class="w">  </span><span class="kt">int</span><span class="w"> </span><span class="n">getBatchSize</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="c1">// HACK: Torch-TensorRT only uses explict batch sizing, INT8 Calibrator does not</span>
+<span class="w">    </span><span class="c1">// work when reporting the batch size here and having explicity batching.</span>
+<span class="w">    </span><span class="c1">// So we just report batch size 1 (warnings will still be printed out).</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">getBatch</span><span class="p">(</span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">bindings</span><span class="p">[],</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">names</span><span class="p">[],</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">nbBindings</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">readCalibrationCache</span><span class="p">(</span><span class="kt">size_t</span><span class="o">&amp;</span><span class="w"> </span><span class="n">length</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">stringstream</span><span class="w"> </span><span class="n">ss</span><span class="p">;</span>
+<span class="w">    </span><span class="n">ss</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Reading Calibration Cache from &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span>
+<span class="w">    </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kINFO</span><span class="p">,</span><span class="w"> </span><span class="n">ss</span><span class="p">.</span><span class="n">str</span><span class="p">());</span>
+
+<span class="w">    </span><span class="n">cache_</span><span class="p">.</span><span class="n">clear</span><span class="p">();</span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ifstream</span><span class="w"> </span><span class="nf">input</span><span class="p">(</span><span class="n">cache_file_path_</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ios</span><span class="o">::</span><span class="n">binary</span><span class="p">);</span>
+<span class="w">    </span><span class="n">input</span><span class="w"> </span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">noskipws</span><span class="p">;</span>
+<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="p">(</span><span class="n">input</span><span class="p">.</span><span class="n">good</span><span class="p">())</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">copy</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">istream_iterator</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="p">(</span><span class="n">input</span><span class="p">),</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">istream_iterator</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="p">(),</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">back_inserter</span><span class="p">(</span><span class="n">cache_</span><span class="p">));</span>
+<span class="w">      </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kDEBUG</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;Cache read&quot;</span><span class="p">);</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="w">    </span><span class="n">length</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">cache_</span><span class="p">.</span><span class="n">size</span><span class="p">();</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">length</span><span class="w"> </span><span class="o">?</span><span class="w"> </span><span class="n">cache_</span><span class="p">.</span><span class="n">data</span><span class="p">()</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">nullptr</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="kt">void</span><span class="w"> </span><span class="n">writeCalibrationCache</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">void</span><span class="o">*</span><span class="w"> </span><span class="n">cache</span><span class="p">,</span><span class="w"> </span><span class="kt">size_t</span><span class="w"> </span><span class="n">length</span><span class="p">)</span><span class="w"> </span><span class="k">noexcept</span><span class="w"> </span><span class="k">override</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="nf">cache_file</span><span class="p">(</span><span class="n">cache_file_path_</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ios</span><span class="o">::</span><span class="n">binary</span><span class="p">);</span>
+<span class="w">    </span><span class="n">cache_file</span><span class="p">.</span><span class="n">write</span><span class="p">(</span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*&gt;</span><span class="p">(</span><span class="n">cache</span><span class="p">),</span><span class="w"> </span><span class="n">length</span><span class="p">);</span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">stringstream</span><span class="w"> </span><span class="n">ss</span><span class="p">;</span>
+<span class="w">    </span><span class="n">ss</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;Saved Calibration Cache to &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span>
+<span class="w">    </span><span class="n">logging</span><span class="o">::</span><span class="n">log</span><span class="p">(</span><span class="n">logging</span><span class="o">::</span><span class="n">Level</span><span class="o">::</span><span class="n">kINFO</span><span class="p">,</span><span class="w"> </span><span class="n">ss</span><span class="p">.</span><span class="n">str</span><span class="p">());</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w">  </span><span class="k">operator</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="k">reinterpret_cast</span><span class="o">&lt;</span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*&gt;</span><span class="p">(</span><span class="k">this</span><span class="p">);</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span>
+<span class="w">  </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path_</span><span class="p">;</span>
+<span class="w">  </span><span class="kt">size_t</span><span class="w"> </span><span class="n">cache_size_</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">char</span><span class="o">&gt;</span><span class="w"> </span><span class="n">cache_</span><span class="p">;</span>
+<span class="p">};</span>
+
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Algorithm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8EntropyCalibrator2</span><span class="p">,</span><span class="w"> </span><span class="k">typename</span><span class="w"> </span><span class="nc">DataLoader</span><span class="o">&gt;</span>
+<span class="kr">inline</span><span class="w"> </span><span class="n">Int8Calibrator</span><span class="o">&lt;</span><span class="n">Algorithm</span><span class="p">,</span><span class="w"> </span><span class="n">DataLoader</span><span class="o">&gt;</span><span class="w"> </span><span class="n">make_int8_calibrator</span><span class="p">(</span>
+<span class="w">    </span><span class="n">DataLoader</span><span class="w"> </span><span class="n">dataloader</span><span class="p">,</span>
+<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">,</span>
+<span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="n">use_cache</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="n">Int8Calibrator</span><span class="o">&lt;</span><span class="n">Algorithm</span><span class="p">,</span><span class="w"> </span><span class="n">DataLoader</span><span class="o">&gt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">,</span><span class="w"> </span><span class="n">use_cache</span><span class="p">);</span>
+<span class="p">}</span>
+
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">typename</span><span class="w"> </span><span class="nc">Algorithm</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8EntropyCalibrator2</span><span class="o">&gt;</span>
+<span class="kr">inline</span><span class="w"> </span><span class="n">Int8CacheCalibrator</span><span class="o">&lt;</span><span class="n">Algorithm</span><span class="o">&gt;</span><span class="w"> </span><span class="n">make_int8_cache_calibrator</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">cache_file_path</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="k">return</span><span class="w"> </span><span class="n">Int8CacheCalibrator</span><span class="o">&lt;</span><span class="n">Algorithm</span><span class="o">&gt;</span><span class="p">(</span><span class="n">cache_file_path</span><span class="p">);</span>
+<span class="p">}</span>
+
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace ptq</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+</pre></div>
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+</section>
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+<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>
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+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
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+              
+  <section id="program-listing-for-file-torch-tensorrt-h">
+<span id="program-listing-file-cpp-include-torch-tensorrt-torch-tensorrt-h"></span><h1>Program Listing for File torch_tensorrt.h<a class="headerlink" href="#program-listing-for-file-torch-tensorrt-h" title="Permalink to this headline">¶</a></h1>
+<p>↰ <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">Return to documentation for file</span></a> (<code class="docutils literal notranslate"><span class="pre">cpp/include/torch_tensorrt/torch_tensorrt.h</span></code>)</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cm">/*</span>
+<span class="cm"> * Copyright (c) NVIDIA Corporation.</span>
+<span class="cm"> * All rights reserved.</span>
+<span class="cm"> *</span>
+<span class="cm"> * This library is licensed under the BSD-style license found in the</span>
+<span class="cm"> * LICENSE file in the root directory of this source tree.</span>
+<span class="cm"> */</span>
+
+<span class="cp">#pragma once</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cuda_runtime.h&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;memory&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;set&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;string&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;vector&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/custom_class.h&quot;</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/macros.h&quot;</span>
+
+<span class="c1">// Just include the .h?</span>
+<span class="cp">#ifndef DOXYGEN_SHOULD_SKIP_THIS</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch</span><span class="w"> </span><span class="p">{</span>
+<span class="k">namespace</span><span class="w"> </span><span class="nn">jit</span><span class="w"> </span><span class="p">{</span>
+<span class="k">struct</span><span class="w"> </span><span class="nc">Graph</span><span class="p">;</span>
+<span class="k">struct</span><span class="w"> </span><span class="nc">Module</span><span class="p">;</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace jit</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">c10</span><span class="w"> </span><span class="p">{</span>
+<span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">DeviceType</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="p">;</span>
+<span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">ScalarType</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="p">;</span>
+<span class="k">template</span><span class="w"> </span><span class="o">&lt;</span><span class="k">class</span><span class="o">&gt;</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">ArrayRef</span><span class="p">;</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace c10</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">nvinfer1</span><span class="w"> </span><span class="p">{</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">IInt8Calibrator</span><span class="p">;</span>
+<span class="p">}</span>
+<span class="cp">#endif </span><span class="c1">// DOXYGEN_SHOULD_SKIP_THIS</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torch_tensorrt</span><span class="w"> </span><span class="p">{</span>
+<span class="k">class</span><span class="w"> </span><span class="nc">DataType</span><span class="w"> </span><span class="p">{</span>
+<span class="w"> </span><span class="k">public</span><span class="o">:</span>
+<span class="w">  </span><span class="k">enum</span><span class="w"> </span><span class="nc">Value</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="n">kLong</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kDouble</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kFloat</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kHalf</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kChar</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kInt</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kBool</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kUnknown</span>
+<span class="w">  </span><span class="p">};</span>
+
+<span class="w">  </span><span class="n">DataType</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="n">DataType</span><span class="p">(</span><span class="n">Value</span><span class="w"> </span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">value</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">DataType</span><span class="p">(</span><span class="n">c10</span><span class="o">::</span><span class="n">ScalarType</span><span class="w"> </span><span class="n">t</span><span class="p">);</span>
+<span class="w">  </span><span class="k">operator</span><span class="w"> </span><span class="n">Value</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="w">  </span><span class="k">explicit</span><span class="w"> </span><span class="k">operator</span><span class="w"> </span><span class="kt">bool</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">DataType</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">DataType</span><span class="o">::</span><span class="n">Value</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">DataType</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">DataType</span><span class="o">::</span><span class="n">Value</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span>
+<span class="w">  </span><span class="k">friend</span><span class="w"> </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">&lt;&lt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="n">os</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">DataType</span><span class="o">&amp;</span><span class="w"> </span><span class="n">dtype</span><span class="p">);</span>
+<span class="w">  </span><span class="n">Value</span><span class="w"> </span><span class="n">value</span><span class="p">;</span>
+<span class="p">};</span>
+
+<span class="k">struct</span><span class="w"> </span><span class="nc">Device</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="k">class</span><span class="w"> </span><span class="nc">DeviceType</span><span class="w"> </span><span class="p">{</span>
+<span class="w">   </span><span class="k">public</span><span class="o">:</span>
+<span class="w">    </span><span class="k">enum</span><span class="w"> </span><span class="nc">Value</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="n">kGPU</span><span class="p">,</span>
+<span class="w">      </span><span class="n">kDLA</span><span class="p">,</span>
+<span class="w">    </span><span class="p">};</span>
+
+<span class="w">    </span><span class="n">DeviceType</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span>
+<span class="w">    </span><span class="k">constexpr</span><span class="w"> </span><span class="n">DeviceType</span><span class="p">(</span><span class="n">Value</span><span class="w"> </span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">value</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span>
+<span class="w">    </span><span class="n">DeviceType</span><span class="p">(</span><span class="n">c10</span><span class="o">::</span><span class="n">DeviceType</span><span class="w"> </span><span class="n">t</span><span class="p">);</span>
+<span class="w">    </span><span class="k">operator</span><span class="w"> </span><span class="n">Value</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="p">;</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="w">    </span><span class="k">explicit</span><span class="w"> </span><span class="k">operator</span><span class="w"> </span><span class="kt">bool</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
+<span class="w">    </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">DeviceType</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="w">    </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">DeviceType</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">      </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span>
+<span class="w">    </span><span class="p">}</span>
+
+<span class="w">   </span><span class="k">private</span><span class="o">:</span>
+<span class="w">    </span><span class="n">Value</span><span class="w"> </span><span class="n">value</span><span class="p">;</span>
+<span class="w">  </span><span class="p">};</span>
+
+<span class="w">  </span><span class="n">DeviceType</span><span class="w"> </span><span class="n">device_type</span><span class="p">;</span>
+
+<span class="w">  </span><span class="cm">/*</span>
+<span class="cm">   * Target gpu id</span>
+<span class="cm">   */</span>
+<span class="w">  </span><span class="kt">int64_t</span><span class="w"> </span><span class="n">gpu_id</span><span class="p">;</span>
+
+<span class="w">  </span><span class="cm">/*</span>
+<span class="cm">   * When using DLA core on NVIDIA AGX platforms gpu_id should be set as Xavier device</span>
+<span class="cm">   */</span>
+<span class="w">  </span><span class="kt">int64_t</span><span class="w"> </span><span class="n">dla_core</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="p">;</span>
+
+<span class="w">  </span><span class="n">Device</span><span class="p">()</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">device_type</span><span class="p">(</span><span class="n">DeviceType</span><span class="o">::</span><span class="n">kGPU</span><span class="p">),</span><span class="w"> </span><span class="n">gpu_id</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="n">dla_core</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="p">(</span><span class="nb">false</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span>
+<span class="p">};</span>
+
+<span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">EngineCapability</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="n">kSTANDARD</span><span class="p">,</span>
+<span class="w">  </span><span class="n">kSAFETY</span><span class="p">,</span>
+<span class="w">  </span><span class="n">kDLA_STANDALONE</span><span class="p">,</span>
+<span class="p">};</span>
+
+<span class="k">class</span><span class="w"> </span><span class="nc">TensorFormat</span><span class="w"> </span><span class="p">{</span>
+<span class="w"> </span><span class="k">public</span><span class="o">:</span>
+<span class="w">  </span><span class="k">enum</span><span class="w"> </span><span class="nc">Value</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="kt">int8_t</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="n">kContiguous</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kChannelsLast</span><span class="p">,</span>
+<span class="w">    </span><span class="n">kUnknown</span><span class="p">,</span>
+<span class="w">  </span><span class="p">};</span>
+
+<span class="w">  </span><span class="n">TensorFormat</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">default</span><span class="p">;</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="n">TensorFormat</span><span class="p">(</span><span class="n">Value</span><span class="w"> </span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">value</span><span class="p">(</span><span class="n">t</span><span class="p">)</span><span class="w"> </span><span class="p">{}</span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">TensorFormat</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">MemoryFormat</span><span class="w"> </span><span class="n">t</span><span class="p">);</span>
+<span class="w">  </span><span class="k">operator</span><span class="w"> </span><span class="n">Value</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="w">  </span><span class="k">explicit</span><span class="w"> </span><span class="k">operator</span><span class="w"> </span><span class="kt">bool</span><span class="p">()</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">delete</span><span class="p">;</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">==</span><span class="p">(</span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">Value</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">==</span><span class="w"> </span><span class="n">other</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">.</span><span class="n">value</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="w">  </span><span class="k">constexpr</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="k">operator</span><span class="o">!=</span><span class="p">(</span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">Value</span><span class="w"> </span><span class="n">other</span><span class="p">)</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">value</span><span class="w"> </span><span class="o">!=</span><span class="w"> </span><span class="n">other</span><span class="p">;</span>
+<span class="w">  </span><span class="p">}</span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span>
+<span class="w">  </span><span class="k">friend</span><span class="w"> </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">&lt;&lt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="n">os</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">&amp;</span><span class="w"> </span><span class="n">format</span><span class="p">);</span>
+<span class="w">  </span><span class="n">Value</span><span class="w"> </span><span class="n">value</span><span class="p">;</span>
+<span class="p">};</span>
+
+<span class="k">struct</span><span class="w"> </span><span class="nc">Input</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">CustomClassHolder</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">;</span>
+<span class="w">  </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">;</span>
+<span class="w">  </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">;</span>
+
+<span class="w">  </span><span class="n">Input</span><span class="p">()</span><span class="w"> </span><span class="p">{}</span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">Input</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span><span class="w"> </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span><span class="w"> </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">min_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">opt_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">max_shape</span><span class="p">,</span>
+<span class="w">      </span><span class="n">DataType</span><span class="w"> </span><span class="n">dtype</span><span class="p">,</span>
+<span class="w">      </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">double</span><span class="o">&gt;</span><span class="w"> </span><span class="n">tensor_domain</span><span class="p">,</span>
+<span class="w">      </span><span class="n">TensorFormat</span><span class="w"> </span><span class="n">format</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">TensorFormat</span><span class="o">::</span><span class="n">kContiguous</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">Input</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">tensor</span><span class="p">);</span>
+
+<span class="w"> </span><span class="k">private</span><span class="o">:</span>
+<span class="w">  </span><span class="k">friend</span><span class="w"> </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="k">operator</span><span class="o">&lt;&lt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">ostream</span><span class="o">&amp;</span><span class="w"> </span><span class="n">os</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">Input</span><span class="o">&amp;</span><span class="w"> </span><span class="n">input</span><span class="p">);</span>
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">input_is_dynamic</span><span class="p">;</span>
+<span class="p">};</span>
+
+<span class="k">struct</span><span class="w"> </span><span class="nc">GraphInputs</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">IValue</span><span class="w"> </span><span class="n">input_signature</span><span class="p">;</span><span class="w"> </span><span class="c1">// nested Input, full input spec</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">Input</span><span class="o">&gt;</span><span class="w"> </span><span class="n">inputs</span><span class="p">;</span><span class="w"> </span><span class="c1">// flatten input spec</span>
+<span class="p">};</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">get_build_info</span><span class="p">();</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">dump_build_info</span><span class="p">();</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">void</span><span class="w"> </span><span class="n">set_device</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="kt">int</span><span class="w"> </span><span class="n">gpu_id</span><span class="p">);</span>
+
+<span class="k">namespace</span><span class="w"> </span><span class="nn">torchscript</span><span class="w"> </span><span class="p">{</span>
+<span class="k">struct</span><span class="w"> </span><span class="nc">CompileSpec</span><span class="w"> </span><span class="p">{</span>
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">CompileSpec</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">fixed_sizes</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">CompileSpec</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">c10</span><span class="o">::</span><span class="n">ArrayRef</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">fixed_sizes</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">CompileSpec</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">Input</span><span class="o">&gt;</span><span class="w"> </span><span class="n">inputs</span><span class="p">);</span>
+
+<span class="w">  </span><span class="n">TORCHTRT_API</span><span class="w"> </span><span class="nf">CompileSpec</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">IValue</span><span class="w"> </span><span class="n">input_signature</span><span class="p">);</span>
+<span class="w">  </span><span class="c1">// Defaults should reflect TensorRT defaults for BuilderConfig</span>
+
+<span class="w">  </span><span class="n">GraphInputs</span><span class="w"> </span><span class="n">graph_inputs</span><span class="p">;</span>
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">set</span><span class="o">&lt;</span><span class="n">DataType</span><span class="o">&gt;</span><span class="w"> </span><span class="n">enabled_precisions</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">DataType</span><span class="o">::</span><span class="n">kFloat</span><span class="p">};</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">disable_tf32</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">sparse_weights</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">refit</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">debug</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">truncate_long_and_double</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">allow_shape_tensors</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+
+<span class="w">  </span><span class="n">Device</span><span class="w"> </span><span class="n">device</span><span class="p">;</span>
+
+<span class="w">  </span><span class="n">EngineCapability</span><span class="w"> </span><span class="n">capability</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">EngineCapability</span><span class="o">::</span><span class="n">kSTANDARD</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">num_avg_timing_iters</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">workspace_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">dla_sram_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1048576</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">dla_local_dram_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1073741824</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">dla_global_dram_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">536870912</span><span class="p">;</span>
+
+<span class="w">  </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8Calibrator</span><span class="o">*</span><span class="w"> </span><span class="n">ptq_calibrator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="k">nullptr</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">bool</span><span class="w"> </span><span class="n">require_full_compilation</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">false</span><span class="p">;</span>
+
+<span class="w">  </span><span class="kt">uint64_t</span><span class="w"> </span><span class="n">min_block_size</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">3</span><span class="p">;</span>
+
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span><span class="w"> </span><span class="n">torch_executed_ops</span><span class="p">;</span>
+
+<span class="w">  </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span><span class="w"> </span><span class="n">torch_executed_modules</span><span class="p">;</span>
+<span class="p">};</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="n">check_method_operator_support</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="o">&amp;</span><span class="w"> </span><span class="k">module</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">method_name</span><span class="p">);</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="n">compile</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="o">&amp;</span><span class="w"> </span><span class="k">module</span><span class="p">,</span><span class="w"> </span><span class="n">CompileSpec</span><span class="w"> </span><span class="n">info</span><span class="p">);</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">convert_method_to_trt_engine</span><span class="p">(</span>
+<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="o">&amp;</span><span class="w"> </span><span class="k">module</span><span class="p">,</span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="w"> </span><span class="n">method_name</span><span class="p">,</span>
+<span class="w">    </span><span class="n">CompileSpec</span><span class="w"> </span><span class="n">info</span><span class="p">);</span>
+
+<span class="n">TORCHTRT_API</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="n">embed_engine_in_new_module</span><span class="p">(</span>
+<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">engine</span><span class="p">,</span>
+<span class="w">    </span><span class="n">Device</span><span class="w"> </span><span class="n">device</span><span class="p">,</span>
+<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">input_binding_names</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span><span class="p">(),</span>
+<span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;&amp;</span><span class="w"> </span><span class="n">output_binding_names</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&gt;</span><span class="p">());</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torchscript</span>
+<span class="p">}</span><span class="w"> </span><span class="c1">// namespace torch_tensorrt</span>
+</pre></div>
+</div>
+</section>
+
+
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diff --git a/docs/v2.2.0/_cpp_api/structtorch__tensorrt_1_1Device.html b/docs/v2.2.0/_cpp_api/structtorch__tensorrt_1_1Device.html
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a></li>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</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>
+<ul>
+<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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+              
+  <section id="struct-device">
+<span id="exhale-struct-structtorch-tensorrt-1-1device"></span><h1>Struct Device<a class="headerlink" href="#struct-device" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="nested-relationships">
+<h2>Nested Relationships<a class="headerlink" href="#nested-relationships" title="Permalink to this headline">¶</a></h2>
+<section id="nested-types">
+<h3>Nested Types<a class="headerlink" href="#nested-types" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#exhale-class-classtorch-tensorrt-1-1device-1-1devicetype"><span class="std std-ref">Class Device::DeviceType</span></a></p></li>
+</ul>
+</section>
+</section>
+<section id="struct-documentation">
+<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp struct">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6DeviceE">
+<span class="target" id="structtorch__tensorrt_1_1Device"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Device</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6DeviceE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Setting data structure for Target device. </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device6DeviceEv">
+<span class="target" id="structtorch__tensorrt_1_1Device_1afb5a5a4833237e1a106094c95cafbdf8"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Device</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device6DeviceEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Constructor for <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> structure </p>
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device11device_typeE">
+<span class="target" id="structtorch__tensorrt_1_1Device_1abaa77ed18f126bf6cae9305b6660c9d6"></span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">device_type</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device11device_typeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Setting data structure for device This struct will hold Target device related parameters such as device_type, gpu_id, dla_core. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device6gpu_idE">
+<span class="target" id="structtorch__tensorrt_1_1Device_1a5498d9e53e3e6598e1ea0b4158ae9105"></span><span class="n"><span class="pre">int64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">gpu_id</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device6gpu_idE" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device8dla_coreE">
+<span class="target" id="structtorch__tensorrt_1_1Device_1ae1d7bcca2367c3eff77a725c5be8d549"></span><span class="n"><span class="pre">int64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dla_core</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device8dla_coreE" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device18allow_gpu_fallbackE">
+<span class="target" id="structtorch__tensorrt_1_1Device_1a0ccaae1d02a1078175fd3a91e74fc398"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">allow_gpu_fallback</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt6Device18allow_gpu_fallbackE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>(Only used when targeting DLA (device)) Lets engine run layers on GPU if they are not supported on DLA </p>
+</dd></dl>
+
+</div>
+<dl class="cpp class">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceTypeE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType"></span><span class="k"><span class="pre">class</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><br /></dt>
+<dd><p>Supported <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Types that can be used with TensorRT engines</p>
+<p>This class is compatable with c10::DeviceTypes (but will check for TRT support) but the only applicable value is at::kCUDA, which maps to <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a></p>
+<p>To use the <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#classtorch__tensorrt_1_1DataType"><span class="std std-ref">DataType</span></a> class itself, interface using the enum vs. normal instatination</p>
+<p>ex. torch_tensorrt::DeviceType type = <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a>; </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-types">Public Types</p>
+<dl class="cpp enum">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350"></span><span class="k"><span class="pre">enum</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Value</span></span></span><br /></dt>
+<dd><p>Underlying enum class to support the <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a> Class</p>
+<p>In the case that you need to use the <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a> class itself, interface using this enum vs. normal instatination</p>
+<p>ex. torch_tensorrt::DeviceType type = <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"><span class="std std-ref">DeviceType::kGPU</span></a>; </p>
+<p><em>Values:</em></p>
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350a7096aeeb6744e578d5afd98e334f866c"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kGPU</span></span></span><br /></dt>
+<dd><p>Target GPU to run engine. </p>
+</dd></dl>
+
+<dl class="cpp enumerator">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a23c188ccbece0f27609e8b1c7bf7e350aa13fb3ec20096f0d0c205b67ca05c98e"></span><span class="k"><span class="pre">enumerator</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">kDLA</span></span></span><br /></dt>
+<dd><p>Target DLA to run engine. </p>
+</dd></dl>
+
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1afe573ee91b95b81be8a0f1503a00ca45"></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">default</span></span><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a5a7e6c4eb98321bd53869a8ec08e2a2c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="torch_tensorrt::Device::DeviceType::Value"><span class="n"><span class="pre">Value</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object from internal enum. </p>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a499573dd56df8fa1479dcec95dfda711"></span><span class="sig-name descname"><span class="n"><span class="pre">DeviceType</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">DeviceType</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">t</span></span><span class="sig-paren">)</span><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> Type object from torch device enums Note: The only valid value is torch::kCUDA (torch::kCPU is not supported) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>t</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a4afa0b396b7a076e37054754a367f2f4"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE" title="torch_tensorrt::Device::DeviceType::Value"><span class="n"><span class="pre">Value</span></span></a></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><br /></dt>
+<dd><p>Get the internal value from the <a class="reference internal" href="#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> object. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Value </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a1e78ae4780df8bd258ee8703a22d9a4d"></span><span class="k"><span class="pre">explicit</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">delete</span></span><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a0177ee9788edabb89a8cb64234d2a60c"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">==</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><br /></dt>
+<dd><p>Comparison operator for <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType">
+<span class="target" id="classtorch__tensorrt_1_1Device_1_1DeviceType_1a0d8973b436226f17597d94f4e8f63336"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="k"><span class="pre">constexpr</span></span><span class="w"> </span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="k"><span class="pre">operator</span></span><span class="o"><span class="pre">!=</span></span></span><span class="sig-paren">(</span><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE" title="torch_tensorrt::Device::DeviceType"><span class="n"><span class="pre">DeviceType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">other</span></span><span class="sig-paren">)</span><span class="w"> </span><span class="k"><span class="pre">const</span></span><br /></dt>
+<dd><p>Comparison operator for <a class="reference internal" href="#classtorch__tensorrt_1_1Device_1_1DeviceType"><span class="std std-ref">DeviceType</span></a>. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>other</strong> – </p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>true </p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>false </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+</section>
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+<li><a class="reference internal" href="#nested-relationships">Nested Relationships</a><ul>
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diff --git a/docs/v2.2.0/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html b/docs/v2.2.0/_cpp_api/structtorch__tensorrt_1_1GraphInputs.html
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+  <section id="struct-graphinputs">
+<span id="exhale-struct-structtorch-tensorrt-1-1graphinputs"></span><h1>Struct GraphInputs<a class="headerlink" href="#struct-graphinputs" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
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+<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
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+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11GraphInputsE">
+<span class="target" id="structtorch__tensorrt_1_1GraphInputs"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">GraphInputs</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11GraphInputsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>A struct to hold complex inputs. </p>
+<p>This struct can either hold a complex inputs of shape or a flattened one, </p>
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+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
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+<span class="target" id="structtorch__tensorrt_1_1GraphInputs_1a3d2a9ff85bc319c29793b2177085cb0f"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5InputE" title="torch_tensorrt::Input"><span class="n"><span class="pre">Input</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">inputs</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11GraphInputs6inputsE" title="Permalink to this definition">¶</a><br /></dt>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
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+              
+  <section id="struct-input">
+<span id="exhale-struct-structtorch-tensorrt-1-1input"></span><h1>Struct Input<a class="headerlink" href="#struct-input" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="inheritance-relationships">
+<h2>Inheritance Relationships<a class="headerlink" href="#inheritance-relationships" title="Permalink to this headline">¶</a></h2>
+<section id="base-type">
+<h3>Base Type<a class="headerlink" href="#base-type" title="Permalink to this headline">¶</a></h3>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">public</span> <span class="pre">torch::CustomClassHolder</span></code></p></li>
+</ul>
+</section>
+</section>
+<section id="struct-documentation">
+<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp struct">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5InputE">
+<span class="target" id="structtorch__tensorrt_1_1Input"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="w"> </span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="k"><span class="pre">public</span></span><span class="w"> </span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">CustomClassHolder</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5InputE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>A struct to hold an input range (used by TensorRT Optimization profile) </p>
+<p>This struct can either hold a single vector representing an input shape, signifying a static input shape or a set of three input shapes representing the min, optiminal and max input shapes allowed for the engine. </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEv">
+<span class="target" id="structtorch__tensorrt_1_1Input_1ae217f6c7c13be354e03d3f9b5b6b5a5e"></span><span class="k"><span class="pre">inline</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEv" title="Permalink to this definition">¶</a><br /></dt>
+<dd></dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a0328d70f3daf18b425d459689be2591a"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorIdEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a37680c5f9c4cf60554886d6a7a901356"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor_domain</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorIdEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>tensor_domain</strong> – Allowed range for tensor inputs [low, high) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1af8b7efdf0e96b4221742a658b214738b"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1afcef85da60683b3ae525980987f27be2"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor_domain</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from vector, optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>tensor_domain</strong> – Allowed range for tensor inputs [low, high) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a70d54a3caadadab68faad230ff302f23"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEENSt6vectorIdEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a13b156bc402c2510878ed5b95b7c8c69"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor_domain</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEENSt6vectorIdEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>tensor_domain</strong> – Allowed range for tensor inputs [low, high) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a7069ee5a6db2f840423a923d08d08c6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1aab1c2c4c7010cea352927b483294c1de"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor_domain</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for static input size from c10::ArrayRef (the type produced by tensor.sizes()), vector, optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape</strong> – <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> tensor shape </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>tensor_domain</strong> – Allowed range for tensor inputs [low, high) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a6f305b4f28ce0a7c76d39ed61dc9a964"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorIdEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a30701fe073b8704dda1124c959270297"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor_domain</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorIdEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>tensor_domain</strong> – Allowed range for tensor inputs [low, high) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a93370fc5773212005972056184a527a5"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for a dynamic input size from vectors for minimum shape, optimal shape, and max shape supported sizes optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1af4ae7c7d52629e7b05ce35ecad1e930a"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor_domain</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object for a dynamic input size from vectors for minimum shape, optimal shape, and max shape supported sizes optional arguments allow the user to configure expected input shape tensor format. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>tensor_domain</strong> – Allowed range for tensor inputs [low, high) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1abda696b9f2f689af6f3577e51647ad6f"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEENSt6vectorIdEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1aa12df4599d04730d82f30cdd1cdd0929"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor_domain</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEENSt6vectorIdEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. dtype (Expected data type for the input) defaults to PyTorch / traditional TRT convection (FP32 for FP32 only, FP16 for FP32 and FP16, FP32 for Int8) </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>tensor_domain</strong> – Allowed range for tensor inputs [low, high) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a59eb3e2d3455017d19c9b498eefbb35e"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a45b72eca2d91ab101062a1189ad477e9"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">min_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">opt_shape</span></span>, <span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">max_shape</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">dtype</span></span>, <span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor_domain</span></span>, <a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="n sig-param"><span class="pre">format</span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE" title="torch_tensorrt::TensorFormat::kContiguous"><span class="n"><span class="pre">kContiguous</span></span></a><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object dynamic input size from c10::ArrayRef (the type produced by tensor.sizes()) for min, opt, and max supported sizes. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>min_shape</strong> – Minimum shape for input tensor </p></li>
+<li><p><strong>opt_shape</strong> – Target optimization shape for input tensor </p></li>
+<li><p><strong>max_shape</strong> – Maximum acceptible shape for input tensor </p></li>
+<li><p><strong>dtype</strong> – Expected data type for the input (Defaults to the type of the weights in the first tensor calculation if detectable else Float32) </p></li>
+<li><p><strong>tensor_domain</strong> – Allowed range for tensor inputs [low, high) </p></li>
+<li><p><strong>format</strong> – Expected tensor format for the input (Defaults to contiguous) </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a3b472eb488183b47f470507860b49f1c"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">Input</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">at</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">Tensor</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">tensor</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new <a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> spec object using a torch tensor as an example The tensor’s shape, type and layout inform the spec’s values. </p>
+<p>Note: You cannot set dynamic shape through this method, you must use an alternative constructor</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>tensor</strong> – Reference tensor to set shape, type and layout </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9min_shapeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1af95ee167b1e25e1a599a8fc67ffea9ac"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">min_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9min_shapeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Minimum acceptable input size into the engine. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9opt_shapeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a63666d30decf34115f642e75f0cf8256"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">opt_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9opt_shapeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Optimal input size into the engine (size optimized for given kernels accept any size in min max range) </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input9max_shapeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1ad3dfdcaaa9126dcb8dfa08dec085d589"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">max_shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input9max_shapeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Maximum acceptable input size into the engine. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5shapeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a26997c30b04c8013a5afadb181b70377"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">shape</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5shapeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p><a class="reference internal" href="#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> shape to be fed to TensorRT, in the event of a dynamic shape, -1’s will hold the place of variable dimensions </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input5dtypeE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a05d520340d486851d9e8a4797611895e"></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dtype</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input5dtypeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Expected data type for the input. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input6formatE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1ad544c1a1584d2f45bccf0e4963b8d292"></span><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE" title="torch_tensorrt::TensorFormat"><span class="n"><span class="pre">TensorFormat</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">format</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input6formatE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Expected tensor format for the input. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt5Input13tensor_domainE">
+<span class="target" id="structtorch__tensorrt_1_1Input_1a7dce978909f79925de41da652f1d4d32"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="kt"><span class="pre">double</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">tensor_domain</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt5Input13tensor_domainE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Expected allowed domain for tensor input. </p>
+</dd></dl>
+
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diff --git a/docs/v2.2.0/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html b/docs/v2.2.0/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+  <section id="struct-compilespec">
+<span id="exhale-struct-structtorch-tensorrt-1-1torchscript-1-1compilespec"></span><h1>Struct CompileSpec<a class="headerlink" href="#struct-compilespec" title="Permalink to this headline">¶</a></h1>
+<ul class="simple">
+<li><p>Defined in <a class="reference internal" href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt-h"><span class="std std-ref">File torch_tensorrt.h</span></a></p></li>
+</ul>
+<section id="struct-documentation">
+<h2>Struct Documentation<a class="headerlink" href="#struct-documentation" title="Permalink to this headline">¶</a></h2>
+<dl class="cpp struct">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpecE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec"></span><span class="k"><span class="pre">struct</span></span><span class="w"> </span><span class="sig-prename descclassname"><span class="n"><span class="pre">torch_tensorrt</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">torchscript</span></span><span class="p"><span class="pre">::</span></span></span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpecE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Settings data structure for Torch-TensorRT TorchScript compilation </p>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-functions">Public Functions</p>
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorINSt6vectorI7int64_tEEEE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a18fd2eb916e1f5358ae536e6cc064377"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">fixed_sizes</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorINSt6vectorI7int64_tEEEE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Compile Spec object Convienence constructor to set fixed input size from vectors describing size of input tensors. Each entry in the vector represents a input and should be provided in call order. </p>
+<p>This constructor should be use as a convience in the case that all inputs are static sized and you are okay with default input dtype and formats (FP32 for FP32 and INT8 weights, FP16 for FP16 weights, contiguous)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>fixed_sizes</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorIN3c108ArrayRefI7int64_tEEEE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a594ca70c02cf2be7fb0f198ca15a0e73"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">c10</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">ArrayRef</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">int64_t</span></span><span class="p"><span class="pre">&gt;</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">fixed_sizes</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorIN3c108ArrayRefI7int64_tEEEE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Compile Spec object Convienence constructor to set fixed input size from c10::ArrayRef’s (the output of tensor.sizes()) describing size of input tensors. Each entry in the vector represents a input and should be provided in call order. </p>
+<p>This constructor should be use as a convience in the case that all inputs are static sized and you are okay with default input dtype and formats (FP32 for FP32 and INT8 weights, FP16 for FP16 weights, contiguous)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>fixed_sizes</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorI5InputEE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a9599633a5a3e893a6e63dc12980e5be8"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5InputE" title="torch_tensorrt::Input"><span class="n"><span class="pre">Input</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">inputs</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorI5InputEE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Compile Spec object from input ranges. Each entry in the vector represents a input and should be provided in call order. </p>
+<p>Use this constructor to define inputs with dynamic shape, specific input types or tensor formats</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>inputs</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="cpp function">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecEN5torch3jit6IValueE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1af8550292118cd8dd139edebe33b99f8c"></span><span class="pre">TORCHTRT_API</span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">CompileSpec</span></span></span><span class="sig-paren">(</span><span class="n"><span class="pre">torch</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">jit</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IValue</span></span><span class="w"> </span><span class="n sig-param"><span class="pre">input_signature</span></span><span class="sig-paren">)</span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecEN5torch3jit6IValueE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Construct a new Compile Spec object from IValue which represents the nesting of input tensors for a module. </p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>input_signature</strong> – </p>
+</dd>
+</dl>
+</dd></dl>
+
+</div>
+<div class="breathe-sectiondef docutils container">
+<p class="breathe-sectiondef-title rubric" id="breathe-section-title-public-members">Public Members</p>
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec12graph_inputsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a72536728d26e36c8b9d64dd7711fea1f"></span><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html#_CPPv4N14torch_tensorrt11GraphInputsE" title="torch_tensorrt::GraphInputs"><span class="n"><span class="pre">GraphInputs</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">graph_inputs</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec12graph_inputsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Specifications for inputs to the engine, can store a IValue which has stored complex <a class="reference internal" href="structtorch__tensorrt_1_1Input.html#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a> or a flatened <a class="reference internal" href="structtorch__tensorrt_1_1Input.html#structtorch__tensorrt_1_1Input"><span class="std std-ref">Input</span></a>. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec18enabled_precisionsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a83ce8e55ab1e1830a440513fbabae043"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">set</span></span><span class="p"><span class="pre">&lt;</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">enabled_precisions</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="p"><span class="pre">{</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE" title="torch_tensorrt::DataType"><span class="n"><span class="pre">DataType</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value6kFloatE" title="torch_tensorrt::DataType::kFloat"><span class="n"><span class="pre">kFloat</span></span></a><span class="p"><span class="pre">}</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18enabled_precisionsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>The set of precisions TensorRT is allowed to use for kernels during compilation. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec12disable_tf32E">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1ac1d3b92892fbeee1a77c6ec89b451fb2"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">disable_tf32</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec12disable_tf32E" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Prevent Float32 layers from using TF32 data format</p>
+<p>TF32 computes inner products by rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas. This is the behavior of FP32 layers by default. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec14sparse_weightsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a5b4ed74b243c63a708c7bcb8e48ab307"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">sparse_weights</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14sparse_weightsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Enable sparsity for weights of conv and FC layers </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec5refitE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a5aa4e3201f756a2a952f01f08902cd7d"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">refit</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec5refitE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Build a refitable engine </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec5debugE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a973775a29be52d24983b02073c4e1592"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">debug</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec5debugE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Build a debugable engine </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec24truncate_long_and_doubleE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1ad47a41aa623a797c0212a157a4091a16"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">truncate_long_and_double</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec24truncate_long_and_doubleE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Truncate long/double type to int/float type </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec19allow_shape_tensorsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1aefb73212e355cc1edcd35786440ad06e"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">allow_shape_tensors</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec19allow_shape_tensorsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Allow shape tensors (from IShape layer) in the graph </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec6deviceE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a7eec2ecd57b3debd73239a7d0d552ffd"></span><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6DeviceE" title="torch_tensorrt::Device"><span class="n"><span class="pre">Device</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">device</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec6deviceE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Target <a class="reference internal" href="structtorch__tensorrt_1_1Device.html#structtorch__tensorrt_1_1Device"><span class="std std-ref">Device</span></a> </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec10capabilityE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1aa9f0624c0b2a01589bdf67343435b517"></span><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapabilityE" title="torch_tensorrt::EngineCapability"><span class="n"><span class="pre">EngineCapability</span></span></a><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">capability</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapabilityE" title="torch_tensorrt::EngineCapability"><span class="n"><span class="pre">EngineCapability</span></span></a><span class="p"><span class="pre">::</span></span><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapability9kSTANDARDE" title="torch_tensorrt::EngineCapability::kSTANDARD"><span class="n"><span class="pre">kSTANDARD</span></span></a><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec10capabilityE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Sets the restrictions for the engine (CUDA Safety) </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec20num_avg_timing_itersE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a0b08c3de8acd85c6c7d277984aadd63d"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">num_avg_timing_iters</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">1</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec20num_avg_timing_itersE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Number of averaging timing iterations used to select kernels </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec14workspace_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a2dee309060f0bf4052c9b734d91feef1"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">workspace_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">0</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14workspace_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Maximum size of workspace given to TensorRT </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec13dla_sram_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1ab895c21f1ab78b2b2790b6664ea1d0b7"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dla_sram_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">1048576</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec13dla_sram_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Fast software managed RAM used by DLA to communicate within a layer. </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec19dla_local_dram_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a7f98e1909d484589228fce66945b218a"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dla_local_dram_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">1073741824</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec19dla_local_dram_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Host RAM used by DLA to share intermediate tensor data across operations </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec20dla_global_dram_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a3cdc173909775362b3e41efa844b6c89"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">dla_global_dram_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">536870912</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec20dla_global_dram_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>host RAM used by DLA to store weights and metadata for execution </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec14ptq_calibratorE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a4d4a1651c2a1516d96374dedd566f96b"></span><span class="n"><span class="pre">nvinfer1</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">IInt8Calibrator</span></span><span class="w"> </span><span class="p"><span class="pre">*</span></span><span class="sig-name descname"><span class="n"><span class="pre">ptq_calibrator</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">nullptr</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14ptq_calibratorE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Calibration dataloaders for each input for post training quantizatiom </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec24require_full_compilationE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a2b1e0e8adbbaf0192e6d30502f867a85"></span><span class="kt"><span class="pre">bool</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">require_full_compilation</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="k"><span class="pre">false</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec24require_full_compilationE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Require the full module be compiled to TensorRT instead of potentially running unsupported operations in PyTorch </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec14min_block_sizeE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1ad75441730fd7776726a7baed34d8d2bb"></span><span class="n"><span class="pre">uint64_t</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">min_block_size</span></span></span><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="m"><span class="pre">3</span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14min_block_sizeE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>Minimum number of contiguous supported operators to compile a subgraph to TensorRT </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec18torch_executed_opsE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a3c60fb30768398ff1c75b25f5969ebc6"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">torch_executed_ops</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18torch_executed_opsE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True </p>
+</dd></dl>
+
+<dl class="cpp var">
+<dt class="sig sig-object cpp" id="_CPPv4N14torch_tensorrt11torchscript11CompileSpec22torch_executed_modulesE">
+<span class="target" id="structtorch__tensorrt_1_1torchscript_1_1CompileSpec_1a878095a83d812f67e3b6f27085abc43b"></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">vector</span></span><span class="p"><span class="pre">&lt;</span></span><span class="n"><span class="pre">std</span></span><span class="p"><span class="pre">::</span></span><span class="n"><span class="pre">string</span></span><span class="p"><span class="pre">&gt;</span></span><span class="w"> </span><span class="sig-name descname"><span class="n"><span class="pre">torch_executed_modules</span></span></span><a class="headerlink" href="#_CPPv4N14torch_tensorrt11torchscript11CompileSpec22torch_executed_modulesE" title="Permalink to this definition">¶</a><br /></dt>
+<dd><p>List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True </p>
+</dd></dl>
+
+</div>
+</dd></dl>
+
+</section>
+</section>
+
+
+             </article>
+             
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
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+<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>
+</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>
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+<li class="toctree-l1"><a class="reference internal" href="../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../user_guide/using_dla.html">DLA</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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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+              
+  <section id="torch-tensorrt-c-api">
+<h1>Torch-TensorRT C++ API<a class="headerlink" href="#torch-tensorrt-c-api" title="Permalink to this headline">¶</a></h1>
+<section id="class-hierarchy">
+<h2>Class Hierarchy<a class="headerlink" href="#class-hierarchy" title="Permalink to this headline">¶</a></h2>
+<ul class="treeView" id="class-treeView">
+  <li>
+    <ul class="collapsibleList">
+      <li class="lastChild">Namespace <a href="namespace_torch_tensorrt.html#namespace-torch-tensorrt">torch_tensorrt</a><ul><li>Namespace <a href="namespace_torch_tensorrt__logging.html#namespace-torch-tensorrt-logging">torch_tensorrt::logging</a><ul><li class="lastChild">Enum <a href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#exhale-enum-logging-8h-1a130f65408ad8cbaee060f05e8db69558">Level</a></li></ul></li><li>Namespace <a href="namespace_torch_tensorrt__ptq.html#namespace-torch-tensorrt-ptq">torch_tensorrt::ptq</a><ul><li>Template Class <a href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1Int8CacheCalibrator">Int8CacheCalibrator</a></li><li class="lastChild">Template Class <a href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#exhale-class-classtorch-tensorrt-1-1ptq-1-1Int8Calibrator">Int8Calibrator</a></li></ul></li><li>Namespace <a href="namespace_torch_tensorrt__torchscript.html#namespace-torch-tensorrt-torchscript">torch_tensorrt::torchscript</a><ul><li class="lastChild">Struct <a href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#exhale-struct-structtorch-tensorrt-1-1torchscript-1-1CompileSpec">CompileSpec</a></li></ul></li><li>Struct <a href="structtorch__tensorrt_1_1Device.html#exhale-struct-structtorch-tensorrt-1-1Device">Device</a><ul><li class="lastChild">Class <a href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#exhale-class-classtorch-tensorrt-1-1Device-1-1DeviceType">Device::DeviceType</a></li></ul></li><li>Struct <a href="structtorch__tensorrt_1_1GraphInputs.html#exhale-struct-structtorch-tensorrt-1-1GraphInputs">GraphInputs</a></li><li>Struct <a href="structtorch__tensorrt_1_1Input.html#exhale-struct-structtorch-tensorrt-1-1Input">Input</a></li><li>Class <a href="classtorch__tensorrt_1_1DataType.html#exhale-class-classtorch-tensorrt-1-1DataType">DataType</a></li><li>Class <a href="classtorch__tensorrt_1_1TensorFormat.html#exhale-class-classtorch-tensorrt-1-1TensorFormat">TensorFormat</a></li><li class="lastChild">Enum <a href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#exhale-enum-torch-tensorrt-8h-1a3fbe5d72e4fc624dbd038853079620eb">EngineCapability</a></li></ul></li>
+    </ul>
+  </li><!-- only tree view element -->
+</ul><!-- /treeView class-treeView --></section>
+<section id="file-hierarchy">
+<h2>File Hierarchy<a class="headerlink" href="#file-hierarchy" title="Permalink to this headline">¶</a></h2>
+<ul class="treeView" id="file-treeView">
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+    <ul class="collapsibleList">
+      <li class="lastChild">Directory <a href="dir_cpp.html#dir-cpp">cpp</a><ul><li class="lastChild">Directory <a href="dir_cpp_include.html#dir-cpp-include">include</a><ul><li class="lastChild">Directory <a href="dir_cpp_include_torch_tensorrt.html#dir-cpp-include-torch-tensorrt">torch_tensorrt</a><ul><li>File <a href="file_cpp_include_torch_tensorrt_logging.h.html#file-cpp-include-torch-tensorrt-logging.h">logging.h</a></li><li>File <a href="file_cpp_include_torch_tensorrt_macros.h.html#file-cpp-include-torch-tensorrt-macros.h">macros.h</a></li><li>File <a href="file_cpp_include_torch_tensorrt_ptq.h.html#file-cpp-include-torch-tensorrt-ptq.h">ptq.h</a></li><li class="lastChild">File <a href="file_cpp_include_torch_tensorrt_torch_tensorrt.h.html#file-cpp-include-torch-tensorrt-torch-tensorrt.h">torch_tensorrt.h</a></li></ul></li></ul></li></ul></li>
+    </ul>
+  </li><!-- only tree view element -->
+</ul><!-- /treeView file-treeView --></section>
+<section id="full-api">
+<h2>Full API<a class="headerlink" href="#full-api" title="Permalink to this headline">¶</a></h2>
+<section id="namespaces">
+<h3>Namespaces<a class="headerlink" href="#namespaces" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch.html">Namespace torch</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt.html">Namespace torch_tensorrt</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt.html#namespaces">Namespaces</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt.html#functions">Functions</a></li>
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+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__logging.html">Namespace torch_tensorrt::logging</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__logging.html#enums">Enums</a></li>
+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__logging.html#functions">Functions</a></li>
+</ul>
+</li>
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+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__ptq.html#functions">Functions</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html">Namespace torch_tensorrt::torchscript</a><ul>
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+<li class="toctree-l2"><a class="reference internal" href="namespace_torch_tensorrt__torchscript.html#functions">Functions</a></li>
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+</ul>
+</div>
+</section>
+<section id="classes-and-structs">
+<h3>Classes and Structs<a class="headerlink" href="#classes-and-structs" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html">Struct Device</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#nested-relationships">Nested Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#nested-types">Nested Types</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Device.html#struct-documentation">Struct Documentation</a></li>
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+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html">Struct GraphInputs</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1GraphInputs.html#struct-documentation">Struct Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html">Struct Input</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1Input.html#struct-documentation">Struct Documentation</a></li>
+</ul>
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+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html">Struct CompileSpec</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#struct-documentation">Struct Documentation</a></li>
+</ul>
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+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html">Class DataType</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1DataType.html#class-documentation">Class Documentation</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html">Class Device::DeviceType</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#nested-relationships">Nested Relationships</a></li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1Device_1_1DeviceType.html#class-documentation">Class Documentation</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html">Template Class Int8CacheCalibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html">Template Class Int8Calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#inheritance-relationships">Inheritance Relationships</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#base-type">Base Type</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html">Class TensorFormat</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="classtorch__tensorrt_1_1TensorFormat.html#class-documentation">Class Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="enums">
+<h3>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html">Enum EngineCapability</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#enum-documentation">Enum Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html">Enum Level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#enum-documentation">Enum Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="functions">
+<h3>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html">Function torch_tensorrt::dump_build_info</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html">Function torch_tensorrt::get_build_info</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html">Function torch_tensorrt::logging::get_is_colored_output_on</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html">Function torch_tensorrt::logging::get_logging_prefix</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html">Function torch_tensorrt::logging::get_reportable_log_level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html">Function torch_tensorrt::logging::log</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html">Function torch_tensorrt::logging::set_is_colored_output_on</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html">Function torch_tensorrt::logging::set_logging_prefix</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html">Function torch_tensorrt::logging::set_reportable_log_level</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html">Template Function torch_tensorrt::ptq::make_int8_cache_calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html">Template Function torch_tensorrt::ptq::make_int8_calibrator</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html">Function torch_tensorrt::set_device</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html">Function torch_tensorrt::torchscript::check_method_operator_support</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html">Function torch_tensorrt::torchscript::compile</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html">Function torch_tensorrt::torchscript::convert_method_to_trt_engine</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.html">Function torch_tensorrt::torchscript::embed_engine_in_new_module</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.html#function-documentation">Function Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="defines">
+<h3>Defines<a class="headerlink" href="#defines" title="Permalink to this headline">¶</a></h3>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html">Define STR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html">Define TORCH_TENSORRT_MAJOR_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html">Define TORCH_TENSORRT_MINOR_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html">Define TORCH_TENSORRT_PATCH_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html">Define TORCH_TENSORRT_VERSION</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html">Define TORCHTRT_API</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html">Define TORCHTRT_HIDDEN</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html">Define XSTR</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.html#define-documentation">Define Documentation</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
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diff --git a/docs/v2.2.0/_cpp_api/unabridged_orphan.html b/docs/v2.2.0/_cpp_api/unabridged_orphan.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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+<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>
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+<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>
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+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</a></li>
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+<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>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+            <li>
+              <a href="https://pytorch.org/torchrec">TorchRec</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/serve/">TorchServe</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/torchx/">TorchX</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/xla">PyTorch on XLA Devices</a>
+            </li>
+          </ul>
+
+          <li class="resources-mobile-menu-title">
+            Resources
+          </li>
+            
+           <ul class="resources-mobile-menu-items">
+
+            <li>
+              <a href="https://pytorch.org/features">About</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/foundation">PyTorch Foundation</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/#community-module">Community</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/community-stories">Community Stories</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/resources">Developer Resources</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/events">Events</a>
+            </li>
+
+            <li>
+              <a href="https://discuss.pytorch.org/">Forums</a>
+            </li>
+
+            <li>
+              <a href="https://pytorch.org/hub">Models (Beta)</a>
+            </li>
+          </ul>
+
+          <li>
+            <a href="https://github.com/pytorch/pytorch">Github</a>
+          </li>
+        </ul>
+      </div>
+    </div>
+  </div>
+
+  <!-- End Mobile Menu -->
+
+  <script type="text/javascript" src="../_static/js/vendor/anchor.min.js"></script>
+
+  <script type="text/javascript">
+    $(document).ready(function() {
+      mobileMenu.bind();
+      mobileTOC.bind();
+      pytorchAnchors.bind();
+      sideMenus.bind();
+      scrollToAnchor.bind();
+      highlightNavigation.bind();
+      mainMenuDropdown.bind();
+      filterTags.bind();
+
+      // Add class to links that have code blocks, since we cannot create links in code blocks
+      $("article.pytorch-article a span.pre").each(function(e) {
+        $(this).closest("a").addClass("has-code");
+      });
+    })
+  </script>
+</body>
+</html>
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diff --git a/docs/v2.2.0/_downloads/0daf1d0af656cac7b808856b71e6616f/torch_compile_resnet_example.ipynb b/docs/v2.2.0/_downloads/0daf1d0af656cac7b808856b71e6616f/torch_compile_resnet_example.ipynb
new file mode 100644
index 0000000000..2aa89e8169
--- /dev/null
+++ b/docs/v2.2.0/_downloads/0daf1d0af656cac7b808856b71e6616f/torch_compile_resnet_example.ipynb
@@ -0,0 +1,158 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n\n# Compiling ResNet using the Torch-TensorRT `torch.compile` Backend\n\nThis interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a ResNet model.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Imports and Model Definition\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport torch_tensorrt\nimport torchvision.models as models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize model with half precision and sample inputs\nmodel = models.resnet18(pretrained=True).half().eval().to(\"cuda\")\ninputs = [torch.randn((1, 3, 224, 224)).to(\"cuda\").half()]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Optional Input Arguments to `torch_tensorrt.compile`\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Enabled precision for TensorRT optimization\nenabled_precisions = {torch.half}\n\n# Whether to print verbose logs\ndebug = True\n\n# Workspace size for TensorRT\nworkspace_size = 20 << 30\n\n# Maximum number of TRT Engines\n# (Lower value allows more graph segmentation)\nmin_block_size = 7\n\n# Operations to Run in Torch, regardless of converter support\ntorch_executed_ops = {}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compilation with `torch_tensorrt.compile`\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Build and compile the model with torch.compile, using Torch-TensorRT backend\noptimized_model = torch_tensorrt.compile(\n    model,\n    ir=\"torch_compile\",\n    inputs=inputs,\n    enabled_precisions=enabled_precisions,\n    debug=debug,\n    workspace_size=workspace_size,\n    min_block_size=min_block_size,\n    torch_executed_ops=torch_executed_ops,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Equivalently, we could have run the above via the torch.compile frontend, as so:\n`optimized_model = torch.compile(model, backend=\"torch_tensorrt\", options={\"enabled_precisions\": enabled_precisions, ...}); optimized_model(*inputs)`\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Inference\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Does not cause recompilation (same batch size as input)\nnew_inputs = [torch.randn((1, 3, 224, 224)).half().to(\"cuda\")]\nnew_outputs = optimized_model(*new_inputs)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Does cause recompilation (new batch size)\nnew_batch_size_inputs = [torch.randn((8, 3, 224, 224)).half().to(\"cuda\")]\nnew_batch_size_outputs = optimized_model(*new_batch_size_inputs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cleanup\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Finally, we use Torch utilities to clean up the workspace\ntorch._dynamo.reset()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cuda Driver Error Note\n\nOccasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,\none may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052\nand can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::\n\n      if __name__ == '__main__':\n          compile_engine_and_infer()\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v2.2.0/_downloads/0e30a6276601af7e5fc4d5166e2e3d37/torch_compile_advanced_usage.py b/docs/v2.2.0/_downloads/0e30a6276601af7e5fc4d5166e2e3d37/torch_compile_advanced_usage.py
new file mode 100644
index 0000000000..8ebedab111
--- /dev/null
+++ b/docs/v2.2.0/_downloads/0e30a6276601af7e5fc4d5166e2e3d37/torch_compile_advanced_usage.py
@@ -0,0 +1,107 @@
+"""
+.. _torch_compile_advanced_usage:
+
+Torch Compile Advanced Usage
+======================================================
+
+This interactive script is intended as an overview of the process by which `torch_tensorrt.compile(..., ir="torch_compile", ...)` works, and how it integrates with the `torch.compile` API."""
+
+# %%
+# Imports and Model Definition
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+import torch
+import torch_tensorrt
+
+# %%
+
+
+# We begin by defining a model
+class Model(torch.nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+        self.relu = torch.nn.ReLU()
+
+    def forward(self, x: torch.Tensor, y: torch.Tensor):
+        x_out = self.relu(x)
+        y_out = self.relu(y)
+        x_y_out = x_out + y_out
+        return torch.mean(x_y_out)
+
+
+# %%
+# Compilation with `torch.compile` Using Default Settings
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Define sample float inputs and initialize model
+sample_inputs = [torch.rand((5, 7)).cuda(), torch.rand((5, 7)).cuda()]
+model = Model().eval().cuda()
+
+# %%
+
+# Next, we compile the model using torch.compile
+# For the default settings, we can simply call torch.compile
+# with the backend "torch_tensorrt", and run the model on an
+# input to cause compilation, as so:
+optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False)
+optimized_model(*sample_inputs)
+
+# %%
+# Compilation with `torch.compile` Using Custom Settings
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# First, we use Torch utilities to clean up the workspace
+# after the previous compile invocation
+torch._dynamo.reset()
+
+# Define sample half inputs and initialize model
+sample_inputs_half = [
+    torch.rand((5, 7)).half().cuda(),
+    torch.rand((5, 7)).half().cuda(),
+]
+model_half = Model().eval().cuda()
+
+# %%
+
+# If we want to customize certain options in the backend,
+# but still use the torch.compile call directly, we can provide
+# custom options to the backend via the "options" keyword
+# which takes in a dictionary mapping options to values.
+#
+# For accepted backend options, see the CompilationSettings dataclass:
+# py/torch_tensorrt/dynamo/_settings.py
+backend_kwargs = {
+    "enabled_precisions": {torch.half},
+    "debug": True,
+    "min_block_size": 2,
+    "torch_executed_ops": {"torch.ops.aten.sub.Tensor"},
+    "optimization_level": 4,
+    "use_python_runtime": False,
+}
+
+# Run the model on an input to cause compilation, as so:
+optimized_model_custom = torch.compile(
+    model_half,
+    backend="torch_tensorrt",
+    options=backend_kwargs,
+    dynamic=False,
+)
+optimized_model_custom(*sample_inputs_half)
+
+# %%
+# Cleanup
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Finally, we use Torch utilities to clean up the workspace
+torch._dynamo.reset()
+
+# %%
+# Cuda Driver Error Note
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Occasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,
+# one may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052
+# and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::
+#
+#       if __name__ == '__main__':
+#           compile_engine_and_infer()
diff --git a/docs/v2.2.0/_downloads/26d49aeeb9c710e27197fda28b7c3516/yi_jing_01_chien.jpg b/docs/v2.2.0/_downloads/26d49aeeb9c710e27197fda28b7c3516/yi_jing_01_chien.jpg
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diff --git a/docs/v2.2.0/_downloads/46b3e6febaab06324aa2715896895544/torch_compile_stable_diffusion.py b/docs/v2.2.0/_downloads/46b3e6febaab06324aa2715896895544/torch_compile_stable_diffusion.py
new file mode 100644
index 0000000000..0511e5a363
--- /dev/null
+++ b/docs/v2.2.0/_downloads/46b3e6febaab06324aa2715896895544/torch_compile_stable_diffusion.py
@@ -0,0 +1,55 @@
+"""
+.. _torch_compile_stable_diffusion:
+
+Torch Compile Stable Diffusion
+======================================================
+
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a Stable Diffusion model. A sample output is featured below:
+
+.. image:: /tutorials/images/majestic_castle.png
+   :width: 512px
+   :height: 512px
+   :scale: 50 %
+   :align: right
+"""
+
+# %%
+# Imports and Model Definition
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+import torch
+from diffusers import DiffusionPipeline
+
+import torch_tensorrt
+
+model_id = "CompVis/stable-diffusion-v1-4"
+device = "cuda:0"
+
+# Instantiate Stable Diffusion Pipeline with FP16 weights
+pipe = DiffusionPipeline.from_pretrained(
+    model_id, revision="fp16", torch_dtype=torch.float16
+)
+pipe = pipe.to(device)
+
+backend = "torch_tensorrt"
+
+# Optimize the UNet portion with Torch-TensorRT
+pipe.unet = torch.compile(
+    pipe.unet,
+    backend=backend,
+    options={
+        "truncate_long_and_double": True,
+        "precision": torch.float16,
+    },
+    dynamic=False,
+)
+
+# %%
+# Inference
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+prompt = "a majestic castle in the clouds"
+image = pipe(prompt).images[0]
+
+image.save("images/majestic_castle.png")
+image.show()
diff --git a/docs/v2.2.0/_downloads/6a6052d9668b2cb8332d349d328e21c1/_rendered_examples_jupyter.zip b/docs/v2.2.0/_downloads/6a6052d9668b2cb8332d349d328e21c1/_rendered_examples_jupyter.zip
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HcmV?d00001

diff --git a/docs/v2.2.0/_downloads/b35883282793ac3413933fdb22d00d81/torch_compile_advanced_usage.ipynb b/docs/v2.2.0/_downloads/b35883282793ac3413933fdb22d00d81/torch_compile_advanced_usage.ipynb
new file mode 100644
index 0000000000..1d2c305b62
--- /dev/null
+++ b/docs/v2.2.0/_downloads/b35883282793ac3413933fdb22d00d81/torch_compile_advanced_usage.ipynb
@@ -0,0 +1,144 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n\n# Torch Compile Advanced Usage\n\nThis interactive script is intended as an overview of the process by which `torch_tensorrt.compile(..., ir=\"torch_compile\", ...)` works, and how it integrates with the `torch.compile` API.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Imports and Model Definition\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport torch_tensorrt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# We begin by defining a model\nclass Model(torch.nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n        self.relu = torch.nn.ReLU()\n\n    def forward(self, x: torch.Tensor, y: torch.Tensor):\n        x_out = self.relu(x)\n        y_out = self.relu(y)\n        x_y_out = x_out + y_out\n        return torch.mean(x_y_out)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compilation with `torch.compile` Using Default Settings\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Define sample float inputs and initialize model\nsample_inputs = [torch.rand((5, 7)).cuda(), torch.rand((5, 7)).cuda()]\nmodel = Model().eval().cuda()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Next, we compile the model using torch.compile\n# For the default settings, we can simply call torch.compile\n# with the backend \"torch_tensorrt\", and run the model on an\n# input to cause compilation, as so:\noptimized_model = torch.compile(model, backend=\"torch_tensorrt\", dynamic=False)\noptimized_model(*sample_inputs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compilation with `torch.compile` Using Custom Settings\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# First, we use Torch utilities to clean up the workspace\n# after the previous compile invocation\ntorch._dynamo.reset()\n\n# Define sample half inputs and initialize model\nsample_inputs_half = [\n    torch.rand((5, 7)).half().cuda(),\n    torch.rand((5, 7)).half().cuda(),\n]\nmodel_half = Model().eval().cuda()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# If we want to customize certain options in the backend,\n# but still use the torch.compile call directly, we can provide\n# custom options to the backend via the \"options\" keyword\n# which takes in a dictionary mapping options to values.\n#\n# For accepted backend options, see the CompilationSettings dataclass:\n# py/torch_tensorrt/dynamo/_settings.py\nbackend_kwargs = {\n    \"enabled_precisions\": {torch.half},\n    \"debug\": True,\n    \"min_block_size\": 2,\n    \"torch_executed_ops\": {\"torch.ops.aten.sub.Tensor\"},\n    \"optimization_level\": 4,\n    \"use_python_runtime\": False,\n}\n\n# Run the model on an input to cause compilation, as so:\noptimized_model_custom = torch.compile(\n    model_half,\n    backend=\"torch_tensorrt\",\n    options=backend_kwargs,\n    dynamic=False,\n)\noptimized_model_custom(*sample_inputs_half)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cleanup\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Finally, we use Torch utilities to clean up the workspace\ntorch._dynamo.reset()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cuda Driver Error Note\n\nOccasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,\none may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052\nand can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::\n\n      if __name__ == '__main__':\n          compile_engine_and_infer()\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v2.2.0/_downloads/b776287bc876f7ce24942b82a66beb05/torch_compile_stable_diffusion.ipynb b/docs/v2.2.0/_downloads/b776287bc876f7ce24942b82a66beb05/torch_compile_stable_diffusion.ipynb
new file mode 100644
index 0000000000..cceefefcc1
--- /dev/null
+++ b/docs/v2.2.0/_downloads/b776287bc876f7ce24942b82a66beb05/torch_compile_stable_diffusion.ipynb
@@ -0,0 +1,68 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n\n# Torch Compile Stable Diffusion\n\nThis interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a Stable Diffusion model. A sample output is featured below:\n\n<img src=\"file://tutorials/images/majestic_castle.png\" width=\"512px\" height=\"512px\" scale=\"50 %\" align=\"right\">\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Imports and Model Definition\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nfrom diffusers import DiffusionPipeline\n\nimport torch_tensorrt\n\nmodel_id = \"CompVis/stable-diffusion-v1-4\"\ndevice = \"cuda:0\"\n\n# Instantiate Stable Diffusion Pipeline with FP16 weights\npipe = DiffusionPipeline.from_pretrained(\n    model_id, revision=\"fp16\", torch_dtype=torch.float16\n)\npipe = pipe.to(device)\n\nbackend = \"torch_tensorrt\"\n\n# Optimize the UNet portion with Torch-TensorRT\npipe.unet = torch.compile(\n    pipe.unet,\n    backend=backend,\n    options={\n        \"truncate_long_and_double\": True,\n        \"precision\": torch.float16,\n    },\n    dynamic=False,\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Inference\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "prompt = \"a majestic castle in the clouds\"\nimage = pipe(prompt).images[0]\n\nimage.save(\"images/majestic_castle.png\")\nimage.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v2.2.0/_downloads/ce102e287ddb5744f0a1364e8c0c7f68/torch_compile_transformers_example.ipynb b/docs/v2.2.0/_downloads/ce102e287ddb5744f0a1364e8c0c7f68/torch_compile_transformers_example.ipynb
new file mode 100644
index 0000000000..6a649f6b38
--- /dev/null
+++ b/docs/v2.2.0/_downloads/ce102e287ddb5744f0a1364e8c0c7f68/torch_compile_transformers_example.ipynb
@@ -0,0 +1,158 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n\n# Compiling a Transformer using torch.compile and TensorRT\n\nThis interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a transformer-based model.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Imports and Model Definition\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import torch\nimport torch_tensorrt\nfrom transformers import BertModel"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Initialize model with float precision and sample inputs\nmodel = BertModel.from_pretrained(\"bert-base-uncased\").eval().to(\"cuda\")\ninputs = [\n    torch.randint(0, 2, (1, 14), dtype=torch.int32).to(\"cuda\"),\n    torch.randint(0, 2, (1, 14), dtype=torch.int32).to(\"cuda\"),\n]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Optional Input Arguments to `torch_tensorrt.compile`\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Enabled precision for TensorRT optimization\nenabled_precisions = {torch.float}\n\n# Whether to print verbose logs\ndebug = True\n\n# Workspace size for TensorRT\nworkspace_size = 20 << 30\n\n# Maximum number of TRT Engines\n# (Lower value allows more graph segmentation)\nmin_block_size = 7\n\n# Operations to Run in Torch, regardless of converter support\ntorch_executed_ops = {}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Compilation with `torch.compile`\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Define backend compilation keyword arguments\ncompilation_kwargs = {\n    \"enabled_precisions\": enabled_precisions,\n    \"debug\": debug,\n    \"workspace_size\": workspace_size,\n    \"min_block_size\": min_block_size,\n    \"torch_executed_ops\": torch_executed_ops,\n}\n\n# Build and compile the model with torch.compile, using Torch-TensorRT backend\noptimized_model = torch.compile(\n    model,\n    backend=\"torch_tensorrt\",\n    dynamic=False,\n    options=compilation_kwargs,\n)\noptimized_model(*inputs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Equivalently, we could have run the above via the convenience frontend, as so:\n`torch_tensorrt.compile(model, ir=\"torch_compile\", inputs=inputs, **compilation_kwargs)`\n\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Inference\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Does not cause recompilation (same batch size as input)\nnew_inputs = [\n    torch.randint(0, 2, (1, 14), dtype=torch.int32).to(\"cuda\"),\n    torch.randint(0, 2, (1, 14), dtype=torch.int32).to(\"cuda\"),\n]\nnew_outputs = optimized_model(*new_inputs)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Does cause recompilation (new batch size)\nnew_inputs = [\n    torch.randint(0, 2, (4, 14), dtype=torch.int32).to(\"cuda\"),\n    torch.randint(0, 2, (4, 14), dtype=torch.int32).to(\"cuda\"),\n]\nnew_outputs = optimized_model(*new_inputs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cleanup\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "# Finally, we use Torch utilities to clean up the workspace\ntorch._dynamo.reset()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Cuda Driver Error Note\n\nOccasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,\none may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052\nand can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::\n\n      if __name__ == '__main__':\n          compile_engine_and_infer()\n\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/docs/v2.2.0/_downloads/d6e1bb6ec5f884994554d9d12e37a0f6/torch_compile_resnet_example.py b/docs/v2.2.0/_downloads/d6e1bb6ec5f884994554d9d12e37a0f6/torch_compile_resnet_example.py
new file mode 100644
index 0000000000..9015538fec
--- /dev/null
+++ b/docs/v2.2.0/_downloads/d6e1bb6ec5f884994554d9d12e37a0f6/torch_compile_resnet_example.py
@@ -0,0 +1,93 @@
+"""
+.. _torch_compile_resnet:
+
+Compiling ResNet using the Torch-TensorRT `torch.compile` Backend
+==========================================================
+
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a ResNet model."""
+
+# %%
+# Imports and Model Definition
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+import torch
+import torch_tensorrt
+import torchvision.models as models
+
+# %%
+
+# Initialize model with half precision and sample inputs
+model = models.resnet18(pretrained=True).half().eval().to("cuda")
+inputs = [torch.randn((1, 3, 224, 224)).to("cuda").half()]
+
+# %%
+# Optional Input Arguments to `torch_tensorrt.compile`
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Enabled precision for TensorRT optimization
+enabled_precisions = {torch.half}
+
+# Whether to print verbose logs
+debug = True
+
+# Workspace size for TensorRT
+workspace_size = 20 << 30
+
+# Maximum number of TRT Engines
+# (Lower value allows more graph segmentation)
+min_block_size = 7
+
+# Operations to Run in Torch, regardless of converter support
+torch_executed_ops = {}
+
+# %%
+# Compilation with `torch_tensorrt.compile`
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Build and compile the model with torch.compile, using Torch-TensorRT backend
+optimized_model = torch_tensorrt.compile(
+    model,
+    ir="torch_compile",
+    inputs=inputs,
+    enabled_precisions=enabled_precisions,
+    debug=debug,
+    workspace_size=workspace_size,
+    min_block_size=min_block_size,
+    torch_executed_ops=torch_executed_ops,
+)
+
+# %%
+# Equivalently, we could have run the above via the torch.compile frontend, as so:
+# `optimized_model = torch.compile(model, backend="torch_tensorrt", options={"enabled_precisions": enabled_precisions, ...}); optimized_model(*inputs)`
+
+# %%
+# Inference
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Does not cause recompilation (same batch size as input)
+new_inputs = [torch.randn((1, 3, 224, 224)).half().to("cuda")]
+new_outputs = optimized_model(*new_inputs)
+
+# %%
+
+# Does cause recompilation (new batch size)
+new_batch_size_inputs = [torch.randn((8, 3, 224, 224)).half().to("cuda")]
+new_batch_size_outputs = optimized_model(*new_batch_size_inputs)
+
+# %%
+# Cleanup
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Finally, we use Torch utilities to clean up the workspace
+torch._dynamo.reset()
+
+# %%
+# Cuda Driver Error Note
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Occasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,
+# one may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052
+# and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::
+#
+#       if __name__ == '__main__':
+#           compile_engine_and_infer()
diff --git a/docs/v2.2.0/_downloads/dfa60e8f9850fd7761f3e7da81304d32/torch_compile_transformers_example.py b/docs/v2.2.0/_downloads/dfa60e8f9850fd7761f3e7da81304d32/torch_compile_transformers_example.py
new file mode 100644
index 0000000000..01d46e96f6
--- /dev/null
+++ b/docs/v2.2.0/_downloads/dfa60e8f9850fd7761f3e7da81304d32/torch_compile_transformers_example.py
@@ -0,0 +1,109 @@
+"""
+.. _torch_compile_transformer:
+
+Compiling a Transformer using torch.compile and TensorRT
+==============================================================
+
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a transformer-based model."""
+
+# %%
+# Imports and Model Definition
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+import torch
+import torch_tensorrt
+from transformers import BertModel
+
+# %%
+
+# Initialize model with float precision and sample inputs
+model = BertModel.from_pretrained("bert-base-uncased").eval().to("cuda")
+inputs = [
+    torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+    torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+]
+
+
+# %%
+# Optional Input Arguments to `torch_tensorrt.compile`
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Enabled precision for TensorRT optimization
+enabled_precisions = {torch.float}
+
+# Whether to print verbose logs
+debug = True
+
+# Workspace size for TensorRT
+workspace_size = 20 << 30
+
+# Maximum number of TRT Engines
+# (Lower value allows more graph segmentation)
+min_block_size = 7
+
+# Operations to Run in Torch, regardless of converter support
+torch_executed_ops = {}
+
+# %%
+# Compilation with `torch.compile`
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Define backend compilation keyword arguments
+compilation_kwargs = {
+    "enabled_precisions": enabled_precisions,
+    "debug": debug,
+    "workspace_size": workspace_size,
+    "min_block_size": min_block_size,
+    "torch_executed_ops": torch_executed_ops,
+}
+
+# Build and compile the model with torch.compile, using Torch-TensorRT backend
+optimized_model = torch.compile(
+    model,
+    backend="torch_tensorrt",
+    dynamic=False,
+    options=compilation_kwargs,
+)
+optimized_model(*inputs)
+
+# %%
+# Equivalently, we could have run the above via the convenience frontend, as so:
+# `torch_tensorrt.compile(model, ir="torch_compile", inputs=inputs, **compilation_kwargs)`
+
+# %%
+# Inference
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Does not cause recompilation (same batch size as input)
+new_inputs = [
+    torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+    torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+]
+new_outputs = optimized_model(*new_inputs)
+
+# %%
+
+# Does cause recompilation (new batch size)
+new_inputs = [
+    torch.randint(0, 2, (4, 14), dtype=torch.int32).to("cuda"),
+    torch.randint(0, 2, (4, 14), dtype=torch.int32).to("cuda"),
+]
+new_outputs = optimized_model(*new_inputs)
+
+# %%
+# Cleanup
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+# Finally, we use Torch utilities to clean up the workspace
+torch._dynamo.reset()
+
+# %%
+# Cuda Driver Error Note
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+#
+# Occasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,
+# one may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052
+# and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::
+#
+#       if __name__ == '__main__':
+#           compile_engine_and_infer()
diff --git a/docs/v2.2.0/_images/majestic_castle.png b/docs/v2.2.0/_images/majestic_castle.png
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+              
+  <h1>Source code for torch_tensorrt._Device</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">11</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Self</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">Self</span>
+
+<span class="kn">import</span> <span class="nn">warnings</span>
+
+<span class="c1"># from torch_tensorrt import _enums</span>
+<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">logging</span>
+
+<span class="k">try</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
+<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
+    <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+        <span class="s2">&quot;Unable to import torchscript frontend core and torch-tensorrt runtime. Some dependent features may be unavailable.&quot;</span>
+    <span class="p">)</span>
+
+
+<div class="viewcode-block" id="Device"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Device">[docs]</a><span class="k">class</span> <span class="nc">Device</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Defines a device that can be used to specify target devices for engines</span>
+
+<span class="sd">    Attributes:</span>
+<span class="sd">        device_type (torch_tensorrt.DeviceType): Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</span>
+<span class="sd">        gpu_id (int): Device ID for target GPU</span>
+<span class="sd">        dla_core (int): Core ID for target DLA core</span>
+<span class="sd">        allow_gpu_fallback (bool): Whether falling back to GPU if DLA cannot support an op should be allowed</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">device_type</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="kc">None</span>  <span class="c1">#: Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</span>
+    <span class="p">)</span>
+    <span class="n">gpu_id</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>  <span class="c1">#: Device ID for target GPU</span>
+    <span class="n">dla_core</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>  <span class="c1">#: Core ID for target DLA core</span>
+    <span class="n">allow_gpu_fallback</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="kc">False</span>  <span class="c1">#: Whether falling back to GPU if DLA cannot support an op should be allowed</span>
+    <span class="p">)</span>
+
+<div class="viewcode-block" id="Device.__init__"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Device.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;__init__ Method for torch_tensorrt.Device</span>
+
+<span class="sd">        Device accepts one of a few construction patterns</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            spec (str): String with device spec e.g. &quot;dla:0&quot; for dla, core_id 0</span>
+
+<span class="sd">        Keyword Arguments:</span>
+<span class="sd">            gpu_id (int): ID of target GPU (will get overrided if dla_core is specified to the GPU managing DLA). If specified, no positional arguments should be provided</span>
+<span class="sd">            dla_core (int): ID of target DLA core. If specified, no positional arguments should be provided.</span>
+<span class="sd">            allow_gpu_fallback (bool): Allow TensorRT to schedule operations on GPU if they are not supported on DLA (ignored if device type is not DLA)</span>
+
+<span class="sd">        Examples:</span>
+<span class="sd">            - Device(&quot;gpu:1&quot;)</span>
+<span class="sd">            - Device(&quot;cuda:1&quot;)</span>
+<span class="sd">            - Device(&quot;dla:0&quot;, allow_gpu_fallback=True)</span>
+<span class="sd">            - Device(gpu_id=0, dla_core=0, allow_gpu_fallback=True)</span>
+<span class="sd">            - Device(dla_core=0, allow_gpu_fallback=True)</span>
+<span class="sd">            - Device(gpu_id=1)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;When specifying Device through positional argument, argument must be str&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device_type</span><span class="p">,</span> <span class="nb">id</span><span class="p">)</span> <span class="o">=</span> <span class="n">Device</span><span class="o">.</span><span class="n">_parse_device_str</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">==</span> <span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="nb">id</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">dla_core</span> <span class="o">=</span> <span class="nb">id</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+                        <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">,</span>
+                        <span class="s2">&quot;Setting GPU id to 0 for device because device 0 manages DLA on Xavier&quot;</span><span class="p">,</span>
+                    <span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s2">&quot;gpu_id&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">or</span> <span class="s2">&quot;dla_core&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="s2">&quot;dla_core&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">dla_core</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dla_core&quot;</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="s2">&quot;gpu_id&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;gpu_id&quot;</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="mi">0</span>
+                        <span class="n">logging</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+                            <span class="n">logging</span><span class="o">.</span><span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">,</span>
+                            <span class="s2">&quot;Setting GPU id to 0 for device because device 0 manages DLA on Xavier&quot;</span><span class="p">,</span>
+                        <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;gpu_id&quot;</span><span class="p">]</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Either gpu_id or dla_core or both must be defined if no string with device specs is provided as an arg&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Unexpected number of positional arguments for class Device </span><span class="se">\n</span><span class="s2">    Found </span><span class="si">{}</span><span class="s2"> arguments, expected either zero or a single positional arguments&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;allow_gpu_fallback&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;allow_gpu_fallback must be a bool&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">allow_gpu_fallback</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">]</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="s2">&quot;Device(type=</span><span class="si">{}</span><span class="s2">, gpu_id=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device_type</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">==</span> <span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span>
+            <span class="k">else</span> <span class="s2">&quot;, dla_core=</span><span class="si">{}</span><span class="s2">, allow_gpu_fallback=</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dla_core</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">allow_gpu_fallback</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__str__</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_to_internal</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_C</span><span class="o">.</span><span class="n">Device</span><span class="p">:</span>
+        <span class="n">internal_dev</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">Device</span><span class="p">()</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">==</span> <span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">:</span>
+            <span class="n">internal_dev</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_type</span> <span class="o">==</span> <span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span><span class="p">:</span>
+            <span class="n">internal_dev</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Invalid DeviceType detected while parsing the Device class&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">internal_dev</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpu_id</span>
+        <span class="n">internal_dev</span><span class="o">.</span><span class="n">dla_core</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dla_core</span>
+        <span class="n">internal_dev</span><span class="o">.</span><span class="n">allow_gpu_fallback</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">allow_gpu_fallback</span>
+        <span class="k">return</span> <span class="n">internal_dev</span>
+
+    <span class="k">def</span> <span class="nf">_to_serialized_rt_device</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="n">internal_dev</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span>
+        <span class="n">serialized_rt_device</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">internal_dev</span><span class="o">.</span><span class="n">_to_serialized_rt_device</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">serialized_rt_device</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_from_torch_device</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">torch_dev</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Self</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">torch_dev</span><span class="o">.</span><span class="n">type</span> <span class="o">!=</span> <span class="s2">&quot;cuda&quot;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Torch Device specs must have type &quot;cuda&quot;&#39;</span><span class="p">)</span>
+        <span class="n">gpu_id</span> <span class="o">=</span> <span class="n">torch_dev</span><span class="o">.</span><span class="n">index</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">gpu_id</span><span class="o">=</span><span class="n">gpu_id</span><span class="p">)</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">_current_device</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Self</span><span class="p">:</span>
+        <span class="n">dev</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">_get_current_device</span><span class="p">()</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">gpu_id</span><span class="o">=</span><span class="n">dev</span><span class="o">.</span><span class="n">gpu_id</span><span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_parse_device_str</span><span class="p">(</span><span class="n">s</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
+        <span class="n">s</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+        <span class="n">spec</span> <span class="o">=</span> <span class="n">s</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;:&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">spec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span> <span class="ow">or</span> <span class="n">spec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">spec</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="k">elif</span> <span class="n">spec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;dla&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">spec</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unknown device type </span><span class="si">{</span><span class="n">spec</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
+             
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\ No newline at end of file
diff --git a/docs/v2.2.0/_modules/torch_tensorrt/_Input.html b/docs/v2.2.0/_modules/torch_tensorrt/_Input.html
new file mode 100644
index 0000000000..21d96c0fe3
--- /dev/null
+++ b/docs/v2.2.0/_modules/torch_tensorrt/_Input.html
@@ -0,0 +1,1183 @@
+
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
+<head>
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+  <h1>Source code for torch_tensorrt._Input</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>
+
+
+<div class="viewcode-block" id="Input"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input">[docs]</a><span class="k">class</span> <span class="nc">Input</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Defines an input to a module in terms of expected shape, data type and tensor format.</span>
+
+<span class="sd">    Attributes:</span>
+<span class="sd">        shape_mode (torch_tensorrt.Input._ShapeMode): Is input statically or dynamically shaped</span>
+<span class="sd">        shape (Tuple or Dict): Either a single Tuple or a dict of tuples defining the input shape.</span>
+<span class="sd">            Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form</span>
+<span class="sd">            ``{</span>
+<span class="sd">                &quot;min_shape&quot;: Tuple,</span>
+<span class="sd">                &quot;opt_shape&quot;: Tuple,</span>
+<span class="sd">                &quot;max_shape&quot;: Tuple</span>
+<span class="sd">            }``</span>
+<span class="sd">        dtype (torch_tensorrt.dtype): The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</span>
+<span class="sd">        format (torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">class</span> <span class="nc">_ShapeMode</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+        <span class="n">STATIC</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">DYNAMIC</span> <span class="o">=</span> <span class="mi">1</span>
+
+    <span class="n">shape_mode</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">_ShapeMode</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="kc">None</span>  <span class="c1">#: Is input statically or dynamically shaped</span>
+    <span class="p">)</span>
+    <span class="n">shape</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]]</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="kc">None</span>  <span class="c1">#: Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form ``{ &quot;min_shape&quot;: Tuple, &quot;opt_shape&quot;: Tuple, &quot;max_shape&quot;: Tuple }``</span>
+    <span class="p">)</span>
+    <span class="n">dtype</span><span class="p">:</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">unknown</span>
+    <span class="p">)</span>  <span class="c1">#: The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</span>
+    <span class="n">_explicit_set_dtype</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="nb">format</span><span class="p">:</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">contiguous</span>
+    <span class="p">)</span>  <span class="c1">#: The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
+
+    <span class="n">DOMAIN_OFFSET</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span>
+    <span class="n">low_tensor_domain_incl</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">high_tensor_domain_excl</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">low_tensor_domain_incl</span> <span class="o">+</span> <span class="n">DOMAIN_OFFSET</span>
+    <span class="n">torch_dtype</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
+    <span class="n">torch_tensor</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+
+<div class="viewcode-block" id="Input.__init__"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;__init__ Method for torch_tensorrt.Input</span>
+
+<span class="sd">        Input accepts one of a few construction patterns</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            shape (Tuple or List, optional): Static shape of input tensor</span>
+
+<span class="sd">        Keyword Arguments:</span>
+<span class="sd">            shape (Tuple or List, optional): Static shape of input tensor</span>
+<span class="sd">            min_shape (Tuple or List, optional): Min size of input tensor&#39;s shape range</span>
+<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
+<span class="sd">            opt_shape (Tuple or List, optional): Opt size of input tensor&#39;s shape range</span>
+<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
+<span class="sd">            max_shape (Tuple or List, optional): Max size of input tensor&#39;s shape range</span>
+<span class="sd">                Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input&#39;s shape_mode to DYNAMIC</span>
+<span class="sd">            dtype (torch.dtype or torch_tensorrt.dtype): Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</span>
+<span class="sd">            format (torch.memory_format or torch_tensorrt.TensorFormat): The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</span>
+<span class="sd">            tensor_domain (Tuple(float, float), optional): The domain of allowed values for the tensor, as interval notation: [tensor_domain[0], tensor_domain[1]).</span>
+<span class="sd">                Note: Entering &quot;None&quot; (or not specifying) will set the bound to [0, 2)</span>
+<span class="sd">            torch_tensor (torch.Tensor): Holds a corresponding torch tensor with this Input.</span>
+<span class="sd">            name (str, optional): Name of this input in the input nn.Module&#39;s forward function. Used to specify dynamic shapes for the corresponding input in dynamo tracer.</span>
+<span class="sd">        Examples:</span>
+<span class="sd">            - Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</span>
+<span class="sd">            - Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</span>
+<span class="sd">            - Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Compatibility code for switching over from InputTensorSpec</span>
+        <span class="k">if</span> <span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> <span class="s2">&quot;shape_ranges&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="p">(</span>
+                <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">3</span>
+            <span class="p">)</span>
+            <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]</span>
+
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape_ranges&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="p">)</span>
+            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Found that both shape (as a positional argument), and one or more of min_shape, opt_shape, max_shape were specified</span><span class="se">\n</span><span class="s2">class Input expects that only either shape or all three of min_shape, opt_shape, max_shape are defined&quot;</span>
+                <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>
+
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="s2">&quot;shape&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="p">(</span>
+                <span class="nb">all</span><span class="p">(</span><span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">])</span>
+            <span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Missing required arguments for class Input</span><span class="se">\n</span><span class="s2">Either shape or all three of min_shape, opt_shape, max_shape must be defined&quot;</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="p">(</span><span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">all</span><span class="p">(</span>
+                <span class="n">k</span> <span class="ow">in</span> <span class="n">kwargs</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>
+            <span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Found that both shape, and one or more of min_shape, opt_shape, max_shape were specified</span><span class="se">\n</span><span class="s2">class Input expects that only either shape or all three of min_shape, opt_shape, max_shape are defined&quot;</span>
+                <span class="p">)</span>
+
+            <span class="k">if</span> <span class="s2">&quot;shape&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">]))</span>
+                    <span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;shape&quot;</span><span class="p">])</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]))</span>
+                        <span class="o">+</span> <span class="s2">&quot; for min_shape&quot;</span>
+                    <span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]))</span>
+                        <span class="o">+</span> <span class="s2">&quot; for opt_shape&quot;</span>
+                    <span class="p">)</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">Input</span><span class="o">.</span><span class="n">_supported_input_size_type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]):</span>
+                    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                        <span class="s2">&quot;Input shape specifications for inputs are required to be a List, tuple or torch.Size, found type: &quot;</span>
+                        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]))</span>
+                        <span class="o">+</span> <span class="s2">&quot; for max_shape&quot;</span>
+                    <span class="p">)</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span> <span class="o">=</span> <span class="p">{</span>
+                    <span class="s2">&quot;min_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]),</span>
+                    <span class="s2">&quot;opt_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]),</span>
+                    <span class="s2">&quot;max_shape&quot;</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]),</span>
+                <span class="p">}</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Unexpected number of positional arguments for class Input </span><span class="se">\n</span><span class="s2">    Found </span><span class="si">{}</span><span class="s2"> arguments, expected either zero or a single positional arguments&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;dtype&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">torch_dtype</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">]</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_dtype</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;dtype&quot;</span><span class="p">])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">torch_dtype</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_to_torch_dtype</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_explicit_set_dtype</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">if</span> <span class="s2">&quot;format&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">format</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_format</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;format&quot;</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="s2">&quot;tensor_domain&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">domain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;tensor_domain&quot;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">domain</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span> <span class="o">=</span> <span class="n">Input</span><span class="o">.</span><span class="n">_parse_tensor_domain</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;torch_tensor&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">torch_tensor</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;torch_tensor&quot;</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">torch_tensor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">example_tensor</span><span class="p">(</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">torch_tensor</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">example_tensor</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="s2">&quot;name&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;name&quot;</span><span class="p">]</span></div>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;Input(shape=</span><span class="si">{}</span><span class="s2">, dtype=</span><span class="si">{}</span><span class="s2">, format=</span><span class="si">{}</span><span class="s2">, domain=[</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">))&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
+                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span>
+                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">),</span>
+                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
+                <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                <span class="k">return</span> <span class="s2">&quot;Input(min_shape=</span><span class="si">{}</span><span class="s2">, opt_shape=</span><span class="si">{}</span><span class="s2">, max_shape=</span><span class="si">{}</span><span class="s2">, dtype=</span><span class="si">{}</span><span class="s2">, format=</span><span class="si">{}</span><span class="s2">, domain=[</span><span class="si">{}</span><span class="s2">, </span><span class="si">{}</span><span class="s2">))&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">],</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">],</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">],</span>
+                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">),</span>
+                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">format</span><span class="p">),</span>
+                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span>
+                    <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Input shape is dynamic but shapes are not provided as dictionary (found: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
+                <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Unknown input shape mode&quot;</span><span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_supported_input_size_type</span><span class="p">(</span><span class="n">input_size</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">return</span> <span class="kc">True</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="kc">False</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_parse_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">long</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">long</span>
+            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">int32</span>
+            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span>
+            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span>
+            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">double</span>
+            <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">bool</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">bool</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                    <span class="s2">&quot;Provided an unsupported data type as an input data type (support: bool, int32, long, half, float), got: &quot;</span>
+                    <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
+                <span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+            <span class="k">return</span> <span class="n">dtype</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Input data type needs to be specified with a torch.dtype or a torch_tensorrt.dtype, got: &quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">dtype</span><span class="p">))</span>
+            <span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_to_torch_dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">:</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">long</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">long</span>
+        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">int32</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">int32</span>
+        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span>
+        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span>
+        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">bool</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">bool</span>
+        <span class="k">elif</span> <span class="n">dtype</span> <span class="o">==</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">double</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">float64</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Default torch_dtype used in FX path</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
+
+    <span class="k">def</span> <span class="nf">is_trt_dtype</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">bool</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">!=</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">long</span><span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_parse_format</span><span class="p">(</span><span class="nb">format</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">format</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">memory_format</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">format</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">contiguous</span>
+            <span class="k">elif</span> <span class="nb">format</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="o">.</span><span class="n">channels_last</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Provided an unsupported tensor format (support: NCHW/contiguous_format, NHWC/channel_last)&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">format</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">TensorFormat</span><span class="p">):</span>
+            <span class="k">return</span> <span class="nb">format</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Tensor format needs to be specified with either torch.memory_format or torch_tensorrt.TensorFormat&quot;</span>
+            <span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_parse_tensor_domain</span><span class="p">(</span>
+        <span class="n">domain</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce a tuple of integers which specifies a tensor domain in the interval format: [lo, hi)</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            domain (Tuple[int, int]): A tuple of integers (or NoneTypes) to verify</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            A tuple of two int32_t-valid integers</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">domain</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">result_domain</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">Input</span><span class="o">.</span><span class="n">low_tensor_domain_incl</span><span class="p">,</span>
+                <span class="n">Input</span><span class="o">.</span><span class="n">high_tensor_domain_excl</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">domain_lo</span><span class="p">,</span> <span class="n">domain_hi</span> <span class="o">=</span> <span class="n">domain</span>
+
+            <span class="c1"># Validate type and provided values for domain</span>
+            <span class="n">valid_type_lo</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_lo</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">))</span>
+            <span class="n">valid_type_hi</span> <span class="o">=</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">domain_hi</span><span class="p">,</span> <span class="p">(</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">valid_type_lo</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Expected value for tensor domain low specifier, got </span><span class="si">{</span><span class="n">domain_lo</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="ow">not</span> <span class="n">valid_type_hi</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Expected value for tensor domain high specifier, got </span><span class="si">{</span><span class="n">domain_hi</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">domain_hi</span> <span class="o">&lt;=</span> <span class="n">domain_lo</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Expected provided integer range to have low tensor domain value &quot;</span>
+                    <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;&lt; high tensor domain value, got invalid range [</span><span class="si">{</span><span class="n">domain_lo</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">domain_hi</span><span class="si">}</span><span class="s2">)&quot;</span>
+                <span class="p">)</span>
+            <span class="n">result_domain</span> <span class="o">=</span> <span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">domain_lo</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="n">domain_hi</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Expected 2 values for domain, got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">domain</span><span class="p">)</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">domain</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">result_domain</span>
+
+<div class="viewcode-block" id="Input.from_tensor"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.from_tensor">[docs]</a>    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_tensor</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">disable_memory_format_check</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Input&quot;</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce a Input which contains the information of the given PyTorch tensor.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            tensor (torch.Tensor): A PyTorch tensor.</span>
+<span class="sd">            disable_memory_format_check (bool): Whether to validate the memory formats of input tensors</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            A Input object.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span>
+            <span class="n">disable_memory_format_check</span>
+            <span class="ow">or</span> <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">)</span>
+            <span class="ow">or</span> <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span><span class="p">)</span>
+        <span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Tensor does not have a supported memory format, supported formats are contiguous or channel_last&quot;</span>
+            <span class="p">)</span>
+        <span class="n">frmt</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span>
+            <span class="k">if</span> <span class="p">(</span>
+                <span class="n">disable_memory_format_check</span>
+                <span class="ow">or</span> <span class="n">t</span><span class="o">.</span><span class="n">is_contiguous</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</span><span class="p">)</span>
+            <span class="p">)</span>
+            <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">channels_last</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">t</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">t</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="n">frmt</span><span class="p">,</span> <span class="n">torch_tensor</span><span class="o">=</span><span class="n">t</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="Input.from_tensors"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.from_tensors">[docs]</a>    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_tensors</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span> <span class="n">ts</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">disable_memory_format_check</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;Input&quot;</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce a list of Inputs which contain</span>
+<span class="sd">        the information of all the given PyTorch tensors.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            tensors (Iterable[torch.Tensor]): A list of PyTorch tensors.</span>
+<span class="sd">            disable_memory_format_check (bool): Whether to validate the memory formats of input tensors</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            A list of Inputs.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
+        <span class="k">return</span> <span class="p">[</span>
+            <span class="bp">cls</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">disable_memory_format_check</span><span class="o">=</span><span class="n">disable_memory_format_check</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">ts</span>
+        <span class="p">]</span></div>
+
+<div class="viewcode-block" id="Input.example_tensor"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.Input.example_tensor">[docs]</a>    <span class="k">def</span> <span class="nf">example_tensor</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">optimization_profile_field</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Get an example tensor of the shape specified by the Input object</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            optimization_profile_field (Optional(str)): Name of the field to use for shape in the case the Input is dynamically shaped</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            A PyTorch Tensor</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">optimization_profile_field</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Specified a optimization profile field but the input is static&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+                    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">torch_dtype</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="ne">RuntimeError</span><span class="p">(</span>
+                        <span class="sa">f</span><span class="s2">&quot;Input shape is dynamic but shapes are not provided as sequence (found: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
+                    <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">optimization_profile_field</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="k">assert</span> <span class="nb">any</span><span class="p">(</span>
+                        <span class="n">optimization_profile_field</span> <span class="o">==</span> <span class="n">field_name</span>
+                        <span class="k">for</span> <span class="n">field_name</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;opt_shape&quot;</span><span class="p">,</span> <span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>
+                    <span class="p">)</span>
+                <span class="k">except</span> <span class="ne">AssertionError</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                        <span class="s2">&quot;Invalid field name, expected one of min_shape, opt_shape, max_shape&quot;</span>
+                    <span class="p">)</span>
+
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="n">optimization_profile_field</span><span class="p">])</span><span class="o">.</span><span class="n">to</span><span class="p">(</span>
+                        <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">torch_dtype</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                        <span class="sa">f</span><span class="s2">&quot;Input shape is dynamic but shapes are not provided as dictionary (found: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">)&quot;</span>
+                    <span class="p">)</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Requested an example tensor from a dynamic shaped input but did not specific which profile field to use.&quot;</span>
+                <span class="p">)</span>
+        <span class="k">raise</span></div></div>
+</pre></div>
+
+             </article>
+             
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+              
+  <h1>Source code for torch_tensorrt._compile</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Set</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.fx</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt.ts</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._enums</span> <span class="kn">import</span> <span class="n">dtype</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Input</span> <span class="kn">import</span> <span class="n">Input</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._utils</span> <span class="kn">import</span> <span class="n">sanitized_torch_version</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.fx</span> <span class="kn">import</span> <span class="n">InputTensorSpec</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.fx.lower</span> <span class="kn">import</span> <span class="nb">compile</span> <span class="k">as</span> <span class="n">fx_compile</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.fx.utils</span> <span class="kn">import</span> <span class="n">LowerPrecision</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.ts._compiler</span> <span class="kn">import</span> <span class="nb">compile</span> <span class="k">as</span> <span class="n">torchscript_compile</span>
+<span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">TypeGuard</span>
+
+<span class="kn">from</span> <span class="nn">packaging</span> <span class="kn">import</span> <span class="n">version</span>
+
+<span class="n">DYNAMO_ENABLED</span> <span class="o">=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">sanitized_torch_version</span><span class="p">())</span> <span class="o">&gt;=</span> <span class="n">version</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="s2">&quot;2.1.dev&quot;</span><span class="p">)</span>
+
+<span class="k">if</span> <span class="n">DYNAMO_ENABLED</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">torch._export</span> <span class="kn">import</span> <span class="n">ExportedProgram</span>
+    <span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo._compiler</span> <span class="kn">import</span> <span class="nb">compile</span> <span class="k">as</span> <span class="n">dynamo_compile</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="s2">&quot;compile&quot;</span><span class="p">,</span>
+    <span class="s2">&quot;convert_method_to_trt_engine&quot;</span><span class="p">,</span>
+<span class="p">]</span>
+
+
+<span class="k">def</span> <span class="nf">_non_fx_input_interface</span><span class="p">(</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">InputTensorSpec</span><span class="p">],</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TypeGuard</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]:</span>
+    <span class="k">return</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Input</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_fx_input_interface</span><span class="p">(</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">InputTensorSpec</span><span class="p">],</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TypeGuard</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">InputTensorSpec</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]:</span>
+    <span class="k">return</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">InputTensorSpec</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">_IRType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Enum to determine the type of IR selected for model compilation&quot;&quot;&quot;</span>
+
+    <span class="n">ts</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">fx</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">dynamo</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="n">torch_compile</span> <span class="o">=</span> <span class="mi">3</span>
+    <span class="n">exported_program</span> <span class="o">=</span> <span class="mi">4</span>
+
+
+<span class="k">class</span> <span class="nc">_ModuleType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Enum to determine the type of model provided as input&quot;&quot;&quot;</span>
+
+    <span class="n">nn</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">ts</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="n">fx</span> <span class="o">=</span> <span class="mi">2</span>
+    <span class="n">ep</span> <span class="o">=</span> <span class="mi">3</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_ModuleType</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span>
+        <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptFunction</span><span class="p">]</span>
+    <span class="p">):</span>
+        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span>
+    <span class="k">elif</span> <span class="n">DYNAMO_ENABLED</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">ExportedProgram</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ep</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format&quot;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">:</span> <span class="n">_ModuleType</span><span class="p">,</span> <span class="n">ir</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_IRType</span><span class="p">:</span>
+    <span class="n">module_is_tsable</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">module_type</span> <span class="o">==</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">,</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ts</span><span class="p">])</span>
+    <span class="n">module_is_fxable</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">module_type</span> <span class="o">==</span> <span class="n">t</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="p">[</span><span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">,</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">fx</span><span class="p">])</span>
+    <span class="n">module_is_exportable</span> <span class="o">=</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">ep</span>
+
+    <span class="n">ir_targets_torchscript</span> <span class="o">=</span> <span class="nb">any</span><span class="p">(</span><span class="n">ir</span> <span class="o">==</span> <span class="n">opt</span> <span class="k">for</span> <span class="n">opt</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;torchscript&quot;</span><span class="p">,</span> <span class="s2">&quot;ts&quot;</span><span class="p">])</span>
+    <span class="n">ir_targets_fx</span> <span class="o">=</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;fx&quot;</span>
+    <span class="n">ir_targets_dynamo</span> <span class="o">=</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;dynamo&quot;</span>
+    <span class="n">ir_targets_torch_compile</span> <span class="o">=</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;torch_compile&quot;</span>
+
+    <span class="k">if</span> <span class="n">module_is_tsable</span> <span class="ow">and</span> <span class="n">ir_targets_torchscript</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span>
+    <span class="k">elif</span> <span class="n">module_is_fxable</span> <span class="ow">and</span> <span class="n">ir_targets_fx</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span>
+    <span class="k">elif</span> <span class="n">module_is_fxable</span> <span class="ow">and</span> <span class="n">ir_targets_dynamo</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span>
+    <span class="k">elif</span> <span class="n">module_is_fxable</span> <span class="ow">and</span> <span class="n">ir_targets_torch_compile</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">torch_compile</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;default&quot;</span><span class="p">:</span>
+            <span class="c1"># Options are listed in order of preference</span>
+            <span class="k">if</span> <span class="n">DYNAMO_ENABLED</span> <span class="ow">and</span> <span class="n">module_is_fxable</span><span class="p">:</span>
+                <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;ir was set to default, using dynamo as ir&quot;</span><span class="p">)</span>
+                <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span>
+            <span class="k">elif</span> <span class="n">module_is_tsable</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">DYNAMO_ENABLED</span><span class="p">:</span>
+                    <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+                        <span class="s2">&quot;Input graph is a Torchscript module but the ir provided is default (dynamo). Please set ir=torchscript to suppress the warning. Compiling the module with ir=torchscript&quot;</span>
+                    <span class="p">)</span>
+                <span class="k">return</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span>
+            <span class="k">elif</span> <span class="n">module_is_exportable</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="s2">&quot;Input graph is an ExportedProgram which is not currently supported. Please provide torch.nn.Module or torch.fx.GraphModule as input.&quot;</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Module was provided in an unsupported format&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">ir</span> <span class="o">==</span> <span class="s2">&quot;exported_program&quot;</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;ir=exported_program is not currently supported. Supported ir options : ts|fx|dynamo&quot;</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown ir was requested&quot;</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
+    <span class="n">ir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;default&quot;</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">InputTensorSpec</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Set</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span> <span class="o">|</span> <span class="n">dtype</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span> <span class="o">|</span> <span class="n">Callable</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile a PyTorch module for NVIDIA GPUs using TensorRT</span>
+
+<span class="sd">    Takes a existing PyTorch module and a set of settings to configure the compiler</span>
+<span class="sd">    and using the path specified in ``ir`` lower and compile the module to TensorRT</span>
+<span class="sd">    returning a PyTorch Module back</span>
+
+<span class="sd">    Converts specifically the forward method of a Module</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</span>
+<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.nn.Module: Compiled Module, when run it will execute via TensorRT</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">input_list</span> <span class="o">=</span> <span class="n">inputs</span> <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
+    <span class="n">enabled_precisions_set</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">}</span>
+    <span class="p">)</span>
+
+    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+    <span class="n">target_ir</span> <span class="o">=</span> <span class="n">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">,</span> <span class="n">ir</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
+        <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">module</span>
+        <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
+            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+                <span class="s2">&quot;Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript&quot;</span>
+            <span class="p">)</span>
+            <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">_non_fx_input_interface</span><span class="p">(</span><span class="n">input_list</span><span class="p">)</span>
+        <span class="n">compiled_ts_module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span> <span class="o">=</span> <span class="n">torchscript_compile</span><span class="p">(</span>
+            <span class="n">ts_mod</span><span class="p">,</span>
+            <span class="n">inputs</span><span class="o">=</span><span class="n">input_list</span><span class="p">,</span>
+            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">compiled_ts_module</span>
+    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> <span class="ow">in</span> <span class="n">enabled_precisions_set</span>
+            <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span> <span class="ow">in</span> <span class="n">enabled_precisions_set</span>
+        <span class="p">):</span>
+            <span class="n">lower_precision</span> <span class="o">=</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
+        <span class="k">elif</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">float32</span> <span class="ow">in</span> <span class="n">enabled_precisions_set</span>
+            <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span> <span class="ow">in</span> <span class="n">enabled_precisions_set</span>
+        <span class="p">):</span>
+            <span class="n">lower_precision</span> <span class="o">=</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP32</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Precision </span><span class="si">{</span><span class="n">enabled_precisions_set</span><span class="si">}</span><span class="s2"> not supported on FX&quot;</span><span class="p">)</span>
+
+        <span class="k">assert</span> <span class="n">_fx_input_interface</span><span class="p">(</span><span class="n">input_list</span><span class="p">)</span>
+        <span class="n">compiled_fx_module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">fx_compile</span><span class="p">(</span>
+            <span class="n">module</span><span class="p">,</span>
+            <span class="n">input_list</span><span class="p">,</span>
+            <span class="n">lower_precision</span><span class="o">=</span><span class="n">lower_precision</span><span class="p">,</span>
+            <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">compiled_fx_module</span>
+    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span><span class="p">:</span>
+        <span class="c1"># Prepare torch and torchtrt inputs</span>
+        <span class="kn">import</span> <span class="nn">collections.abc</span>
+
+        <span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo.utils</span> <span class="kn">import</span> <span class="n">prepare_inputs</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_list</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
+            <span class="n">input_list</span> <span class="o">=</span> <span class="p">[</span><span class="n">input_list</span><span class="p">]</span>
+
+        <span class="c1"># Export the module</span>
+        <span class="n">torchtrt_inputs</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">input_list</span><span class="p">)</span>
+        <span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torchtrt_inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">trt_graph_module</span> <span class="o">=</span> <span class="n">dynamo_compile</span><span class="p">(</span>
+            <span class="n">exp_program</span><span class="p">,</span>
+            <span class="n">inputs</span><span class="o">=</span><span class="n">torchtrt_inputs</span><span class="p">,</span>
+            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">trt_graph_module</span>
+    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">torch_compile</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch_compile</span><span class="p">(</span>
+            <span class="n">module</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format or the ir requested is unknown&quot;</span><span class="p">)</span></div>
+
+
+<span class="k">def</span> <span class="nf">torch_compile</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns a boxed model which is the output of torch.compile.</span>
+<span class="sd">    This does not compile the model to TRT. Execute this model on</span>
+<span class="sd">    sample inputs to compile the model to TRT.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo.backend</span> <span class="kn">import</span> <span class="n">torch_tensorrt_backend</span>
+
+    <span class="c1"># TODO: Remove dynamic=False when SymInt Dynamic shape support is ready</span>
+    <span class="n">boxed_fn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+        <span class="n">module</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="n">torch_tensorrt_backend</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="o">**</span><span class="n">kwargs</span><span class="p">}</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">boxed_fn</span>
+
+
+<div class="viewcode-block" id="convert_method_to_trt_engine"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.convert_method_to_trt_engine">[docs]</a><span class="k">def</span> <span class="nf">convert_method_to_trt_engine</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
+    <span class="n">method_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;forward&quot;</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">ir</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;default&quot;</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Set</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span> <span class="o">|</span> <span class="n">dtype</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bytes</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a TorchScript module method to a serialized TensorRT engine</span>
+
+<span class="sd">    Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (Union(torch.nn.Module,torch.jit.ScriptModule): Source module</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        ir (str): The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</span>
+<span class="sd">        **kwargs: Additional settings for the specific requested strategy (See submodules for more info)</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">enabled_precisions_set</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">}</span>
+    <span class="p">)</span>
+
+    <span class="n">module_type</span> <span class="o">=</span> <span class="n">_parse_module_type</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+    <span class="n">target_ir</span> <span class="o">=</span> <span class="n">_get_target_ir</span><span class="p">(</span><span class="n">module_type</span><span class="p">,</span> <span class="n">ir</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">ts</span><span class="p">:</span>
+        <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">module</span>
+        <span class="k">if</span> <span class="n">module_type</span> <span class="o">==</span> <span class="n">_ModuleType</span><span class="o">.</span><span class="n">nn</span><span class="p">:</span>
+            <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+                <span class="s2">&quot;Module was provided as a torch.nn.Module, trying to script the module with torch.jit.script. In the event of a failure please preconvert your module to TorchScript&quot;</span>
+            <span class="p">)</span>
+            <span class="n">ts_mod</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ts</span><span class="o">.</span><span class="n">convert_method_to_trt_engine</span><span class="p">(</span>  <span class="c1"># type: ignore[no-any-return]</span>
+            <span class="n">ts_mod</span><span class="p">,</span>
+            <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span>
+            <span class="n">method_name</span><span class="o">=</span><span class="n">method_name</span><span class="p">,</span>
+            <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions_set</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">fx</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+            <span class="s2">&quot;convert_method_to_trt_engine call is not supported for ir=fx&quot;</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">dynamo</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+            <span class="s2">&quot;convert_method_to_trt_engine call is not supported for ir=dynamo.&quot;</span>
+        <span class="p">)</span>
+    <span class="k">elif</span> <span class="n">target_ir</span> <span class="o">==</span> <span class="n">_IRType</span><span class="o">.</span><span class="n">torch_compile</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+            <span class="s2">&quot;convert_method_to_trt_engine call is not supported for ir=torch_compile&quot;</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Module is an unknown format or the ir requested is unknown&quot;</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
+             
+            </div>
+            <footer>
+  
+
+  
+
+    <hr>
+
+  
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2022, NVIDIA Corporation.
+
+    </p>
+  </div>
+    
+      <div>
+        Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
+      </div>
+     
+
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+        <div class="pytorch-content-right" id="pytorch-content-right">
+          <div class="pytorch-right-menu" id="pytorch-right-menu">
+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              
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+<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
+<ul>
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+<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>
+</ul>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
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+</ul>
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+              
+  <h1>Source code for torch_tensorrt._utils</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._version</span> <span class="kn">import</span> <span class="n">__version__</span>
+
+
+<div class="viewcode-block" id="dump_build_info"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.dump_build_info">[docs]</a><span class="k">def</span> <span class="nf">dump_build_info</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Prints build information about the torch_tensorrt distribution to stdout&quot;&quot;&quot;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">get_build_info</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="get_build_info"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.get_build_info">[docs]</a><span class="k">def</span> <span class="nf">get_build_info</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a string containing the build information of torch_tensorrt distribution</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        str: String containing the build information for torch_tensorrt distribution</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">core_build_info</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">get_build_info</span><span class="p">()</span>
+    <span class="n">build_info</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span>
+        <span class="s2">&quot;Torch-TensorRT Version: &quot;</span>
+        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">__version__</span><span class="p">)</span>
+        <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="s2">&quot;Using PyTorch Version: &quot;</span>
+        <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
+        <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="o">+</span> <span class="n">core_build_info</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">build_info</span></div>
+
+
+<div class="viewcode-block" id="set_device"><a class="viewcode-back" href="../../py_api/torch_tensorrt.html#torch_tensorrt.set_device">[docs]</a><span class="k">def</span> <span class="nf">set_device</span><span class="p">(</span><span class="n">gpu_id</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="n">_C</span><span class="o">.</span><span class="n">set_device</span><span class="p">(</span><span class="n">gpu_id</span><span class="p">)</span></div>
+
+
+<span class="k">def</span> <span class="nf">sanitized_torch_version</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">__version__</span>
+        <span class="k">if</span> <span class="s2">&quot;.nv&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">torch</span><span class="o">.</span><span class="n">__version__</span>
+        <span class="k">else</span> <span class="n">torch</span><span class="o">.</span><span class="n">__version__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;.nv&quot;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="p">)</span>
+</pre></div>
+
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
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+      <li>torch_tensorrt.dynamo._SourceIR</li>
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+        
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+              
+  <h1>Source code for torch_tensorrt.dynamo._SourceIR</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span><span class="p">,</span> <span class="n">auto</span>
+
+
+<div class="viewcode-block" id="SourceIR"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.SourceIR">[docs]</a><span class="k">class</span> <span class="nc">SourceIR</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+    <span class="n">NN</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">ACC</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">ATEN</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">PRIM</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">TORCHTRT_LOWERED</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+    <span class="n">UNKNOWN</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+        <span class="k">if</span> <span class="bp">self</span> <span class="o">==</span> <span class="n">SourceIR</span><span class="o">.</span><span class="n">NN</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;nn&quot;</span>
+        <span class="k">elif</span> <span class="bp">self</span> <span class="o">==</span> <span class="n">SourceIR</span><span class="o">.</span><span class="n">ACC</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;acc&quot;</span>
+        <span class="k">elif</span> <span class="bp">self</span> <span class="o">==</span> <span class="n">SourceIR</span><span class="o">.</span><span class="n">ATEN</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;aten&quot;</span>
+        <span class="k">elif</span> <span class="bp">self</span> <span class="o">==</span> <span class="n">SourceIR</span><span class="o">.</span><span class="n">PRIM</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;prim&quot;</span>
+        <span class="k">elif</span> <span class="bp">self</span> <span class="o">==</span> <span class="n">SourceIR</span><span class="o">.</span><span class="n">TORCHTRT_LOWERED</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;torchtrt_lowered&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="s2">&quot;unknown_ir&quot;</span></div>
+</pre></div>
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diff --git a/docs/v2.2.0/_modules/torch_tensorrt/dynamo/_compiler.html b/docs/v2.2.0/_modules/torch_tensorrt/dynamo/_compiler.html
new file mode 100644
index 0000000000..c1d553c4c2
--- /dev/null
+++ b/docs/v2.2.0/_modules/torch_tensorrt/dynamo/_compiler.html
@@ -0,0 +1,1098 @@
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+              
+  <h1>Source code for torch_tensorrt.dynamo._compiler</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">collections.abc</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Collection</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Set</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="kn">from</span> <span class="nn">torch.export</span> <span class="kn">import</span> <span class="n">ExportedProgram</span>
+<span class="kn">from</span> <span class="nn">torch.fx.node</span> <span class="kn">import</span> <span class="n">Target</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._enums</span> <span class="kn">import</span> <span class="p">(</span>  <span class="c1"># TODO: Should probabably be the TRT EngineCapability Enum</span>
+    <span class="n">EngineCapability</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Input</span> <span class="kn">import</span> <span class="n">Input</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo</span> <span class="kn">import</span> <span class="n">partitioning</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo._defaults</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">DEBUG</span><span class="p">,</span>
+    <span class="n">DEVICE</span><span class="p">,</span>
+    <span class="n">DISABLE_TF32</span><span class="p">,</span>
+    <span class="n">DLA_GLOBAL_DRAM_SIZE</span><span class="p">,</span>
+    <span class="n">DLA_LOCAL_DRAM_SIZE</span><span class="p">,</span>
+    <span class="n">DLA_SRAM_SIZE</span><span class="p">,</span>
+    <span class="n">ENABLE_EXPERIMENTAL_DECOMPOSITIONS</span><span class="p">,</span>
+    <span class="n">ENGINE_CAPABILITY</span><span class="p">,</span>
+    <span class="n">MAX_AUX_STREAMS</span><span class="p">,</span>
+    <span class="n">MIN_BLOCK_SIZE</span><span class="p">,</span>
+    <span class="n">NUM_AVG_TIMING_ITERS</span><span class="p">,</span>
+    <span class="n">OPTIMIZATION_LEVEL</span><span class="p">,</span>
+    <span class="n">OUTPUT_FORMAT</span><span class="p">,</span>
+    <span class="n">PASS_THROUGH_BUILD_FAILURES</span><span class="p">,</span>
+    <span class="n">PRECISION</span><span class="p">,</span>
+    <span class="n">REFIT</span><span class="p">,</span>
+    <span class="n">REQUIRE_FULL_COMPILATION</span><span class="p">,</span>
+    <span class="n">SPARSE_WEIGHTS</span><span class="p">,</span>
+    <span class="n">TRUNCATE_LONG_AND_DOUBLE</span><span class="p">,</span>
+    <span class="n">USE_FAST_PARTITIONER</span><span class="p">,</span>
+    <span class="n">USE_PYTHON_RUNTIME</span><span class="p">,</span>
+    <span class="n">VERSION_COMPATIBLE</span><span class="p">,</span>
+    <span class="n">WORKSPACE_SIZE</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo._exporter</span> <span class="kn">import</span> <span class="n">export</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo.conversion</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">CompilationSettings</span><span class="p">,</span>
+    <span class="n">convert_module</span><span class="p">,</span>
+    <span class="n">repair_long_or_double_inputs</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo.conversion._ConverterRegistry</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">DYNAMO_CONVERTERS</span> <span class="k">as</span> <span class="n">CONVERTERS</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo.lowering</span> <span class="kn">import</span> <span class="n">apply_lowering_passes</span><span class="p">,</span> <span class="n">get_decompositions</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo.utils</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">get_torch_inputs</span><span class="p">,</span>
+    <span class="n">prepare_inputs</span><span class="p">,</span>
+    <span class="n">set_log_level</span><span class="p">,</span>
+    <span class="n">to_torch_device</span><span class="p">,</span>
+    <span class="n">to_torch_tensorrt_device</span><span class="p">,</span>
+<span class="p">)</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">exported_program</span><span class="p">:</span> <span class="n">ExportedProgram</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
+    <span class="o">*</span><span class="p">,</span>
+    <span class="n">device</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="n">DEVICE</span><span class="p">,</span>
+    <span class="n">disable_tf32</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">DISABLE_TF32</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">SPARSE_WEIGHTS</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Set</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">|</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,),</span>
+    <span class="n">engine_capability</span><span class="p">:</span> <span class="n">EngineCapability</span> <span class="o">=</span> <span class="n">ENGINE_CAPABILITY</span><span class="p">,</span>
+    <span class="n">refit</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">REFIT</span><span class="p">,</span>
+    <span class="n">debug</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">DEBUG</span><span class="p">,</span>
+    <span class="n">num_avg_timing_iters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">NUM_AVG_TIMING_ITERS</span><span class="p">,</span>
+    <span class="n">workspace_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">WORKSPACE_SIZE</span><span class="p">,</span>
+    <span class="n">dla_sram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DLA_SRAM_SIZE</span><span class="p">,</span>
+    <span class="n">dla_local_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DLA_LOCAL_DRAM_SIZE</span><span class="p">,</span>
+    <span class="n">dla_global_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DLA_GLOBAL_DRAM_SIZE</span><span class="p">,</span>
+    <span class="n">truncate_long_and_double</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">TRUNCATE_LONG_AND_DOUBLE</span><span class="p">,</span>
+    <span class="n">require_full_compilation</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">REQUIRE_FULL_COMPILATION</span><span class="p">,</span>
+    <span class="n">min_block_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">MIN_BLOCK_SIZE</span><span class="p">,</span>
+    <span class="n">torch_executed_ops</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Collection</span><span class="p">[</span><span class="n">Target</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">torch_executed_modules</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">pass_through_build_failures</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">PASS_THROUGH_BUILD_FAILURES</span><span class="p">,</span>
+    <span class="n">max_aux_streams</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">MAX_AUX_STREAMS</span><span class="p">,</span>
+    <span class="n">version_compatible</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">VERSION_COMPATIBLE</span><span class="p">,</span>
+    <span class="n">optimization_level</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">OPTIMIZATION_LEVEL</span><span class="p">,</span>
+    <span class="n">use_python_runtime</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">USE_PYTHON_RUNTIME</span><span class="p">,</span>
+    <span class="n">use_fast_partitioner</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">USE_FAST_PARTITIONER</span><span class="p">,</span>
+    <span class="n">enable_experimental_decompositions</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">ENABLE_EXPERIMENTAL_DECOMPOSITIONS</span><span class="p">,</span>
+    <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">OUTPUT_FORMAT</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile a TorchScript module for NVIDIA GPUs using TensorRT</span>
+
+<span class="sd">    Takes a existing TorchScript module and a set of settings to configure the compiler</span>
+<span class="sd">    and will convert methods to JIT Graphs which call equivalent TensorRT engines</span>
+
+<span class="sd">    Converts specifically the forward method of a TorchScript Module</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        exported_program (torch.export.ExportedProgram): Source module, running torch.export on a ``torch.nn.Module``</span>
+<span class="sd">        inputs (Tuple[Any, ...]): List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>
+
+<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>
+
+<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        refit (bool): Enable refitting</span>
+<span class="sd">        debug (bool): Enable debuggable engine</span>
+<span class="sd">        capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
+<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
+<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
+<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
+<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
+<span class="sd">        truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32</span>
+<span class="sd">        calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration</span>
+<span class="sd">        require_full_compilation (bool): Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</span>
+<span class="sd">        min_block_size (int): The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</span>
+<span class="sd">        torch_executed_ops (Collection[Target]): Set of aten operators that must be run in PyTorch. An error will be thrown if this set is not empty but ``require_full_compilation`` is True</span>
+<span class="sd">        torch_executed_modules (List[str]): List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
+<span class="sd">        pass_through_build_failures (bool): Error out if there are issues during compilation (only applicable to torch.compile workflows)</span>
+<span class="sd">        max_aux_stream (Optional[int]): Maximum streams in the engine</span>
+<span class="sd">        version_compatible (bool): Build the TensorRT engines compatible with future versions of TensorRT (Restrict to lean runtime operators to provide version forward compatibility for the engines)</span>
+<span class="sd">        optimization_level: (Optional[int]): Setting a higher optimization level allows TensorRT to spend longer engine building time searching for more optimization options. The resulting engine may have better performance compared to an engine built with a lower optimization level. The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, which is currently 5. Setting it to be greater than the maximum level results in identical behavior to the maximum level.</span>
+<span class="sd">        use_python_runtime: (bool): Return a graph using a pure Python runtime, reduces options for serialization</span>
+<span class="sd">        use_fast_partitioner: (bool): Use the adjacency based partitioning scheme instead of the global partitioner. Adjacency partitioning is faster but may not be optiminal. Use the global paritioner (``False``) if looking for best performance</span>
+<span class="sd">        enable_experimental_decompositions (bool): Use the full set of operator decompositions. These decompositions may not be tested but serve to make the grap easier to covert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</span>
+<span class="sd">        output_format (str): Output format of the result of TRT compilation. Options include &quot;exported_program&quot; (or) &quot;ep&quot; | &quot;torchscript&quot; (or) &quot;ts&quot; | &quot;graph_module&quot; (or) &quot;fx&quot;. Default is &quot;exported_program&quot;</span>
+<span class="sd">        **kwargs: Any,</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.fx.GraphModule: Compiled FX Module, when run it will execute via TensorRT</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">debug</span><span class="p">:</span>
+        <span class="n">set_log_level</span><span class="p">(</span><span class="n">logger</span><span class="o">.</span><span class="n">parent</span><span class="p">,</span> <span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">torch_executed_modules</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">torch_executed_modules</span><span class="p">:</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Detected torch_executed_modules was non-empty: </span><span class="si">{</span><span class="n">torch_executed_modules</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">This feature is unimplemented in Torch-TRT Dynamo currently.&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">collections</span><span class="o">.</span><span class="n">abc</span><span class="o">.</span><span class="n">Sequence</span><span class="p">):</span>
+        <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">inputs</span><span class="p">]</span>
+
+    <span class="c1"># Prepare torch_trt inputs</span>
+    <span class="n">inputs</span> <span class="o">=</span> <span class="n">prepare_inputs</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+    <span class="n">device</span> <span class="o">=</span> <span class="n">to_torch_tensorrt_device</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">exported_program</span><span class="p">,</span> <span class="n">ExportedProgram</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Input graph should be an ExportedProgram but got type </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">exported_program</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+    <span class="n">exported_program</span> <span class="o">=</span> <span class="n">exported_program</span><span class="o">.</span><span class="n">run_decompositions</span><span class="p">(</span>
+        <span class="n">get_decompositions</span><span class="p">(</span><span class="n">enable_experimental_decompositions</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="n">gm</span> <span class="o">=</span> <span class="n">exported_program</span><span class="o">.</span><span class="n">module</span><span class="p">()</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>
+
+    <span class="c1"># Apply lowering on the graph module</span>
+    <span class="n">torch_inputs</span> <span class="o">=</span> <span class="n">get_torch_inputs</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+    <span class="n">gm</span> <span class="o">=</span> <span class="n">apply_lowering_passes</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">torch_inputs</span><span class="p">)</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Lowered Input graph: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">))</span>
+
+    <span class="n">enabled_precisions</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">enabled_precisions</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="p">(</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">float16</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
+        <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
+    <span class="p">):</span>
+        <span class="n">precision</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span>
+    <span class="k">elif</span> <span class="p">(</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">float32</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
+        <span class="ow">or</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span> <span class="ow">in</span> <span class="n">enabled_precisions</span>
+    <span class="p">):</span>
+        <span class="n">precision</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span>
+    <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">enabled_precisions</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No precision specified, defaulting to </span><span class="si">{</span><span class="n">PRECISION</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">precision</span> <span class="o">=</span> <span class="n">PRECISION</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Precision </span><span class="si">{</span><span class="n">enabled_precisions</span><span class="si">}</span><span class="s2"> not supported in the Dynamo Path&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">compilation_options</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;precision&quot;</span><span class="p">:</span> <span class="n">precision</span><span class="p">,</span>
+        <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="n">debug</span><span class="p">,</span>
+        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>
+        <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="n">min_block_size</span><span class="p">,</span>
+        <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="p">(</span>
+            <span class="n">torch_executed_ops</span> <span class="k">if</span> <span class="n">torch_executed_ops</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">set</span><span class="p">()</span>
+        <span class="p">),</span>
+        <span class="s2">&quot;pass_through_build_failures&quot;</span><span class="p">:</span> <span class="n">pass_through_build_failures</span><span class="p">,</span>
+        <span class="s2">&quot;max_aux_streams&quot;</span><span class="p">:</span> <span class="n">max_aux_streams</span><span class="p">,</span>
+        <span class="s2">&quot;version_compatible&quot;</span><span class="p">:</span> <span class="n">version_compatible</span><span class="p">,</span>
+        <span class="s2">&quot;optimization_level&quot;</span><span class="p">:</span> <span class="n">optimization_level</span><span class="p">,</span>
+        <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">:</span> <span class="n">use_python_runtime</span><span class="p">,</span>
+        <span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="n">truncate_long_and_double</span><span class="p">,</span>
+        <span class="s2">&quot;use_fast_partitioner&quot;</span><span class="p">:</span> <span class="n">use_fast_partitioner</span><span class="p">,</span>
+        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>
+        <span class="s2">&quot;enable_experimental_decompositions&quot;</span><span class="p">:</span> <span class="n">enable_experimental_decompositions</span><span class="p">,</span>
+        <span class="s2">&quot;require_full_compilation&quot;</span><span class="p">:</span> <span class="n">require_full_compilation</span><span class="p">,</span>
+        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>
+        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>
+        <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="n">refit</span><span class="p">,</span>
+        <span class="s2">&quot;engine_capability&quot;</span><span class="p">:</span> <span class="n">engine_capability</span><span class="p">,</span>
+        <span class="s2">&quot;dla_sram_size&quot;</span><span class="p">:</span> <span class="n">dla_sram_size</span><span class="p">,</span>
+        <span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_local_dram_size</span><span class="p">,</span>
+        <span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_global_dram_size</span><span class="p">,</span>
+        <span class="s2">&quot;output_format&quot;</span><span class="p">:</span> <span class="n">output_format</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="n">settings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(</span><span class="o">**</span><span class="n">compilation_options</span><span class="p">)</span>
+    <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Compilation Settings: </span><span class="si">%s</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
+    <span class="n">trt_gm</span> <span class="o">=</span> <span class="n">compile_module</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">settings</span><span class="p">)</span>
+    <span class="n">trt_result</span> <span class="o">=</span> <span class="n">export</span><span class="p">(</span><span class="n">trt_gm</span><span class="p">,</span> <span class="n">torch_inputs</span><span class="p">,</span> <span class="n">output_format</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">trt_result</span></div>
+
+
+<span class="k">def</span> <span class="nf">compile_module</span><span class="p">(</span>
+    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
+    <span class="n">sample_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span><span class="p">],</span>
+    <span class="n">settings</span><span class="p">:</span> <span class="n">CompilationSettings</span> <span class="o">=</span> <span class="n">CompilationSettings</span><span class="p">(),</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile a traced FX module</span>
+
+<span class="sd">    Includes: Partitioning + Conversion Phases</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        module: FX GraphModule to convert</span>
+<span class="sd">        inputs: Inputs to the module</span>
+<span class="sd">        settings: Compilation settings</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        Compiled FX GraphModule</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Set torch-executed ops</span>
+    <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">set_disallowed_targets</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span><span class="p">)</span>
+
+    <span class="c1"># Check the number of supported operations in the graph</span>
+    <span class="n">num_supported_ops</span><span class="p">,</span> <span class="n">total_ops</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">get_graph_converter_support</span><span class="p">(</span>
+        <span class="n">gm</span><span class="p">,</span> <span class="n">settings</span><span class="o">.</span><span class="n">debug</span><span class="p">,</span> <span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span>
+    <span class="p">)</span>
+
+    <span class="c1"># If the number of supported operations is 0 or less than the block size, skip the subgraph</span>
+    <span class="c1"># TODO: Add condition to second expression below when require_full_compilation is added</span>
+    <span class="k">if</span> <span class="n">num_supported_ops</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="p">(</span><span class="n">num_supported_ops</span> <span class="o">&lt;</span> <span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">):</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">num_supported_ops</span><span class="si">}</span><span class="s2"> supported operations detected in subgraph containing </span><span class="si">{</span><span class="n">total_ops</span><span class="si">}</span><span class="s2"> computational nodes. &quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;Skipping this subgraph, since min_block_size was detected to be </span><span class="si">{</span><span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">gm</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Detected support for </span><span class="si">{</span><span class="n">num_supported_ops</span><span class="si">}</span><span class="s2"> operators out of </span><span class="si">{</span><span class="n">total_ops</span><span class="si">}</span><span class="s2"> in subgraph.&quot;</span>
+        <span class="p">)</span>
+
+    <span class="c1"># Partition module into components that can be TRT-accelerated</span>
+    <span class="n">fast_partitioner_failed</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="c1"># If specified, try using the fast partitioner and fall back to the global one on failure</span>
+    <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span><span class="p">:</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">partitioned_module</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">fast_partition</span><span class="p">(</span>
+                <span class="n">gm</span><span class="p">,</span>
+                <span class="n">verbose</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">debug</span><span class="p">,</span>
+                <span class="n">min_block_size</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">,</span>
+                <span class="n">torch_executed_ops</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">except</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">passes</span><span class="o">.</span><span class="n">splitter_base</span><span class="o">.</span><span class="n">FxNetSplitterInternalError</span><span class="p">:</span>
+            <span class="n">logger</span><span class="o">.</span><span class="n">error</span><span class="p">(</span>
+                <span class="s2">&quot;Partitioning failed on the subgraph with fast partition. See trace above. &quot;</span>
+                <span class="o">+</span> <span class="s2">&quot;Retrying with global partition.&quot;</span><span class="p">,</span>
+                <span class="n">exc_info</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="p">)</span>
+
+            <span class="n">fast_partitioner_failed</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span><span class="p">:</span>
+        <span class="n">partitioned_module</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">global_partition</span><span class="p">(</span>
+            <span class="n">gm</span><span class="p">,</span>
+            <span class="n">verbose</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">debug</span><span class="p">,</span>
+            <span class="n">min_block_size</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">,</span>
+            <span class="n">torch_executed_ops</span><span class="o">=</span><span class="n">settings</span><span class="o">.</span><span class="n">torch_executed_ops</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="c1"># Store TRT replicas of Torch subgraphs</span>
+    <span class="n">trt_modules</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="c1"># Iterate over all components that can be accelerated</span>
+    <span class="c1"># Generate the corresponding TRT Module for those</span>
+    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">partitioned_module</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
+        <span class="n">submodule</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">partitioned_module</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
+        <span class="c1"># Criteria for a module to be convertible to TRT</span>
+        <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span> <span class="ow">and</span> <span class="s2">&quot;_run_on_acc&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
+            <span class="k">continue</span>
+
+        <span class="c1"># Get the submodule inputs for min, opt, max shapes of the graph inputs</span>
+        <span class="n">submodule_inputs</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">get_submod_inputs</span><span class="p">(</span>
+            <span class="n">partitioned_module</span><span class="p">,</span>
+            <span class="n">submodule</span><span class="p">,</span>
+            <span class="n">sample_inputs</span><span class="p">,</span>
+            <span class="n">to_torch_device</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+        <span class="p">)</span>
+
+        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span>
+            <span class="s2">&quot;Submodule name: </span><span class="si">%s</span><span class="se">\n</span><span class="s2"> Input shapes: </span><span class="si">%s</span><span class="se">\n</span><span class="s2"> </span><span class="si">%s</span><span class="s2">&quot;</span><span class="p">,</span>
+            <span class="nb">str</span><span class="p">(</span><span class="n">name</span><span class="p">),</span>
+            <span class="p">[</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">submodule_inputs</span><span class="p">],</span>
+            <span class="nb">str</span><span class="p">(</span><span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="p">),</span>
+        <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="n">submodule_inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="c1"># Handle long/double inputs if requested by the user</span>
+        <span class="k">if</span> <span class="n">settings</span><span class="o">.</span><span class="n">truncate_long_and_double</span><span class="p">:</span>
+            <span class="n">submodule_inputs</span> <span class="o">=</span> <span class="n">repair_long_or_double_inputs</span><span class="p">(</span>
+                <span class="n">partitioned_module</span><span class="p">,</span>
+                <span class="n">submodule</span><span class="p">,</span>
+                <span class="n">submodule_inputs</span><span class="p">,</span>
+                <span class="n">to_torch_device</span><span class="p">(</span><span class="n">settings</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+                <span class="n">name</span><span class="p">,</span>
+            <span class="p">)</span>
+
+        <span class="c1"># Create TRT engines from submodule</span>
+        <span class="n">trt_module</span> <span class="o">=</span> <span class="n">convert_module</span><span class="p">(</span>
+            <span class="n">submodule</span><span class="p">,</span>
+            <span class="n">submodule_inputs</span><span class="p">,</span>
+            <span class="n">settings</span><span class="o">=</span><span class="n">settings</span><span class="p">,</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">trt_modules</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">trt_module</span>
+
+    <span class="c1"># Replace all FX Modules with TRT Modules</span>
+    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">trt_module</span> <span class="ow">in</span> <span class="n">trt_modules</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="nb">setattr</span><span class="p">(</span><span class="n">partitioned_module</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">trt_module</span><span class="p">)</span>
+
+    <span class="c1"># Reset settings object to user specification after fallback to global partitioning mode</span>
+    <span class="k">if</span> <span class="n">fast_partitioner_failed</span><span class="p">:</span>
+        <span class="n">settings</span><span class="o">.</span><span class="n">use_fast_partitioner</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="k">return</span> <span class="n">partitioned_module</span>
+</pre></div>
+
+             </article>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
+</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>
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+<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/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/using_dla.html">DLA</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/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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+  <h1>Source code for torch_tensorrt.dynamo._exporter</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">operator</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">cast</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch._guards</span> <span class="kn">import</span> <span class="n">detect_fake_mode</span>
+<span class="kn">from</span> <span class="nn">torch._subclasses.fake_tensor</span> <span class="kn">import</span> <span class="n">FakeTensor</span>
+<span class="kn">from</span> <span class="nn">torch.export</span> <span class="kn">import</span> <span class="n">ExportedProgram</span><span class="p">,</span> <span class="n">ExportGraphSignature</span>
+<span class="kn">from</span> <span class="nn">torch.export.exported_program</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">InputKind</span><span class="p">,</span>
+    <span class="n">InputSpec</span><span class="p">,</span>
+    <span class="n">OutputKind</span><span class="p">,</span>
+    <span class="n">OutputSpec</span><span class="p">,</span>
+    <span class="n">TensorArgument</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo</span> <span class="kn">import</span> <span class="n">partitioning</span>
+
+
+<div class="viewcode-block" id="export"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.export">[docs]</a><span class="k">def</span> <span class="nf">export</span><span class="p">(</span>
+    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
+    <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Export the result of TensorRT compilation into the desired output format.</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        gm (torch.fx.GraphModule): Compiled Torch-TensorRT module, generated by ``torch_tensorrt.dynamo.compile``</span>
+<span class="sd">        inputs (torch.Tensor): Torch input tensors</span>
+<span class="sd">        output_format (str): Output format of the result of TRT compilation. Options include &quot;exported_program&quot; (or) &quot;ep&quot; | &quot;torchscript&quot; (or) &quot;ts&quot; | &quot;graph_module&quot; (or) &quot;fx&quot;. Default is &quot;exported_program&quot;</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;torchscript&quot;</span> <span class="ow">or</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;ts&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;exported_program&quot;</span> <span class="ow">or</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;ep&quot;</span><span class="p">:</span>
+        <span class="n">patched_module</span> <span class="o">=</span> <span class="n">transform</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+        <span class="n">exp_program</span> <span class="o">=</span> <span class="n">create_trt_exp_program</span><span class="p">(</span><span class="n">patched_module</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">exp_program</span>
+    <span class="k">elif</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;graph_module&quot;</span> <span class="ow">or</span> <span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;fx&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">gm</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Invalid output format </span><span class="si">{</span><span class="n">output_format</span><span class="si">}</span><span class="s2"> specified. Supported options include exported_program (or) ep | torchscript (or) ts | graph_module (or) fx&quot;</span>
+        <span class="p">)</span></div>
+
+
+<span class="k">def</span> <span class="nf">transform</span><span class="p">(</span>
+    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Transforms the graphmodule by inlining Pytorch and TensorRT submodules.</span>
+<span class="sd">    Inlining collapses submodules into nodes which is necessary for torch.export</span>
+<span class="sd">    serialization.</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        gm (torch.fx.GraphModule): Compiled Torch-TensorRT module, generated by ``torch_tensorrt.dynamo.compile``</span>
+<span class="sd">        inputs (torch.Tensor): Torch input tensors</span>
+
+<span class="sd">    Returns an inlined torch.fx.GraphModule</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Run shape analysis</span>
+    <span class="n">_</span><span class="p">,</span> <span class="n">outputs_map</span> <span class="o">=</span> <span class="n">partitioning</span><span class="o">.</span><span class="n">run_shape_analysis</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+
+    <span class="c1"># Inline TensorRT submodules</span>
+    <span class="n">inline_trt_modules</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">outputs_map</span><span class="p">)</span>
+
+    <span class="c1"># Inline pytorch submodules</span>
+    <span class="n">inline_torch_modules</span><span class="p">(</span><span class="n">gm</span><span class="p">)</span>
+
+    <span class="c1"># Clean the graph</span>
+    <span class="n">gm</span><span class="o">.</span><span class="n">delete_all_unused_submodules</span><span class="p">()</span>
+    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
+    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">gm</span>
+
+
+<span class="k">def</span> <span class="nf">lift</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">graph_signature</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Given an unlifted fx.GraphModule, lift all parameters, buffers into placeholders.</span>
+<span class="sd">    Arguments:</span>
+<span class="sd">        gm (torch.fx.GraphModule): Unlifted GraphModule which contains parameters and buffers as get_attr nodes.</span>
+<span class="sd">        graph_signature (torch.export.ExportGraphSignature): Instance of ExportGraphSignature class created for the output ExportedProgram.</span>
+<span class="sd">        After lifting, this graph_signature will be modified with the parameters and buffers added appropriately.</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        A lifted fx.GraphModule, modified graph_signature and a new state_dict</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Get the state_dict of graph_module. This is different from exported_program.state_dict</span>
+    <span class="c1"># exp_program.state_dict contains parameters and buffers whereas a graph_module&#39;s state_dict</span>
+    <span class="c1"># has all parameters registered as torch.tensors.</span>
+    <span class="n">state_dict</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span>
+
+    <span class="n">fake_mode</span> <span class="o">=</span> <span class="n">detect_fake_mode</span><span class="p">(</span>
+        <span class="nb">tuple</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span><span class="p">)</span>
+    <span class="p">)</span>
+    <span class="k">assert</span> <span class="n">fake_mode</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+
+    <span class="c1"># Locate the user input to insert new placeholders before them</span>
+    <span class="n">first_user_input</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span> <span class="ow">and</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">graph_signature</span><span class="o">.</span><span class="n">user_inputs</span><span class="p">:</span>
+            <span class="n">first_user_input</span> <span class="o">=</span> <span class="n">node</span>
+            <span class="k">break</span>
+
+    <span class="c1"># At first the user_inputs are only present in the graph_signature.input_specs and hence non_user_input_idx=0</span>
+    <span class="c1"># The input_specs should be of the form [params, buffers, constant_tensors, user_inputs]</span>
+    <span class="n">non_user_input_idx</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;get_attr&quot;</span><span class="p">:</span>
+            <span class="n">constant_tensor</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
+            <span class="n">input_kind</span> <span class="o">=</span> <span class="n">InputKind</span><span class="o">.</span><span class="n">CONSTANT_TENSOR</span>
+
+            <span class="c1"># state_dict has these parameters/buffers as torch.Tensors. We override them as torch.nn.Parameter/torch.Tensors respectively.</span>
+            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span> <span class="o">==</span> <span class="n">name</span><span class="p">:</span>
+                    <span class="n">input_kind</span> <span class="o">=</span> <span class="n">InputKind</span><span class="o">.</span><span class="n">PARAMETER</span>
+                    <span class="n">state_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">constant_tensor</span>
+                    <span class="k">break</span>
+            <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">named_buffers</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span> <span class="o">==</span> <span class="n">name</span><span class="p">:</span>
+                    <span class="n">input_kind</span> <span class="o">=</span> <span class="n">InputKind</span><span class="o">.</span><span class="n">BUFFER</span>
+                    <span class="n">state_dict</span><span class="p">[</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">constant_tensor</span>
+                    <span class="k">break</span>
+
+            <span class="c1"># Replace get_attr nodes with placeholder nodes and copy metadata.</span>
+            <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_before</span><span class="p">(</span><span class="n">first_user_input</span><span class="p">):</span>
+                <span class="n">const_placeholder_node</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">placeholder</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">node</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="n">const_placeholder_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">v</span>
+                <span class="n">const_placeholder_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fake_mode</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span>
+                    <span class="n">constant_tensor</span>
+                <span class="p">)</span>
+                <span class="n">node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">const_placeholder_node</span><span class="p">)</span>
+                <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">node</span><span class="p">)</span>
+
+                <span class="c1"># Add these parameters/buffers/constants to the existing graph signature</span>
+                <span class="c1"># before user inputs. These specs are looked up in the state_dict during ExportedProgram creation.</span>
+                <span class="n">graph_signature</span><span class="o">.</span><span class="n">input_specs</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span>
+                    <span class="n">non_user_input_idx</span><span class="p">,</span>
+                    <span class="n">InputSpec</span><span class="p">(</span>
+                        <span class="n">kind</span><span class="o">=</span><span class="n">input_kind</span><span class="p">,</span>
+                        <span class="n">arg</span><span class="o">=</span><span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">const_placeholder_node</span><span class="o">.</span><span class="n">name</span><span class="p">),</span>
+                        <span class="n">target</span><span class="o">=</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">,</span>
+                    <span class="p">),</span>
+                <span class="p">)</span>
+                <span class="n">non_user_input_idx</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
+    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">gm</span><span class="p">,</span> <span class="n">graph_signature</span><span class="p">,</span> <span class="n">state_dict</span>
+
+
+<span class="k">def</span> <span class="nf">get_duplicate_nodes</span><span class="p">(</span>
+    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">submodule</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">],</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    We check if there are duplicate nodes when we copy submodule graph into gm.</span>
+<span class="sd">    Handle the case where the subgraph input placeholders are same as</span>
+<span class="sd">    gm placeholders. This happens when the first submodule in the graph is</span>
+<span class="sd">    a pytorch submodule</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">submodule_placeholder_inputs</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span>
+    <span class="p">]</span>
+    <span class="n">submodule_input_node_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule_placeholder_inputs</span><span class="p">]</span>
+    <span class="n">gm_node_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">]</span>
+    <span class="n">submodule_duplicate_inputs</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule_placeholder_inputs</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">gm_node_names</span>
+    <span class="p">]</span>
+    <span class="n">gm_duplicate_inputs</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">submodule_input_node_names</span>
+    <span class="p">]</span>
+    <span class="k">return</span> <span class="n">submodule_duplicate_inputs</span><span class="p">,</span> <span class="n">gm_duplicate_inputs</span>
+
+
+<span class="k">def</span> <span class="nf">inline_torch_modules</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Inline a submodule within the parent graph (gm). All `call_module` nodes</span>
+<span class="sd">    should be replaced by their nodes in the submodule.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Clean the graph</span>
+    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
+    <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">gm_node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_module&quot;</span> <span class="ow">and</span> <span class="s2">&quot;_run_on_gpu&quot;</span> <span class="ow">in</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
+            <span class="n">submodule</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+            <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_before</span><span class="p">(</span><span class="n">gm_node</span><span class="p">):</span>
+                <span class="c1"># Get inputs of submodule node which are most likely outputs of a previous TRT node</span>
+                <span class="c1"># or a placeholder of the main graph</span>
+                <span class="n">submodule_inputs</span> <span class="o">=</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">args</span>
+
+                <span class="n">submodule_duplicate_inputs</span><span class="p">,</span> <span class="n">gm_duplicate_inputs</span> <span class="o">=</span> <span class="n">get_duplicate_nodes</span><span class="p">(</span>
+                    <span class="n">gm</span><span class="p">,</span> <span class="n">submodule</span>
+                <span class="p">)</span>
+                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">submodule_duplicate_inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">gm_duplicate_inputs</span><span class="p">)</span>
+                <span class="c1"># Avoid creating new copies of duplicate inputs by creating a mapping</span>
+                <span class="n">val_map</span> <span class="o">=</span> <span class="p">{}</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">submodule_duplicate_inputs</span><span class="p">)):</span>
+                    <span class="n">val_map</span><span class="p">[</span><span class="n">submodule_duplicate_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="n">gm_duplicate_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+                <span class="c1"># Copy all nodes in the submodule into gm and</span>
+                <span class="c1"># store the output node of this submodule which is now present in gm</span>
+                <span class="n">submodule_output</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">graph_copy</span><span class="p">(</span><span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> <span class="n">val_map</span><span class="p">)</span>
+
+                <span class="c1"># Get their references (since we copied) in the parent graph (gm)</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">submodule_duplicate_inputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">submodule_placeholder_input_names</span> <span class="o">=</span> <span class="p">[</span>
+                        <span class="n">node</span><span class="o">.</span><span class="n">name</span>
+                        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">submodule</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span>
+                        <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span>
+                    <span class="p">]</span>
+                    <span class="n">gm_added_placeholder_inputs</span> <span class="o">=</span> <span class="p">[</span>
+                        <span class="n">node</span>
+                        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span>
+                        <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">submodule_placeholder_input_names</span>
+                    <span class="p">]</span>
+
+                    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">submodule_inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">gm_added_placeholder_inputs</span><span class="p">)</span>
+
+                    <span class="c1"># Replace the added placeholder inputs with original inputs to this submodule node</span>
+                    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">gm_added_placeholder_inputs</span><span class="p">)):</span>
+                        <span class="n">gm_added_placeholder_inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span>
+                            <span class="n">submodule_inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+                        <span class="p">)</span>
+
+                    <span class="c1"># Erase the placeholder input nodes in the gm</span>
+                    <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">gm_added_placeholder_inputs</span><span class="p">)):</span>
+                        <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">gm_added_placeholder_inputs</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>
+
+                <span class="c1"># Replace the pytorch submodule node (call_module) with the inlined subgraph output</span>
+                <span class="n">gm_node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">submodule_output</span><span class="p">)</span>
+
+                <span class="c1"># copy the attributes of the submodule into gm (graph_copy doesn&#39;t do this)</span>
+                <span class="n">copy_submodule_attributes</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">gm_node</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+
+            <span class="c1"># Erase the pytorch submodule (call_module) node</span>
+            <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">gm_node</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">gm</span>
+
+
+<span class="k">def</span> <span class="nf">copy_submodule_attributes</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">submod_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Copy the getattr attriibutes from submodule to parent module gm.</span>
+<span class="sd">    The graph_copy call doesn&#39;t do this for us unfortunately.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">param</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="n">submod_name</span> <span class="o">+</span> <span class="s2">&quot;.&quot;</span><span class="p">):</span>
+            <span class="n">attr_name</span> <span class="o">=</span> <span class="n">param</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">submod_name</span> <span class="o">+</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+            <span class="n">gm</span><span class="o">.</span><span class="n">register_parameter</span><span class="p">(</span><span class="n">attr_name</span><span class="p">,</span> <span class="n">param</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="k">for</span> <span class="n">buffer</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">named_buffers</span><span class="p">():</span>
+        <span class="k">if</span> <span class="n">buffer</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="n">submod_name</span> <span class="o">+</span> <span class="s2">&quot;.&quot;</span><span class="p">):</span>
+            <span class="n">attr_name</span> <span class="o">=</span> <span class="n">buffer</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">submod_name</span> <span class="o">+</span> <span class="s2">&quot;.&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
+            <span class="n">gm</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="n">attr_name</span><span class="p">,</span> <span class="n">buffer</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+
+<span class="k">def</span> <span class="nf">create_trt_exp_program</span><span class="p">(</span>
+    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ExportedProgram</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Creates a new Exported Program. This function takes an torch.fx.GraphModule which has TRT engines</span>
+<span class="sd">    and constructs an Exported Program object with the new IO node names and state_dict</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">input_nodes</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span><span class="p">]</span>
+    <span class="n">output_nodes</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;output&quot;</span><span class="p">]</span>
+    <span class="k">assert</span> <span class="n">output_nodes</span>
+    <span class="n">output_nodes</span> <span class="o">=</span> <span class="n">output_nodes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="n">input_specs</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">InputSpec</span><span class="p">(</span><span class="n">InputKind</span><span class="o">.</span><span class="n">USER_INPUT</span><span class="p">,</span> <span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">),</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">input_nodes</span>
+    <span class="p">]</span>
+    <span class="n">output_specs</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">OutputSpec</span><span class="p">(</span><span class="n">OutputKind</span><span class="o">.</span><span class="n">USER_OUTPUT</span><span class="p">,</span> <span class="n">TensorArgument</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">node</span><span class="o">.</span><span class="n">name</span><span class="p">),</span> <span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">output_nodes</span>
+    <span class="p">]</span>
+
+    <span class="n">trt_graph_signature</span> <span class="o">=</span> <span class="n">ExportGraphSignature</span><span class="p">(</span>
+        <span class="n">input_specs</span><span class="o">=</span><span class="n">input_specs</span><span class="p">,</span> <span class="n">output_specs</span><span class="o">=</span><span class="n">output_specs</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Lift parameters/buffers/constants in the graph</span>
+    <span class="c1"># torch.export serialization expects them to be lifted</span>
+    <span class="n">gm</span><span class="p">,</span> <span class="n">trt_graph_signature</span><span class="p">,</span> <span class="n">state_dict</span> <span class="o">=</span> <span class="n">lift</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">trt_graph_signature</span><span class="p">)</span>
+
+    <span class="n">trt_exp_program</span> <span class="o">=</span> <span class="n">ExportedProgram</span><span class="p">(</span>
+        <span class="n">gm</span><span class="p">,</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="p">,</span> <span class="n">trt_graph_signature</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="p">{},</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+    <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">trt_exp_program</span>
+
+
+<span class="k">def</span> <span class="nf">inline_trt_modules</span><span class="p">(</span>
+    <span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">outputs_map</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Replace TRT submodules with trt engine nodes.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
+        <span class="k">if</span> <span class="s2">&quot;_run_on_acc&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
+            <span class="k">continue</span>
+        <span class="c1"># Get the TRT submodule</span>
+        <span class="n">trt_module</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">gm</span><span class="p">,</span> <span class="n">name</span><span class="p">)</span>
+
+        <span class="c1"># Ensure the trt module node in the main graph (gm) has inputs</span>
+        <span class="n">trt_module_node</span> <span class="o">=</span> <span class="p">[</span><span class="n">node</span> <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span> <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="n">name</span><span class="p">]</span>
+        <span class="k">assert</span> <span class="n">trt_module_node</span>
+        <span class="n">trt_module_node</span> <span class="o">=</span> <span class="n">trt_module_node</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">assert</span> <span class="n">trt_module_node</span><span class="o">.</span><span class="n">args</span>
+
+        <span class="n">num_outputs</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs_map</span><span class="p">[</span><span class="n">trt_module_node</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
+        <span class="c1"># Insert a call_function node to perform inference on TRT engine</span>
+        <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_before</span><span class="p">(</span><span class="n">trt_module_node</span><span class="p">):</span>
+            <span class="n">trt_node</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">execute_engine</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+                <span class="p">(</span><span class="n">trt_module_node</span><span class="o">.</span><span class="n">args</span><span class="p">,</span> <span class="n">trt_module</span><span class="o">.</span><span class="n">engine</span><span class="p">),</span>
+            <span class="p">)</span>
+            <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">assert</span> <span class="n">num_outputs</span> <span class="o">&gt;</span> <span class="mi">0</span>
+            <span class="c1"># Generate meta data for TRT node (a FakeTensor with corresponding output shape)</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_outputs</span><span class="p">):</span>
+                <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="n">cast</span><span class="p">(</span>
+                        <span class="n">FakeTensor</span><span class="p">,</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">empty_strided</span><span class="p">(</span>
+                            <span class="nb">tuple</span><span class="p">(</span><span class="n">outputs_map</span><span class="p">[</span><span class="n">trt_module_node</span><span class="o">.</span><span class="n">name</span><span class="p">][</span><span class="n">idx</span><span class="p">]),</span>
+                            <span class="nb">tuple</span><span class="p">([</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs_map</span><span class="p">[</span><span class="n">trt_module_node</span><span class="o">.</span><span class="n">name</span><span class="p">][</span><span class="n">idx</span><span class="p">])),</span>
+                        <span class="p">),</span>
+                    <span class="p">)</span>
+                <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">num_outputs</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="c1"># Insert getitem nodes as outputs (for export serialization to work)</span>
+            <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_after</span><span class="p">(</span><span class="n">trt_node</span><span class="p">):</span>
+                <span class="n">getitem_output</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span><span class="n">operator</span><span class="o">.</span><span class="n">getitem</span><span class="p">,</span> <span class="p">(</span><span class="n">trt_node</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+                <span class="n">getitem_output</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span>
+            <span class="n">trt_module_node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">getitem_output</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Multiple outputs case:</span>
+            <span class="c1"># Replace uses of submodule with the trt_node.</span>
+            <span class="c1"># getitem nodes are already added inherently by the partitioner</span>
+            <span class="n">trt_module_node</span><span class="o">.</span><span class="n">replace_all_uses_with</span><span class="p">(</span><span class="n">trt_node</span><span class="p">)</span>
+            <span class="n">getitem_nodes</span> <span class="o">=</span> <span class="n">trt_node</span><span class="o">.</span><span class="n">users</span>
+            <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">getitem_node</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">getitem_nodes</span><span class="p">):</span>
+                <span class="n">getitem_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">trt_node</span><span class="o">.</span><span class="n">meta</span><span class="p">[</span><span class="s2">&quot;val&quot;</span><span class="p">][</span><span class="n">idx</span><span class="p">]</span>
+
+        <span class="c1"># Erase the TRT submodule (call_module) node.</span>
+        <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">erase_node</span><span class="p">(</span><span class="n">trt_module_node</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">gm</span>
+</pre></div>
+
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diff --git a/docs/v2.2.0/_modules/torch_tensorrt/dynamo/_settings.html b/docs/v2.2.0/_modules/torch_tensorrt/dynamo/_settings.html
new file mode 100644
index 0000000000..2a8e051915
--- /dev/null
+++ b/docs/v2.2.0/_modules/torch_tensorrt/dynamo/_settings.html
@@ -0,0 +1,829 @@
+
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
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+  <meta charset="utf-8">
+  
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  
+  <title>torch_tensorrt.dynamo._settings &mdash; Torch-TensorRT v2.2.0 documentation</title>
+  
+
+  
+  
+  
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+  
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+  <h1>Source code for torch_tensorrt.dynamo._settings</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span><span class="p">,</span> <span class="n">field</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Collection</span><span class="p">,</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">tensorrt</span> <span class="kn">import</span> <span class="n">EngineCapability</span>
+<span class="kn">from</span> <span class="nn">torch.fx.node</span> <span class="kn">import</span> <span class="n">Target</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo._defaults</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">DEBUG</span><span class="p">,</span>
+    <span class="n">DISABLE_TF32</span><span class="p">,</span>
+    <span class="n">DLA_GLOBAL_DRAM_SIZE</span><span class="p">,</span>
+    <span class="n">DLA_LOCAL_DRAM_SIZE</span><span class="p">,</span>
+    <span class="n">DLA_SRAM_SIZE</span><span class="p">,</span>
+    <span class="n">ENABLE_EXPERIMENTAL_DECOMPOSITIONS</span><span class="p">,</span>
+    <span class="n">ENGINE_CAPABILITY</span><span class="p">,</span>
+    <span class="n">MAX_AUX_STREAMS</span><span class="p">,</span>
+    <span class="n">MIN_BLOCK_SIZE</span><span class="p">,</span>
+    <span class="n">NUM_AVG_TIMING_ITERS</span><span class="p">,</span>
+    <span class="n">OPTIMIZATION_LEVEL</span><span class="p">,</span>
+    <span class="n">OUTPUT_FORMAT</span><span class="p">,</span>
+    <span class="n">PASS_THROUGH_BUILD_FAILURES</span><span class="p">,</span>
+    <span class="n">PRECISION</span><span class="p">,</span>
+    <span class="n">REFIT</span><span class="p">,</span>
+    <span class="n">REQUIRE_FULL_COMPILATION</span><span class="p">,</span>
+    <span class="n">SPARSE_WEIGHTS</span><span class="p">,</span>
+    <span class="n">TRUNCATE_LONG_AND_DOUBLE</span><span class="p">,</span>
+    <span class="n">USE_FAST_PARTITIONER</span><span class="p">,</span>
+    <span class="n">USE_PYTHON_RUNTIME</span><span class="p">,</span>
+    <span class="n">VERSION_COMPATIBLE</span><span class="p">,</span>
+    <span class="n">WORKSPACE_SIZE</span><span class="p">,</span>
+    <span class="n">default_device</span><span class="p">,</span>
+<span class="p">)</span>
+
+
+<div class="viewcode-block" id="CompilationSettings"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.CompilationSettings">[docs]</a><span class="nd">@dataclass</span>
+<span class="k">class</span> <span class="nc">CompilationSettings</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compilation settings for Torch-TensorRT Dynamo Paths</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        precision (torch.dtype): Model Layer precision</span>
+<span class="sd">        debug (bool): Whether to print out verbose debugging information</span>
+<span class="sd">        workspace_size (int): Workspace TRT is allowed to use for the module (0 is default)</span>
+<span class="sd">        min_block_size (int): Minimum number of operators per TRT-Engine Block</span>
+<span class="sd">        torch_executed_ops (Collection[Target]): Collection of operations to run in Torch, regardless of converter coverage</span>
+<span class="sd">        pass_through_build_failures (bool): Whether to fail on TRT engine build errors (True) or not (False)</span>
+<span class="sd">        max_aux_streams (Optional[int]): Maximum number of allowed auxiliary TRT streams for each engine</span>
+<span class="sd">        version_compatible (bool): Provide version forward-compatibility for engine plan files</span>
+<span class="sd">        optimization_level (Optional[int]): Builder optimization 0-5, higher levels imply longer build time,</span>
+<span class="sd">            searching for more optimization options. TRT defaults to 3</span>
+<span class="sd">        use_python_runtime (Optional[bool]): Whether to strictly use Python runtime or C++ runtime. To auto-select a runtime</span>
+<span class="sd">            based on C++ dependency presence (preferentially choosing C++ runtime if available), leave the</span>
+<span class="sd">            argument as None</span>
+<span class="sd">        truncate_long_and_double (bool): Whether to truncate int64/float64 TRT engine inputs or weights to int32/float32</span>
+<span class="sd">        use_fast_partitioner (bool): Whether to use the fast or global graph partitioning system</span>
+<span class="sd">        enable_experimental_decompositions (bool): Whether to enable all core aten decompositions</span>
+<span class="sd">            or only a selected subset of them</span>
+<span class="sd">        device (Device): GPU to compile the model on</span>
+<span class="sd">        require_full_compilation (bool): Whether to require the graph is fully compiled in TensorRT.</span>
+<span class="sd">            Only applicable for `ir=&quot;dynamo&quot;`; has no effect for `torch.compile` path</span>
+<span class="sd">        disable_tf32 (bool): Whether to disable TF32 computation for TRT layers</span>
+<span class="sd">        sparse_weights (bool): Whether to allow the builder to use sparse weights</span>
+<span class="sd">        refit (bool): Whether to build a refittable engine</span>
+<span class="sd">        engine_capability (trt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
+<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
+<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
+<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
+<span class="sd">        output_format (str): Output format of the result of TRT compilation. Options include &quot;exported_program&quot; (or) &quot;ep&quot; | &quot;torchscript&quot; (or) &quot;ts&quot; | &quot;graph_module&quot; (or) &quot;fx&quot;. Default is &quot;exported_program&quot;</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">precision</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">PRECISION</span>
+    <span class="n">debug</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">DEBUG</span>
+    <span class="n">workspace_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">WORKSPACE_SIZE</span>
+    <span class="n">min_block_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">MIN_BLOCK_SIZE</span>
+    <span class="n">torch_executed_ops</span><span class="p">:</span> <span class="n">Collection</span><span class="p">[</span><span class="n">Target</span><span class="p">]</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span><span class="n">default_factory</span><span class="o">=</span><span class="nb">set</span><span class="p">)</span>
+    <span class="n">pass_through_build_failures</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">PASS_THROUGH_BUILD_FAILURES</span>
+    <span class="n">max_aux_streams</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">MAX_AUX_STREAMS</span>
+    <span class="n">version_compatible</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">VERSION_COMPATIBLE</span>
+    <span class="n">optimization_level</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">OPTIMIZATION_LEVEL</span>
+    <span class="n">use_python_runtime</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">USE_PYTHON_RUNTIME</span>
+    <span class="n">truncate_long_and_double</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">TRUNCATE_LONG_AND_DOUBLE</span>
+    <span class="n">use_fast_partitioner</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">USE_FAST_PARTITIONER</span>
+    <span class="n">enable_experimental_decompositions</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">ENABLE_EXPERIMENTAL_DECOMPOSITIONS</span>
+    <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span><span class="n">default_factory</span><span class="o">=</span><span class="n">default_device</span><span class="p">)</span>
+    <span class="n">require_full_compilation</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">REQUIRE_FULL_COMPILATION</span>
+    <span class="n">disable_tf32</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">DISABLE_TF32</span>
+    <span class="n">sparse_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">SPARSE_WEIGHTS</span>
+    <span class="n">refit</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">REFIT</span>
+    <span class="n">engine_capability</span><span class="p">:</span> <span class="n">EngineCapability</span> <span class="o">=</span> <span class="n">ENGINE_CAPABILITY</span>
+    <span class="n">num_avg_timing_iters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">NUM_AVG_TIMING_ITERS</span>
+    <span class="n">dla_sram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DLA_SRAM_SIZE</span>
+    <span class="n">dla_local_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DLA_LOCAL_DRAM_SIZE</span>
+    <span class="n">dla_global_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">DLA_GLOBAL_DRAM_SIZE</span>
+    <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">OUTPUT_FORMAT</span></div>
+</pre></div>
+
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+
+
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+      <li>torch_tensorrt.dynamo._tracer</li>
+    
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+      <li class="pytorch-breadcrumbs-aside">
+        
+      </li>
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <h1>Source code for torch_tensorrt.dynamo._tracer</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch.export</span> <span class="kn">import</span> <span class="n">Dim</span><span class="p">,</span> <span class="n">export</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Input</span> <span class="kn">import</span> <span class="n">Input</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo._defaults</span> <span class="kn">import</span> <span class="n">DEBUG</span><span class="p">,</span> <span class="n">default_device</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo.utils</span> <span class="kn">import</span> <span class="n">get_torch_inputs</span><span class="p">,</span> <span class="n">set_log_level</span><span class="p">,</span> <span class="n">to_torch_device</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="trace"><a class="viewcode-back" href="../../../py_api/dynamo.html#torch_tensorrt.dynamo.trace">[docs]</a><span class="k">def</span> <span class="nf">trace</span><span class="p">(</span>
+    <span class="n">mod</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Any</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">ExportedProgram</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Exports a ``torch.export.ExportedProgram`` from a ``torch.nn.Module`` or ``torch.fx.GraphModule`` specifically targeting being compiled with Torch-TensorRT</span>
+
+<span class="sd">    Exports a ``torch.export.ExportedProgram`` from either a ``torch.nn.Module`` or torch.fx.GraphModule``. Runs specific operator decompositions geared towards</span>
+<span class="sd">    compilation by Torch-TensorRT&#39;s dynamo frontend.</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        mod (torch.nn.Module | torch.fx.GraphModule): Source module to later be compiled by Torch-TensorRT&#39;s dynamo fronted</span>
+<span class="sd">        inputs (Tuple[Any, ...]): List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        device (Union(torch.device, dict)): Target device for TensorRT engines to run on ::</span>
+
+<span class="sd">            device=torch.device(&quot;cuda:0&quot;)</span>
+
+<span class="sd">        debug (bool): Enable debuggable engine</span>
+<span class="sd">        enable_experimental_decompositions (bool): Use the full set of operator decompositions. These decompositions may not be tested but serve to make the grap easier to covert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</span>
+<span class="sd">        **kwargs: Any,</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.fx.GraphModule: Compiled FX Module, when run it will execute via TensorRT</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Set log level at the top of compilation (torch_tensorrt.dynamo)</span>
+    <span class="n">debug</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;debug&quot;</span><span class="p">,</span> <span class="n">DEBUG</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">debug</span><span class="p">:</span>
+        <span class="n">set_log_level</span><span class="p">(</span><span class="n">logger</span><span class="o">.</span><span class="n">parent</span><span class="p">,</span> <span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">)</span>
+
+    <span class="n">device</span> <span class="o">=</span> <span class="n">to_torch_device</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">default_device</span><span class="p">()))</span>
+    <span class="n">torch_inputs</span> <span class="o">=</span> <span class="n">get_torch_inputs</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+    <span class="n">dynamic_shapes</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="nb">input</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">Input</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">input</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">input</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Expected a name for a dynamic input with shape </span><span class="si">{</span><span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2"> but found none&quot;</span>
+                <span class="p">)</span>
+            <span class="n">min_shape</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+            <span class="n">opt_shape</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+            <span class="n">max_shape</span> <span class="o">=</span> <span class="nb">input</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">min_shape</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">opt_shape</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">max_shape</span><span class="p">)</span>
+            <span class="n">dynamic_dims</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">min_shape</span><span class="p">)):</span>
+                <span class="k">if</span> <span class="n">min_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">==</span> <span class="n">opt_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">==</span> <span class="n">max_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]:</span>
+                    <span class="k">continue</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">dynamic_dims</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">=</span> <span class="n">Dim</span><span class="p">(</span>
+                        <span class="nb">input</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">dim</span><span class="p">),</span>
+                        <span class="nb">min</span><span class="o">=</span><span class="n">min_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">],</span>
+                        <span class="nb">max</span><span class="o">=</span><span class="n">max_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">],</span>
+                    <span class="p">)</span>
+
+            <span class="n">dynamic_shapes</span><span class="p">[</span><span class="nb">input</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">dynamic_dims</span>
+
+    <span class="n">exp_program</span> <span class="o">=</span> <span class="n">export</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">torch_inputs</span><span class="p">),</span> <span class="n">dynamic_shapes</span><span class="o">=</span><span class="n">dynamic_shapes</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">exp_program</span></div>
+</pre></div>
+
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diff --git a/docs/v2.2.0/_modules/torch_tensorrt/fx/fx2trt.html b/docs/v2.2.0/_modules/torch_tensorrt/fx/fx2trt.html
new file mode 100644
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+++ b/docs/v2.2.0/_modules/torch_tensorrt/fx/fx2trt.html
@@ -0,0 +1,1123 @@
+
+
+
+<!DOCTYPE html>
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+              
+  <h1>Source code for torch_tensorrt.fx.fx2trt</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">warnings</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NamedTuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span>
+
+<span class="c1"># @manual=//deeplearning/trt/python:py_tensorrt</span>
+<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.fx</span>
+<span class="kn">from</span> <span class="nn">torch._ops</span> <span class="kn">import</span> <span class="n">OpOverload</span>
+<span class="kn">from</span> <span class="nn">torch.fx.node</span> <span class="kn">import</span> <span class="n">_get_qualified_name</span>
+<span class="kn">from</span> <span class="nn">torch.fx.passes.shape_prop</span> <span class="kn">import</span> <span class="n">TensorMetadata</span>
+
+<span class="kn">from</span> <span class="nn">.converter_registry</span> <span class="kn">import</span> <span class="n">CONVERTERS</span>
+<span class="kn">from</span> <span class="nn">.input_tensor_spec</span> <span class="kn">import</span> <span class="n">InputTensorSpec</span>
+<span class="kn">from</span> <span class="nn">.observer</span> <span class="kn">import</span> <span class="n">Observer</span>
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">Frameworks</span><span class="p">,</span> <span class="n">LowerPrecision</span><span class="p">,</span> <span class="n">get_dynamic_dims</span><span class="p">,</span> <span class="n">unified_dtype_converter</span>
+
+<span class="n">_LOGGER</span><span class="p">:</span> <span class="n">logging</span><span class="o">.</span><span class="n">Logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<span class="n">TRT_INTERPRETER_CALL_PRE_OBSERVER</span><span class="p">:</span> <span class="n">Observer</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">],</span> <span class="kc">None</span><span class="p">]]</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="n">Observer</span><span class="p">(</span><span class="s2">&quot;TRT_INTERPRETER_CALL_PRE_OBSERVER&quot;</span><span class="p">)</span>
+<span class="p">)</span>
+
+
+<div class="viewcode-block" id="TRTInterpreterResult"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.TRTInterpreterResult">[docs]</a><span class="k">class</span> <span class="nc">TRTInterpreterResult</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
+    <span class="n">engine</span><span class="p">:</span> <span class="n">Any</span>
+    <span class="n">input_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+    <span class="n">output_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span>
+    <span class="n">serialized_cache</span><span class="p">:</span> <span class="nb">bytearray</span></div>
+
+
+<div class="viewcode-block" id="TRTInterpreter"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.TRTInterpreter">[docs]</a><span class="k">class</span> <span class="nc">TRTInterpreter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">Interpreter</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span>
+        <span class="n">input_specs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">InputTensorSpec</span><span class="p">],</span>
+        <span class="n">explicit_batch_dimension</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">explicit_precision</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">logger_level</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">logger</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="p">(</span><span class="n">logger_level</span> <span class="ow">or</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">WARNING</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Builder</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logger</span><span class="p">)</span>
+
+        <span class="n">flag</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="n">explicit_batch_dimension</span><span class="p">:</span>
+            <span class="n">EXPLICIT_BATCH</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="p">(</span><span class="nb">int</span><span class="p">)(</span>
+                <span class="n">trt</span><span class="o">.</span><span class="n">NetworkDefinitionCreationFlag</span><span class="o">.</span><span class="n">EXPLICIT_BATCH</span>
+            <span class="p">)</span>
+            <span class="n">flag</span> <span class="o">|=</span> <span class="n">EXPLICIT_BATCH</span>
+
+        <span class="k">if</span> <span class="n">explicit_precision</span><span class="p">:</span>
+            <span class="n">EXPLICIT_PRECISION</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="p">(</span><span class="nb">int</span><span class="p">)(</span>
+                <span class="n">trt</span><span class="o">.</span><span class="n">NetworkDefinitionCreationFlag</span><span class="o">.</span><span class="n">EXPLICIT_PRECISION</span>
+            <span class="p">)</span>
+            <span class="n">flag</span> <span class="o">|=</span> <span class="n">EXPLICIT_PRECISION</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">network</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_network</span><span class="p">(</span><span class="n">flag</span><span class="p">)</span>
+
+        <span class="n">missing_ops</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">validate_conversion</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">missing_ops</span><span class="p">:</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span>
+                <span class="s2">&quot;Interpretation will fail due to missing operations </span><span class="se">\n</span><span class="s2">&quot;</span>
+                <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">missing_ops</span><span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span> <span class="o">=</span> <span class="n">input_specs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">validate_input_specs</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">,</span> <span class="n">TensorMetadata</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">dict</span><span class="p">()</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">validate_input_specs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">shape</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">,</span> <span class="n">has_batch_dim</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="p">(</span>
+                    <span class="n">has_batch_dim</span>
+                <span class="p">),</span> <span class="s2">&quot;It&#39;s required to specify batch dimension when it&#39;s explicit in TensorRT network.&quot;</span>
+
+            <span class="n">dynamic_dims</span> <span class="o">=</span> <span class="n">get_dynamic_dims</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dynamic_dims</span><span class="p">):</span>
+                <span class="k">assert</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">,</span> <span class="p">(</span>
+                    <span class="s2">&quot;Can&#39;t have dynamic dim when &quot;</span>
+                    <span class="sa">f</span><span class="s2">&quot;batch dim is implicit, got </span><span class="si">{</span><span class="n">shape</span><span class="si">}</span><span class="s2">.&quot;</span>
+                <span class="p">)</span>
+                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span>
+                    <span class="n">shape_ranges</span>
+                <span class="p">),</span> <span class="s2">&quot;shape_ranges must be provided when shape has dynamic dim.&quot;</span>
+
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
+                    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">),</span> <span class="p">(</span>
+                        <span class="s2">&quot;Number of optimization &quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot;profiles </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">)</span><span class="si">}</span><span class="s2"> doesn&#39;t match with the number of shape_range&quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span><span class="si">}</span><span class="s2"> provided.&quot;</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span> <span class="o">=</span> <span class="p">[</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_optimization_profile</span><span class="p">()</span>
+                        <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">))</span>
+                    <span class="p">]</span>
+
+                <span class="k">for</span> <span class="n">shape_range</span> <span class="ow">in</span> <span class="n">shape_ranges</span><span class="p">:</span>
+                    <span class="k">assert</span> <span class="p">(</span>
+                        <span class="nb">len</span><span class="p">(</span><span class="n">shape_range</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span>
+                    <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Expect three elements in shape_range, got </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape_range</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">s</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shape_range</span><span class="p">),</span> <span class="p">(</span>
+                        <span class="s2">&quot;Expect elements in shape_range&quot;</span>
+                        <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2"> have the same number of dimension as the provided shape </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span>
+                    <span class="p">)</span>
+
+                    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)):</span>
+                        <span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">dynamic_dims</span><span class="p">:</span>
+                            <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span>
+                                <span class="n">shape_range</span><span class="p">[</span><span class="n">j</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">shape_range</span><span class="p">[</span><span class="n">j</span> <span class="o">+</span> <span class="mi">1</span><span class="p">][</span><span class="n">i</span><span class="p">]</span>
+                                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+                            <span class="p">),</span> <span class="p">(</span>
+                                <span class="s2">&quot;Expect dynamic dim&quot;</span>
+                                <span class="sa">f</span><span class="s2">&quot; </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2"> to have incremental value for shapes in shape_range </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2">.&quot;</span>
+                            <span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="k">assert</span> <span class="nb">all</span><span class="p">(</span><span class="n">s</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">==</span> <span class="n">shape</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">shape_range</span><span class="p">),</span> <span class="p">(</span>
+                                <span class="sa">f</span><span class="s2">&quot;Expect non dynamic dim </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2"> to be the same&quot;</span>
+                                <span class="sa">f</span><span class="s2">&quot; for all shapes in shape_range </span><span class="si">{</span><span class="n">shape_range</span><span class="si">}</span><span class="s2">.&quot;</span>
+                            <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span>
+                <span class="p">),</span> <span class="s2">&quot;shape_ranges are provided for input that doesn&#39;t have dynamic dim.&quot;</span>
+
+    <span class="k">def</span> <span class="nf">validate_conversion</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">missing_converter</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_function&quot;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">):</span>
+                <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">_get_qualified_name</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_method&quot;</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">):</span>
+                <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> torch.Tensor.</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;call_module&quot;</span><span class="p">:</span>
+                <span class="n">submod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">target</span><span class="p">)</span>
+                <span class="n">submod_type</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">submod</span><span class="p">,</span> <span class="s2">&quot;_base_class_origin&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">submod</span><span class="p">))</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">submod_type</span><span class="p">):</span>
+                    <span class="n">missing_converter</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">node</span><span class="o">.</span><span class="n">op</span><span class="si">}</span><span class="s2"> </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">typename</span><span class="p">(</span><span class="n">submod_type</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">missing_converter</span>
+
+    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">max_batch_size</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span>
+        <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">25</span><span class="p">,</span>
+        <span class="n">lower_precision</span><span class="o">=</span><span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">,</span>
+        <span class="n">sparse_weights</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">force_fp32_output</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">strict_type_constraints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="n">algorithm_selector</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">timing_cache</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">profiling_verbosity</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">tactic_sources</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TRTInterpreterResult</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Build TensorRT engine with some configs.</span>
+<span class="sd">        Args:</span>
+<span class="sd">            max_batch_size: set accordingly for maximum batch size you will use.</span>
+<span class="sd">            max_workspace_size: set to the maximum size we can afford for temporary buffer</span>
+<span class="sd">            lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).</span>
+<span class="sd">            sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity</span>
+<span class="sd">            force_fp32_output: force output to be fp32</span>
+<span class="sd">            strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric reasons.</span>
+<span class="sd">            algorithm_selector: set up algorithm selection for certain layer</span>
+<span class="sd">            timing_cache: enable timing cache for TensorRT</span>
+<span class="sd">            profiling_verbosity: TensorRT logging level</span>
+<span class="sd">        Return:</span>
+<span class="sd">            TRTInterpreterResult</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">TRT_INTERPRETER_CALL_PRE_OBSERVER</span><span class="o">.</span><span class="n">observe</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">)</span>
+
+        <span class="c1"># For float outputs, we set their dtype to fp16 only if lower_precision == LowerPrecision.FP16 and</span>
+        <span class="c1"># force_fp32_output=False.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_fp16</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="ow">not</span> <span class="n">force_fp32_output</span> <span class="ow">and</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
+        <span class="p">)</span>
+
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">INT8</span>
+            <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">platform_has_fast_int8</span>
+        <span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;Current platform doesn&#39;t support fast native int8!&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
+            <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">platform_has_fast_fp16</span>
+        <span class="p">):</span>
+            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Current platform doesn&#39;t support fast native fp16!&quot;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">run_module_start_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run</span><span class="p">()</span>
+        <span class="n">_LOGGER</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;TRT INetwork construction elapsed time: </span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">run_module_start_time</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+        <span class="n">build_engine_start_time</span> <span class="o">=</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">max_batch_size</span> <span class="o">=</span> <span class="n">max_batch_size</span>
+        <span class="n">builder_config</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">create_builder_config</span><span class="p">()</span>
+        <span class="n">builder_config</span><span class="o">.</span><span class="n">max_workspace_size</span> <span class="o">=</span> <span class="n">max_workspace_size</span>
+
+        <span class="c1"># Speed up TRT build time in the test environment</span>
+        <span class="k">if</span> <span class="n">trt</span><span class="o">.</span><span class="n">__version__</span> <span class="o">&gt;=</span> <span class="s2">&quot;8.6&quot;</span> <span class="ow">and</span> <span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;TRT_TEST_ENV&quot;</span><span class="p">,</span> <span class="s2">&quot;0&quot;</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;1&quot;</span><span class="p">:</span>
+            <span class="n">_LOGGER</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Set TRT optimization level to 0&quot;</span><span class="p">)</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">builder_optimization_level</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="n">cache</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="n">timing_cache</span><span class="p">:</span>
+            <span class="n">cache_file</span> <span class="o">=</span> <span class="n">numpy</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">timing_cache</span><span class="p">)</span>
+            <span class="n">cache</span> <span class="o">=</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">create_timing_cache</span><span class="p">(</span><span class="n">cache_file</span><span class="o">.</span><span class="n">tobytes</span><span class="p">())</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">cache</span> <span class="o">=</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">create_timing_cache</span><span class="p">(</span><span class="sa">b</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="n">builder_config</span><span class="o">.</span><span class="n">set_timing_cache</span><span class="p">(</span><span class="n">cache</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">trt</span><span class="o">.</span><span class="n">__version__</span> <span class="o">&gt;=</span> <span class="s2">&quot;8.2&quot;</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">profiling_verbosity</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">profiling_verbosity</span>
+                <span class="k">if</span> <span class="n">profiling_verbosity</span>
+                <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">LAYER_NAMES_ONLY</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">FP16</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">lower_precision</span> <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">INT8</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">INT8</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">sparse_weights</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">SPARSE_WEIGHTS</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">strict_type_constraints</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">STRICT_TYPES</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">optimization_profile</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">:</span>
+                <span class="n">builder_config</span><span class="o">.</span><span class="n">add_optimization_profile</span><span class="p">(</span><span class="n">optimization_profile</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">algorithm_selector</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_flag</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">BuilderFlag</span><span class="o">.</span><span class="n">DISABLE_TIMING_CACHE</span><span class="p">)</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">algorithm_selector</span> <span class="o">=</span> <span class="n">algorithm_selector</span>
+
+        <span class="k">if</span> <span class="n">tactic_sources</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">builder_config</span><span class="o">.</span><span class="n">set_tactic_sources</span><span class="p">(</span><span class="n">tactic_sources</span><span class="o">=</span><span class="n">tactic_sources</span><span class="p">)</span>
+
+        <span class="n">engine</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">builder</span><span class="o">.</span><span class="n">build_engine</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">builder_config</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">engine</span>
+
+        <span class="n">serialized_cache</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="nb">bytearray</span><span class="p">(</span><span class="n">cache</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
+            <span class="k">if</span> <span class="n">builder_config</span><span class="o">.</span><span class="n">get_timing_cache</span><span class="p">()</span>
+            <span class="k">else</span> <span class="nb">bytearray</span><span class="p">()</span>
+        <span class="p">)</span>
+        <span class="n">_LOGGER</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;Build TRT engine elapsed time: </span><span class="si">{</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">build_engine_start_time</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">TRTInterpreterResult</span><span class="p">(</span>
+            <span class="n">engine</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="p">,</span> <span class="n">serialized_cache</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">run_node</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+        <span class="c1"># add &quot;_itensor_to_tensor_meta&quot;</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;_itensor_to_tensor_meta&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span>
+        <span class="n">n</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>
+
+        <span class="c1"># run the node</span>
+        <span class="n">trt_node</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">run_node</span><span class="p">(</span><span class="n">n</span><span class="p">)</span>
+
+        <span class="c1"># remove &quot;_itensor_to_tensor_meta&quot;</span>
+        <span class="n">kwargs</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">del</span> <span class="n">kwargs</span><span class="p">[</span><span class="s2">&quot;_itensor_to_tensor_meta&quot;</span><span class="p">]</span>
+        <span class="n">n</span><span class="o">.</span><span class="n">kwargs</span> <span class="o">=</span> <span class="n">kwargs</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">trt_node</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_itensor_to_tensor_meta</span><span class="p">[</span><span class="n">trt_node</span><span class="p">]</span> <span class="o">=</span> <span class="n">n</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;tensor_meta&quot;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">trt_node</span>
+
+    <span class="k">def</span> <span class="nf">placeholder</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_input_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+        <span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">,</span> <span class="n">has_batch_dim</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_specs</span><span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_specs_iter</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">has_batch_dim</span><span class="p">:</span>
+                <span class="n">shape</span> <span class="o">=</span> <span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">shape_range</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">shape_ranges</span><span class="p">):</span>
+                <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">optimization_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_shape</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="o">*</span><span class="n">shape_range</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">add_input</span><span class="p">(</span>
+            <span class="n">name</span><span class="o">=</span><span class="n">target</span><span class="p">,</span>
+            <span class="n">shape</span><span class="o">=</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span>
+            <span class="n">dtype</span><span class="o">=</span><span class="n">unified_dtype_converter</span><span class="p">(</span><span class="n">dtype</span><span class="p">,</span> <span class="n">Frameworks</span><span class="o">.</span><span class="n">TRT</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">call_module</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
+        <span class="n">submod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fetch_attr</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+        <span class="n">submod_type</span> <span class="o">=</span> <span class="nb">getattr</span><span class="p">(</span><span class="n">submod</span><span class="p">,</span> <span class="s2">&quot;_base_class_origin&quot;</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="n">submod</span><span class="p">))</span>
+        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">submod_type</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Conversion of module of type </span><span class="si">{</span><span class="n">submod_type</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">submod</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">call_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Conversion of function </span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">typename</span><span class="p">(</span><span class="n">target</span><span class="p">)</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">call_method</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="nb">str</span><span class="p">)</span>
+        <span class="n">converter</span> <span class="o">=</span> <span class="n">CONVERTERS</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">converter</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Conversion of method </span><span class="si">{</span><span class="n">target</span><span class="si">}</span><span class="s2"> not currently supported!&quot;</span>
+            <span class="p">)</span>
+
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+        <span class="k">return</span> <span class="n">converter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cur_node_name</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">output</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">args</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">tuple</span><span class="p">):</span>
+            <span class="n">outputs</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="n">outputs</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">outputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">],)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">output</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">)</span> <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;TensorRT requires all outputs to be Tensor!&quot;</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">output</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">outputs</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">any</span><span class="p">(</span>
+                <span class="n">op_name</span> <span class="ow">in</span> <span class="n">output</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;_&quot;</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">op_name</span> <span class="ow">in</span> <span class="p">(</span>
+                    <span class="s2">&quot;eq&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;gt&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;lt&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;or&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;xor&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;and&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;not&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;ne&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;isinf&quot;</span><span class="p">,</span>
+                    <span class="s2">&quot;any&quot;</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="p">):</span>
+                <span class="n">output_bool</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">output_bool</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="n">name</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;output</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="n">output</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">network</span><span class="o">.</span><span class="n">mark_output</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">output_bool</span><span class="p">:</span>
+                <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">bool</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_fp16</span> <span class="ow">and</span> <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">trt</span><span class="o">.</span><span class="n">float32</span><span class="p">:</span>
+                <span class="n">output</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">float16</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_output_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
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+  <h1>Source code for torch_tensorrt.fx.input_tensor_spec</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">NamedTuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">.types</span> <span class="kn">import</span> <span class="n">Shape</span><span class="p">,</span> <span class="n">ShapeRange</span>
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">get_dynamic_dims</span>
+
+
+<span class="k">def</span> <span class="nf">generate_input_specs</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">,</span> <span class="n">additional_inputs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="c1"># dynamic_batch is TRT only flag.</span>
+    <span class="k">if</span> <span class="p">(</span>
+        <span class="ow">not</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span>
+        <span class="ow">or</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">dynamic_batch</span> <span class="ow">is</span> <span class="kc">False</span>
+    <span class="p">):</span>
+        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+
+    <span class="c1"># If we don&#39;t have additional inputs, we assume the first dimension</span>
+    <span class="c1"># is the dynamic batch dimension. Otherwise, we use the additional</span>
+    <span class="c1"># inputs to determine the batch dimension.</span>
+    <span class="k">if</span> <span class="n">additional_inputs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">batch_dims</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">bs</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+            <span class="n">batch_dims</span> <span class="o">=</span> <span class="kc">None</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="n">bs</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">):</span>
+                <span class="n">batch_dims</span> <span class="o">=</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">find_batch_size_dim</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
+            <span class="n">inputs</span><span class="p">,</span>
+            <span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span>
+                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">lower_setting</span><span class="o">.</span><span class="n">opt_profile_replica</span><span class="p">,</span>
+            <span class="n">batch_dims</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">batch_dims</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">additional_inputs</span><span class="p">):</span>
+            <span class="n">found_batch_dim</span> <span class="o">=</span> <span class="kc">False</span>
+
+            <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">values</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">j</span><span class="o">.</span><span class="n">shape</span><span class="p">)):</span>
+                <span class="k">if</span> <span class="n">values</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">values</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                    <span class="k">assert</span> <span class="p">(</span>
+                        <span class="n">found_batch_dim</span> <span class="ow">is</span> <span class="kc">False</span>
+                    <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;We&#39;ve already found a batch dim, </span><span class="si">{</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="n">j</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">.&quot;</span>
+                    <span class="n">batch_dims</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
+                    <span class="n">found_batch_dim</span> <span class="o">=</span> <span class="kc">True</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">found_batch_dim</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Failed to find batch dimension because shapes are the same, </span><span class="si">{</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">InputTensorSpec</span><span class="o">.</span><span class="n">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
+            <span class="n">inputs</span><span class="p">,</span>
+            <span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span>
+                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+                <span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">lower_setting</span><span class="o">.</span><span class="n">opt_profile_replica</span><span class="p">,</span>
+            <span class="n">batch_dims</span><span class="p">,</span>
+        <span class="p">)</span>
+
+
+<div class="viewcode-block" id="InputTensorSpec"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.InputTensorSpec">[docs]</a><span class="k">class</span> <span class="nc">InputTensorSpec</span><span class="p">(</span><span class="n">NamedTuple</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    This class contains the information of a input tensor.</span>
+
+<span class="sd">    shape: shape of the tensor.</span>
+
+<span class="sd">    dtype: dtyep of the tensor.</span>
+
+<span class="sd">    device: device of the tensor. This is only used to generate inputs to the given model</span>
+<span class="sd">        in order to run shape prop. For TensorRT engine, inputs have to be on cuda device.</span>
+
+<span class="sd">    shape_ranges: If dynamic shape is needed (shape has dimensions of -1), then this field</span>
+<span class="sd">        has to be provided (default is empty list). Every shape_range is a tuple of three</span>
+<span class="sd">        tuples ((min_input_shape), (optimized_input_shape), (max_input_shape)). Each shape_range</span>
+<span class="sd">        is used to populate a TensorRT optimization profile.</span>
+<span class="sd">        e.g. If the input shape varies from (1, 224) to (100, 224) and we want to optimize</span>
+<span class="sd">        for (25, 224) because it&#39;s the most common input shape, then we set shape_ranges to</span>
+<span class="sd">        ((1, 224), (25, 225), (100, 224)).</span>
+
+<span class="sd">    has_batch_dim: Whether the shape includes batch dimension. Batch dimension has to be provided</span>
+<span class="sd">        if the engine want to run with dynamic shape.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">shape</span><span class="p">:</span> <span class="n">Shape</span>
+    <span class="n">dtype</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span>
+    <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+    <span class="n">shape_ranges</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">ShapeRange</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">has_batch_dim</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_tensor</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">tensor</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce an InputTenosrSpec named tuple which contains the</span>
+<span class="sd">        information of the given PyTorch tensor.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            tensor (torch.Tensor): A PyTorch tensor.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            An InputTensorSpec named tuple.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_tensors</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">tensors</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce a list of InputTenosrSpec named tuples which contain</span>
+<span class="sd">        the information of all the given PyTorch tensors.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            tensors (Iterable[torch.Tensor]): A list of PyTorch tensors.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            A list of InputTensorSpec named tuples.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tensors</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">))</span>
+        <span class="k">return</span> <span class="p">[</span><span class="bp">cls</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">t</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">tensors</span><span class="p">]</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">from_tensors_with_dynamic_batch_size</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">tensors</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
+        <span class="n">batch_size_range</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
+        <span class="n">opt_profile_replica</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">batch_dims</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">List</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Produce a list of InputTenosrSpec named tuples which would contain</span>
+<span class="sd">        the information of all the given PyTorch tensors. The produced input</span>
+<span class="sd">        tensor specs will treat all tensors&#39; first dimension as batch dimension</span>
+<span class="sd">        and mark them as dynmaic.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            tensors (Sequence[torch.Tensor]): A list of PyTorch tensors.</span>
+<span class="sd">            batch_size_range (Tuple[int, int, int]): The first integer indicates</span>
+<span class="sd">                the smallest batch size allowed. The second integer indiceates</span>
+<span class="sd">                the batch size that we&#39;ll optimize for. The third integer indicates</span>
+<span class="sd">                the largest batch size allowed.</span>
+<span class="sd">            opt_profile_replica (int): If dynamic shape is enabled, each execution</span>
+<span class="sd">                context requires a different optimization profile. This arg determines</span>
+<span class="sd">                how many optimization profile replicas we want to produce.</span>
+<span class="sd">            batch_dims (Optional[List[int]]): The batch dim might not be the leading dim</span>
+<span class="sd">                and allow user to specify the batch dims using this arg. Default we treat</span>
+<span class="sd">                dim 0 as the batch dim.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            A list of InputTensorSpec named tuples with dynamic ranges.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="n">batch_dims</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">batch_dims</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="n">find_batch_size_dim</span><span class="p">(</span><span class="n">tensors</span><span class="p">)</span>
+
+        <span class="n">input_specs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">batch_size</span> <span class="o">=</span> <span class="n">tensors</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">batch_dims</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tensors</span><span class="p">):</span>
+            <span class="n">batch_dim</span> <span class="o">=</span> <span class="n">batch_dims</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">batch_dim</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                <span class="n">input_specs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">cls</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">tensor</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">shape</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                <span class="k">assert</span> <span class="n">batch_size</span> <span class="o">==</span> <span class="n">tensor</span><span class="o">.</span><span class="n">size</span><span class="p">(</span>
+                    <span class="n">batch_dim</span>
+                <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;The </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th tensor (shape: </span><span class="si">{</span><span class="n">tensor</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">) doesn&#39;t have the correct batch size: </span><span class="si">{</span><span class="n">batch_size</span><span class="si">}</span><span class="s2">.&quot;</span>
+                <span class="n">shape</span><span class="p">[</span><span class="n">batch_dim</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+                <span class="n">shape_ranges</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">ShapeRange</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="nb">tuple</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">batch_dim</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="n">bs</span><span class="p">]</span> <span class="o">+</span> <span class="n">shape</span><span class="p">[</span><span class="n">batch_dim</span> <span class="o">+</span> <span class="mi">1</span> <span class="p">:])</span> <span class="k">for</span> <span class="n">bs</span> <span class="ow">in</span> <span class="n">batch_size_range</span><span class="p">)]</span> <span class="o">*</span> <span class="n">opt_profile_replica</span>  <span class="c1"># type: ignore[list-item]</span>
+                <span class="n">input_specs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="bp">cls</span><span class="p">(</span><span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> <span class="n">tensor</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">tensor</span><span class="o">.</span><span class="n">device</span><span class="p">,</span> <span class="n">shape_ranges</span><span class="p">)</span>
+                <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">input_specs</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="c1"># pyre-ignore [2]: Parameter `sample_input` must have a type other than `Any`</span>
+    <span class="k">def</span> <span class="nf">find_batch_size_dim</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="p">[]:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">shapes</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">]</span>
+        <span class="n">frequency_map</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">first_dims</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">shapes</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="c1"># By pass for rank-1 tensors. MRS model has rank-1 tensor carry no batch_size info</span>
+                <span class="k">continue</span>
+            <span class="c1"># Dedup shape value for single tensor</span>
+            <span class="n">first_dims</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">shape</span><span class="p">:</span>
+                <span class="n">frequency_map</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">frequency_map</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">first_dims</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="c1"># first dim is the same in every input: we use it as batch_size</span>
+            <span class="n">batch_size</span> <span class="o">=</span> <span class="n">first_dims</span><span class="o">.</span><span class="n">pop</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">frequency_map</span><span class="p">:</span>
+            <span class="c1"># first dims are different: we use the most frequent dim as batch_size</span>
+            <span class="n">sorted_frequency</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">frequency_map</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="o">-</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">batch_size</span> <span class="o">=</span> <span class="n">sorted_frequency</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># no dims to sort: no batch_size</span>
+            <span class="n">batch_size</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+        <span class="n">bs_dim</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
+            <span class="c1"># Default batch size dim = -1, indicate no batch_size</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+            <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">val</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">val</span> <span class="o">==</span> <span class="n">batch_size</span><span class="p">:</span>
+                    <span class="n">dim</span> <span class="o">=</span> <span class="n">index</span>
+                    <span class="k">break</span>
+            <span class="n">bs_dim</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">bs_dim</span>
+
+    <span class="k">def</span> <span class="nf">to_random_tensor</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">id</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+        <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">get_dynamic_dims</span><span class="p">(</span><span class="n">shape</span><span class="p">)):</span>
+            <span class="c1"># id=0 -&gt; min shape</span>
+            <span class="c1"># id=1 -&gt; optimal shape</span>
+            <span class="c1"># id=2 -&gt; max shape</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">shape_ranges</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="nb">id</span><span class="p">])</span>
+        <span class="k">elif</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_batch_dim</span><span class="p">:</span>
+            <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_inputs_from_specs</span><span class="p">(</span><span class="n">input_specs</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]):</span>
+        <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">spec</span> <span class="ow">in</span> <span class="n">input_specs</span><span class="p">:</span>
+            <span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">spec</span><span class="o">.</span><span class="n">to_random_tensor</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="n">inputs</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">create_inputs_from_max_specs</span><span class="p">(</span><span class="n">input_specs</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="s2">&quot;InputTensorSpec&quot;</span><span class="p">]):</span>
+        <span class="n">inputs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">spec</span> <span class="ow">in</span> <span class="n">input_specs</span><span class="p">:</span>
+            <span class="n">inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">spec</span><span class="o">.</span><span class="n">to_random_tensor</span><span class="p">(</span><span class="mi">2</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">inputs</span></div>
+</pre></div>
+
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diff --git a/docs/v2.2.0/_modules/torch_tensorrt/fx/lower.html b/docs/v2.2.0/_modules/torch_tensorrt/fx/lower.html
new file mode 100644
index 0000000000..b0cee0b593
--- /dev/null
+++ b/docs/v2.2.0/_modules/torch_tensorrt/fx/lower.html
@@ -0,0 +1,1056 @@
+
+
+
+<!DOCTYPE html>
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+              
+  <h1>Source code for torch_tensorrt.fx.lower</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">dataclasses</span> <span class="k">as</span> <span class="nn">dc</span>
+<span class="kn">import</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span>
+
+<span class="c1"># @manual=//deeplearning/trt/python:py_tensorrt</span>
+<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.fx</span> <span class="k">as</span> <span class="nn">fx</span>
+<span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt.fx.tracer.dispatch_tracer.aten_tracer</span> <span class="k">as</span> <span class="nn">aten_tracer</span>
+<span class="kn">from</span> <span class="nn">torch.fx.passes.splitter_base</span> <span class="kn">import</span> <span class="n">SplitResult</span>
+
+<span class="kn">from</span> <span class="nn">.fx2trt</span> <span class="kn">import</span> <span class="n">TRTInterpreter</span><span class="p">,</span> <span class="n">TRTInterpreterResult</span>
+<span class="kn">from</span> <span class="nn">.lower_setting</span> <span class="kn">import</span> <span class="n">LowerSetting</span>
+<span class="kn">from</span> <span class="nn">.passes.lower_pass_manager_builder</span> <span class="kn">import</span> <span class="n">LowerPassManagerBuilder</span>
+<span class="kn">from</span> <span class="nn">.passes.pass_utils</span> <span class="kn">import</span> <span class="n">PassFunc</span><span class="p">,</span> <span class="n">validate_inference</span>
+<span class="kn">from</span> <span class="nn">.tools.timing_cache_utils</span> <span class="kn">import</span> <span class="n">TimingCacheManager</span>
+<span class="kn">from</span> <span class="nn">.tools.trt_splitter</span> <span class="kn">import</span> <span class="n">TRTSplitter</span><span class="p">,</span> <span class="n">TRTSplitterSetting</span>
+<span class="kn">from</span> <span class="nn">.tracer.acc_tracer</span> <span class="kn">import</span> <span class="n">acc_tracer</span>
+<span class="kn">from</span> <span class="nn">.trt_module</span> <span class="kn">import</span> <span class="n">TRTModule</span>
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">LowerPrecision</span>
+
+<span class="n">logger</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="vm">__name__</span><span class="p">)</span>
+
+<span class="n">Input</span> <span class="o">=</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+    <span class="nb">input</span><span class="p">,</span>
+    <span class="n">min_acc_module_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span>
+    <span class="n">max_batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2048</span><span class="p">,</span>
+    <span class="n">max_workspace_size</span><span class="o">=</span><span class="mi">1</span> <span class="o">&lt;&lt;</span> <span class="mi">25</span><span class="p">,</span>
+    <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">lower_precision</span><span class="o">=</span><span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span><span class="p">,</span>
+    <span class="n">verbose_log</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">timing_cache_prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
+    <span class="n">save_timing_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">cuda_graph_batch_size</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">dynamic_batch</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">is_aten</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">use_experimental_fx_rt</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">correctness_atol</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span>
+    <span class="n">correctness_rtol</span><span class="o">=</span><span class="mf">1e-1</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Takes in original module, input and lowering setting, run lowering workflow to turn module</span>
+<span class="sd">    into lowered module, or so called TRTModule.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        module: Original module for lowering.</span>
+<span class="sd">        input: Input for module.</span>
+<span class="sd">        max_batch_size: Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
+<span class="sd">        min_acc_module_size: Minimal number of nodes for an accelerated submodule</span>
+<span class="sd">        max_workspace_size: Maximum size of workspace given to TensorRT.</span>
+<span class="sd">        explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</span>
+<span class="sd">        lower_precision: lower_precision config given to TRTModule.</span>
+<span class="sd">        verbose_log: Enable verbose log for TensorRT if set True.</span>
+<span class="sd">        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.</span>
+<span class="sd">        save_timing_cache: Update timing cache with current timing cache data if set to True.</span>
+<span class="sd">        cuda_graph_batch_size: Cuda graph batch size, default to be -1.</span>
+<span class="sd">        dynamic_batch: batch dimension (dim=0) is dynamic.</span>
+<span class="sd">        use_experimental_fx_rt: Uses the next generation TRTModule which supports both Python and TorchScript based execution (including in C++).</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        A torch.nn.Module lowered by TensorRT.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">use_experimental_fx_rt</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">explicit_batch_dimension</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;The experimental unifed runtime only supports explicit batch. Please make sure to set explicit_batch_dimension=True when use_experimental_fx_rt=True&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">lower_setting</span> <span class="o">=</span> <span class="n">LowerSetting</span><span class="p">(</span>
+        <span class="n">max_batch_size</span><span class="o">=</span><span class="n">max_batch_size</span><span class="p">,</span>
+        <span class="n">min_acc_module_size</span><span class="o">=</span><span class="n">min_acc_module_size</span><span class="p">,</span>
+        <span class="n">max_workspace_size</span><span class="o">=</span><span class="n">max_workspace_size</span><span class="p">,</span>
+        <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="n">explicit_batch_dimension</span><span class="p">,</span>
+        <span class="n">lower_precision</span><span class="o">=</span><span class="n">lower_precision</span><span class="p">,</span>
+        <span class="n">verbose_log</span><span class="o">=</span><span class="n">verbose_log</span><span class="p">,</span>
+        <span class="n">timing_cache_prefix</span><span class="o">=</span><span class="n">timing_cache_prefix</span><span class="p">,</span>
+        <span class="n">save_timing_cache</span><span class="o">=</span><span class="n">save_timing_cache</span><span class="p">,</span>
+        <span class="n">cuda_graph_batch_size</span><span class="o">=</span><span class="n">cuda_graph_batch_size</span><span class="p">,</span>
+        <span class="n">dynamic_batch</span><span class="o">=</span><span class="n">dynamic_batch</span><span class="p">,</span>
+        <span class="n">is_aten</span><span class="o">=</span><span class="n">is_aten</span><span class="p">,</span>
+        <span class="n">use_experimental_rt</span><span class="o">=</span><span class="n">use_experimental_fx_rt</span><span class="p">,</span>
+        <span class="n">correctness_atol</span><span class="o">=</span><span class="n">correctness_atol</span><span class="p">,</span>
+        <span class="n">correctness_rtol</span><span class="o">=</span><span class="n">correctness_rtol</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">lowerer</span> <span class="o">=</span> <span class="n">Lowerer</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">lower_setting</span><span class="o">=</span><span class="n">lower_setting</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">lowerer</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="nb">input</span><span class="p">)</span></div>
+
+
+<span class="nd">@dc</span><span class="o">.</span><span class="n">dataclass</span>
+<span class="k">class</span> <span class="nc">LowerTrtInterpreter</span><span class="p">:</span>
+    <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span>
+    <span class="n">timing_cache_manager</span><span class="p">:</span> <span class="n">TimingCacheManager</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">create</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">):</span>
+        <span class="n">timing_cache_manager</span> <span class="o">=</span> <span class="n">TimingCacheManager</span><span class="p">(</span>
+            <span class="n">lower_setting</span><span class="o">.</span><span class="n">timing_cache_prefix</span><span class="p">,</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">save_timing_cache</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">LowerTrtInterpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">,</span> <span class="n">timing_cache_manager</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mod</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">split_name</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TRTInterpreterResult</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="p">,</span> <span class="s2">&quot;Can&#39;t find input specs for lowering!&quot;</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;split_name=</span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">, input_specs=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Prepare algorithm selector and timing_cache for TRTInterpreter</span>
+        <span class="n">algo_selector</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">algo_selector</span><span class="p">:</span>
+            <span class="n">algo_selector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">algo_selector</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">.json&quot;</span><span class="p">)</span>
+        <span class="n">cache_data</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">cache_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="o">.</span><span class="n">get_timing_cache_trt</span><span class="p">(</span><span class="n">split_name</span><span class="p">)</span>
+                <span class="n">logger</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Timing cache is used!&quot;</span><span class="p">)</span>
+            <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
+                <span class="n">logger</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Cannot load timing cache for </span><span class="si">{</span><span class="n">split_name</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">e</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+                <span class="n">cache_data</span> <span class="o">=</span> <span class="kc">None</span>
+
+        <span class="n">interpreter</span> <span class="o">=</span> <span class="n">TRTInterpreter</span><span class="p">(</span>
+            <span class="n">mod</span><span class="p">,</span>
+            <span class="n">input_specs</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">input_specs</span><span class="p">,</span>
+            <span class="n">explicit_batch_dimension</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span><span class="p">,</span>
+            <span class="n">explicit_precision</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_precision</span><span class="p">,</span>
+            <span class="n">logger_level</span><span class="o">=</span><span class="p">(</span>
+                <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">VERBOSE</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">verbose_log</span>
+                <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="o">.</span><span class="n">WARNING</span>
+            <span class="p">),</span>
+        <span class="p">)</span>
+
+        <span class="n">interp_result</span><span class="p">:</span> <span class="n">TRTInterpreterResult</span> <span class="o">=</span> <span class="n">interpreter</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
+            <span class="n">max_batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">max_batch_size</span><span class="p">,</span>
+            <span class="n">max_workspace_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">max_workspace_size</span><span class="p">,</span>
+            <span class="n">lower_precision</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">lower_precision</span><span class="p">,</span>
+            <span class="n">strict_type_constraints</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">strict_type_constraints</span><span class="p">,</span>
+            <span class="n">algorithm_selector</span><span class="o">=</span><span class="n">algo_selector</span><span class="p">,</span>
+            <span class="n">timing_cache</span><span class="o">=</span><span class="n">cache_data</span><span class="p">,</span>
+            <span class="n">profiling_verbosity</span><span class="o">=</span><span class="p">(</span>
+                <span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">DETAILED</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">verbose_profile</span>
+                <span class="k">else</span> <span class="n">trt</span><span class="o">.</span><span class="n">ProfilingVerbosity</span><span class="o">.</span><span class="n">LAYER_NAMES_ONLY</span>
+            <span class="p">),</span>
+            <span class="n">tactic_sources</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">tactic_sources</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="c1"># Update timing cache file if needed</span>
+        <span class="n">timing_cache</span> <span class="o">=</span> <span class="n">interp_result</span><span class="o">.</span><span class="n">serialized_cache</span>
+        <span class="k">if</span> <span class="n">timing_cache</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">timing_cache_manager</span><span class="o">.</span><span class="n">update_timing_cache</span><span class="p">(</span><span class="n">split_name</span><span class="p">,</span> <span class="n">timing_cache</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">interp_result</span>
+
+
+<span class="k">def</span> <span class="nf">default_split_function</span><span class="p">(</span>
+    <span class="n">model</span><span class="p">:</span> <span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">SplitResult</span><span class="p">:</span>
+    <span class="n">splitter_setting</span> <span class="o">=</span> <span class="n">TRTSplitterSetting</span><span class="p">()</span>
+    <span class="n">splitter_setting</span><span class="o">.</span><span class="n">use_implicit_batch_dim</span> <span class="o">=</span> <span class="ow">not</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">explicit_batch_dimension</span>
+    <span class="n">splitter_setting</span><span class="o">.</span><span class="n">min_acc_module_size</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">min_acc_module_size</span>
+    <span class="n">splitter_setting</span><span class="o">.</span><span class="n">use_experimental_rt</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">use_experimental_rt</span>
+    <span class="n">splitter</span> <span class="o">=</span> <span class="n">TRTSplitter</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">settings</span><span class="o">=</span><span class="n">splitter_setting</span><span class="p">)</span>
+    <span class="n">splitter</span><span class="o">.</span><span class="n">node_support_preview</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">splitter</span><span class="o">.</span><span class="n">generate_split_results</span><span class="p">()</span>
+
+
+<span class="k">def</span> <span class="nf">create_lower_trt_interpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LowerTrtInterpreter</span><span class="p">:</span>
+    <span class="k">return</span> <span class="n">LowerTrtInterpreter</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">default_lower_pass</span><span class="p">(</span>
+    <span class="n">create_trt_interpreter</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">LowerSetting</span><span class="p">],</span> <span class="n">LowerTrtInterpreter</span><span class="p">],</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">PassFunc</span><span class="p">:</span>
+    <span class="k">def</span> <span class="nf">lower_pass</span><span class="p">(</span>
+        <span class="n">mod</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span> <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">,</span> <span class="n">module_name</span><span class="p">:</span> <span class="nb">str</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a module transformation pass which lowers an `fx.GraphModule` into a</span>
+<span class="sd">        `TRTModule`</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">interpreter</span> <span class="o">=</span> <span class="n">create_trt_interpreter</span><span class="p">(</span><span class="n">lower_setting</span><span class="p">)</span>
+        <span class="n">interp_res</span><span class="p">:</span> <span class="n">TRTInterpreterResult</span> <span class="o">=</span> <span class="n">interpreter</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span> <span class="nb">input</span><span class="p">,</span> <span class="n">module_name</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">use_experimental_rt</span><span class="p">:</span>
+            <span class="kn">import</span> <span class="nn">io</span>
+
+            <span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>
+            <span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo._TorchTensorRTModule</span> <span class="kn">import</span> <span class="n">TorchTensorRTModule</span>
+
+            <span class="k">with</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">()</span> <span class="k">as</span> <span class="n">engine_bytes</span><span class="p">:</span>
+                <span class="n">engine_bytes</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">interp_res</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
+                <span class="n">engine_str</span> <span class="o">=</span> <span class="n">engine_bytes</span><span class="o">.</span><span class="n">getvalue</span><span class="p">()</span>
+
+            <span class="n">trt_module</span> <span class="o">=</span> <span class="n">TorchTensorRTModule</span><span class="p">(</span>
+                <span class="n">engine_str</span><span class="p">,</span>
+                <span class="n">name</span><span class="o">=</span><span class="n">module_name</span><span class="p">,</span>
+                <span class="n">input_binding_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">input_names</span><span class="p">,</span>
+                <span class="n">output_binding_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">output_names</span><span class="p">,</span>
+                <span class="n">target_device</span><span class="o">=</span><span class="n">Device</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cuda:</span><span class="si">{</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">),</span>
+                <span class="c1"># cuda_graph_batch_size=lower_setting.cuda_graph_batch_size, # NOTE: Not sure what this is supposed to do</span>
+            <span class="p">)</span>
+            <span class="k">return</span> <span class="n">trt_module</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">trt_module</span> <span class="o">=</span> <span class="n">TRTModule</span><span class="p">(</span>
+                <span class="n">engine</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">engine</span><span class="p">,</span>
+                <span class="n">input_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">input_names</span><span class="p">,</span>
+                <span class="n">output_names</span><span class="o">=</span><span class="n">interp_res</span><span class="o">.</span><span class="n">output_names</span><span class="p">,</span>
+                <span class="n">cuda_graph_batch_size</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">cuda_graph_batch_size</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="k">return</span> <span class="n">trt_module</span>
+
+    <span class="k">return</span> <span class="n">lower_pass</span>
+
+
+<span class="nd">@dc</span><span class="o">.</span><span class="n">dataclass</span><span class="p">(</span><span class="n">frozen</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="k">class</span> <span class="nc">Lowerer</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Lowers a module using fx2trt.</span>
+
+<span class="sd">    This is a composable class to facilitate fx2trt. A normal fx2trt process</span>
+<span class="sd">    composes of the following passes to transform an `fx.GraphModule`:</span>
+
+<span class="sd">        1. trace - use torch.fx to trace the module so we can get the graph</span>
+<span class="sd">            representation of the model.</span>
+<span class="sd">        2. split - the graph module is split into several submodules,</span>
+<span class="sd">            running either via TensorRT, or via regular CUDA.</span>
+
+<span class="sd">    For each split that need to run via TRT, the following passes are</span>
+<span class="sd">    invoked:</span>
+
+<span class="sd">        3. `TRTInterpreter` - build the TRT engine for the submodule that</span>
+<span class="sd">            can be supported through `TRTInterpreter`.</span>
+<span class="sd">        4. Wraps the executable TRT engine into `TRTModule`, which is an `nn.Module`.</span>
+<span class="sd">        5. The converted submodule is then set back onto the top-level module</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">lower_pass_manager_builder</span><span class="p">:</span> <span class="n">LowerPassManagerBuilder</span>
+
+    <span class="nd">@classmethod</span>
+    <span class="k">def</span> <span class="nf">create</span><span class="p">(</span>
+        <span class="bp">cls</span><span class="p">,</span>
+        <span class="n">lower_setting</span><span class="p">:</span> <span class="n">LowerSetting</span><span class="p">,</span>
+        <span class="n">interpreter_builder</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">create_lower_trt_interpreter</span><span class="p">,</span>
+        <span class="n">split_func</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">default_split_function</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;Lowerer&quot;</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Instantiate a `Lowerer` instance.&quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">is_aten</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
+                <span class="n">lower_pass_manager_builder</span><span class="o">=</span><span class="n">LowerPassManagerBuilder</span><span class="p">(</span>
+                    <span class="n">lower_setting</span><span class="o">=</span><span class="n">lower_setting</span><span class="p">,</span>
+                    <span class="n">trace_func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">acc_tracer</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span>
+                        <span class="n">module</span><span class="p">,</span>
+                        <span class="n">inputs</span><span class="p">,</span>  <span class="c1"># type: ignore[arg-type]</span>
+                        <span class="n">ast_rewriter_allow_list</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">ast_rewriter_allow_list</span><span class="p">,</span>
+                        <span class="n">leaf_module_list</span><span class="o">=</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">leaf_module_list</span><span class="p">,</span>
+                    <span class="p">),</span>
+                    <span class="n">split_func</span><span class="o">=</span><span class="n">split_func</span><span class="p">,</span>
+                    <span class="n">lower_func</span><span class="o">=</span><span class="n">default_lower_pass</span><span class="p">(</span><span class="n">interpreter_builder</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+        <span class="c1"># proxytensor_trace</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">cls</span><span class="p">(</span>
+                <span class="n">lower_pass_manager_builder</span><span class="o">=</span><span class="n">LowerPassManagerBuilder</span><span class="p">(</span>
+                    <span class="n">lower_setting</span><span class="o">=</span><span class="n">lower_setting</span><span class="p">,</span>
+                    <span class="n">trace_func</span><span class="o">=</span><span class="k">lambda</span> <span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">aten_tracer</span><span class="o">.</span><span class="n">opt_trace</span><span class="p">(</span>
+                        <span class="n">module</span><span class="p">,</span> <span class="n">inputs</span>
+                    <span class="p">),</span>
+                    <span class="n">split_func</span><span class="o">=</span><span class="n">split_func</span><span class="p">,</span>
+                    <span class="n">lower_func</span><span class="o">=</span><span class="n">default_lower_pass</span><span class="p">(</span><span class="n">interpreter_builder</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+        <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">,</span>
+        <span class="n">additional_inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Input</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">fp16_conversion_fn</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">[[</span><span class="n">Input</span><span class="p">],</span> <span class="n">Input</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+        <span class="n">lower_setting</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">lower_setting</span>
+        <span class="n">atol</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">correctness_atol</span>
+        <span class="n">rtol</span> <span class="o">=</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">correctness_rtol</span>
+
+        <span class="nd">@validate_inference</span><span class="p">(</span>
+            <span class="n">atol</span><span class="o">=</span><span class="n">atol</span><span class="p">,</span>
+            <span class="n">rtol</span><span class="o">=</span><span class="n">rtol</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">def</span> <span class="nf">do_lower</span><span class="p">(</span><span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">:</span> <span class="n">Input</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">:</span>
+            <span class="n">module</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+            <span class="k">if</span> <span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">lower_setting</span><span class="o">.</span><span class="n">lower_precision</span>
+                <span class="o">==</span> <span class="n">LowerPrecision</span><span class="o">.</span><span class="n">FP16</span>
+            <span class="p">):</span>
+                <span class="n">module</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+                <span class="c1"># A custom conversion function can be passed to the lowerer to</span>
+                <span class="c1"># handle inputs with custom types. By default, just handle</span>
+                <span class="c1"># tensors and NoneType.</span>
+                <span class="k">if</span> <span class="n">fp16_conversion_fn</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">conversion_fn</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="p">(</span>
+                        <span class="n">x</span><span class="o">.</span><span class="n">half</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">x</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float32</span> <span class="k">else</span> <span class="n">x</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">conversion_fn</span> <span class="o">=</span> <span class="n">fp16_conversion_fn</span>
+
+                <span class="n">inputs</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">conversion_fn</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">lower_setting</span><span class="o">.</span><span class="n">is_aten</span><span class="p">:</span>
+                <span class="n">pm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">build_aten2trt_lower_pipeline</span><span class="p">(</span>
+                    <span class="n">inputs</span><span class="p">,</span> <span class="n">additional_inputs</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">pm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_pass_manager_builder</span><span class="o">.</span><span class="n">build_trt_lower_pipeline</span><span class="p">(</span>
+                    <span class="n">inputs</span><span class="p">,</span> <span class="n">additional_inputs</span>
+                <span class="p">)</span>
+            <span class="n">lower_result</span> <span class="o">=</span> <span class="n">pm</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">lower_result</span>
+
+        <span class="k">return</span> <span class="n">do_lower</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+
+             </article>
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+  <h1>Source code for torch_tensorrt.fx.trt_module</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Sequence</span>
+
+<span class="c1"># @manual=//deeplearning/trt/python:py_tensorrt</span>
+<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">.utils</span> <span class="kn">import</span> <span class="n">Frameworks</span><span class="p">,</span> <span class="n">unified_dtype_converter</span>
+
+
+<div class="viewcode-block" id="TRTModule"><a class="viewcode-back" href="../../../py_api/fx.html#torch_tensorrt.fx.TRTModule">[docs]</a><span class="k">class</span> <span class="nc">TRTModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">input_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_names</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">cuda_graph_batch_size</span><span class="o">=-</span><span class="mi">1</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">TRTModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_register_state_dict_hook</span><span class="p">(</span><span class="n">TRTModule</span><span class="o">.</span><span class="n">_on_state_dict</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="n">engine</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span> <span class="o">=</span> <span class="n">input_names</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="o">=</span> <span class="n">output_names</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">cuda_graph_batch_size</span> <span class="o">=</span> <span class="n">cuda_graph_batch_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="n">engine</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_initialize</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">_initialize</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
+
+        <span class="c1"># Indices of inputs/outputs in the trt engine bindings, in the order</span>
+        <span class="c1"># as they are in the original PyTorch model.</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_index</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_index</span><span class="p">(</span><span class="n">name</span><span class="p">)</span> <span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span>
+        <span class="p">]</span>
+        <span class="n">primary_input_outputs</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+        <span class="n">primary_input_outputs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span><span class="p">)</span>
+        <span class="n">primary_input_outputs</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_names</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_bindings</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_optimization_profiles</span>
+        <span class="p">):</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">primary_input_outputs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_names</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_name</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+        <span class="k">assert</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_bindings</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">num_optimization_profiles</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span>
+            <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span>
+            <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">)</span>
+            <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_names</span><span class="p">)</span>
+        <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">unified_dtype_converter</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_dtype</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="n">Frameworks</span><span class="o">.</span><span class="n">TORCH</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_shapes</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_dtypes</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">unified_dtype_converter</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_dtype</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="n">Frameworks</span><span class="o">.</span><span class="n">TORCH</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_shapes</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">(</span>
+                <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span>
+                <span class="k">else</span> <span class="nb">tuple</span><span class="p">()</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_dtypes</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">unified_dtype_converter</span><span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_dtype</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="n">Frameworks</span><span class="o">.</span><span class="n">TORCH</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span>
+        <span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_shapes</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="p">(</span>
+                <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span>
+                <span class="k">else</span> <span class="nb">tuple</span><span class="p">()</span>
+            <span class="p">)</span>
+            <span class="k">for</span> <span class="n">idx</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span>
+        <span class="p">]</span>
+
+    <span class="k">def</span> <span class="nf">_check_initialized</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">initialized</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;TRTModule is not initialized.&quot;</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_on_state_dict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state_dict</span><span class="p">,</span> <span class="n">prefix</span><span class="p">,</span> <span class="n">local_metadata</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
+        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;engine&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
+        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;input_names&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
+        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;output_names&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span>
+        <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;cuda_graph_batch_size&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cuda_graph_batch_size</span>
+
+    <span class="k">def</span> <span class="nf">_load_from_state_dict</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">state_dict</span><span class="p">,</span>
+        <span class="n">prefix</span><span class="p">,</span>
+        <span class="n">local_metadata</span><span class="p">,</span>
+        <span class="n">strict</span><span class="p">,</span>
+        <span class="n">missing_keys</span><span class="p">,</span>
+        <span class="n">unexpected_keys</span><span class="p">,</span>
+        <span class="n">error_msgs</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="n">engine_bytes</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;engine&quot;</span><span class="p">]</span>
+
+        <span class="n">logger</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="p">()</span>
+        <span class="n">runtime</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Runtime</span><span class="p">(</span><span class="n">logger</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">engine</span> <span class="o">=</span> <span class="n">runtime</span><span class="o">.</span><span class="n">deserialize_cuda_engine</span><span class="p">(</span><span class="n">engine_bytes</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;input_names&quot;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_names</span> <span class="o">=</span> <span class="n">state_dict</span><span class="p">[</span><span class="n">prefix</span> <span class="o">+</span> <span class="s2">&quot;output_names&quot;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_initialize</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">__getstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+        <span class="n">state</span><span class="p">[</span><span class="s2">&quot;engine&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">bytearray</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">serialize</span><span class="p">())</span>
+        <span class="n">state</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;context&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">state</span>
+
+    <span class="k">def</span> <span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">):</span>
+        <span class="n">logger</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Logger</span><span class="p">()</span>
+        <span class="n">runtime</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Runtime</span><span class="p">(</span><span class="n">logger</span><span class="p">)</span>
+        <span class="n">state</span><span class="p">[</span><span class="s2">&quot;engine&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">runtime</span><span class="o">.</span><span class="n">deserialize_cuda_engine</span><span class="p">(</span><span class="n">state</span><span class="p">[</span><span class="s2">&quot;engine&quot;</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">):</span>
+        <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;TRTModule:Forward&quot;</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
+
+            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;TRTModule:ProcessInputs&quot;</span><span class="p">):</span>
+                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">input_names</span>
+                <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Wrong number of inputs, expect </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span><span class="si">}</span><span class="s2"> get </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span><span class="si">}</span><span class="s2">.&quot;</span>
+
+                <span class="c1"># This is only used when the trt engine is using implicit batch dim.</span>
+                <span class="n">batch_size</span> <span class="o">=</span> <span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">contiguous_inputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="o">.</span><span class="n">contiguous</span><span class="p">()</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">]</span>
+                <span class="n">bindings</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">)</span>
+                    <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_names</span><span class="p">)</span>
+                    <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_names</span><span class="p">)</span>
+                <span class="p">)</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">input_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_names</span><span class="p">):</span>
+                    <span class="k">assert</span> <span class="n">inputs</span><span class="p">[</span>
+                        <span class="n">i</span>
+                    <span class="p">]</span><span class="o">.</span><span class="n">is_cuda</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th input(</span><span class="si">{</span><span class="n">input_name</span><span class="si">}</span><span class="s2">) is not on cuda device.&quot;</span>
+                    <span class="k">assert</span> <span class="p">(</span>
+                        <span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Dtype mismatch for </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th input(</span><span class="si">{</span><span class="n">input_name</span><span class="si">}</span><span class="s2">). Expect </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">, got </span><span class="si">{</span><span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">dtype</span><span class="si">}</span><span class="s2">.&quot;</span>
+
+                    <span class="n">idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_binding_indices_in_order</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="n">bindings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
+
+                    <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">set_binding_shape</span><span class="p">(</span>
+                            <span class="n">idx</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">contiguous_inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                        <span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">assert</span> <span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="p">(</span>
+                            <span class="sa">f</span><span class="s2">&quot;Shape mismatch for </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">th input(</span><span class="si">{</span><span class="n">input_name</span><span class="si">}</span><span class="s2">). &quot;</span>
+                            <span class="sa">f</span><span class="s2">&quot;Expect </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="si">}</span><span class="s2">, got </span><span class="si">{</span><span class="n">inputs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">size</span><span class="p">()[</span><span class="mi">1</span><span class="p">:]</span><span class="si">}</span><span class="s2">.&quot;</span>
+                        <span class="p">)</span>
+
+            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;TRTModule:ProcessOutputs&quot;</span><span class="p">):</span>
+                <span class="c1"># create output tensors</span>
+                <span class="n">outputs</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_binding_indices_in_order</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                        <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+
+                    <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span>  <span class="c1"># type: ignore[call-overload]</span>
+                        <span class="n">size</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
+                        <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                        <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">(),</span>
+                    <span class="p">)</span>
+                    <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+                    <span class="n">bindings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
+
+                <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_binding_indices_in_order</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                        <span class="n">shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">batch_size</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_shapes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">get_binding_shape</span><span class="p">(</span><span class="n">idx</span><span class="p">))</span>
+
+                    <span class="n">output</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span>  <span class="c1"># type: ignore[call-overload]</span>
+                        <span class="n">size</span><span class="o">=</span><span class="n">shape</span><span class="p">,</span>
+                        <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">hidden_output_dtypes</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                        <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_device</span><span class="p">(),</span>
+                    <span class="p">)</span>
+                    <span class="n">bindings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">()</span>
+
+            <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">profiler</span><span class="o">.</span><span class="n">record_function</span><span class="p">(</span><span class="s2">&quot;TRTModule:TensorRTRuntime&quot;</span><span class="p">):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">has_implicit_batch_dimension</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">execute_async</span><span class="p">(</span>
+                        <span class="n">batch_size</span><span class="p">,</span> <span class="n">bindings</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">cuda_stream</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">execute_async_v2</span><span class="p">(</span>
+                        <span class="n">bindings</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">current_stream</span><span class="p">()</span><span class="o">.</span><span class="n">cuda_stream</span>
+                    <span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">outputs</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">enable_profiling</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">profiler</span><span class="p">:</span> <span class="s2">&quot;trt.IProfiler&quot;</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Enable TensorRT profiling. After calling this function, TensorRT will report</span>
+<span class="sd">        time spent on each layer in stdout for each forward run.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">profiler</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">context</span><span class="o">.</span><span class="n">profiler</span> <span class="o">=</span> <span class="n">trt</span><span class="o">.</span><span class="n">Profiler</span><span class="p">()</span> <span class="k">if</span> <span class="n">profiler</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">profiler</span>
+
+    <span class="k">def</span> <span class="nf">disable_profiling</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Disable TensorRT profiling.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_check_initialized</span><span class="p">()</span>
+
+        <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">synchronize</span><span class="p">()</span>
+        <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">context</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">context</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_execution_context</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">get_layer_info</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Get layer info of the engine. Only support for TRT &gt; 8.2.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">inspector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">engine</span><span class="o">.</span><span class="n">create_engine_inspector</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">inspector</span><span class="o">.</span><span class="n">get_engine_information</span><span class="p">(</span><span class="n">trt</span><span class="o">.</span><span class="n">LayerInformationFormat</span><span class="o">.</span><span class="n">JSON</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
+             
+            </div>
+            <footer>
+  
+
+  
+
+    <hr>
+
+  
+
+  <div role="contentinfo">
+    <p>
+        &copy; Copyright 2022, NVIDIA Corporation.
+
+    </p>
+  </div>
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+  <h1>Source code for torch_tensorrt.logging</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span>
+
+<span class="kn">from</span> <span class="nn">torch_tensorrt._C</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">LogLevel</span><span class="p">,</span>
+    <span class="n">_get_is_colored_output_on</span><span class="p">,</span>
+    <span class="n">_get_logging_prefix</span><span class="p">,</span>
+    <span class="n">_get_reportable_log_level</span><span class="p">,</span>
+    <span class="n">_log</span><span class="p">,</span>
+    <span class="n">_set_is_colored_output_on</span><span class="p">,</span>
+    <span class="n">_set_logging_prefix</span><span class="p">,</span>
+    <span class="n">_set_reportable_log_level</span><span class="p">,</span>
+<span class="p">)</span>
+
+
+<div class="viewcode-block" id="Level"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.Level">[docs]</a><span class="k">class</span> <span class="nc">Level</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Enum to set the minimum required logging level to print a message to stdout&quot;&quot;&quot;</span>
+
+    <span class="n">InternalError</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INTERNAL_ERROR</span>
+    <span class="n">Error</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">ERROR</span>
+    <span class="ne">Warning</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">WARNING</span>
+    <span class="n">Info</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INFO</span>
+    <span class="n">Debug</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">DEBUG</span>
+    <span class="n">Graph</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">GRAPH</span>
+
+    <span class="nd">@staticmethod</span>
+    <span class="k">def</span> <span class="nf">_to_internal_level</span><span class="p">(</span><span class="n">external</span><span class="p">:</span> <span class="s2">&quot;Level&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">LogLevel</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">InternalError</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INTERNAL_ERROR</span>
+        <span class="k">elif</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">ERROR</span>
+        <span class="k">elif</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">WARNING</span>
+        <span class="k">elif</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INFO</span>
+        <span class="k">elif</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">DEBUG</span>
+        <span class="k">elif</span> <span class="n">external</span> <span class="o">==</span> <span class="n">Level</span><span class="o">.</span><span class="n">Graph</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">GRAPH</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Unknown log severity&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="get_logging_prefix"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.get_logging_prefix">[docs]</a><span class="k">def</span> <span class="nf">get_logging_prefix</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the prefix set for logging messages</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        str: Prefix used for logger</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="n">_get_logging_prefix</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="set_logging_prefix"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.set_logging_prefix">[docs]</a><span class="k">def</span> <span class="nf">set_logging_prefix</span><span class="p">(</span><span class="n">prefix</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the prefix used when logging messages</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        prefix (str): Prefix to use for logging messages</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_set_logging_prefix</span><span class="p">(</span><span class="n">prefix</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="get_reportable_log_level"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.get_reportable_log_level">[docs]</a><span class="k">def</span> <span class="nf">get_reportable_log_level</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">Level</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the level required for a message to be printed in the log</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch_tensorrt.logging.Level: The enum representing the level required to print</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">Level</span><span class="p">(</span><span class="n">_get_reportable_log_level</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="set_reportable_log_level"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.set_reportable_log_level">[docs]</a><span class="k">def</span> <span class="nf">set_reportable_log_level</span><span class="p">(</span><span class="n">level</span><span class="p">:</span> <span class="n">Level</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the level required for a message to be printed to the log</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        level (torch_tensorrt.logging.Level): The enum representing the level required to print</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">_to_internal_level</span><span class="p">(</span><span class="n">level</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="get_is_colored_output_on"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.get_is_colored_output_on">[docs]</a><span class="k">def</span> <span class="nf">get_is_colored_output_on</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Get if colored output is enabled for logging</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        bool: If colored output is one</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="nb">bool</span><span class="p">(</span><span class="n">_get_is_colored_output_on</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="set_is_colored_output_on"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.set_is_colored_output_on">[docs]</a><span class="k">def</span> <span class="nf">set_is_colored_output_on</span><span class="p">(</span><span class="n">colored_output_on</span><span class="p">:</span> <span class="nb">bool</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Enable or disable color in the log output</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        colored_output_on (bool): If colored output should be enabled or not</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_set_is_colored_output_on</span><span class="p">(</span><span class="n">colored_output_on</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="log"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.log">[docs]</a><span class="k">def</span> <span class="nf">log</span><span class="p">(</span><span class="n">level</span><span class="p">:</span> <span class="n">Level</span><span class="p">,</span> <span class="n">msg</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Add a new message to the log</span>
+
+<span class="sd">    Adds a new message to the log at a specified level. The message</span>
+<span class="sd">    will only get printed out if Level &gt; reportable_log_level</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        level (torch_tensorrt.logging.Level): Severity of the message</span>
+<span class="sd">        msg (str): Actual message text</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">_to_internal_level</span><span class="p">(</span><span class="n">level</span><span class="p">),</span> <span class="n">msg</span><span class="p">)</span>
+
+    <span class="n">InternalError</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INTERNAL_ERROR</span>
+    <span class="n">Error</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">ERROR</span>
+    <span class="ne">Warning</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">WARNING</span>
+    <span class="n">Info</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">INFO</span>
+    <span class="n">Debug</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">DEBUG</span>
+    <span class="n">Graph</span> <span class="o">=</span> <span class="n">LogLevel</span><span class="o">.</span><span class="n">GRAPH</span></div>
+
+
+<div class="viewcode-block" id="internal_errors"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.internal_errors">[docs]</a><span class="k">class</span> <span class="nc">internal_errors</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Context-manager to limit displayed log messages to just internal errors</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.internal_errors():</span>
+<span class="sd">        outputs = model_torchtrt(inputs)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">InternalError</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="errors"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.errors">[docs]</a><span class="k">class</span> <span class="nc">errors</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Context-manager to limit displayed log messages to just errors and above</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.errors():</span>
+<span class="sd">        outputs = model_torchtrt(inputs)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="warnings"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.warnings">[docs]</a><span class="k">class</span> <span class="nc">warnings</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Context-manager to limit displayed log messages to just warnings and above</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.warnings():</span>
+<span class="sd">        model_trt = torch_tensorrt.compile(model, **spec)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="info"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.info">[docs]</a><span class="k">class</span> <span class="nc">info</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Context-manager to display all info and greater severity messages</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.info():</span>
+<span class="sd">        model_trt = torch_tensorrt.compile(model, **spec)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Info</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="debug"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.debug">[docs]</a><span class="k">class</span> <span class="nc">debug</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Context-manager to display full debug information through the logger</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.debug():</span>
+<span class="sd">        model_trt = torch_tensorrt.compile(model, **spec)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="graphs"><a class="viewcode-back" href="../../py_api/logging.html#torch_tensorrt.logging.graphs">[docs]</a><span class="k">class</span> <span class="nc">graphs</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Context-manager to display the results of intermediate lowering passes</span>
+<span class="sd">    as well as full debug information through the logger</span>
+
+<span class="sd">    Example::</span>
+
+<span class="sd">    with torch_tensorrt.logging.graphs():</span>
+<span class="sd">        model_trt = torch_tensorrt.compile(model, **spec)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__enter__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span> <span class="o">=</span> <span class="n">get_reportable_log_level</span><span class="p">()</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Graph</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__exit__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">exc_type</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_value</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">exc_tb</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">set_reportable_log_level</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">external_lvl</span><span class="p">)</span></div>
+</pre></div>
+
+             </article>
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+              
+  <h1>Source code for torch_tensorrt.ptq</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span>
+
+<span class="k">if</span> <span class="n">sys</span><span class="o">.</span><span class="n">version_info</span> <span class="o">&gt;=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">11</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Self</span>
+<span class="k">else</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">typing_extensions</span> <span class="kn">import</span> <span class="n">Self</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">enum</span> <span class="kn">import</span> <span class="n">Enum</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.logging</span> <span class="kn">import</span> <span class="n">Level</span><span class="p">,</span> <span class="n">log</span>
+
+
+<div class="viewcode-block" id="CalibrationAlgo"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo">[docs]</a><span class="k">class</span> <span class="nc">CalibrationAlgo</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
+    <span class="n">ENTROPY_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span>
+    <span class="n">ENTROPY_CALIBRATION_2</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span>
+    <span class="n">LEGACY_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span>
+    <span class="n">MINMAX_CALIBRATION</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span></div>
+
+
+<div class="viewcode-block" id="get_cache_mode_batch"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_cache_mode_batch">[docs]</a><span class="k">def</span> <span class="nf">get_cache_mode_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">return</span> <span class="kc">None</span></div>
+
+
+<div class="viewcode-block" id="get_batch_size"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_batch_size">[docs]</a><span class="k">def</span> <span class="nf">get_batch_size</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+    <span class="k">return</span> <span class="mi">1</span></div>
+
+
+<div class="viewcode-block" id="get_batch"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.get_batch">[docs]</a><span class="k">def</span> <span class="nf">get_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span> <span class="n">_</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">]]:</span>
+    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_batch_idx</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">&gt;</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_loader</span><span class="o">.</span><span class="n">dataset</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">None</span>
+
+    <span class="n">batch</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dataset_iterator</span><span class="p">)</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">current_batch_idx</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
+    <span class="n">inputs_gpu</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">example</span> <span class="ow">in</span> <span class="n">batch</span><span class="p">:</span>
+            <span class="n">inputs_gpu</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">example</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">inputs_gpu</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">)</span><span class="o">.</span><span class="n">data_ptr</span><span class="p">())</span>
+    <span class="k">return</span> <span class="n">inputs_gpu</span></div>
+
+
+<div class="viewcode-block" id="read_calibration_cache"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.read_calibration_cache">[docs]</a><span class="k">def</span> <span class="nf">read_calibration_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bytes</span><span class="p">:</span>
+    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">use_cache</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">):</span>
+            <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">,</span> <span class="s2">&quot;rb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+                <span class="n">b</span><span class="p">:</span> <span class="nb">bytes</span> <span class="o">=</span> <span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+                <span class="k">return</span> <span class="n">b</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">FileNotFoundError</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="sa">b</span><span class="s2">&quot;&quot;</span></div>
+
+
+<div class="viewcode-block" id="write_calibration_cache"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.write_calibration_cache">[docs]</a><span class="k">def</span> <span class="nf">write_calibration_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">,</span> <span class="n">cache</span><span class="p">:</span> <span class="nb">bytes</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">:</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">cache_file</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+            <span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">cache</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span></div>
+
+
+<span class="c1"># deepcopy (which involves pickling) is performed on the compile_spec internally during compilation.</span>
+<span class="c1"># We register this __reduce__ function for pickler to identity the calibrator object returned by DataLoaderCalibrator during deepcopy.</span>
+<span class="c1"># This should be the object&#39;s local name relative to the module https://docs.python.org/3/library/pickle.html#object.__reduce__</span>
+<span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">:</span> <span class="nb">object</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
+    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span>
+
+
+<div class="viewcode-block" id="DataLoaderCalibrator"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator">[docs]</a><span class="k">class</span> <span class="nc">DataLoaderCalibrator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces</span>
+<span class="sd">    data which is passed during calibration.</span>
+<span class="sd">    Args:</span>
+<span class="sd">        dataloader: an instance of pytorch dataloader which iterates through a given dataset.</span>
+<span class="sd">        algo_type: choice of calibration algorithm.</span>
+<span class="sd">        cache_file: path to cache file.</span>
+<span class="sd">        use_cache: flag which enables usage of pre-existing cache.</span>
+<span class="sd">        device: device on which calibration data is copied to.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="DataLoaderCalibrator.__init__"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Self</span><span class="p">:</span>
+        <span class="n">dataloader</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">algo_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;algo_type&quot;</span><span class="p">,</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">)</span>
+        <span class="n">cache_file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;cache_file&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">use_cache</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;use_cache&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="n">device</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;device&quot;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:0&quot;</span><span class="p">))</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dataloader</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">):</span>
+            <span class="n">log</span><span class="p">(</span>
+                <span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span>
+                <span class="s2">&quot;Dataloader : </span><span class="si">{}</span><span class="s2"> is not a valid instance of torch.utils.data.DataLoader&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="n">dataloader</span>
+                <span class="p">),</span>
+            <span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">cache_file</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">use_cache</span><span class="p">:</span>
+                <span class="n">log</span><span class="p">(</span>
+                    <span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span>
+                    <span class="s2">&quot;Using existing cache_file </span><span class="si">{}</span><span class="s2"> for calibration&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cache_file</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span> <span class="s2">&quot;Overwriting existing calibration cache file.&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">use_cache</span><span class="p">:</span>
+                <span class="n">log</span><span class="p">(</span>
+                    <span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span>
+                    <span class="s2">&quot;Input cache file is None but use_cache is set to True in INT8 mode.&quot;</span><span class="p">,</span>
+                <span class="p">)</span>
+
+        <span class="c1"># Define attributes and member functions for the calibrator class</span>
+        <span class="n">attribute_mapping</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;data_loader&quot;</span><span class="p">:</span> <span class="n">dataloader</span><span class="p">,</span>
+            <span class="s2">&quot;current_batch_idx&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+            <span class="s2">&quot;batch_size&quot;</span><span class="p">:</span> <span class="n">dataloader</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
+            <span class="s2">&quot;dataset_iterator&quot;</span><span class="p">:</span> <span class="nb">iter</span><span class="p">(</span><span class="n">dataloader</span><span class="p">),</span>
+            <span class="s2">&quot;cache_file&quot;</span><span class="p">:</span> <span class="n">cache_file</span><span class="p">,</span>
+            <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+            <span class="s2">&quot;use_cache&quot;</span><span class="p">:</span> <span class="n">use_cache</span><span class="p">,</span>
+            <span class="s2">&quot;get_batch_size&quot;</span><span class="p">:</span> <span class="n">get_batch_size</span><span class="p">,</span>
+            <span class="s2">&quot;get_batch&quot;</span><span class="p">:</span> <span class="n">get_cache_mode_batch</span> <span class="k">if</span> <span class="n">use_cache</span> <span class="k">else</span> <span class="n">get_batch</span><span class="p">,</span>
+            <span class="s2">&quot;read_calibration_cache&quot;</span><span class="p">:</span> <span class="n">read_calibration_cache</span><span class="p">,</span>
+            <span class="s2">&quot;write_calibration_cache&quot;</span><span class="p">:</span> <span class="n">write_calibration_cache</span><span class="p">,</span>
+            <span class="s2">&quot;__reduce__&quot;</span><span class="p">:</span> <span class="n">__reduce__</span><span class="p">,</span>  <span class="c1"># used when you deepcopy the DataLoaderCalibrator object</span>
+        <span class="p">}</span>
+
+        <span class="c1"># Using type metaclass to construct calibrator class based on algorithm type</span>
+        <span class="k">if</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span><span class="p">:</span>
+            <span class="n">calib_ec</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;Int8EntropyCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+            <span class="k">return</span> <span class="n">calib_ec</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">:</span>
+            <span class="n">calib_ec2</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;Int8EntropyCalibrator2&quot;</span><span class="p">,</span>
+                <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator2</span><span class="p">,),</span>
+                <span class="n">attribute_mapping</span><span class="p">,</span>
+            <span class="p">)()</span>
+            <span class="k">return</span> <span class="n">calib_ec2</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span><span class="p">:</span>
+            <span class="n">calib_lc</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;Int8LegacyCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8LegacyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+            <span class="k">return</span> <span class="n">calib_lc</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span><span class="p">:</span>
+            <span class="n">calib_mmc</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;Int8MinMaxCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+            <span class="k">return</span> <span class="n">calib_mmc</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Invalid calibration algorithm type. Please select among ENTROPY_CALIBRATION, ENTROPY_CALIBRATION, LEGACY_CALIBRATION or MINMAX_CALIBRATION&quot;</span>
+            <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="CacheCalibrator"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator">[docs]</a><span class="k">class</span> <span class="nc">CacheCalibrator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.</span>
+<span class="sd">    Args:</span>
+<span class="sd">        cache_file: path to cache file.</span>
+<span class="sd">        algo_type: choice of calibration algorithm.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="CacheCalibrator.__init__"><a class="viewcode-back" href="../../py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator.__init__">[docs]</a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+    <span class="k">def</span> <span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Self</span><span class="p">:</span>
+        <span class="n">cache_file</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">algo_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;algo_type&quot;</span><span class="p">,</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">cache_file</span><span class="p">):</span>
+            <span class="n">log</span><span class="p">(</span>
+                <span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span>
+                <span class="s2">&quot;Using existing cache_file </span><span class="si">{}</span><span class="s2"> for calibration&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">cache_file</span><span class="p">),</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Error</span><span class="p">,</span> <span class="s2">&quot;Invalid calibration cache file.&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Define attributes and member functions for the calibrator class</span>
+        <span class="n">attribute_mapping</span> <span class="o">=</span> <span class="p">{</span>
+            <span class="s2">&quot;use_cache&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+            <span class="s2">&quot;cache_file&quot;</span><span class="p">:</span> <span class="n">cache_file</span><span class="p">,</span>
+            <span class="s2">&quot;get_batch_size&quot;</span><span class="p">:</span> <span class="n">get_batch_size</span><span class="p">,</span>
+            <span class="s2">&quot;get_batch&quot;</span><span class="p">:</span> <span class="n">get_cache_mode_batch</span><span class="p">,</span>
+            <span class="s2">&quot;read_calibration_cache&quot;</span><span class="p">:</span> <span class="n">read_calibration_cache</span><span class="p">,</span>
+            <span class="s2">&quot;write_calibration_cache&quot;</span><span class="p">:</span> <span class="n">write_calibration_cache</span><span class="p">,</span>
+        <span class="p">}</span>
+        <span class="c1"># Using type metaclass to construct calibrator class based on algorithm type</span>
+        <span class="k">if</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION</span><span class="p">:</span>
+            <span class="n">calib_ec</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;DataLoaderCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8EntropyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+            <span class="k">return</span> <span class="n">calib_ec</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">:</span>
+            <span class="n">calib_ec2</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;DataLoaderCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+            <span class="k">return</span> <span class="n">calib_ec2</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">LEGACY_CALIBRATION</span><span class="p">:</span>
+            <span class="n">calib_lc</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;DataLoaderCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8LegacyCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+            <span class="k">return</span> <span class="n">calib_lc</span>
+        <span class="k">elif</span> <span class="n">algo_type</span> <span class="o">==</span> <span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">MINMAX_CALIBRATION</span><span class="p">:</span>
+            <span class="n">calib_mmc</span><span class="p">:</span> <span class="n">Self</span> <span class="o">=</span> <span class="nb">type</span><span class="p">(</span>
+                <span class="s2">&quot;DataLoaderCalibrator&quot;</span><span class="p">,</span> <span class="p">(</span><span class="n">_C</span><span class="o">.</span><span class="n">IInt8MinMaxCalibrator</span><span class="p">,),</span> <span class="n">attribute_mapping</span>
+            <span class="p">)()</span>
+            <span class="k">return</span> <span class="n">calib_mmc</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Invalid calibration algorithm type. Please select among ENTROPY_CALIBRATION, ENTROPY_CALIBRATION, LEGACY_CALIBRATION or MINMAX_CALIBRATION&quot;</span>
+            <span class="p">)</span></div>
+</pre></div>
+
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diff --git a/docs/v2.2.0/_modules/torch_tensorrt/ts/_compile_spec.html b/docs/v2.2.0/_modules/torch_tensorrt/ts/_compile_spec.html
new file mode 100644
index 0000000000..8b87683931
--- /dev/null
+++ b/docs/v2.2.0/_modules/torch_tensorrt/ts/_compile_spec.html
@@ -0,0 +1,1211 @@
+
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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+  <meta charset="utf-8">
+  
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+  
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+              
+  <h1>Source code for torch_tensorrt.ts._compile_spec</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Set</span>
+
+<span class="kn">import</span> <span class="nn">tensorrt</span> <span class="k">as</span> <span class="nn">trt</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt._C.ts</span> <span class="k">as</span> <span class="nn">_ts_C</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_C</span><span class="p">,</span> <span class="n">_enums</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Input</span> <span class="kn">import</span> <span class="n">Input</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.logging</span> <span class="kn">import</span> <span class="n">Level</span><span class="p">,</span> <span class="n">log</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.ts._Input</span> <span class="kn">import</span> <span class="n">TorchScriptInput</span>
+
+
+<span class="k">def</span> <span class="nf">_internal_input_to_torch_class_input</span><span class="p">(</span><span class="n">i</span><span class="p">:</span> <span class="n">_C</span><span class="o">.</span><span class="n">Input</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">_Input</span><span class="p">:</span>
+    <span class="n">clone</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">_Input</span><span class="p">()</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_min</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">min</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_opt</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">opt</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_max</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_dtype</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_tensor_domain</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">tensor_domain</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_format</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">format</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_input_is_dynamic</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">input_is_dynamic</span><span class="p">)</span>
+    <span class="n">clone</span><span class="o">.</span><span class="n">_set_explicit_set_dtype</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">_explicit_set_dtype</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">clone</span>
+
+
+<span class="k">def</span> <span class="nf">_supported_input_size_type</span><span class="p">(</span><span class="n">input_size</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_size</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;Input sizes for inputs are required to be a List, tuple or torch.Size or a Dict of three sizes (min, opt, max), found type: &quot;</span>
+            <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">input_size</span><span class="p">))</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_op_precision</span><span class="p">(</span><span class="n">precision</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">precision</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">precision</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">int8</span>
+        <span class="k">elif</span> <span class="n">precision</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">half</span>
+        <span class="k">elif</span> <span class="n">precision</span> <span class="o">==</span> <span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="o">.</span><span class="n">float</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Provided an unsupported dtype as operating precision (support: int8, half, float), got: &quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">precision</span><span class="p">)</span>
+            <span class="p">)</span>
+
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">precision</span><span class="p">,</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">precision</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;Op precision type needs to be specified with a torch.dtype or a torch_tensorrt.dtype, got: &quot;</span>
+            <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">precision</span><span class="p">))</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_enabled_precisions</span><span class="p">(</span><span class="n">precisions</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Set</span><span class="p">[</span><span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">]:</span>
+    <span class="n">parsed_precisions</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+    <span class="k">if</span> <span class="nb">any</span><span class="p">(</span><span class="nb">isinstance</span><span class="p">(</span><span class="n">precisions</span><span class="p">,</span> <span class="nb">type</span><span class="p">)</span> <span class="k">for</span> <span class="nb">type</span> <span class="ow">in</span> <span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">]):</span>
+        <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">precisions</span><span class="p">:</span>
+            <span class="n">parsed_precisions</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_parse_op_precision</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">parsed_precisions</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">_parse_op_precision</span><span class="p">(</span><span class="n">precisions</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">parsed_precisions</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_device_type</span><span class="p">(</span><span class="n">device</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_enums</span><span class="o">.</span><span class="n">DeviceType</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">device</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s2">&quot;cuda&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">gpu</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Got a device type other than GPU or DLA (type: &quot;</span>
+                <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="o">.</span><span class="n">type</span><span class="p">)</span>
+                <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">_C</span><span class="o">.</span><span class="n">DeviceType</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">device</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="n">trt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span>
+        <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device</span><span class="p">,</span> <span class="nb">str</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;gpu&quot;</span> <span class="ow">or</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;GPU&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span>
+        <span class="k">elif</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;dla&quot;</span> <span class="ow">or</span> <span class="n">device</span> <span class="o">==</span> <span class="s2">&quot;DLA&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">_C</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">DLA</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Got a device type other than GPU or DLA (type: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">device</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+            <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;Device specification must be of type torch.device, string or torch_tensorrt.DeviceType, but got: &quot;</span>
+            <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">device</span><span class="p">))</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_device</span><span class="p">(</span><span class="n">device_info</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">_C</span><span class="o">.</span><span class="n">Device</span><span class="p">:</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+        <span class="n">info</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">Device</span><span class="p">()</span>
+        <span class="k">if</span> <span class="s2">&quot;device_type&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">device_info</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;Device type is required parameter&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">info</span><span class="o">.</span><span class="n">device_type</span> <span class="o">=</span> <span class="n">_parse_device_type</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;device_type&quot;</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="s2">&quot;gpu_id&quot;</span> <span class="ow">in</span> <span class="n">device_info</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;gpu_id&quot;</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span>
+            <span class="n">info</span><span class="o">.</span><span class="n">gpu_id</span> <span class="o">=</span> <span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;gpu_id&quot;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="s2">&quot;dla_core&quot;</span> <span class="ow">in</span> <span class="n">device_info</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;dla_core&quot;</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span>
+            <span class="n">info</span><span class="o">.</span><span class="n">dla_core</span> <span class="o">=</span> <span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;dla_core&quot;</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="s2">&quot;allow_gpu_fallback&quot;</span> <span class="ow">in</span> <span class="n">device_info</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+            <span class="n">info</span><span class="o">.</span><span class="n">allow_gpu_fallback</span> <span class="o">=</span> <span class="n">device_info</span><span class="p">[</span><span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">info</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">,</span> <span class="n">Device</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">device_info</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">device_info</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">(</span><span class="n">Device</span><span class="o">.</span><span class="n">_from_torch_device</span><span class="p">(</span><span class="n">device_info</span><span class="p">))</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;Unsupported data for device specification. Expected either a dict, torch_tensorrt.Device or torch.Device&quot;</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_torch_fallback</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">_ts_C</span><span class="o">.</span><span class="n">TorchFallback</span><span class="p">:</span>
+    <span class="n">info</span> <span class="o">=</span> <span class="n">_ts_C</span><span class="o">.</span><span class="n">TorchFallback</span><span class="p">()</span>
+    <span class="k">if</span> <span class="s2">&quot;enabled&quot;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">fallback_info</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span><span class="s2">&quot;Enabled is required parameter&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;enabled&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">enabled</span> <span class="o">=</span> <span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;enabled&quot;</span><span class="p">]</span>
+    <span class="k">if</span> <span class="s2">&quot;min_block_size&quot;</span> <span class="ow">in</span> <span class="n">fallback_info</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;min_block_size&quot;</span><span class="p">],</span> <span class="nb">int</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">min_block_size</span> <span class="o">=</span> <span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;min_block_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;forced_fallback_ops&quot;</span> <span class="ow">in</span> <span class="n">fallback_info</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">forced_fallback_operators</span> <span class="o">=</span> <span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;forced_fallback_modules&quot;</span> <span class="ow">in</span> <span class="n">fallback_info</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;forced_fallback_modules&quot;</span><span class="p">],</span> <span class="nb">list</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">forced_fallback_modules</span> <span class="o">=</span> <span class="n">fallback_info</span><span class="p">[</span><span class="s2">&quot;forced_fallback_modules&quot;</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="n">info</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_input_signature</span><span class="p">(</span><span class="n">input_signature</span><span class="p">:</span> <span class="n">Any</span><span class="p">,</span> <span class="n">depth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Any</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">depth</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span>
+            <span class="s2">&quot;Input nesting depth exceeds max supported depth, use 1 level: [A, B], or 2 level: [A, (B, C)]&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_signature</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+        <span class="n">input_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">input_signature</span><span class="p">:</span>
+            <span class="nb">input</span> <span class="o">=</span> <span class="n">_parse_input_signature</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">depth</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">input_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">input_list</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_signature</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">input_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">input_signature</span><span class="p">:</span>
+            <span class="nb">input</span> <span class="o">=</span> <span class="n">_parse_input_signature</span><span class="p">(</span><span class="n">item</span><span class="p">,</span> <span class="n">depth</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">input_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">input_list</span>
+    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_signature</span><span class="p">,</span> <span class="p">(</span><span class="n">Input</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)):</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">Input</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">input_signature</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">input_signature</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
+            <span class="k">else</span> <span class="n">input_signature</span>
+        <span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">i</span><span class="o">.</span><span class="n">is_trt_dtype</span><span class="p">():</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+                <span class="s2">&quot;Using non-TRT input types with input_signature is not currently &quot;</span>
+                <span class="o">+</span> <span class="s2">&quot;supported. Please specify inputs individually to use &quot;</span>
+                <span class="o">+</span> <span class="s2">&quot;non-TRT types.&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">ts_i</span> <span class="o">=</span> <span class="n">i</span>
+        <span class="k">if</span> <span class="n">i</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
+            <span class="n">ts_i</span> <span class="o">=</span> <span class="n">TorchScriptInput</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">format</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">i</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                <span class="n">ts_i</span> <span class="o">=</span> <span class="n">TorchScriptInput</span><span class="p">(</span>
+                    <span class="n">min_shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">],</span>
+                    <span class="n">opt_shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">],</span>
+                    <span class="n">max_shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">],</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
+                    <span class="nb">format</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">format</span><span class="p">,</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Input set as dynamic, expected dictionary of shapes but found </span><span class="si">{</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                <span class="s2">&quot;Invalid shape mode detected for input while parsing the input_signature&quot;</span>
+            <span class="p">)</span>
+
+        <span class="n">clone</span> <span class="o">=</span> <span class="n">_internal_input_to_torch_class_input</span><span class="p">(</span><span class="n">ts_i</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">())</span>
+        <span class="k">return</span> <span class="n">clone</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span>
+            <span class="s2">&quot;Input signature contains an unsupported type </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                <span class="nb">type</span><span class="p">(</span><span class="n">input_signature</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_parse_compile_spec</span><span class="p">(</span><span class="n">compile_spec_</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">_ts_C</span><span class="o">.</span><span class="n">CompileSpec</span><span class="p">:</span>
+    <span class="c1"># TODO: Use deepcopy to support partial compilation of collections</span>
+    <span class="n">compile_spec</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">compile_spec_</span><span class="p">)</span>
+    <span class="n">info</span> <span class="o">=</span> <span class="n">_ts_C</span><span class="o">.</span><span class="n">CompileSpec</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;inputs&quot;</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span>
+            <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">Input</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;inputs&quot;</span><span class="p">]</span>
+        <span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span>
+                <span class="s2">&quot;Input specs should be either torch_tensorrt.Input or torch.Tensor, found types: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+                    <span class="p">[</span><span class="nb">type</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;inputs&quot;</span><span class="p">]]</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+
+        <span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">Input</span><span class="o">.</span><span class="n">from_tensor</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="k">else</span> <span class="n">i</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;inputs&quot;</span><span class="p">]</span>
+        <span class="p">]</span>
+        <span class="n">ts_inputs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">inputs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">i</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">STATIC</span><span class="p">:</span>
+                <span class="n">ts_inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="n">TorchScriptInput</span><span class="p">(</span>
+                        <span class="n">shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="nb">format</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">format</span>
+                    <span class="p">)</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="n">i</span><span class="o">.</span><span class="n">shape_mode</span> <span class="o">==</span> <span class="n">Input</span><span class="o">.</span><span class="n">_ShapeMode</span><span class="o">.</span><span class="n">DYNAMIC</span><span class="p">:</span>
+                <span class="n">ts_inputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="n">TorchScriptInput</span><span class="p">(</span>
+                        <span class="n">min_shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;min_shape&quot;</span><span class="p">],</span>
+                        <span class="n">opt_shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;opt_shape&quot;</span><span class="p">],</span>
+                        <span class="n">max_shape</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;max_shape&quot;</span><span class="p">],</span>
+                        <span class="n">dtype</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span>
+                        <span class="nb">format</span><span class="o">=</span><span class="n">i</span><span class="o">.</span><span class="n">format</span><span class="p">,</span>
+                    <span class="p">)</span><span class="o">.</span><span class="n">_to_internal</span><span class="p">()</span>
+                <span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">inputs</span> <span class="o">=</span> <span class="n">ts_inputs</span>
+
+    <span class="k">elif</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;input_signature&quot;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">log</span><span class="p">(</span>
+            <span class="n">Level</span><span class="o">.</span><span class="n">Warning</span><span class="p">,</span>
+            <span class="s2">&quot;Input signature parsing is an experimental feature, behavior and APIs may change&quot;</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">signature</span> <span class="o">=</span> <span class="n">_parse_input_signature</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;input_signature&quot;</span><span class="p">])</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">input_signature</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">InputSignature</span><span class="p">(</span><span class="n">signature</span><span class="p">)</span>  <span class="c1"># py_object</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">KeyError</span><span class="p">(</span>
+            <span class="s1">&#39;Module input definitions are requried to compile module. Provide a list of torch_tensorrt.Input keyed to &quot;inputs&quot; in the compile spec&#39;</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="s2">&quot;enabled_precisions&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">enabled_precisions</span> <span class="o">=</span> <span class="n">_parse_enabled_precisions</span><span class="p">(</span>
+            <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;enabled_precisions&quot;</span><span class="p">]</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="s2">&quot;calibrator&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span> <span class="ow">and</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;calibrator&quot;</span><span class="p">]:</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">ptq_calibrator</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;calibrator&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;sparse_weights&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;sparse_weights&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">sparse_weights</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;sparse_weights&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;disable_tf32&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;disable_tf32&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">disable_tf32</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;disable_tf32&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;refit&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;refit&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">refit</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;refit&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;debug&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;debug&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">debug</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;debug&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;allow_shape_tensors&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;allow_shape_tensors&quot;</span><span class="p">],</span> <span class="nb">bool</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">allow_shape_tensors</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;allow_shape_tensors&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;device&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">_parse_device</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;device&quot;</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="s2">&quot;capability&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;capability&quot;</span><span class="p">],</span> <span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span><span class="p">)</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">capability</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;capability&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;num_avg_timing_iters&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">num_avg_timing_iters</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;workspace_size&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;workspace_size&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">workspace_size</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;workspace_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;dla_sram_size&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_sram_size&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">dla_sram_size</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_sram_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;dla_local_dram_size&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">dla_local_dram_size</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;dla_global_dram_size&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">int</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">dla_global_dram_size</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;truncate_long_and_double&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">type</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">])</span> <span class="ow">is</span> <span class="nb">bool</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">truncate_long_and_double</span> <span class="o">=</span> <span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="s2">&quot;torch_fallback&quot;</span> <span class="ow">in</span> <span class="n">compile_spec</span><span class="p">:</span>
+        <span class="n">info</span><span class="o">.</span><span class="n">torch_fallback</span> <span class="o">=</span> <span class="n">_parse_torch_fallback</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">[</span><span class="s2">&quot;torch_fallback&quot;</span><span class="p">])</span>
+
+    <span class="n">log</span><span class="p">(</span><span class="n">Level</span><span class="o">.</span><span class="n">Debug</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="n">info</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">info</span>
+
+
+<div class="viewcode-block" id="TensorRTCompileSpec"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.TensorRTCompileSpec">[docs]</a><span class="k">def</span> <span class="nf">TensorRTCompileSpec</span><span class="p">(</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">Input</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">input_signature</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">device</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span> <span class="o">|</span> <span class="n">Device</span> <span class="o">=</span> <span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">(),</span>
+    <span class="n">disable_tf32</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Set</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span> <span class="o">|</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">refit</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">debug</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">capability</span><span class="p">:</span> <span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span> <span class="o">=</span> <span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+    <span class="n">num_avg_timing_iters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">workspace_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">dla_sram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1048576</span><span class="p">,</span>
+    <span class="n">dla_local_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1073741824</span><span class="p">,</span>
+    <span class="n">dla_global_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">536870912</span><span class="p">,</span>
+    <span class="n">truncate_long_and_double</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">calibrator</span><span class="p">:</span> <span class="nb">object</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">allow_shape_tensors</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">CompileSpec</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</span>
+
+<span class="sd">    Keyword Args:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>
+
+<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>
+
+<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        refit (bool): Enable refitting</span>
+<span class="sd">        debug (bool): Enable debuggable engine</span>
+<span class="sd">        capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
+<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
+<span class="sd">        truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32</span>
+<span class="sd">        calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration</span>
+<span class="sd">        allow_shape_tensors: (Experimental) Allow aten::size to output shape tensors using IShapeLayer in TensorRT</span>
+
+<span class="sd">      Returns:</span>
+<span class="sd">        torch.classes.tensorrt.CompileSpec: List of methods and formated spec objects to be provided to ``torch._C._jit_to_tensorrt``</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">compile_spec</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="n">inputs</span> <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[],</span>
+        <span class="c1"># &quot;input_signature&quot;: input_signature,</span>
+        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>  <span class="c1"># Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>  <span class="c1"># Enable sparsity for convolution and fully connected layers.</span>
+        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="p">(</span>
+            <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">set</span><span class="p">()</span>
+        <span class="p">),</span>  <span class="c1"># Enabling FP16 kernels</span>
+        <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="n">refit</span><span class="p">,</span>  <span class="c1"># enable refit</span>
+        <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="n">debug</span><span class="p">,</span>  <span class="c1"># enable debuggable engine</span>
+        <span class="s2">&quot;capability&quot;</span><span class="p">:</span> <span class="n">capability</span><span class="p">,</span>  <span class="c1"># Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>  <span class="c1"># Number of averaging timing iterations used to select kernels</span>
+        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>  <span class="c1"># Maximum size of workspace given to TensorRT</span>
+        <span class="s2">&quot;dla_sram_size&quot;</span><span class="p">:</span> <span class="n">dla_sram_size</span><span class="p">,</span>  <span class="c1"># Fast software managed RAM used by DLA to communicate within a layer.</span>
+        <span class="s2">&quot;dla_local_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_local_dram_size</span><span class="p">,</span>  <span class="c1"># Host RAM used by DLA to share intermediate tensor data across operations</span>
+        <span class="s2">&quot;dla_global_dram_size&quot;</span><span class="p">:</span> <span class="n">dla_global_dram_size</span><span class="p">,</span>  <span class="c1"># Host RAM used by DLA to store weights and metadata for execution</span>
+        <span class="s2">&quot;calibrator&quot;</span><span class="p">:</span> <span class="n">calibrator</span><span class="p">,</span>
+        <span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="n">truncate_long_and_double</span><span class="p">,</span>
+        <span class="s2">&quot;allow_shape_tensors&quot;</span><span class="p">:</span> <span class="n">allow_shape_tensors</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="n">parsed_spec</span> <span class="o">=</span> <span class="n">_parse_compile_spec</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">)</span>
+
+    <span class="n">backend_spec</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">CompileSpec</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">input_signature</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;Input signature parsing is not currently supported in the TorchScript backend integration&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">parsed_spec</span><span class="o">.</span><span class="n">inputs</span><span class="p">:</span>
+        <span class="n">clone</span> <span class="o">=</span> <span class="n">_internal_input_to_torch_class_input</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+        <span class="n">backend_spec</span><span class="o">.</span><span class="n">_append_input</span><span class="p">(</span><span class="n">clone</span><span class="p">)</span>
+
+    <span class="n">d</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">_Device</span><span class="p">()</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">_set_device_type</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">device_type</span><span class="p">))</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">_set_gpu_id</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">gpu_id</span><span class="p">)</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">_set_dla_core</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">dla_core</span><span class="p">)</span>
+    <span class="n">d</span><span class="o">.</span><span class="n">_set_allow_gpu_fallback</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">device</span><span class="o">.</span><span class="n">allow_gpu_fallback</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">enabled</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span>
+            <span class="s2">&quot;Partial module compilation is not currently supported via the PyTorch TensorRT backend. If you need partial compilation, use torch_tensorrt.compile&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">torch_fallback</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">classes</span><span class="o">.</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">_TorchFallback</span><span class="p">()</span>
+    <span class="n">torch_fallback</span><span class="o">.</span><span class="n">_set_enabled</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">enabled</span><span class="p">)</span>
+    <span class="n">torch_fallback</span><span class="o">.</span><span class="n">_set_min_block_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">min_block_size</span><span class="p">)</span>
+    <span class="n">torch_fallback</span><span class="o">.</span><span class="n">_set_forced_fallback_operators</span><span class="p">(</span>
+        <span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">forced_fallback_operators</span>
+    <span class="p">)</span>
+    <span class="n">torch_fallback</span><span class="o">.</span><span class="n">_set_forced_fallback_modules</span><span class="p">(</span>
+        <span class="n">parsed_spec</span><span class="o">.</span><span class="n">torch_fallback</span><span class="o">.</span><span class="n">forced_fallback_modules</span>
+    <span class="p">)</span>
+
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_device</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_torch_fallback</span><span class="p">(</span><span class="n">torch_fallback</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_precisions</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">parsed_spec</span><span class="o">.</span><span class="n">enabled_precisions</span><span class="p">])</span>
+
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_disable_tf32</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">disable_tf32</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_refit</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">refit</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_debug</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">debug</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_refit</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">refit</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_capability</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">capability</span><span class="p">))</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_num_avg_timing_iters</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">num_avg_timing_iters</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_workspace_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">workspace_size</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_dla_sram_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">dla_sram_size</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_dla_local_dram_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">dla_local_dram_size</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_dla_global_dram_size</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">dla_global_dram_size</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_truncate_long_and_double</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">truncate_long_and_double</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_allow_shape_tensors</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">allow_shape_tensors</span><span class="p">)</span>
+    <span class="n">backend_spec</span><span class="o">.</span><span class="n">_set_ptq_calibrator</span><span class="p">(</span><span class="n">parsed_spec</span><span class="o">.</span><span class="n">_get_calibrator_handle</span><span class="p">())</span>
+
+    <span class="k">return</span> <span class="n">backend_spec</span></div>
+</pre></div>
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diff --git a/docs/v2.2.0/_modules/torch_tensorrt/ts/_compiler.html b/docs/v2.2.0/_modules/torch_tensorrt/ts/_compiler.html
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+  <h1>Source code for torch_tensorrt.ts._compiler</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">,</span> <span class="n">Set</span><span class="p">,</span> <span class="n">Tuple</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt._C.ts</span> <span class="k">as</span> <span class="nn">_C</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt</span> <span class="kn">import</span> <span class="n">_enums</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Device</span> <span class="kn">import</span> <span class="n">Device</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt._Input</span> <span class="kn">import</span> <span class="n">Input</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.ts._compile_spec</span> <span class="kn">import</span> <span class="n">_parse_compile_spec</span><span class="p">,</span> <span class="n">_parse_device</span>
+
+
+<div class="viewcode-block" id="compile"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.compile">[docs]</a><span class="k">def</span> <span class="nf">compile</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">input_signature</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">Any</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">=</span> <span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">(),</span>
+    <span class="n">disable_tf32</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Set</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span> <span class="o">|</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">refit</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">debug</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">capability</span><span class="p">:</span> <span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span> <span class="o">=</span> <span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+    <span class="n">num_avg_timing_iters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">workspace_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">dla_sram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1048576</span><span class="p">,</span>
+    <span class="n">dla_local_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1073741824</span><span class="p">,</span>
+    <span class="n">dla_global_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">536870912</span><span class="p">,</span>
+    <span class="n">calibrator</span><span class="p">:</span> <span class="nb">object</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">truncate_long_and_double</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">require_full_compilation</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">min_block_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+    <span class="n">torch_executed_ops</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">torch_executed_modules</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">allow_shape_tensors</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Compile a TorchScript module for NVIDIA GPUs using TensorRT</span>
+
+<span class="sd">    Takes a existing TorchScript module and a set of settings to configure the compiler</span>
+<span class="sd">    and will convert methods to JIT Graphs which call equivalent TensorRT engines</span>
+
+<span class="sd">    Converts specifically the forward method of a TorchScript Module</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch</span>
+<span class="sd">            ``torch.nn.Module``</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        input_signature Union(List, Tuple, torch_tensorrt.Input, torch.Tensor): A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. **This API should be considered beta-level stable and may change in the future** ::</span>
+
+<span class="sd">                input_signature=([</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                ], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3</span>
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>
+
+<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>
+
+<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        refit (bool): Enable refitting</span>
+<span class="sd">        debug (bool): Enable debuggable engine</span>
+<span class="sd">        capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
+<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
+<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
+<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
+<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
+<span class="sd">        truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32</span>
+<span class="sd">        calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration</span>
+<span class="sd">        require_full_compilation (bool): Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</span>
+<span class="sd">        min_block_size (int): The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</span>
+<span class="sd">        torch_executed_ops (List[str]): List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
+<span class="sd">        torch_executed_modules (List[str]): List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but ``require_full_compilation`` is True</span>
+<span class="sd">        allow_shape_tensors: (Experimental) Allow aten::size to output shape tensors using IShapeLayer in TensorRT</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.jit.ScriptModule: Compiled TorchScript Module, when run it will execute via TensorRT</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">input_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
+    <span class="n">enabled_precisions_set</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">set</span><span class="p">()</span>
+    <span class="p">)</span>
+    <span class="n">torch_executed_module_list</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">torch_executed_modules</span> <span class="k">if</span> <span class="n">torch_executed_modules</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
+    <span class="p">)</span>
+    <span class="n">torch_executed_op_list</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">torch_executed_ops</span> <span class="k">if</span> <span class="n">torch_executed_ops</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
+    <span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptFunction</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;torch.jit.ScriptFunction currently is not directly supported, wrap the function in a module to compile&quot;</span>
+        <span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">require_full_compilation</span> <span class="ow">and</span> <span class="p">(</span>
+        <span class="nb">len</span><span class="p">(</span><span class="n">torch_executed_module_list</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">torch_executed_op_list</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span>
+    <span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;require_full_compilation is enabled however the list of modules and ops to run in torch is not empty. Found: torch_executed_ops: </span><span class="si">{</span><span class="n">torch_executed_ops</span><span class="si">}</span><span class="s2">, torch_executed_modules: </span><span class="si">{</span><span class="n">torch_executed_modules</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="n">input_list</span><span class="p">,</span>
+        <span class="s2">&quot;input_signature&quot;</span><span class="p">:</span> <span class="n">input_signature</span><span class="p">,</span>
+        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>  <span class="c1"># Force FP32 layers to use traditional as FP32 format</span>
+        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>  <span class="c1"># Enable sparsity for convolution and fully connected layers.</span>
+        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="n">enabled_precisions_set</span><span class="p">,</span>  <span class="c1"># Enabling FP16 kernels</span>
+        <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="n">refit</span><span class="p">,</span>  <span class="c1"># enable refit</span>
+        <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="n">debug</span><span class="p">,</span>  <span class="c1"># enable debuggable engine</span>
+        <span class="s2">&quot;capability&quot;</span><span class="p">:</span> <span class="n">capability</span><span class="p">,</span>  <span class="c1"># Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>  <span class="c1"># Number of averaging timing iterations used to select kernels</span>
+        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>  <span class="c1"># Maximum size of workspace given to TensorRT</span>
+        <span class="s2">&quot;calibrator&quot;</span><span class="p">:</span> <span class="n">calibrator</span><span class="p">,</span>
+        <span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="n">truncate_long_and_double</span><span class="p">,</span>
+        <span class="s2">&quot;torch_fallback&quot;</span><span class="p">:</span> <span class="p">{</span>
+            <span class="s2">&quot;enabled&quot;</span><span class="p">:</span> <span class="ow">not</span> <span class="n">require_full_compilation</span><span class="p">,</span>
+            <span class="s2">&quot;forced_fallback_ops&quot;</span><span class="p">:</span> <span class="n">torch_executed_op_list</span><span class="p">,</span>
+            <span class="s2">&quot;forced_fallback_modules&quot;</span><span class="p">:</span> <span class="n">torch_executed_module_list</span><span class="p">,</span>
+            <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="n">min_block_size</span><span class="p">,</span>
+        <span class="p">},</span>
+        <span class="s2">&quot;allow_shape_tensors&quot;</span><span class="p">:</span> <span class="n">allow_shape_tensors</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="n">compiled_cpp_mod</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">compile_graph</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">_c</span><span class="p">,</span> <span class="n">_parse_compile_spec</span><span class="p">(</span><span class="n">spec</span><span class="p">))</span>
+    <span class="n">compiled_module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">_recursive</span><span class="o">.</span><span class="n">wrap_cpp_module</span><span class="p">(</span>
+        <span class="n">compiled_cpp_mod</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">compiled_module</span></div>
+
+
+<div class="viewcode-block" id="convert_method_to_trt_engine"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.convert_method_to_trt_engine">[docs]</a><span class="k">def</span> <span class="nf">convert_method_to_trt_engine</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span>
+    <span class="n">method_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;forward&quot;</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Sequence</span><span class="p">[</span><span class="n">Input</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">=</span> <span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">(),</span>
+    <span class="n">disable_tf32</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">sparse_weights</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Set</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">dtype</span> <span class="o">|</span> <span class="n">_enums</span><span class="o">.</span><span class="n">dtype</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">refit</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">debug</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">capability</span><span class="p">:</span> <span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span> <span class="o">=</span> <span class="n">_enums</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+    <span class="n">num_avg_timing_iters</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">workspace_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">dla_sram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1048576</span><span class="p">,</span>
+    <span class="n">dla_local_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1073741824</span><span class="p">,</span>
+    <span class="n">dla_global_dram_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">536870912</span><span class="p">,</span>
+    <span class="n">truncate_long_and_double</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">calibrator</span><span class="p">:</span> <span class="nb">object</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">allow_shape_tensors</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bytes</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a TorchScript module method to a serialized TensorRT engine</span>
+
+<span class="sd">    Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch</span>
+<span class="sd">            ``torch.nn.Module``</span>
+
+<span class="sd">    Keyword Args:</span>
+<span class="sd">        inputs (List[Union(torch_tensorrt.Input, torch.Tensor)]): **Required** List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum</span>
+<span class="sd">            to select device type. ::</span>
+
+<span class="sd">                input=[</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings</span>
+<span class="sd">                ]</span>
+
+<span class="sd">        method_name (str): Name of method to convert</span>
+<span class="sd">        input_signature Union(List, Tuple, torch_tensorrt.Input, torch.Tensor): A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using</span>
+<span class="sd">            torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. **This API should be considered beta-level stable and may change in the future** ::</span>
+
+<span class="sd">                input_signature=([</span>
+<span class="sd">                    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1</span>
+<span class="sd">                    torch_tensorrt.Input(</span>
+<span class="sd">                        min_shape=(1, 224, 224, 3),</span>
+<span class="sd">                        opt_shape=(1, 512, 512, 3),</span>
+<span class="sd">                        max_shape=(1, 1024, 1024, 3),</span>
+<span class="sd">                        dtype=torch.int32</span>
+<span class="sd">                        format=torch.channel_last</span>
+<span class="sd">                    ), # Dynamic input shape for input #2</span>
+<span class="sd">                ], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3</span>
+
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device for TensorRT engines to run on ::</span>
+
+<span class="sd">            device=torch_tensorrt.Device(&quot;dla:1&quot;, allow_gpu_fallback=True)</span>
+
+<span class="sd">        disable_tf32 (bool): Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+<span class="sd">        sparse_weights (bool): Enable sparsity for convolution and fully connected layers.</span>
+<span class="sd">        enabled_precision (Set(Union(torch.dtype, torch_tensorrt.dtype))): The set of datatypes that TensorRT can use when selecting kernels</span>
+<span class="sd">        refit (bool): Enable refitting</span>
+<span class="sd">        debug (bool): Enable debuggable engine</span>
+<span class="sd">        capability (torch_tensorrt.EngineCapability): Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+<span class="sd">        num_avg_timing_iters (int): Number of averaging timing iterations used to select kernels</span>
+<span class="sd">        workspace_size (int): Maximum size of workspace given to TensorRT</span>
+<span class="sd">        dla_sram_size (int): Fast software managed RAM used by DLA to communicate within a layer.</span>
+<span class="sd">        dla_local_dram_size (int): Host RAM used by DLA to share intermediate tensor data across operations</span>
+<span class="sd">        dla_global_dram_size (int): Host RAM used by DLA to store weights and metadata for execution</span>
+<span class="sd">        truncate_long_and_double (bool): Truncate weights provided in int64 or double (float64) to int32 and float32</span>
+<span class="sd">        calibrator (Union(torch_tensorrt._C.IInt8Calibrator, tensorrt.IInt8Calibrator)): Calibrator object which will provide data to the PTQ system for INT8 Calibration</span>
+<span class="sd">        allow_shape_tensors: (Experimental) Allow aten::size to output shape tensors using IShapeLayer in TensorRT</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        bytes: Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">input_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span> <span class="k">if</span> <span class="n">inputs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
+    <span class="n">enabled_precisions_set</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">enabled_precisions</span> <span class="k">if</span> <span class="n">enabled_precisions</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">}</span>
+    <span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptFunction</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span>
+            <span class="s2">&quot;torch.jit.ScriptFunctions currently are not directly supported, wrap the function in a module to compile&quot;</span>
+        <span class="p">)</span>
+
+    <span class="n">compile_spec</span> <span class="o">=</span> <span class="p">{</span>
+        <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="n">input_list</span><span class="p">,</span>
+        <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">device</span><span class="p">,</span>
+        <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="n">disable_tf32</span><span class="p">,</span>  <span class="c1"># Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</span>
+        <span class="s2">&quot;sparse_weights&quot;</span><span class="p">:</span> <span class="n">sparse_weights</span><span class="p">,</span>  <span class="c1"># Enable sparsity for convolution and fully connected layers.</span>
+        <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="n">enabled_precisions_set</span><span class="p">,</span>  <span class="c1"># Enabling FP16 kernels</span>
+        <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="n">refit</span><span class="p">,</span>  <span class="c1"># enable refit</span>
+        <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="n">debug</span><span class="p">,</span>  <span class="c1"># enable debuggable engine</span>
+        <span class="s2">&quot;capability&quot;</span><span class="p">:</span> <span class="n">capability</span><span class="p">,</span>  <span class="c1"># Restrict kernel selection to safe gpu kernels or safe dla kernels</span>
+        <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="n">num_avg_timing_iters</span><span class="p">,</span>  <span class="c1"># Number of averaging timing iterations used to select kernels</span>
+        <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>  <span class="c1"># Maximum size of workspace given to TensorRT</span>
+        <span class="s2">&quot;calibrator&quot;</span><span class="p">:</span> <span class="n">calibrator</span><span class="p">,</span>
+        <span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="n">truncate_long_and_double</span><span class="p">,</span>
+        <span class="s2">&quot;allow_shape_tensors&quot;</span><span class="p">:</span> <span class="n">allow_shape_tensors</span><span class="p">,</span>
+    <span class="p">}</span>
+
+    <span class="n">engine_str</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">convert_graph_to_trt_engine</span><span class="p">(</span>
+        <span class="n">module</span><span class="o">.</span><span class="n">_c</span><span class="p">,</span> <span class="n">method_name</span><span class="p">,</span> <span class="n">_parse_compile_spec</span><span class="p">(</span><span class="n">compile_spec</span><span class="p">)</span>
+    <span class="p">)</span>
+
+    <span class="kn">import</span> <span class="nn">io</span>
+
+    <span class="k">with</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">()</span> <span class="k">as</span> <span class="n">engine_bytes</span><span class="p">:</span>
+        <span class="n">engine_bytes</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">engine_str</span><span class="p">)</span>
+        <span class="n">engine_bytearray</span> <span class="o">=</span> <span class="n">engine_bytes</span><span class="o">.</span><span class="n">getvalue</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">engine_bytearray</span></div>
+
+
+<div class="viewcode-block" id="embed_engine_in_new_module"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.embed_engine_in_new_module">[docs]</a><span class="k">def</span> <span class="nf">embed_engine_in_new_module</span><span class="p">(</span>
+    <span class="n">serialized_engine</span><span class="p">:</span> <span class="nb">bytes</span><span class="p">,</span>
+    <span class="n">input_binding_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">output_binding_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">List</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">device</span><span class="p">:</span> <span class="n">Device</span> <span class="o">=</span> <span class="n">Device</span><span class="o">.</span><span class="n">_current_device</span><span class="p">(),</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module</span>
+
+<span class="sd">    Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.</span>
+<span class="sd">    Registers the forward method to execute the TensorRT engine with the function signature:</span>
+
+<span class="sd">        forward(Tensor[]) -&gt; Tensor[]</span>
+
+<span class="sd">    TensorRT bindings either be explicitly specified using ``[in/out]put_binding_names`` or have names with the following format:</span>
+<span class="sd">      - [symbol].[index in input / output array]</span>
+<span class="sd">      ex.</span>
+<span class="sd">      - [x.0, x.1, x.2] -&gt; [y.0]</span>
+
+<span class="sd">    Module can be save with engine embedded with torch.jit.save and moved / loaded according to torch_tensorrt portability rules</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        serialized_engine (bytearray): Serialized TensorRT engine from either torch_tensorrt or TensorRT APIs</span>
+
+<span class="sd">    Keyword Arguments:</span>
+<span class="sd">        input_binding_names (List[str]): List of names of TensorRT bindings in order to be passed to the encompassing PyTorch module</span>
+<span class="sd">        output_binding_names (List[str]): List of names of TensorRT bindings in order that should be returned from the encompassing PyTorch module</span>
+<span class="sd">        device (Union(torch_tensorrt.Device, torch.device, dict)): Target device to run engine on. Must be compatible with engine provided. Default: Current active device</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.jit.ScriptModule: New TorchScript module with engine embedded</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">input_binding_name_list</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">input_binding_names</span> <span class="k">if</span> <span class="n">input_binding_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
+    <span class="p">)</span>
+    <span class="n">output_binding_name_list</span> <span class="o">=</span> <span class="p">(</span>
+        <span class="n">output_binding_names</span> <span class="k">if</span> <span class="n">output_binding_names</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
+    <span class="p">)</span>
+    <span class="n">cpp_mod</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">embed_engine_in_new_module</span><span class="p">(</span>
+        <span class="n">serialized_engine</span><span class="p">,</span>
+        <span class="n">_parse_device</span><span class="p">(</span><span class="n">device</span><span class="p">),</span>
+        <span class="n">input_binding_name_list</span><span class="p">,</span>
+        <span class="n">output_binding_name_list</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="n">wrapped_mod</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">_recursive</span><span class="o">.</span><span class="n">wrap_cpp_module</span><span class="p">(</span><span class="n">cpp_mod</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">wrapped_mod</span></div>
+
+
+<div class="viewcode-block" id="check_method_op_support"><a class="viewcode-back" href="../../../py_api/ts.html#torch_tensorrt.ts.check_method_op_support">[docs]</a><span class="k">def</span> <span class="nf">check_method_op_support</span><span class="p">(</span>
+    <span class="n">module</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">ScriptModule</span><span class="p">,</span> <span class="n">method_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;forward&quot;</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Checks to see if a method is fully supported by torch_tensorrt</span>
+
+<span class="sd">    Checks if a method of a TorchScript module can be compiled by torch_tensorrt, if not, a list of operators</span>
+<span class="sd">    that are not supported are printed out and the function returns false, else true.</span>
+
+<span class="sd">    Arguments:</span>
+<span class="sd">        module (torch.jit.ScriptModule): Source module, a result of tracing or scripting a PyTorch</span>
+<span class="sd">            ``torch.nn.Module``</span>
+<span class="sd">        method_name (str): Name of method to check</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        bool: True if supported Method</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">supported</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">_C</span><span class="o">.</span><span class="n">check_method_op_support</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">_c</span><span class="p">,</span> <span class="n">method_name</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">supported</span></div>
+</pre></div>
+
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+            </li>
+
+            <li>
+              <a href="https://pytorch.org/hub">Models (Beta)</a>
+            </li>
+          </ul>
+
+          <li>
+            <a href="https://github.com/pytorch/pytorch">Github</a>
+          </li>
+        </ul>
+      </div>
+    </div>
+  </div>
+
+  <!-- End Mobile Menu -->
+
+  <script type="text/javascript" src="../../../_static/js/vendor/anchor.min.js"></script>
+
+  <script type="text/javascript">
+    $(document).ready(function() {
+      mobileMenu.bind();
+      mobileTOC.bind();
+      pytorchAnchors.bind();
+      sideMenus.bind();
+      scrollToAnchor.bind();
+      highlightNavigation.bind();
+      mainMenuDropdown.bind();
+      filterTags.bind();
+
+      // Add class to links that have code blocks, since we cannot create links in code blocks
+      $("article.pytorch-article a span.pre").each(function(e) {
+        $(this).closest("a").addClass("has-code");
+      });
+    })
+  </script>
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1DataType.rst.txt b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1DataType.rst.txt
new file mode 100644
index 0000000000..6a40d653a7
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1DataType.rst.txt
@@ -0,0 +1,17 @@
+.. _exhale_class_classtorch__tensorrt_1_1DataType:
+
+Class DataType
+==============
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::DataType
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.rst.txt b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.rst.txt
new file mode 100644
index 0000000000..5479dea0ee
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.rst.txt
@@ -0,0 +1,23 @@
+.. _exhale_class_classtorch__tensorrt_1_1Device_1_1DeviceType:
+
+Class Device::DeviceType
+========================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Nested Relationships
+--------------------
+
+This class is a nested type of :ref:`exhale_struct_structtorch__tensorrt_1_1Device`.
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::Device::DeviceType
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1TensorFormat.rst.txt b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1TensorFormat.rst.txt
new file mode 100644
index 0000000000..3f2030e8f2
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1TensorFormat.rst.txt
@@ -0,0 +1,17 @@
+.. _exhale_class_classtorch__tensorrt_1_1TensorFormat:
+
+Class TensorFormat
+==================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::TensorFormat
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.rst.txt b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.rst.txt
new file mode 100644
index 0000000000..7e76229f19
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.rst.txt
@@ -0,0 +1,26 @@
+.. _exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator:
+
+Template Class Int8CacheCalibrator
+==================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Inheritance Relationships
+-------------------------
+
+Base Type
+*********
+
+- ``private Algorithm``
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::ptq::Int8CacheCalibrator
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.rst.txt b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.rst.txt
new file mode 100644
index 0000000000..6ef15f8ff1
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.rst.txt
@@ -0,0 +1,26 @@
+.. _exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8Calibrator:
+
+Template Class Int8Calibrator
+=============================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Inheritance Relationships
+-------------------------
+
+Base Type
+*********
+
+- ``private Algorithm``
+
+
+Class Documentation
+-------------------
+
+
+.. doxygenclass:: torch_tensorrt::ptq::Int8Calibrator
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.rst.txt b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.rst.txt
new file mode 100644
index 0000000000..70809499b3
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1a18d295a837ac71add5578860b55e5502:
+
+Define STR
+==========
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: STR
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.rst.txt b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.rst.txt
new file mode 100644
index 0000000000..10850f77a2
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268:
+
+Define TORCH_TENSORRT_PATCH_VERSION
+===================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCH_TENSORRT_PATCH_VERSION
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.rst.txt b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.rst.txt
new file mode 100644
index 0000000000..9a3d3c8aa8
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e:
+
+Define TORCH_TENSORRT_MAJOR_VERSION
+===================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCH_TENSORRT_MAJOR_VERSION
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.rst.txt b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.rst.txt
new file mode 100644
index 0000000000..0c495bc60c
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827:
+
+Define TORCH_TENSORRT_MINOR_VERSION
+===================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCH_TENSORRT_MINOR_VERSION
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.rst.txt b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.rst.txt
new file mode 100644
index 0000000000..40ad2fd99f
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b:
+
+Define TORCHTRT_API
+===================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCHTRT_API
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.rst.txt b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.rst.txt
new file mode 100644
index 0000000000..75d62d3b65
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da:
+
+Define XSTR
+===========
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: XSTR
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.rst.txt b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.rst.txt
new file mode 100644
index 0000000000..262bd89acd
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1ad19939408f7be171a74a89928b36eb59:
+
+Define TORCHTRT_HIDDEN
+======================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCHTRT_HIDDEN
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.rst.txt b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.rst.txt
new file mode 100644
index 0000000000..ea5d28d205
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883:
+
+Define TORCH_TENSORRT_VERSION
+=============================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_macros.h`
+
+
+Define Documentation
+--------------------
+
+
+.. doxygendefine:: TORCH_TENSORRT_VERSION
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/dir_cpp.rst.txt b/docs/v2.2.0/_sources/_cpp_api/dir_cpp.rst.txt
new file mode 100644
index 0000000000..f09e39c518
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/dir_cpp.rst.txt
@@ -0,0 +1,16 @@
+.. _dir_cpp:
+
+
+Directory cpp
+=============
+
+
+*Directory path:* ``cpp``
+
+Subdirectories
+--------------
+
+- :ref:`dir_cpp_include`
+
+
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/dir_cpp_include.rst.txt b/docs/v2.2.0/_sources/_cpp_api/dir_cpp_include.rst.txt
new file mode 100644
index 0000000000..e262b4a9af
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/dir_cpp_include.rst.txt
@@ -0,0 +1,20 @@
+.. _dir_cpp_include:
+
+
+Directory include
+=================
+
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp>` (``cpp``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+*Directory path:* ``cpp/include``
+
+Subdirectories
+--------------
+
+- :ref:`dir_cpp_include_torch_tensorrt`
+
+
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/dir_cpp_include_torch_tensorrt.rst.txt b/docs/v2.2.0/_sources/_cpp_api/dir_cpp_include_torch_tensorrt.rst.txt
new file mode 100644
index 0000000000..f1da041e12
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/dir_cpp_include_torch_tensorrt.rst.txt
@@ -0,0 +1,23 @@
+.. _dir_cpp_include_torch_tensorrt:
+
+
+Directory torch_tensorrt
+========================
+
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include>` (``cpp/include``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+*Directory path:* ``cpp/include/torch_tensorrt``
+
+
+Files
+-----
+
+- :ref:`file_cpp_include_torch_tensorrt_logging.h`
+- :ref:`file_cpp_include_torch_tensorrt_macros.h`
+- :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+- :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.rst.txt b/docs/v2.2.0/_sources/_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.rst.txt
new file mode 100644
index 0000000000..6a1c2671ae
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558:
+
+Enum Level
+==========
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Enum Documentation
+------------------
+
+
+.. doxygenenum:: torch_tensorrt::logging::Level
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.rst.txt b/docs/v2.2.0/_sources/_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.rst.txt
new file mode 100644
index 0000000000..a004103bfe
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb:
+
+Enum EngineCapability
+=====================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Enum Documentation
+------------------
+
+
+.. doxygenenum:: torch_tensorrt::EngineCapability
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.rst.txt b/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.rst.txt
new file mode 100644
index 0000000000..ea89d38038
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_logging.h.rst.txt
@@ -0,0 +1,80 @@
+
+.. _file_cpp_include_torch_tensorrt_logging.h:
+
+File logging.h
+==============
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include_torch_tensorrt>` (``cpp/include/torch_tensorrt``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+Definition (``cpp/include/torch_tensorrt/logging.h``)
+-----------------------------------------------------
+
+
+.. toctree::
+   :maxdepth: 1
+
+   program_listing_file_cpp_include_torch_tensorrt_logging.h.rst
+
+
+
+
+
+Includes
+--------
+
+
+- ``string``
+
+- ``torch_tensorrt/macros.h`` (:ref:`file_cpp_include_torch_tensorrt_macros.h`)
+
+
+
+Included By
+-----------
+
+
+- :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt`
+
+- :ref:`namespace_torch_tensorrt__logging`
+
+
+Enums
+-----
+
+
+- :ref:`exhale_enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a`
+
+- :ref:`exhale_function_logging_8h_1a0593f776f469c20469e2f729fc7861a3`
+
+- :ref:`exhale_function_logging_8h_1a0c012cb374addd90eb1f42eaec570650`
+
+- :ref:`exhale_function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8`
+
+- :ref:`exhale_function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5`
+
+- :ref:`exhale_function_logging_8h_1af8f3443813315af7901903d25dd495cc`
+
+- :ref:`exhale_function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2`
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.rst.txt b/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.rst.txt
new file mode 100644
index 0000000000..61447e1ada
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_macros.h.rst.txt
@@ -0,0 +1,71 @@
+
+.. _file_cpp_include_torch_tensorrt_macros.h:
+
+File macros.h
+=============
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include_torch_tensorrt>` (``cpp/include/torch_tensorrt``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+Definition (``cpp/include/torch_tensorrt/macros.h``)
+----------------------------------------------------
+
+
+.. toctree::
+   :maxdepth: 1
+
+   program_listing_file_cpp_include_torch_tensorrt_macros.h.rst
+
+
+
+
+
+
+
+Included By
+-----------
+
+
+- :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+- :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+- :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt`
+
+- :ref:`namespace_torch_tensorrt__torchscript`
+
+
+Defines
+-------
+
+
+- :ref:`exhale_define_macros_8h_1a18d295a837ac71add5578860b55e5502`
+
+- :ref:`exhale_define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e`
+
+- :ref:`exhale_define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827`
+
+- :ref:`exhale_define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268`
+
+- :ref:`exhale_define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883`
+
+- :ref:`exhale_define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b`
+
+- :ref:`exhale_define_macros_8h_1ad19939408f7be171a74a89928b36eb59`
+
+- :ref:`exhale_define_macros_8h_1abe87b341f562fd1cf40b7672e4d759da`
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.rst.txt b/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.rst.txt
new file mode 100644
index 0000000000..34b64af6e0
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_ptq.h.rst.txt
@@ -0,0 +1,84 @@
+
+.. _file_cpp_include_torch_tensorrt_ptq.h:
+
+File ptq.h
+==========
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include_torch_tensorrt>` (``cpp/include/torch_tensorrt``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+Definition (``cpp/include/torch_tensorrt/ptq.h``)
+-------------------------------------------------
+
+
+.. toctree::
+   :maxdepth: 1
+
+   program_listing_file_cpp_include_torch_tensorrt_ptq.h.rst
+
+
+
+
+
+Includes
+--------
+
+
+- ``NvInfer.h``
+
+- ``fstream``
+
+- ``iostream``
+
+- ``iterator``
+
+- ``memory``
+
+- ``sstream``
+
+- ``string``
+
+- ``torch/torch.h``
+
+- ``torch_tensorrt/logging.h`` (:ref:`file_cpp_include_torch_tensorrt_logging.h`)
+
+- ``torch_tensorrt/macros.h`` (:ref:`file_cpp_include_torch_tensorrt_macros.h`)
+
+- ``vector``
+
+
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt`
+
+- :ref:`namespace_torch_tensorrt__ptq`
+
+
+Classes
+-------
+
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8Calibrator`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c`
+
+- :ref:`exhale_function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178`
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt b/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
new file mode 100644
index 0000000000..8057364274
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
@@ -0,0 +1,105 @@
+
+.. _file_cpp_include_torch_tensorrt_torch_tensorrt.h:
+
+File torch_tensorrt.h
+=====================
+
+|exhale_lsh| :ref:`Parent directory <dir_cpp_include_torch_tensorrt>` (``cpp/include/torch_tensorrt``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+Definition (``cpp/include/torch_tensorrt/torch_tensorrt.h``)
+------------------------------------------------------------
+
+
+.. toctree::
+   :maxdepth: 1
+
+   program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst
+
+
+
+
+
+Includes
+--------
+
+
+- ``cuda_runtime.h``
+
+- ``iostream``
+
+- ``memory``
+
+- ``set``
+
+- ``string``
+
+- ``torch/custom_class.h``
+
+- ``torch_tensorrt/macros.h`` (:ref:`file_cpp_include_torch_tensorrt_macros.h`)
+
+- ``vector``
+
+
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt`
+
+- :ref:`namespace_torch_tensorrt__torchscript`
+
+
+Classes
+-------
+
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1Device`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1GraphInputs`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1Input`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1torchscript_1_1CompileSpec`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1DataType`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1Device_1_1DeviceType`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1TensorFormat`
+
+
+Enums
+-----
+
+
+- :ref:`exhale_enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9`
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.rst.txt
new file mode 100644
index 0000000000..8cab943a9a
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1a0593f776f469c20469e2f729fc7861a3:
+
+Function torch_tensorrt::logging::get_logging_prefix
+====================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::get_logging_prefix()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.rst.txt
new file mode 100644
index 0000000000..55bb2bde5c
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1a0c012cb374addd90eb1f42eaec570650:
+
+Function torch_tensorrt::logging::get_reportable_log_level
+==========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::get_reportable_log_level()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.rst.txt
new file mode 100644
index 0000000000..e8b0ac046d
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a:
+
+Function torch_tensorrt::logging::get_is_colored_output_on
+==========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::get_is_colored_output_on()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.rst.txt
new file mode 100644
index 0000000000..95380eb157
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2:
+
+Function torch_tensorrt::logging::set_reportable_log_level
+==========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::set_reportable_log_level(Level)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.rst.txt
new file mode 100644
index 0000000000..0ca83fe039
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8:
+
+Function torch_tensorrt::logging::log
+=====================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::log(Level, std::string)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.rst.txt
new file mode 100644
index 0000000000..86c2c7d61e
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5:
+
+Function torch_tensorrt::logging::set_is_colored_output_on
+==========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::set_is_colored_output_on(bool)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.rst.txt
new file mode 100644
index 0000000000..9d0a42d2bd
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_logging_8h_1af8f3443813315af7901903d25dd495cc:
+
+Function torch_tensorrt::logging::set_logging_prefix
+====================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_logging.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::logging::set_logging_prefix(std::string)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.rst.txt
new file mode 100644
index 0000000000..5d9188270f
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c:
+
+Template Function torch_tensorrt::ptq::make_int8_cache_calibrator
+=================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::ptq::make_int8_cache_calibrator(const std::string&)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.rst.txt
new file mode 100644
index 0000000000..9551f48bb6
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178:
+
+Template Function torch_tensorrt::ptq::make_int8_calibrator
+===========================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_ptq.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::ptq::make_int8_calibrator(DataLoader, const std::string&, bool)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.rst.txt
new file mode 100644
index 0000000000..54f7c29df3
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797:
+
+Function torch_tensorrt::torchscript::check_method_operator_support
+===================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::torchscript::check_method_operator_support(const torch::jit::Module&, std::string)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.rst.txt
new file mode 100644
index 0000000000..b80699688a
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9:
+
+Function torch_tensorrt::torchscript::compile
+=============================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::torchscript::compile(const torch::jit::Module&, CompileSpec)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.rst.txt
new file mode 100644
index 0000000000..e01e80e211
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9:
+
+Function torch_tensorrt::torchscript::embed_engine_in_new_module
+================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::torchscript::embed_engine_in_new_module(const std::string&, Device, const std::vector<std::string>&, const std::vector<std::string>&)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.rst.txt
new file mode 100644
index 0000000000..d551c4894d
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528:
+
+Function torch_tensorrt::get_build_info
+=======================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::get_build_info()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.rst.txt
new file mode 100644
index 0000000000..e296177bfa
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384:
+
+Function torch_tensorrt::set_device
+===================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::set_device(const int)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.rst.txt
new file mode 100644
index 0000000000..06b03cbfd9
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1:
+
+Function torch_tensorrt::dump_build_info
+========================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::dump_build_info()
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.rst.txt b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.rst.txt
new file mode 100644
index 0000000000..2a1f0c89d0
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.rst.txt
@@ -0,0 +1,14 @@
+.. _exhale_function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2:
+
+Function torch_tensorrt::torchscript::convert_method_to_trt_engine
+==================================================================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Function Documentation
+----------------------
+
+
+.. doxygenfunction:: torch_tensorrt::torchscript::convert_method_to_trt_engine(const torch::jit::Module&, std::string, CompileSpec)
+   :project: Torch-TensorRT
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/namespace_torch.rst.txt b/docs/v2.2.0/_sources/_cpp_api/namespace_torch.rst.txt
new file mode 100644
index 0000000000..23f54f958d
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/namespace_torch.rst.txt
@@ -0,0 +1,13 @@
+
+.. _namespace_torch:
+
+Namespace torch
+===============
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt.rst.txt b/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt.rst.txt
new file mode 100644
index 0000000000..43a0afb20d
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt.rst.txt
@@ -0,0 +1,59 @@
+
+.. _namespace_torch_tensorrt:
+
+Namespace torch_tensorrt
+========================
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
+
+
+Namespaces
+----------
+
+
+- :ref:`namespace_torch_tensorrt__logging`
+
+- :ref:`namespace_torch_tensorrt__ptq`
+
+- :ref:`namespace_torch_tensorrt__torchscript`
+
+
+Classes
+-------
+
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1Device`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1GraphInputs`
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1Input`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1DataType`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1Device_1_1DeviceType`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1TensorFormat`
+
+
+Enums
+-----
+
+
+- :ref:`exhale_enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384`
diff --git a/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__logging.rst.txt b/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__logging.rst.txt
new file mode 100644
index 0000000000..49f946f937
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__logging.rst.txt
@@ -0,0 +1,39 @@
+
+.. _namespace_torch_tensorrt__logging:
+
+Namespace torch_tensorrt::logging
+=================================
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
+
+
+Enums
+-----
+
+
+- :ref:`exhale_enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a`
+
+- :ref:`exhale_function_logging_8h_1a0593f776f469c20469e2f729fc7861a3`
+
+- :ref:`exhale_function_logging_8h_1a0c012cb374addd90eb1f42eaec570650`
+
+- :ref:`exhale_function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8`
+
+- :ref:`exhale_function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5`
+
+- :ref:`exhale_function_logging_8h_1af8f3443813315af7901903d25dd495cc`
+
+- :ref:`exhale_function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2`
diff --git a/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__ptq.rst.txt b/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__ptq.rst.txt
new file mode 100644
index 0000000000..bdc39cb326
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__ptq.rst.txt
@@ -0,0 +1,31 @@
+
+.. _namespace_torch_tensorrt__ptq:
+
+Namespace torch_tensorrt::ptq
+=============================
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
+
+
+Classes
+-------
+
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator`
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1ptq_1_1Int8Calibrator`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c`
+
+- :ref:`exhale_function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178`
diff --git a/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__torchscript.rst.txt b/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__torchscript.rst.txt
new file mode 100644
index 0000000000..fa21b92a73
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/namespace_torch_tensorrt__torchscript.rst.txt
@@ -0,0 +1,33 @@
+
+.. _namespace_torch_tensorrt__torchscript:
+
+Namespace torch_tensorrt::torchscript
+=====================================
+
+
+.. contents:: Contents
+   :local:
+   :backlinks: none
+
+
+
+
+
+Classes
+-------
+
+
+- :ref:`exhale_struct_structtorch__tensorrt_1_1torchscript_1_1CompileSpec`
+
+
+Functions
+---------
+
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2`
+
+- :ref:`exhale_function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9`
diff --git a/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.rst.txt b/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.rst.txt
new file mode 100644
index 0000000000..ec413cb1b7
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_logging.h.rst.txt
@@ -0,0 +1,51 @@
+
+.. _program_listing_file_cpp_include_torch_tensorrt_logging.h:
+
+Program Listing for File logging.h
+==================================
+
+|exhale_lsh| :ref:`Return to documentation for file <file_cpp_include_torch_tensorrt_logging.h>` (``cpp/include/torch_tensorrt/logging.h``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. code-block:: cpp
+
+   /*
+    * Copyright (c) NVIDIA Corporation.
+    * All rights reserved.
+    *
+    * This library is licensed under the BSD-style license found in the
+    * LICENSE file in the root directory of this source tree.
+    */
+   #pragma once
+   
+   #include <string>
+   #include "torch_tensorrt/macros.h"
+   
+   namespace torch_tensorrt {
+   namespace logging {
+   enum Level {
+     kINTERNAL_ERROR,
+     kERROR,
+     kWARNING,
+     kINFO,
+     kDEBUG,
+     kGRAPH,
+   };
+   
+   // Are these ones necessary for the user?
+   TORCHTRT_API std::string get_logging_prefix();
+   TORCHTRT_API void set_logging_prefix(std::string prefix);
+   
+   TORCHTRT_API void set_reportable_log_level(Level lvl);
+   
+   TORCHTRT_API void set_is_colored_output_on(bool colored_output_on);
+   
+   TORCHTRT_API Level get_reportable_log_level();
+   
+   TORCHTRT_API bool get_is_colored_output_on();
+   
+   // Dont know if we want this?
+   TORCHTRT_API void log(Level lvl, std::string msg);
+   } // namespace logging
+   } // namespace torch_tensorrt
diff --git a/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.rst.txt b/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.rst.txt
new file mode 100644
index 0000000000..5bda443ab9
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_macros.h.rst.txt
@@ -0,0 +1,50 @@
+
+.. _program_listing_file_cpp_include_torch_tensorrt_macros.h:
+
+Program Listing for File macros.h
+=================================
+
+|exhale_lsh| :ref:`Return to documentation for file <file_cpp_include_torch_tensorrt_macros.h>` (``cpp/include/torch_tensorrt/macros.h``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. code-block:: cpp
+
+   /*
+    * Copyright (c) NVIDIA Corporation.
+    * All rights reserved.
+    *
+    * This library is licensed under the BSD-style license found in the
+    * LICENSE file in the root directory of this source tree.
+    */
+   #pragma once
+   
+   #if defined(USE_CMAKE_GENERATED_EXPORT_HEADER)
+   #include <torch_tensorrt_export.h>
+   #else
+   #if defined(__GNUC__)
+   #define TORCHTRT_API __attribute__((__visibility__("default")))
+   #define TORCHTRT_HIDDEN __attribute__((__visibility__("hidden")))
+   #else
+   #define TORCHTRT_API
+   #define TORCHTRT_HIDDEN
+   #endif // defined(__GNUC__)
+   #endif // defined(USE_CMAKE_GENERATED_EXPORT_HEADER)
+   
+   // Does this need to be gaurded or something?
+   #define XSTR(x) #x
+   #define STR(x) XSTR(x)
+   
+   #define TORCH_TENSORRT_MAJOR_VERSION 2
+   #define TORCH_TENSORRT_MINOR_VERSION 2
+   #define TORCH_TENSORRT_PATCH_VERSION 0
+   #define TORCH_TENSORRT_VERSION      \
+     STR(TORCH_TENSORRT_MAJOR_VERSION) \
+     "." STR(TORCH_TENSORRT_MINOR_VERSION) "." STR(TORCH_TENSORRT_PATCH_VERSION)
+   
+   // Setup namespace aliases for ease of use
+   namespace torch_tensorrt {
+   namespace torchscript {}
+   namespace ts = torchscript;
+   } // namespace torch_tensorrt
+   namespace torchtrt = torch_tensorrt;
diff --git a/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.rst.txt b/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.rst.txt
new file mode 100644
index 0000000000..8f4e8e4865
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_ptq.h.rst.txt
@@ -0,0 +1,190 @@
+
+.. _program_listing_file_cpp_include_torch_tensorrt_ptq.h:
+
+Program Listing for File ptq.h
+==============================
+
+|exhale_lsh| :ref:`Return to documentation for file <file_cpp_include_torch_tensorrt_ptq.h>` (``cpp/include/torch_tensorrt/ptq.h``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. code-block:: cpp
+
+   /*
+    * Copyright (c) NVIDIA Corporation.
+    * All rights reserved.
+    *
+    * This library is licensed under the BSD-style license found in the
+    * LICENSE file in the root directory of this source tree.
+    */
+   #pragma once
+   
+   #include <fstream>
+   #include <iostream>
+   #include <iterator>
+   #include <memory>
+   #include <sstream>
+   #include <string>
+   #include <vector>
+   
+   #include "NvInfer.h"
+   #include "torch/torch.h"
+   #include "torch_tensorrt/logging.h"
+   #include "torch_tensorrt/macros.h"
+   
+   #ifndef DOXYGEN_SHOULD_SKIP_THIS
+   namespace nvinfer1 {
+   class IInt8Calibrator;
+   class IInt8EntropyCalibrator2;
+   } // namespace nvinfer1
+   
+   namespace torch_tensorrt {
+   namespace ptq {
+   TORCHTRT_API bool get_batch_impl(void* bindings[], const char* names[], int nbBindings, torch::Tensor& data);
+   }
+   } // namespace torch_tensorrt
+   #endif // DOXYGEN_SHOULD_SKIP_THIS
+   
+   namespace torch_tensorrt {
+   namespace ptq {
+   
+   template <typename Algorithm, typename DataLoaderUniquePtr>
+   class Int8Calibrator : Algorithm {
+     using DataLoader = typename DataLoaderUniquePtr::element_type;
+     using Batch = typename DataLoader::super::BatchType;
+   
+    public:
+     Int8Calibrator(DataLoaderUniquePtr dataloader, const std::string& cache_file_path, bool use_cache)
+         : dataloader_(dataloader.get()), cache_file_path_(cache_file_path), use_cache_(use_cache) {
+       for (auto batch : *dataloader_) {
+         batched_data_.push_back(batch.data);
+       }
+       it_ = batched_data_.begin();
+     }
+   
+     int getBatchSize() const noexcept override {
+       // HACK: Torch-TensorRT only uses explict batch sizing, INT8 Calibrator does not
+       // work when reporting the batch size here and having explicity batching.
+       // So we just report batch size 1 (warnings will still be printed out).
+       return 1;
+       // return static_cast<int>(dataloader_->options().batch_size);
+     }
+   
+     bool getBatch(void* bindings[], const char* names[], int nbBindings) noexcept override {
+       if (it_ != batched_data_.end()) {
+         auto status = get_batch_impl(bindings, names, nbBindings, *it_);
+         it_ = ++it_;
+         return status;
+       } else {
+         // Reset iterator if incase calibrator is going to be used again
+         it_ = batched_data_.begin();
+         return false;
+       }
+     }
+   
+     const void* readCalibrationCache(size_t& length) noexcept override {
+       if (use_cache_) {
+         std::stringstream ss;
+         ss << "Reading Calibration Cache from " << cache_file_path_;
+         logging::log(logging::Level::kINFO, ss.str());
+   
+         cache_.clear();
+         std::ifstream input(cache_file_path_, std::ios::binary);
+         input >> std::noskipws;
+         if (input.good()) {
+           std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(), std::back_inserter(cache_));
+           logging::log(logging::Level::kDEBUG, "Cache read");
+         }
+         length = cache_.size();
+         return length ? cache_.data() : nullptr;
+       }
+       return nullptr;
+     }
+   
+     void writeCalibrationCache(const void* cache, size_t length) noexcept override {
+       std::ofstream cache_file(cache_file_path_, std::ios::binary);
+       cache_file.write(reinterpret_cast<const char*>(cache), length);
+       std::stringstream ss;
+       ss << "Saved Calibration Cache to " << cache_file_path_;
+       logging::log(logging::Level::kINFO, ss.str());
+     }
+   
+     operator nvinfer1::IInt8Calibrator*() {
+       return reinterpret_cast<nvinfer1::IInt8Calibrator*>(this);
+     }
+   
+    private:
+     DataLoader* dataloader_;
+     const std::string& cache_file_path_;
+     size_t cache_size_ = 0;
+     bool use_cache_;
+     std::vector<char> cache_;
+     std::vector<torch::Tensor> batched_data_;
+     std::vector<torch::Tensor>::iterator it_;
+   };
+   
+   template <typename Algorithm>
+   class Int8CacheCalibrator : Algorithm {
+    public:
+     Int8CacheCalibrator(const std::string& cache_file_path) : cache_file_path_(cache_file_path) {}
+   
+     int getBatchSize() const noexcept override {
+       // HACK: Torch-TensorRT only uses explict batch sizing, INT8 Calibrator does not
+       // work when reporting the batch size here and having explicity batching.
+       // So we just report batch size 1 (warnings will still be printed out).
+       return 1;
+     }
+   
+     bool getBatch(void* bindings[], const char* names[], int nbBindings) noexcept override {
+       return false;
+     }
+   
+     const void* readCalibrationCache(size_t& length) noexcept override {
+       std::stringstream ss;
+       ss << "Reading Calibration Cache from " << cache_file_path_;
+       logging::log(logging::Level::kINFO, ss.str());
+   
+       cache_.clear();
+       std::ifstream input(cache_file_path_, std::ios::binary);
+       input >> std::noskipws;
+       if (input.good()) {
+         std::copy(std::istream_iterator<char>(input), std::istream_iterator<char>(), std::back_inserter(cache_));
+         logging::log(logging::Level::kDEBUG, "Cache read");
+       }
+       length = cache_.size();
+       return length ? cache_.data() : nullptr;
+     }
+   
+     void writeCalibrationCache(const void* cache, size_t length) noexcept override {
+       std::ofstream cache_file(cache_file_path_, std::ios::binary);
+       cache_file.write(reinterpret_cast<const char*>(cache), length);
+       std::stringstream ss;
+       ss << "Saved Calibration Cache to " << cache_file_path_;
+       logging::log(logging::Level::kINFO, ss.str());
+     }
+   
+     operator nvinfer1::IInt8Calibrator*() {
+       return reinterpret_cast<nvinfer1::IInt8Calibrator*>(this);
+     }
+   
+    private:
+     const std::string& cache_file_path_;
+     size_t cache_size_ = 0;
+     std::vector<char> cache_;
+   };
+   
+   template <typename Algorithm = nvinfer1::IInt8EntropyCalibrator2, typename DataLoader>
+   inline Int8Calibrator<Algorithm, DataLoader> make_int8_calibrator(
+       DataLoader dataloader,
+       const std::string& cache_file_path,
+       bool use_cache) {
+     return Int8Calibrator<Algorithm, DataLoader>(std::move(dataloader), cache_file_path, use_cache);
+   }
+   
+   template <typename Algorithm = nvinfer1::IInt8EntropyCalibrator2>
+   inline Int8CacheCalibrator<Algorithm> make_int8_cache_calibrator(const std::string& cache_file_path) {
+     return Int8CacheCalibrator<Algorithm>(cache_file_path);
+   }
+   
+   } // namespace ptq
+   } // namespace torch_tensorrt
diff --git a/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt b/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
new file mode 100644
index 0000000000..9a34bf9d4f
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst.txt
@@ -0,0 +1,354 @@
+
+.. _program_listing_file_cpp_include_torch_tensorrt_torch_tensorrt.h:
+
+Program Listing for File torch_tensorrt.h
+=========================================
+
+|exhale_lsh| :ref:`Return to documentation for file <file_cpp_include_torch_tensorrt_torch_tensorrt.h>` (``cpp/include/torch_tensorrt/torch_tensorrt.h``)
+
+.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS
+
+.. code-block:: cpp
+
+   /*
+    * Copyright (c) NVIDIA Corporation.
+    * All rights reserved.
+    *
+    * This library is licensed under the BSD-style license found in the
+    * LICENSE file in the root directory of this source tree.
+    */
+   
+   #pragma once
+   
+   #include <cuda_runtime.h>
+   #include <iostream>
+   #include <memory>
+   #include <set>
+   #include <string>
+   #include <vector>
+   #include "torch/custom_class.h"
+   
+   #include "torch_tensorrt/macros.h"
+   
+   // Just include the .h?
+   #ifndef DOXYGEN_SHOULD_SKIP_THIS
+   namespace torch {
+   namespace jit {
+   struct Graph;
+   struct Module;
+   } // namespace jit
+   } // namespace torch
+   
+   namespace c10 {
+   enum class DeviceType : int8_t;
+   enum class ScalarType : int8_t;
+   template <class>
+   class ArrayRef;
+   } // namespace c10
+   
+   namespace nvinfer1 {
+   class IInt8Calibrator;
+   }
+   #endif // DOXYGEN_SHOULD_SKIP_THIS
+   
+   namespace torch_tensorrt {
+   class DataType {
+    public:
+     enum Value : int8_t {
+       kLong,
+       kDouble,
+       kFloat,
+       kHalf,
+       kChar,
+       kInt,
+       kBool,
+       kUnknown
+     };
+   
+     DataType() = default;
+     constexpr DataType(Value t) : value(t) {}
+     TORCHTRT_API DataType(c10::ScalarType t);
+     operator Value() const {
+       return value;
+     }
+     explicit operator bool() = delete;
+     constexpr bool operator==(DataType other) const {
+       return value == other.value;
+     }
+     constexpr bool operator==(DataType::Value other) const {
+       return value == other;
+     }
+     constexpr bool operator!=(DataType other) const {
+       return value != other.value;
+     }
+     constexpr bool operator!=(DataType::Value other) const {
+       return value != other;
+     }
+   
+    private:
+     friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const DataType& dtype);
+     Value value;
+   };
+   
+   struct Device {
+     class DeviceType {
+      public:
+       enum Value : int8_t {
+         kGPU,
+         kDLA,
+       };
+   
+       DeviceType() = default;
+       constexpr DeviceType(Value t) : value(t) {}
+       DeviceType(c10::DeviceType t);
+       operator Value() const {
+         return value;
+       }
+       explicit operator bool() = delete;
+       constexpr bool operator==(DeviceType other) const {
+         return value == other.value;
+       }
+       constexpr bool operator!=(DeviceType other) const {
+         return value != other.value;
+       }
+   
+      private:
+       Value value;
+     };
+   
+     DeviceType device_type;
+   
+     /*
+      * Target gpu id
+      */
+     int64_t gpu_id;
+   
+     /*
+      * When using DLA core on NVIDIA AGX platforms gpu_id should be set as Xavier device
+      */
+     int64_t dla_core;
+   
+     bool allow_gpu_fallback;
+   
+     Device() : device_type(DeviceType::kGPU), gpu_id(0), dla_core(0), allow_gpu_fallback(false) {}
+   };
+   
+   enum class EngineCapability : int8_t {
+     kSTANDARD,
+     kSAFETY,
+     kDLA_STANDALONE,
+   };
+   
+   class TensorFormat {
+    public:
+     enum Value : int8_t {
+       kContiguous,
+       kChannelsLast,
+       kUnknown,
+     };
+   
+     TensorFormat() = default;
+     constexpr TensorFormat(Value t) : value(t) {}
+     TORCHTRT_API TensorFormat(at::MemoryFormat t);
+     operator Value() const {
+       return value;
+     }
+     explicit operator bool() = delete;
+     constexpr bool operator==(TensorFormat other) const {
+       return value == other.value;
+     }
+     constexpr bool operator==(TensorFormat::Value other) const {
+       return value == other;
+     }
+     constexpr bool operator!=(TensorFormat other) const {
+       return value != other.value;
+     }
+     constexpr bool operator!=(TensorFormat::Value other) const {
+       return value != other;
+     }
+   
+    private:
+     friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const TensorFormat& format);
+     Value value;
+   };
+   
+   struct Input : torch::CustomClassHolder {
+     std::vector<int64_t> min_shape;
+     std::vector<int64_t> opt_shape;
+     std::vector<int64_t> max_shape;
+     std::vector<int64_t> shape;
+     DataType dtype;
+     TensorFormat format;
+     std::vector<double> tensor_domain;
+   
+     Input() {}
+     TORCHTRT_API Input(std::vector<int64_t> shape, TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         std::vector<int64_t> shape,
+         std::vector<double> tensor_domain,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(std::vector<int64_t> shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         std::vector<int64_t> shape,
+         DataType dtype,
+         std::vector<double> tensor_domain,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(c10::ArrayRef<int64_t> shape, TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         c10::ArrayRef<int64_t> shape,
+         std::vector<double> tensor_domain,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(c10::ArrayRef<int64_t> shape, DataType dtype, TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         c10::ArrayRef<int64_t> shape,
+         DataType dtype,
+         std::vector<double> tensor_domain,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         std::vector<int64_t> min_shape,
+         std::vector<int64_t> opt_shape,
+         std::vector<int64_t> max_shape,
+         TensorFormat format = TensorFormat::kContiguous);
+     TORCHTRT_API Input(
+         std::vector<int64_t> min_shape,
+         std::vector<int64_t> opt_shape,
+         std::vector<int64_t> max_shape,
+         std::vector<double> tensor_domain,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         std::vector<int64_t> min_shape,
+         std::vector<int64_t> opt_shape,
+         std::vector<int64_t> max_shape,
+         DataType dtype,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         std::vector<int64_t> min_shape,
+         std::vector<int64_t> opt_shape,
+         std::vector<int64_t> max_shape,
+         DataType dtype,
+         std::vector<double> tensor_domain,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         c10::ArrayRef<int64_t> min_shape,
+         c10::ArrayRef<int64_t> opt_shape,
+         c10::ArrayRef<int64_t> max_shape,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         c10::ArrayRef<int64_t> min_shape,
+         c10::ArrayRef<int64_t> opt_shape,
+         c10::ArrayRef<int64_t> max_shape,
+         std::vector<double> tensor_domain,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         c10::ArrayRef<int64_t> min_shape,
+         c10::ArrayRef<int64_t> opt_shape,
+         c10::ArrayRef<int64_t> max_shape,
+         DataType dtype,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(
+         c10::ArrayRef<int64_t> min_shape,
+         c10::ArrayRef<int64_t> opt_shape,
+         c10::ArrayRef<int64_t> max_shape,
+         DataType dtype,
+         std::vector<double> tensor_domain,
+         TensorFormat format = TensorFormat::kContiguous);
+   
+     TORCHTRT_API Input(at::Tensor tensor);
+   
+    private:
+     friend TORCHTRT_API std::ostream& operator<<(std::ostream& os, const Input& input);
+     bool input_is_dynamic;
+   };
+   
+   struct GraphInputs {
+     torch::jit::IValue input_signature; // nested Input, full input spec
+     std::vector<Input> inputs; // flatten input spec
+   };
+   
+   TORCHTRT_API std::string get_build_info();
+   
+   TORCHTRT_API void dump_build_info();
+   
+   TORCHTRT_API void set_device(const int gpu_id);
+   
+   namespace torchscript {
+   struct CompileSpec {
+     TORCHTRT_API CompileSpec(std::vector<std::vector<int64_t>> fixed_sizes);
+   
+     TORCHTRT_API CompileSpec(std::vector<c10::ArrayRef<int64_t>> fixed_sizes);
+   
+     TORCHTRT_API CompileSpec(std::vector<Input> inputs);
+   
+     TORCHTRT_API CompileSpec(torch::jit::IValue input_signature);
+     // Defaults should reflect TensorRT defaults for BuilderConfig
+   
+     GraphInputs graph_inputs;
+     std::set<DataType> enabled_precisions = {DataType::kFloat};
+   
+     bool disable_tf32 = false;
+   
+     bool sparse_weights = false;
+   
+     bool refit = false;
+   
+     bool debug = false;
+   
+     bool truncate_long_and_double = false;
+   
+     bool allow_shape_tensors = false;
+   
+     Device device;
+   
+     EngineCapability capability = EngineCapability::kSTANDARD;
+   
+     uint64_t num_avg_timing_iters = 1;
+   
+     uint64_t workspace_size = 0;
+   
+     uint64_t dla_sram_size = 1048576;
+   
+     uint64_t dla_local_dram_size = 1073741824;
+   
+     uint64_t dla_global_dram_size = 536870912;
+   
+     nvinfer1::IInt8Calibrator* ptq_calibrator = nullptr;
+   
+     bool require_full_compilation = false;
+   
+     uint64_t min_block_size = 3;
+   
+     std::vector<std::string> torch_executed_ops;
+   
+     std::vector<std::string> torch_executed_modules;
+   };
+   
+   TORCHTRT_API bool check_method_operator_support(const torch::jit::Module& module, std::string method_name);
+   
+   TORCHTRT_API torch::jit::Module compile(const torch::jit::Module& module, CompileSpec info);
+   
+   TORCHTRT_API std::string convert_method_to_trt_engine(
+       const torch::jit::Module& module,
+       std::string method_name,
+       CompileSpec info);
+   
+   TORCHTRT_API torch::jit::Module embed_engine_in_new_module(
+       const std::string& engine,
+       Device device,
+       const std::vector<std::string>& input_binding_names = std::vector<std::string>(),
+       const std::vector<std::string>& output_binding_names = std::vector<std::string>());
+   } // namespace torchscript
+   } // namespace torch_tensorrt
diff --git a/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Device.rst.txt b/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Device.rst.txt
new file mode 100644
index 0000000000..a090eaa324
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Device.rst.txt
@@ -0,0 +1,27 @@
+.. _exhale_struct_structtorch__tensorrt_1_1Device:
+
+Struct Device
+=============
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Nested Relationships
+--------------------
+
+
+Nested Types
+************
+
+- :ref:`exhale_class_classtorch__tensorrt_1_1Device_1_1DeviceType`
+
+
+Struct Documentation
+--------------------
+
+
+.. doxygenstruct:: torch_tensorrt::Device
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1GraphInputs.rst.txt b/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1GraphInputs.rst.txt
new file mode 100644
index 0000000000..60bc3285c6
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1GraphInputs.rst.txt
@@ -0,0 +1,17 @@
+.. _exhale_struct_structtorch__tensorrt_1_1GraphInputs:
+
+Struct GraphInputs
+==================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Struct Documentation
+--------------------
+
+
+.. doxygenstruct:: torch_tensorrt::GraphInputs
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Input.rst.txt b/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Input.rst.txt
new file mode 100644
index 0000000000..64144c4dd7
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1Input.rst.txt
@@ -0,0 +1,26 @@
+.. _exhale_struct_structtorch__tensorrt_1_1Input:
+
+Struct Input
+============
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Inheritance Relationships
+-------------------------
+
+Base Type
+*********
+
+- ``public torch::CustomClassHolder``
+
+
+Struct Documentation
+--------------------
+
+
+.. doxygenstruct:: torch_tensorrt::Input
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.rst.txt b/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.rst.txt
new file mode 100644
index 0000000000..31f2296658
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.rst.txt
@@ -0,0 +1,17 @@
+.. _exhale_struct_structtorch__tensorrt_1_1torchscript_1_1CompileSpec:
+
+Struct CompileSpec
+==================
+
+- Defined in :ref:`file_cpp_include_torch_tensorrt_torch_tensorrt.h`
+
+
+Struct Documentation
+--------------------
+
+
+.. doxygenstruct:: torch_tensorrt::torchscript::CompileSpec
+   :project: Torch-TensorRT
+   :members:
+   :protected-members:
+   :undoc-members:
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/_cpp_api/torch_tensort_cpp.rst.txt b/docs/v2.2.0/_sources/_cpp_api/torch_tensort_cpp.rst.txt
new file mode 100644
index 0000000000..1b34d45a77
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/torch_tensort_cpp.rst.txt
@@ -0,0 +1,10 @@
+======================
+Torch-TensorRT C++ API
+======================
+
+.. include:: class_view_hierarchy.rst.include
+
+.. include:: file_view_hierarchy.rst.include
+
+.. include:: unabridged_api.rst.include
+
diff --git a/docs/v2.2.0/_sources/_cpp_api/unabridged_orphan.rst.txt b/docs/v2.2.0/_sources/_cpp_api/unabridged_orphan.rst.txt
new file mode 100644
index 0000000000..5789e581e8
--- /dev/null
+++ b/docs/v2.2.0/_sources/_cpp_api/unabridged_orphan.rst.txt
@@ -0,0 +1,48 @@
+:orphan:
+
+
+Full API
+========
+
+Directories
+***********
+
+
+.. toctree::
+   :maxdepth: 5
+
+   dir_cpp.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   dir_cpp_include.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   dir_cpp_include_torch_tensorrt.rst
+
+Files
+*****
+
+
+.. toctree::
+   :maxdepth: 5
+
+   file_cpp_include_torch_tensorrt_logging.h.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   file_cpp_include_torch_tensorrt_macros.h.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   file_cpp_include_torch_tensorrt_ptq.h.rst
+
+.. toctree::
+   :maxdepth: 5
+
+   file_cpp_include_torch_tensorrt_torch_tensorrt.h.rst
diff --git a/docs/v2.2.0/_sources/cli/torchtrtc.rst.txt b/docs/v2.2.0/_sources/cli/torchtrtc.rst.txt
new file mode 100644
index 0000000000..69292c491c
--- /dev/null
+++ b/docs/v2.2.0/_sources/cli/torchtrtc.rst.txt
@@ -0,0 +1,149 @@
+.. _torchtrtc:
+
+torchtrtc
+=================================
+
+``torchtrtc`` is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
+TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of
+a deployment pipeline. All basic features of the compiler are supported including post training
+quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can
+output two formats, either a TorchScript program with the TensorRT engine embedded or
+the TensorRT engine itself as a PLAN file.
+
+All that is required to run the program after compilation is for C++ linking against ``libtorchtrt.so``
+or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
+to standard TorchScript. Load with ``torch.jit.load()`` and run like you would run any other module.
+
+.. code-block:: txt
+
+    torchtrtc [input_file_path] [output_file_path]
+      [input_specs...] {OPTIONS}
+
+      torchtrtc is a compiler for TorchScript, it will compile and optimize
+      TorchScript programs to run on NVIDIA GPUs using TensorRT
+
+    OPTIONS:
+
+        -h, --help                        Display this help menu
+        Verbiosity of the compiler
+          -v, --verbose                     Dumps debugging information about the
+                                            compilation process onto the console
+          -w, --warnings                    Disables warnings generated during
+                                            compilation onto the console (warnings
+                                            are on by default)
+          --i, --info                       Dumps info messages generated during
+                                            compilation onto the console
+        --build-debuggable-engine         Creates a debuggable engine
+        --allow-gpu-fallback              (Only used when targeting DLA
+                                          (device-type)) Lets engine run layers on
+                                          GPU if they are not supported on DLA
+        --require-full-compilation        Require that the model should be fully
+                                          compiled to TensorRT or throw an error
+        --check-method-support=[method_name]
+                                          Check the support for end to end
+                                          compilation of a specified method in the
+                                          TorchScript module
+        --disable-tf32                    Prevent Float32 layers from using the
+                                          TF32 data format
+        --sparse-weights                  Enable sparsity for weights of conv and
+                                          FC layers
+        -p[precision...],
+        --enable-precision=[precision...] (Repeatable) Enabling an operating
+                                          precision for kernels to use when
+                                          building the engine (Int8 requires a
+                                          calibration-cache argument) [ float |
+                                          float32 | f32 | fp32 | half | float16 |
+                                          f16 | fp16 | int8 | i8 | char ]
+                                          (default: float)
+        -d[type], --device-type=[type]    The type of device the engine should be
+                                          built for [ gpu | dla ] (default: gpu)
+        --gpu-id=[gpu_id]                 GPU id if running on multi-GPU platform
+                                          (defaults to 0)
+        --dla-core=[dla_core]             DLACore id if running on available DLA
+                                          (defaults to 0)
+        --engine-capability=[capability]  The type of device the engine should be
+                                          built for [ standard | safety |
+                                          dla_standalone ]
+        --calibration-cache-file=[file_path]
+                                          Path to calibration cache file to use
+                                          for post training quantization
+        --teo=[op_name...],
+        --torch-executed-op=[op_name...]  (Repeatable) Operator in the graph that
+                                          should always be run in PyTorch for
+                                          execution (partial compilation must be
+                                          enabled)
+        --tem=[module_name...],
+        --torch-executed-mod=[module_name...]
+                                          (Repeatable) Module that should always
+                                          be run in Pytorch for execution (partial
+                                          compilation must be enabled)
+        --mbs=[num_ops],
+        --min-block-size=[num_ops]        Minimum number of contiguous TensorRT
+                                          supported ops to compile a subgraph to
+                                          TensorRT
+        --embed-engine                    Whether to treat input file as a
+                                          serialized TensorRT engine and embed it
+                                          into a TorchScript module (device spec
+                                          must be provided)
+        --num-avg-timing-iters=[num_iters]
+                                          Number of averaging timing iterations
+                                          used to select kernels
+        --workspace-size=[workspace_size] Maximum size of workspace given to
+                                          TensorRT
+        --dla-sram-size=[dla_sram_size]   Fast software managed RAM used by DLA
+                                          to communicate within a layer.
+        --dla-local-dram-size=[dla_local_dram_size]  Host RAM used by DLA to share
+                                          intermediate tensor data across operations.
+        --dla-global-dram-size=[dla_global_dram_size] Host RAM used by DLA to store
+                                          weights and metadata for execution
+        --atol=[atol]                     Absolute tolerance threshold for acceptable
+                                          numerical deviation from standard torchscript
+                                          output (default 1e-8)
+        --rtol=[rtol]                     Relative tolerance threshold for acceptable
+                                          numerical deviation from standard torchscript
+                                          output  (default 1e-5)
+        --no-threshold-check              Skip checking threshold compliance
+        --truncate-long-double,
+        --truncate, --truncate-64bit      Truncate weights that are provided in
+                                          64bit to 32bit (Long, Double to Int,
+                                          Float)
+        --save-engine                     Instead of compiling a full a
+                                          TorchScript program, save the created
+                                          engine to the path specified as the
+                                          output path
+        --custom-torch-ops                (repeatable) Shared object/DLL containing custom torch operators
+        --custom-converters               (repeatable) Shared object/DLL containing custom converters
+        input_file_path                   Path to input TorchScript file
+        output_file_path                  Path for compiled TorchScript (or
+                                          TensorRT engine) file
+        input_specs...                    Specs for inputs to engine, can either
+                                          be a single size or a range defined by
+                                          Min, Optimal, Max sizes, e.g.
+                                          "(N,..,C,H,W)"
+                                          "[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]".
+                                          Data Type and format can be specified by
+                                          adding an "@" followed by dtype and "%"
+                                          followed by format to the end of the
+                                          shape spec. e.g. "(3, 3, 32,
+                                          32)@f16%NHWC"
+        "--" can be used to terminate flag options and force all following
+        arguments to be treated as positional options
+
+e.g.
+
+.. code-block:: shell
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous" -p f16
+
+- To include a set of custom operators
+
+.. code-block:: shell
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-torch-ops=<path to custom library .so file> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
+
+
+- To include a set of custom converters
+
+.. code-block:: shell
+
+    torchtrtc tests/modules/ssd_traced.jit.pt ssd_trt.ts --custom-converters=<path to custom library .so file> "[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous" -p f16
diff --git a/docs/v2.2.0/_sources/contributors/conversion.rst.txt b/docs/v2.2.0/_sources/contributors/conversion.rst.txt
new file mode 100644
index 0000000000..f19fc5eba8
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/conversion.rst.txt
@@ -0,0 +1,53 @@
+.. _conversion:
+
+Conversion Phase
+==================
+
+Once the graph has be simplified to a form thats easy to convert, we then set up a conversion context
+to manage the construction of a TensorRT ``INetworkDefinition`` from the blocks nodes. The conversion context
+records the set of converted nodes, block inputs and outputs and other information about the conversion
+of the graph. This data is then used to help converters link together layers and also hold build time
+information like weights required to construct the engine. After the context is created, the block
+converter starts iterating through the list of nodes, for each node, the converter will look at its
+inputs and assemble an array of resources to pass to the converter. Inputs can be in a couple of states:
+
+*  The input is a block parameter
+
+   *  In this case the input should have already been stored in as an IValue in the
+      conversion context ``evaluated_value_map``. The conversion stage will add the IValue to the list of args for the
+      converter
+
+*  The input is an output of a node that has already been converted
+
+   *  In this case the ITensor of the output has added to the ``value_tensor_map``,
+      The conversion stage will add the ITensor to the list of args for the converter
+
+*  The input is from a node that produces a static value
+
+   *  There are nodes that produce static values, typically used to store parameters for operators, we need to
+      evaluate these nodes at conversion time to be able to convert a op. The conversion system will look for a node
+      evaluator in the evaluator registry and run it on the node. The IValue produced will be entered in the
+      conversion context ``evaluated_value_map`` and added to the list of args for the converter. If the node
+      to be evaluated takes inputs, the conversion stage will recursively resolve dependencies until the final
+      static value has been evaluated
+
+*  The input is from a node that has not been converted
+
+   *  Torch-TensorRT will error out here
+
+Node Evaluation
+-----------------
+There are some nodes that contain static data and are resources for operations. These can be evaluated at
+conversion time so that you can use those values when doing node conversion. In theory any node kind can have
+a conversion time evaluator as long as it produces a static IValue, This IValue will be stored in the conversion
+context so it can be consumed by any node that takes the evaluated node as an input. Common node types are
+``prim::Constant`` which emits a constant and ``prim::ListConstruct`` which makes lists.
+
+Node Converters
+----------------
+
+Node converters map JIT nodes to layers or subgraphs of layers. They then associate outputs from the JIT graph
+and the TRT graph together in the conversion context. This allows the conversion stage to assemble the inputs
+for the next node. There are some cases where a node produces an output that is not a Tensor but a static result
+from a calculation done on inputs which need to be converted first. In this case the converter may associate the outputs in
+the ``evaluated_value_map`` instead of the ``value_tensor_map``. For more information take a look at: :ref:`writing_converters`
diff --git a/docs/v2.2.0/_sources/contributors/dynamo_converters.rst.txt b/docs/v2.2.0/_sources/contributors/dynamo_converters.rst.txt
new file mode 100644
index 0000000000..3238d609f3
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/dynamo_converters.rst.txt
@@ -0,0 +1,131 @@
+.. _dynamo_converters:
+
+Writing Dynamo Converters
+=============================
+The dynamo converter library in Torch-TensorRT is located in ``TensorRT/py/torch_tensorrt/dynamo/conversion``.
+
+Converter implementation
+------------------------
+
+Registration
+^^^^^^^^^^^^^^^^
+
+A converter is a function decrorated with  ``torch_tensorrt.dynamo.dynamo_tensorrt_converter`` that follows the function signature:
+
+
+.. code-block:: python
+
+    @torch_tensorrt.dynamo.conversion.dynamo_tensorrt_converter(torch.ops.aten.leaky_relu.default)
+    def leaky_relu_converter(
+        ctx: torch_tensorrt.dynamo.conversion.ConversionCtx,
+        target: Target,
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        name: str,
+    ) -> Union[tensorrt.ITensor, Sequence[tensorrt.ITensor]]:
+
+
+
+The decorator takes a number of arguments:
+
+    * ``key``: Node target for which the converter is implemented for (for example, torch.ops.aten.leaky_relu.default)
+    * ``enabled``: Whether the converter should be enabled as a converter that can be used in the converter registry
+    * ``capability_validator``: A lambda that can take a ``torch.fx.Node`` and determine if the converter can properly handle this Node. If the validator returns ``False``, the subgraph partitioner will make sure this Node is run in PyTorch in the compiled graph.
+    * ``priority``: Allows developers to override existing converters in the converter registry
+
+All that is required for a converter is the key.
+
+The function body is responsible for taking the current state of the network and adding the next subgraph to perform the op specified in the decorator with TensorRT operations.
+The function is provided arguments as the native PyTorch op would be provided with the added case of numpy arrays for frozen Tensor attributes or TensorRT ITensors which are ouput Tensors of previous nodes, correspoding to edges/output Tensors of intermediate operations in the graph.
+To determine the types expected as well as the return type of the converter, look at the definition of the op being converted. In the case of ``aten`` operations, this file will be the source of truth: https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml
+Since many converters a developer may write are a composition of lower level operators, instead of needing to implement the converter in raw TensorRT, the ``torch_tensorrt.dynamo.conversion.impl`` subpackage contains many implementations of operations that can be chained to create a TensorRT subgraph.
+
+    * ``ctx`` : The current state of the compiler. Converters primarily will manipulate ctx.net which is the ``tensorrt.INetworkDefinition`` being constructed. Additional metadata including user provided settings is available in this struct as well.
+    * ``target``: Target key in the ``call_module`` or ``call_function`` above. eg: ``torch.ops.aten_.leaky_relu.default``. Note that ``torch.ops.aten._leaky_relu`` is the ``OpOverloadPacket`` while ``torch.ops.aten_.leaky_relu.default`` is ``OpOverload``.
+    * ``args``: The arguments being passed to a particular Node (as collected by the ``torch_tensorrt.dynamo.conversion.TRTInterpreter``). These arguments along with the kwargs are to be used to construct a specific TensorRT subgraph representing the current node in the INetworkDefinition.
+    * ``kwargs``: The arguments being passed to a particular Node (as collected by the ``torch_tensorrt.dynamo.conversion.TRTInterpreter``).
+    * ``name``: String containing the name of the target
+
+The function is expected to return the ``tensorrt.ITensor`` or some collection of ``tensorrt.ITensor`` for use in the ``torch_tensorrt.dynamo.conversion.TRTInterpreter`` matching the output signature of the operation being converted
+
+Capability Validation
+^^^^^^^^^^^^^^^^^^^^^^^
+
+There are some converters which have special cases to be accounted for. In those cases, one should use ``capability_validators`` to register the converter using ``@dynamo_tensorrt_converter``
+We illustrate this through ``torch.ops.aten.embedding.default``. It has parameters - ``scale_grad_by_freq`` and ``sparse`` which are not currently supported by the implementation.
+In such cases we can write validator ``embedding_param_validator`` which implements that given those paramters the converter is not supported and register the converter by
+
+
+Type Contract
+^^^^^^^^^^^^^^^
+
+The function is expected to follow the type contract established by the signature. This includes accepting the union of valid PyTorch types + numpy arrays for constant tensors and TensorRT ITensors.
+In the case that only a subset of types is supported in the converter, you can also add the ``torch_tensorrt.dynamo.conversion.converter_utils.enforce_tensor_types``, which allows you to specify a dictionary mapping between input positions and types that those inputs can take. Where possible the decorator will convert inputs to match these types prefering the order provided.
+``int`` keys in the dictionary will refer to positional arguments in ``args``. ``str`` keys will refer to keyword arguments in ``kwargs``.
+
+
+Example: ``Convolution``
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The default convolution converter both uses a capability validator and type enforcement to prevent being run in unsupported situations.
+The capability validator is run during partitioning to determine if a particular convolution node can be converted to TensorRT or needs to run in PyTorch. Here the validator ensures that the convolution is no greater than 3D.
+The type enforcer will autocast before the converter is called, inputs to the supported type in the converter, thereby limiting the number of cases an author must handle.
+
+.. code-block:: python
+
+    @dynamo_tensorrt_converter(
+        torch.ops.aten.convolution.default, capability_validator=lambda conv_node: conv_node.args[7] in ([0], [0, 0], [0, 0, 0])
+    )  # type: ignore[misc]
+    @enforce_tensor_types(
+        {
+            0: (TRTTensor,),
+            1: (np.ndarray, torch.Tensor, TRTTensor),
+            2: (np.ndarray, torch.Tensor, TRTTensor),
+        }
+    )  # type: ignore[misc]
+    def aten_ops_convolution(
+        ctx: ConversionContext,
+        target: Target,
+        args: Tuple[Argument, ...],
+        kwargs: Dict[str, Argument],
+        name: str,
+    ) -> Union[TRTTensor, Sequence[TRTTensor]]:
+
+Evaluators
+------------------------
+
+Some operations do not produce TensorRT subgraphs as a side-effect. These are termed evaluators.
+
+    Example: ``operator.getitem``
+
+    Evaluators are categorized as so since they do not make any modification to the graph. This is implemented in ``py/torch_tensorrt/dynamo/conversion/op_evaluators.py``, with the corresponding ``capbility_validator``.
+    The opcode is ``operator.getitem``.
+
+
+Operator Decomposition
+-----------------------
+
+There are some converters which can be decomposed into suboperations in PyTorch and need not have seperate converter registration.
+Such converters can be implemented via a decomposition
+
+Example: ``addmm``
+^^^^^^^^^^^^^^^^^^^^^^^
+
+The decompositions are registered via ``register_torch_trt_decomposition`` decorator
+We define ``addmm_replacement`` and replace it with the torch ops, which will have their corresponding converters called.
+
+.. code-block:: python
+
+    @torch_tensorrt.dynamo.lowering.register_torch_trt_decomposition(torch.ops.aten.addmm)
+    def addmm_replacement(
+        input_: torch.Tensor, mat1: torch.Tensor, mat2: torch.Tensor, *, beta=1, alpha=1
+    ) -> torch.Tensor:
+        return torch.add(
+            torch.mul(input_, beta), torch.mul(torch.matmul(mat1, mat2), alpha)
+        )
+
+You can modify the decompositions run by editing ``torch_tensorrt.dynamo.lowering.torch_enabled_decompositions`` and ``torch_tensorrt.dynamo.lowering.torch_disabled_decompositions``
+
+    Note: ``torch_tensorrt.dynamo.lowering.torch_enabled_decompositions`` and ``torch_tensorrt.dynamo.lowering.torch_disabled_decompositions`` must be disjoint sets and that the decompositions already defined in ``torch_tensorrt.dynamo.lowering`` will take precedence over torch lowering ops.
+
+Much of the time, this is significantly easier than implementing a converter. So where possible, this is what should be tried first.
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/contributors/lowering.rst.txt b/docs/v2.2.0/_sources/contributors/lowering.rst.txt
new file mode 100644
index 0000000000..a82f497ed2
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/lowering.rst.txt
@@ -0,0 +1,214 @@
+.. _lowering:
+
+Lowering Phase
+===============
+
+The lowering phase is made up out of passes which are operations which map a graph from a high level representation
+to a lower level one. Each pass does something specific for instance inlining method calls. The idea is to
+significantly reduce what the conversion phase needs to be able to handle when actually mapping to TensorRT.
+We aim for closer to 1->1 op conversion vs looking for applicable subgraphs, limiting the number of converters and
+reduce the scope of each converter.
+
+You can see the effects of each pass by setting the log level to ``Level::kGraph``
+
+Passes Used
+-------------
+
+EliminateCommonSubexpression
+***********************************
+
+    `torch/csrc/jit/passes/common_subexpression_elimination.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/common_subexpression_elimination.h>`_
+
+Removes common subexpressions in the graph
+
+
+
+Eliminate Dead Code
+**************************
+
+    `torch/csrc/jit/passes/dead_code_elimination.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/dead_code_elimination.h>`_
+
+Dead code elimination will check if a node has side effects and not delete it if it does.
+
+Eliminate Exeception Or Pass Pattern
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/exception_elimination.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/exception_elimination.cpp>`_
+
+A common pattern in scripted modules are dimension gaurds which will throw execptions if
+the input dimension is not what was expected.
+
+.. code-block:: none
+
+    %1013 : bool = aten::ne(%1012, %24) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:248:11
+        = prim::If(%1013) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:248:8
+        block0():
+            = prim::RaiseException(%23) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:249:12
+        -> ()
+        block1():
+        -> ()
+
+Since we are resolving all of this at compile time and there are no execptions in the TensorRT graph, we just remove it.
+
+Eliminate Redundant Gaurds
+***************************************
+
+    `torch/csrc/jit/passes/guard_elimination.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/guard_elimination.h>`_
+
+Eliminate redundant guards for ops whose outputs are fully determined by their inputs i.e. if inputs to such ops are
+guarded we are allowed to remove a guard on ops' outputs
+
+Freeze Module
+***************************************
+
+    `torch/csrc/jit/passes/freeze_module.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/freeze_module.h>`_
+
+Freeze attributes and inline constants and modules. Propogates constants in the graph.
+
+Fuse AddMM Branches
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/fuse_addmm_branches.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_addmm_branches.cpp>`_
+
+A common pattern in scripted modules is tensors of different dimensions use different constructions for implementing linear layers. We fuse these
+different varients into a single one that will get caught by the Unpack AddMM pass.
+
+.. code-block:: none
+
+    %ret : Tensor = prim::If(%622)
+    block0():
+      %ret.1 : Tensor = aten::addmm(%self.fc.bias, %x9.1, %3677, %3, %3)
+      -> (%ret.1)
+    block1():
+      %output.1 : Tensor = aten::matmul(%x9.1, %3677)
+      %output0.1 : Tensor = aten::add_(%output.1, %self.fc.bias, %3)
+      -> (%output0.1)
+
+We fuse this set of blocks into a graph like this:
+
+.. code-block:: none
+
+    %ret : Tensor = aten::addmm(%self.fc.bias, %x9.1, %3677, %3, %3)
+
+Fuse Linear
+***************************************
+
+    `torch/csrc/jit/passes/fuse_linear.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/fuse_linear.h>`_
+
+Match the ``aten::linear`` pattern and fuse it into a single ``aten::linear``
+This pass fuse the addmm or matmul + add generated by JIT back to linear
+
+Fuse Flatten Linear
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/fuse_flatten_linear.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_flatten_linear.cpp>`_
+
+TensorRT implicity flattens input layers into fully connected layers when they are higher than 1D. So when there is a
+``aten::flatten`` -> ``aten::linear`` pattern we remove the ``aten::flatten``.
+
+Lower Graph
+***************************************
+
+    `torch/csrc/jit/passes/lower_graph.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/lower_graph.h>`_
+
+Given a graph with of a method which first argument is %self, lower it to a graph where
+all attributes accesses are replaced with explicit inputs of the graph
+(rather than results of prim::GetAttr executed on %self). Returns a tuple
+(graph, parameters) where the last module.parameters.size() inputs to the
+graph are the trainable parameters used in this method. The remaining inputs
+are the true inputs to the function.
+
+Lower Tuples
+***************************************
+
+    `torch/csrc/jit/passes/lower_tuples.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/lower_tuples.h>`_
+
+* ``LowerSimpleTuples``:
+
+Removes tuples where TupleConstruct and TupleUnpack are matched but leaves tuples in place across if statements, loops, and as inputs/outputs
+
+* ``LowerAllTuples``:
+
+Removes _all_ tuples and raises an error if some cannot be removed, this is used by ONNX to ensure there are not tuples before conversion, but will not work on graphs whose inputs contain tuples.
+
+Module Fallback
+*****************
+
+    `Torch-TensorRT/core/lowering/passes/module_fallback.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/module_fallback.cpp>`_
+
+Module fallback consists of two lowering passes that must be run as a pair. The first pass is run before freezing to place delimiters in the graph around modules
+that should run in PyTorch. The second pass marks nodes between these delimiters after freezing to signify they should run in PyTorch.
+
+* ``NotateModuleForFallback``
+
+Places delimiting nodes around module calls pre freezing to signify where in the graph nodes should run in PyTorch
+
+* ``MarkNodesForFallback``
+
+Looks for delimiters then marks all nodes between the delimiters to tell partitioning to run them in PyTorch
+
+Peephole Optimze
+***************************************
+
+    `torch/csrc/jit/passes/peephole_optimze.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/ppeephole_optimze.h>`_
+
+The intent for this optimization pass is to catch all of the small, easy to catch peephole optimizations you might be interested in doing.
+
+Right now, it does:
+    - Eliminate no-op 'expand' nodes
+    - Simply x.t().t() to x
+
+
+Remove Contiguous
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/remove_contiguous.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_contiguous.cpp>`_
+
+Removes contiguous operators since we are doing TensorRT memory is already contiguous.
+
+
+Remove Dropout
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/remove_dropout.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_dropout.cpp>`_
+
+Removes dropout operators since we are doing inference.
+
+Remove To
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/remove_to.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_to.cpp>`_
+
+Removes ``aten::to`` operators that do casting, since TensorRT mangages it itself. It is important that this is one of the last passes run so that
+other passes have a change to move required cast operators out of the main namespace.
+
+Unpack AddMM
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/unpack_addmm.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/unpack_addmm.cpp>`_
+
+Unpacks ``aten::addmm`` into ``aten::matmul`` and ``aten::add_`` (with an additional ``trt::const``
+op to freeze the bias in the TensorRT graph). This lets us reuse the ``aten::matmul`` and ``aten::add_``
+converters instead of needing a dedicated converter.
+
+Unpack LogSoftmax
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/unpack_log_softmax.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/unpack_log_softmax.cpp>`_
+
+Unpacks ``aten::logsoftmax`` into ``aten::softmax`` and ``aten::log``. This lets us reuse the
+``aten::softmax`` and ``aten::log`` converters instead of needing a dedicated converter.
+
+Unroll Loops
+***************************************
+
+    `torch/csrc/jit/passes/loop_unrolling.h <https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/loop_unrolling.h>`_
+
+Unrolls the operations of compatable loops (e.g. sufficently short) so that you only have to go through the loop once.
+
+Replace Tile with Repeat
+***************************************
+
+    `Torch-TensorRT/core/lowering/passes/tile_to_repeat.cpp <https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/tile_to_repeat.cpp>`_
+
+Removes dropout operators since we are doing inference.
diff --git a/docs/v2.2.0/_sources/contributors/partitioning.rst.txt b/docs/v2.2.0/_sources/contributors/partitioning.rst.txt
new file mode 100644
index 0000000000..8c83ddcadc
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/partitioning.rst.txt
@@ -0,0 +1,241 @@
+.. _partitioning:
+
+Partitioning Phase
+====================
+
+The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.
+
+On a high level, Torch-TensorRT partitioning phase does the following:
+
+* Segmentation. Go through the set of operators in order and verify if there is converter for each operator. Then, roughly separate the graph into parts that Torch-TensorRT can support and parts Torch-TensorRT cannot.
+
+* Dependency Analysis. For every to be compiled operator there is a "complete dependency graph", which means that every input can to traced back to an input as Tensor or TensorList. Go through all segments after segmentation then do dependency analysis to ensure that there are only Tensor/TensorList inputs and outputs for TensorRT segments.
+
+* Shape Analysis. For each segments, figure out the input and outputs shapes starting from the provided input shape from the user. Shapes can be calculated by running the graphs with JIT.
+
+* Conversion. Every TensorRT segments will be converted to TensorRT engine. This part is done in compiler.cpp, but it's still a phase in our partitioning process.
+
+* Stitching. Stitch all TensorRT engines with PyTorch nodes altogether.
+
+Here are the brief description of these functions of each file:
+
+PartitonInfo.h/.cpp
+***********************************
+
+    `core/partitioning/PartitionInfo.h <https://github.com/pytorch/TensorRT/blob/master/core/partitioning/PartitionInfo.h>`_
+
+The automatic fallback APIs that is used for partitioning.
+
+
+SegmentedBlock.h/.cpp
+***********************************
+
+    `core/partitioning/SegmentedBlock.h <https://github.com/pytorch/TensorRT/blob/master/core/partitioning/SegmentedBlock.h>`_
+
+The main data structures that is used to maintain information for each segments after segmentation.
+
+
+shape_analysis.h/.cpp
+***********************************
+
+    `core/partitioning/shape_analysis.h <https://github.com/pytorch/TensorRT/blob/master/core/partitioning/shape_analysis.h>`_
+
+Code implementation to get the shapes for each segments by running them in JIT.
+
+
+partitioning.h/.cpp
+***********************************
+    `core/partitioning/partitioning.h <https://github.com/pytorch/TensorRT/blob/master/core/partitioning/partitioning.h>`_
+
+APIs and main code implementation for partitioning phase.
+
+Automatic Fallback
+====================
+
+To enable automatic fallback feature, you can set following attributes in Python:
+
+.. code-block:: none
+
+      import torch
+      import torch_tensorrt as torchtrt
+
+      ...
+      model = MyModel()
+      ts_model = torch.jit.script(model)
+      trt_model = torchtrt.ts.compile(model, **{
+        ...
+        "min_block_size" : 3,
+        "torch_executed_ops": ["aten::add"],
+        "torch_executed_modules": [],
+      })
+
+* enabled: By default automatic fallback will be off. It is enabled by setting it to True.
+* min_block_size: The minimum number of consecutive operations that must satisfy to be converted to TensorRT. For example, if it's set to 3, then there must be 3 consecutive supported operators then this segments will be converted.
+* forced_fallback_ops: A list of strings that will be the names of operations that the user explicitly want to be in PyTorch nodes.
+
+
+.. code-block:: none
+
+      #include "torch/script.h"
+      #include "torch_tensorrt/torch_tensorrt.h"
+
+      ...
+      auto in = torch::randn({1, 3, 224, 224}, {torch::kCUDA});
+
+      auto mod = torch::jit::load("trt_ts_module.ts");
+      auto input_sizes =  std::vector<torchtrt::InputRange>{{in.sizes()}};
+      torchtrt::ts::CompileSpec cfg(input_sizes);
+      cfg.min_block_size = 2;
+      cfg.torch_executed_ops.push_back("aten::relu");
+      auto trt_mod = torchtrt::ts::compile(mod, cfg);
+      auto out = trt_mod.forward({in});
+
+Dependency Aware Partitioning
+====================
+During segmentation, Torch-TensorRT uses a dependency graph of the input TorchScript nodes to reduce the number of segments created. Consider this example from test Partitioning.SegmentModelWithDependencyAwareness in `tests/core/partitioning/test_segmentation.cpp <https://github.com/pytorch/TensorRT/blob/master/tests/core/partitioning/test_segmentation.cpp>`_
+
+.. code-block:: none
+
+    graph(%x : Tensor, %y : Tensor):
+        %3 : int = prim::Constant[value=0]()
+        %20 : int = prim::Constant[value=1]()
+        %add : Tensor = aten::add(%x, %y, %20)
+        %x_lgamma : Tensor = aten::lgamma(%x)
+        %mul : Tensor = aten::mul(%x, %y)
+        %y_lgamma : Tensor = aten::lgamma(%y)
+        %div : Tensor = aten::div(%x, %y)
+        %div_lgamma : Tensor = aten::lgamma(%div)
+        %27 : Tensor[] = prim::ListConstruct(%x_lgamma, %y_lgamma, %div_lgamma, %add, %mul)
+        %12 : Tensor = aten::cat(%27, %3)
+        return (%12)
+
+In this graph `aten::lgamma` is not supported by conversion and must be partitioned in a Torch fallback segment. If Torch-TensorRT uses a greedy segmentation strategy that traverses nodes in the input graph in order and gathers ops with the same target (TensorRT or Torch) into a segment until it encounters an op with a different target, the resulting partition includes 7 segments, many with just a single op.
+
+.. code-block:: none
+
+    Segment Block @0:
+        Target: TensorRT
+
+        Graph: graph(%x : Tensor,
+            %y : Tensor):
+        %3 : int = prim::Constant[value=1]()
+        %0 : Tensor = aten::add(%x, %y, %3)
+        return ()
+
+    Segment Block @1:
+        Target: Torch
+
+        Graph: graph(%x : Tensor):
+        %0 : Tensor = aten::lgamma(%x)
+        return ()
+
+    Segment Block @2:
+        Target: TensorRT
+
+        Graph: graph(%x : Tensor,
+            %y : Tensor):
+        %0 : Tensor = aten::mul(%x, %y)
+        return ()
+
+    Segment Block @3:
+        Target: Torch
+
+        Graph: graph(%y : Tensor):
+        %0 : Tensor = aten::lgamma(%y)
+        return ()
+
+    Segment Block @4:
+        Target: TensorRT
+
+        Graph: graph(%x : Tensor,
+            %y : Tensor):
+        %0 : Tensor = aten::div(%x, %y)
+        return ()
+
+    Segment Block @5:
+        Target: Torch
+
+        Graph: graph(%1 : Tensor):
+        %0 : Tensor = aten::lgamma(%1)
+        return ()
+
+    Segment Block @6:
+        Target: TensorRT
+
+        Graph: graph(%1 : Tensor,
+            %2 : Tensor,
+            %3 : Tensor,
+            %4 : Tensor,
+            %5 : Tensor):
+        %7 : int = prim::Constant[value=0]()
+        %0 : Tensor[] = prim::ListConstruct(%1, %2, %3, %4, %5)
+        %6 : Tensor = aten::cat(%0, %7)
+        return ()
+
+This partition is valid, but the segmentation is suboptimal. These arithmetic ops and `aten::lgamma` ops are each split into their own segment as we alternate between Torch and TensorRT targets in the linear traversal of the graph.
+
+.. code-block:: none
+
+    %add : Tensor = aten::add(%x, %y, %20)
+    %x_lgamma : Tensor = aten::lgamma(%x)
+    %mul : Tensor = aten::mul(%x, %y)
+    %y_lgamma : Tensor = aten::lgamma(%y)
+    %div : Tensor = aten::div(%x, %y)
+    %div_lgamma : Tensor = aten::lgamma(%div)
+
+Each of the arithmetic ops in this segment is only dependent on constants and the inputs `%x` and `%y`. The `aten::lgamma` ops are dependent on the inputs `%x`, `%y` and the output of the `aten::div`. This means that we could rewrite this portion of the input graph as below without changing the behavior of the graph. This reordered series of ops could be cleanly partitioned into just 2 segments using the greedy segmentation approach described above.
+
+.. code-block:: none
+
+    %add : Tensor = aten::add(%x, %y, %20)
+    %mul : Tensor = aten::mul(%x, %y)
+    %div : Tensor = aten::div(%x, %y)
+    %x_lgamma : Tensor = aten::lgamma(%x)
+    %y_lgamma : Tensor = aten::lgamma(%y)
+    %div_lgamma : Tensor = aten::lgamma(%div)
+
+By adding awareness of the dependencies between ops to the basic greedy segmentation approach we can achieve the same partition without rewriting the graph. Now we will maintain both Torch and TensorRT targeted segments at the same time as we traverse the graph. We will only finalize a segment once we hit an op that is both dependent on an op in the segment and has a different target. This will allow the partition to create larger segments by reordering nodes across the segment boundary while guaranteeing that we will not modify the behavior of the graph by reordering nodes relative to their dependencies.
+In this example we will collect the arithmetic ops in a TensorRT segment and the `aten::lgamma` ops in a Torch segment. When we encounter the `%div_lgamma : Tensor = aten::lgamma(%div)` op we can see it is dependent on `%div : Tensor = aten::div(%x, %y)` in the current TensorRT segment. This triggers finalization of the TensorRT segment containing the `aten::div` op to guarantee it will appear before its dependency in the final partition. The Torch segment containing the `aten::lgamma` op is finalized when we encounter the `prim::ListConstruct` op which targets TensorRT and is dependent on the results of the `aten::lgamma` ops.
+
+.. code-block:: none
+
+    Segment Block @0:
+        Target: TensorRT
+
+        Graph: graph(%x : Tensor,
+            %y : Tensor):
+        %3 : int = prim::Constant[value=1]()
+        %0 : Tensor = aten::add(%x, %y, %3)
+        %4 : Tensor = aten::mul(%x, %y)
+        %5 : Tensor = aten::div(%x, %y)
+        return ()
+
+    Segment Block @1:
+        Target: Torch
+
+        Graph: graph(%x : Tensor,
+            %y : Tensor,
+            %5 : Tensor):
+        %0 : Tensor = aten::lgamma(%x)
+        %2 : Tensor = aten::lgamma(%y)
+        %4 : Tensor = aten::lgamma(%5)
+        return ()
+
+    Segment Block @2:
+        Target: TensorRT
+
+        Graph: graph(%1 : Tensor,
+            %2 : Tensor,
+            %3 : Tensor,
+            %4 : Tensor,
+            %5 : Tensor):
+        %7 : int = prim::Constant[value=0]()
+        %0 : Tensor[] = prim::ListConstruct(%1, %2, %3, %4, %5)
+        %6 : Tensor = aten::cat(%0, %7)
+        return ()
+
+In some cases this approach may create adjacent segments in the partition which have the same target. As a clean-up step we can consolidate these adjacent segments to further reduce the number of segments in the final partition.
+The merge segments step identifies a list of segments that are adjacent in the graph, have the same target, and are not marked as `do_not_merge`. The nodes from these segments will be combined into a single new segment that will replace the merged segments in the partition.
+The `do_not_merge` marking is used to prevent merging of segments created for conditional nodes and loops that are handled as special cases in graph stitching and should not be merged with adjacent segments of the same type.
diff --git a/docs/v2.2.0/_sources/contributors/phases.rst.txt b/docs/v2.2.0/_sources/contributors/phases.rst.txt
new file mode 100644
index 0000000000..e99af77faa
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/phases.rst.txt
@@ -0,0 +1,46 @@
+Compiler Phases
+----------------
+
+.. toctree::
+    :caption: Compiler Phases
+    :maxdepth: 1
+    :hidden:
+
+    lowering
+    partitioning
+    conversion
+    runtime
+
+Lowering
+^^^^^^^^^^^
+:ref:`lowering`
+
+The lowering is made up of a set of passes (some from PyTorch and some specific to Torch-TensorRT)
+run over the graph IR to map the large PyTorch opset to a reduced opset that is easier to convert to
+TensorRT.
+
+Partitioning
+^^^^^^^^^^^^^
+:ref:`partitioning`
+
+The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.
+
+Conversion
+^^^^^^^^^^^
+:ref:`conversion`
+
+In the conversion phase we traverse the lowered graph and construct an equivalent TensorRT graph.
+The conversion phase is made up of three main components, a context to manage compile time data,
+a evaluator library which will execute operations that can be resolved at compile time and a converter
+library which maps an op from JIT to TensorRT.
+
+Compilation and Runtime
+^^^^^^^^^^^^^^^^^^^^^^^^
+:ref:`runtime`
+
+The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
+takes a serialized engine and instantiates it within a engine manager, then the compiler will
+build out a JIT graph that references this engine and wraps it in a module to return to the user.
+When the user executes the module, the JIT program run in the JIT runtime extended by Torch-TensorRT with the data providied from the user.
diff --git a/docs/v2.2.0/_sources/contributors/runtime.rst.txt b/docs/v2.2.0/_sources/contributors/runtime.rst.txt
new file mode 100644
index 0000000000..23d83b6db2
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/runtime.rst.txt
@@ -0,0 +1,85 @@
+.. _execution:
+
+Runtime Phase
+================
+
+The Runtime phase is responsible for constructing self standing TorchScript graphs with embedded TensorRT engines and serving as the runtime
+when these engines are called. The main interface accepts a serialized TensorRT engine. The execution phase
+will deserialize and wrap this engine in a class which maintains a execution context for each engine
+and some metadata about its inputs and outputs and is compatable with the TorchScript interpreter so that
+it can be moved around and used like other TorchScript IValues. The engine is run by providing it and inputs
+to the ``tensorrt::execute_engine`` operator which will take the engine and its inputs and return the results of engine exeuction.
+
+
+Background
+------------
+PyTorch JIT's runtime is based around a stack machine, all operators pop off arguments from the stack, pass them to
+some implementation of the operator then push results back onto the stack. The actual elements of the stack
+are ``torch::jit::IValues``, the same type we evaluate in the conversion phase (the realization of the abstract
+torch::jit::Value type).
+
+TensorRT Engine Executor Op
+----------------------------
+
+When the Torch-TensorRT is loaded, it registers an operator in the PyTorch JIT operator library called
+``trt::execute_engine(Tensor[] inputs, __torch__.torch.classes.tensorrt.Engine engine) -> Tensor[]`` which takes an
+instantiated engine and list of inputs. Compiled graphs store this engine in an attribute so that it is portable and serializable.
+When the op is called, an instnantiated engine and input tensors are popped off the runtime stack. These inputs are passed into a generic engine execution function which
+will run the tensors through the TensorRT engine and return new tensors as results. These tensors are pushed on to the
+stack so that the next op whatever it is can use it.
+
+Constructing the Resulting Graph
+-----------------------------------
+
+Once the engine is deserialized and instantiated, the compiler will construct a graph that will execute the engine when the module is called.
+Here is an example:
+
+.. code-block::
+
+    graph(%self_1 : __torch__.torchvision.models.resnet.___torch_mangle_4847.ResNet_trt,
+      %input_0 : Tensor):
+        %1 : __torch__.torch.classes.tensorrt.Engine = prim::GetAttr[name="__torch___torchvision_models_resnet____torch_mangle_4847_ResNet_trt_engine"](%self_1)
+        %3 : Tensor[] = prim::ListConstruct(%input_0)
+        %4 : Tensor[] = trt::execute_engine(%3, %1)
+        %5 : Tensor = prim::ListUnpack(%4)
+    return (%5)
+
+You can see the engine attribute in the graph and the ``trt::execute_engine`` op taking a list of input tensors and an engine in
+and produces a list of output tensors which is returned. When ``forward`` is called on the module this graph is executed, thereby
+running the TensorRT engine.
+
+In the case of multiple outputs, the compiled graph may repack output tensors into a Tuple to return back to the user.
+
+.. code-block::
+
+    graph(%self_1 : __torch__.PyTorch.Detection.SSD.src.model.SSD300_trt,
+      %input_0 : Tensor):
+        %1 : __torch__.torch.classes.tensorrt.Engine = prim::GetAttr[name="__torch___PyTorch_Detection_SSD_src_model_SSD300_trt_engine"](%self_1)
+        %3 : Tensor[] = prim::ListConstruct(%input_0)
+        %4 : Tensor[] = trt::execute_engine(%3, %1)
+        %5 : Tensor, %6 : Tensor = prim::ListUnpack(%4)
+        %7 : (Tensor, Tensor) = prim::TupleConstruct(%5, %6)
+    return (%7)
+
+Serialization and Deserialization
+----------------------------------
+
+Serialization and deserialization of TensorRT engines embedded in TorchScript graphs are handled by the holder class for the engine and TorchBind.
+When a TorchScript module is saved, the pickler will run serilization on the cuda engine and store the serialized engine in the zip file created.
+When deserializing, the depickler will call a constructor for the engine holder class with the serialized engine so that it can be set up again for
+execution.
+
+ABI Versioning and Serialization Format
+=========================================
+
+Torch-TensorRT programs are standard TorchScript with TensorRT engines as objects embedded in the graph. Therefore there is a serialization format
+for the TensorRT engines. The format for Torch-TensorRT serialized programs are versioned with an "ABI" version which tells the runtime about runtime compatibility.
+
+> Current ABI version is 3
+
+The format is a vector of serialized strings. They encode the following information
+
+* ABI Version for the program
+* Name of the TRT engine
+* Device information: Includes the target device the engine was built on, SM capability and other device information. This information is used at deserialization time to select the correct device to run the engine
+* Serialized TensorRT engine
diff --git a/docs/v2.2.0/_sources/contributors/system_overview.rst.txt b/docs/v2.2.0/_sources/contributors/system_overview.rst.txt
new file mode 100644
index 0000000000..d16697e3a3
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/system_overview.rst.txt
@@ -0,0 +1,29 @@
+.. _system_overview:
+
+System Overview
+================
+
+Torch-TensorRT is primarily a C++ Library with a Python API planned. We use Bazel as our build system and target Linux x86_64 and
+Linux aarch64 (only natively) right now. The compiler we use is GCC 7.5.0 and the library is untested with compilers before that
+version so there may be compilation errors if you try to use an older compiler.
+
+The repository is structured into:
+
+* core: Main compiler source code
+* cpp: C++ API
+* tests: tests of the C++ API, the core and converters
+* py: Python API
+* notebooks: Example applications built with Torch-TensorRT
+* docs: Documentation
+* docsrc: Documentation Source
+* third_party: BUILD files for dependency libraries
+* toolchains: Toolchains for different platforms
+
+
+The C++ API is unstable and subject to change until the library matures, though most work is done under the hood in the core.
+
+The core has a couple major parts: The top level compiler interface which coordinates ingesting a module, lowering,
+converting and generating a new module and returning it back to the user. There are the three main phases of the
+compiler, the lowering phase, the conversion phase, and the execution phase.
+
+.. include:: phases.rst
diff --git a/docs/v2.2.0/_sources/contributors/ts_converters.rst.txt b/docs/v2.2.0/_sources/contributors/ts_converters.rst.txt
new file mode 100644
index 0000000000..3266354ad8
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/ts_converters.rst.txt
@@ -0,0 +1,129 @@
+.. _ts_converters:
+
+Writing TorchScript Converters
+=================================
+
+Background
+------------
+
+In the JIT IR, operations are represented as nodes in a graph. A node has inputs and outputs, represented by ``torch::jit::Values``
+which are typed abstract representation of data flowing into and out of a node. TensorRT represents its graph though the
+use of ``nvinfer1::ILayers`` and ``nvinfer1::ITensors`` which are its analogues to nodes and values. The goal of
+converters create new ILayers and subgraphs that do operation specified by the node and associate produced ITensors
+and Values together.
+
+Converters
+------------
+
+Converters should be functions which will use a list of inputs (either ``nvinfer1::ITensors`` or ``torch::jit::IValues``) to
+construct an equivalent layer to the LibTorch op.
+
+Converters can be registered using the ``RegisterNodeConversionPatterns`` helper class where you instantiate a
+RegisterNodeConversionPatterns object and call the pattern function on it (like below) which takes a string
+which describes the function schema of the op that will cause the converter to be run and a lambda or function
+which will do the actual conversion:
+
+    Note the pattern function can be chained
+
+.. code-block:: c++
+
+    auto acthardtanh TORCHTRT_UNUSED = RegisterNodeConversionPatterns()
+        .pattern({
+            "aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> (Tensor)",
+            [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+                auto in = args[0].ITensor();
+                auto min = args[1].unwrapToDouble();
+                auto max = args[2].unwrapToDouble();
+
+                auto new_layer = ctx->net->addActivation(*in, nvinfer1::ActivationType::kCLIP);
+                TORCHTRT_CHECK(new_layer, "Unable to create layer for aten::hardtanh");
+
+                new_layer->setAlpha(min);
+                new_layer->setBeta(max);
+
+                new_layer->setName(util::node_info(n).c_str());
+                auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], new_layer->getOutput(0));
+
+                LOG_DEBUG("Output shape: " << out_tensor->getDimensions());
+                return true;
+            }
+        });
+
+
+Converter Contract
+----------------------
+
+What is guaranteed to converters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+1. In the args there will be an entry for each node input value, either a ITensor or IValue
+2. Inputs will be provided in order according to the function schema
+
+Responsibilities of a converter
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+1.  Args must be guaranteed to be a type to unwrap the Arg union without checking, typically input tensor arguments can be expected to be ITensors
+2.  Any weights or static values must guaranteed to be valid until the end of conversion time
+
+    a. A helpful tool is the Weights helper class described below
+
+3.  Converters are expected to produce an IValue or ITensor for each output of a node. The compiler will check this and produce warnings if there are Values that don't have associated ITensors or IValues.
+4.  Outputs must be annotated
+
+    a.  There must be an association between a JIT nodes output values and the new TRT layers output tensors in the ``value_tensor_map`` in the conversion context
+
+5.  Name your layers
+
+    a.  Its much easier to debug when we can track which layers and nodes correspond with each other. The system we are currently using is to use the "node info" of the node as the name of the layer
+
+6.  Name your tensors
+
+    a.  Use the output value debug name as the name for the new ITensor (again for debugging)
+
+Conversion Context
+--------------------
+
+The conversion context maintains the state of conversion, it manages the Network Definition, two maps
+one that stores associations between Values and IValues (the evaluated_value_map) and one that stores
+associations between Values and ITensors, and any sort of memory that needs to live until the end of
+conversion. The main apis that you will interface with in converters is directly accessing the network
+definition to add layers ``ctx->net`` and data association functions ``ctx->AssociateValueAndTensor()``
+and ``ctx->AssociateValueAndIValue()``, which you will use to add layers to the TRT layers and log
+pairs of node outputs and static values or TensorRT layer outputs.
+
+Args
+-------
+
+Arguments provided to the converter are inspectable unions of ``nvinfer1::ITensors`` and ``torch::jit::IValues`` (i.e.
+abstract dataflow in the TensorRT graph and static values). You are guaranteed that you will have some
+argument for each input value for the node. They are provided in the order of the function schema.
+It can be expected that inputs (meaning the parameters that would be passed into the forward
+function of a module in PyTorch) will be ITensors but the Arg class also has mechanisms to inspect arguments safely
+before unwrapping if you are unsure. Args also have deep unwrap methods that let you get straight to the
+underlying data in an IValue if you know it's safe. You can also pass in a fallback value if there is a
+chance the IValue is None. IValues have been extended to be able to hold a wrapper around ITensors only in the case of TensorLists.
+You can get an ITensor from an IValue by a pattern similar to this: ``ivalue.toCustomClass<TensorContainer>()->tensor()``.
+You can tell if an IValue contains a Tensor or an ITensor by using ``ivalue.isTensor()`` or ``ivalue.isCustomClass()``.
+
+
+Weights
+--------------
+
+Weights are used during build time, so any weights need to be guaranteed to live until the end of the conversion phase.
+TensorRT also uses its own weights structure to hold the weights. There is a wrapper around this class available
+to converts which abstracts a lot of this.
+
+The weights wrapper class can accept either ``at::Tensors`` or singular values (right now). You also need to pass the
+conversion context when constructing these weights because internally the weights class will allocate memory managed
+by the conversion context to store a copy of the tensor data. This data gets freed when the conversion context
+destructor gets destroyed so converters don't really need to think about it.
+
+There is metadata generated from the shape of the input data which becomes useful in interfacing with TensorRT, such
+as number of input maps, number of output maps and kernel shape.
+
+Other advice
+--------------
+
+You have the benefit of the full aten library when dealing with weights and other static values. This means that you
+can do quite a bit of work during conversion time to produce efficient conversion. A good example is batch_norm
+converter where the converter does fusion of operations with PyTorch before creating the TensorRT layer.
diff --git a/docs/v2.2.0/_sources/contributors/useful_links.rst.txt b/docs/v2.2.0/_sources/contributors/useful_links.rst.txt
new file mode 100644
index 0000000000..d5903a16b0
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/useful_links.rst.txt
@@ -0,0 +1,34 @@
+.. _useful_links:
+
+Useful Links for Torch-TensorRT Development
+==============================================
+
+TensorRT Available Layers and Expected Dimensions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://docs.nvidia.com/deeplearning/sdk/tensorrt-support-matrix/index.html#layers-matrix
+
+TensorRT C++ Documentation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://docs.nvidia.com/deeplearning/tensorrt/api/c_api/index.html
+
+TensorRT Python Documentation (Sometimes easier to read)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://docs.nvidia.com/deeplearning/sdk/tensorrt-api/python_api/index.html
+
+PyTorch Functional API
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://pytorch.org/docs/stable/nn.functional.html
+
+PyTorch native_ops
+^^^^^^^^^^^^^^^^^^^^^
+
+* https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/native_functions.yaml
+
+PyTorch IR Documentation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+* https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/OVERVIEW.md
diff --git a/docs/v2.2.0/_sources/contributors/writing_dynamo_aten_lowering_passes.rst.txt b/docs/v2.2.0/_sources/contributors/writing_dynamo_aten_lowering_passes.rst.txt
new file mode 100644
index 0000000000..4c29bc9b75
--- /dev/null
+++ b/docs/v2.2.0/_sources/contributors/writing_dynamo_aten_lowering_passes.rst.txt
@@ -0,0 +1,109 @@
+.. _writing_dynamo_aten_lowering_passes:
+
+Writing Dynamo ATen Lowering Passes
+===================
+
+Basics of a Lowering Pass
+------------
+
+ATen lowering passes are Python functions which take as input a graph of ATen operators, apply some desired modification such as operator coalescing/fusion, operator replacement, subgraph rewriting, custom operator insertion, or other operation on a `torch.fx.GraphModule`, then return the modified graph to the caller. These lowering passes generally modify the graph in-place and return the same input object.
+
+Lowering Pass Requirements
+------------
+
+An ATen lowering pass function in Torch-TRT must satisfy two requirements:
+- The function must take as input a `torch.fx.GraphModule` and a sequence of torch Tensors, `Sequence[torch.Tensor]`, and return the lowered `torch.fx.GraphModule`
+- The function must leave the graph in a valid and invoke-able state, including performing any necessary linting and recompilation
+
+See this link for information on `Graph Manipulations <https://pytorch.org/docs/stable/fx.html#graph-manipulation>`_ in FX. See below for an example of a lowering pass which repairs graphs that have inputs which are also outputs, a disallowed configuration for TRT Engines.
+
+Example Lowering Pass
+------------
+
+.. code-block:: python
+
+    def repair_input_as_output(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+        """Repair scenarios where inputs are also outputs of the graph
+
+        TRT does not allow such cases, so we insert a clone (identity) layer
+        """
+        modified_graph = False
+
+        # Extract graph placeholder Tensors
+        placeholders = [
+            node
+            for node in gm.graph.nodes
+            if (
+                node.op == "placeholder"
+                and isinstance(node.type, type)
+                and issubclass(node.type, torch.Tensor)
+            )
+        ]
+
+        for placeholder in placeholders:
+            # If any placeholder has any users which are direct graph outputs
+            if len(placeholder.users) >= 1 and any(
+                user.op == "output" for user in placeholder.users
+            ):
+                modified_graph = True
+
+                # Get direct graph outputs which are direct uses of placeholders
+                direct_outputs = [user for user in placeholder.users if user.op == "output"]
+
+                # Insert clone node for placeholder to ensure
+                # placeholder is not a direct output
+                with gm.graph.inserting_after(placeholder):
+                    cloned_placeholder = gm.graph.call_function(
+                        torch.ops.aten.clone.default,
+                        args=(placeholder,),
+                    )
+
+                # Replace placeholder as output with cloned version
+                for output in direct_outputs:
+                    output.replace_input_with(placeholder, cloned_placeholder)
+
+        # If the graph was modified, clean up the graph and ensure it is up-to-date
+        if modified_graph:
+            gm.graph.eliminate_dead_code()
+            gm.graph.lint()
+            gm.recompile()
+            logger.debug(f"Graph after repair_input_as_output:\n{gm.graph}")
+
+        return gm
+
+
+Registering Lowering Passes
+----------------------
+
+Lowering passes are currently registered in `py/torch_tensorrt/dynamo/lowering/passes/__init__.py`, using the `torch.fx.passes.pass_manager.PassManager` utility to assemble the list of passes in a desired order. New passes added directly to that list will be applied to graphs in the Torch-TensorRT `torch.compile` backend. Currently, we offer an ATen lowering pass registration decorator for convenience, which can be invoked either directly, or with the optional `index` keyword argument which controls where in the pass list the lowering pass will be inserted.
+
+For instance, to insert the pass at the default location (end of the list), the following code can be used:
+
+.. code-block:: python
+
+    @_aten_lowering_pass
+    def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+        ...
+
+Alternatively, to insert the pass at a custom index (such as the front of the list) in the passlist, the following code can be used:
+
+.. code-block:: python
+
+    @_aten_lowering_pass(index=0)
+    def my_custom_pass(gm: torch.fx.GraphModule, sample_inputs: Sequence[torch.Tensor]) -> torch.fx.GraphModule:
+        ...
+
+There are also provided utilities in `torch_tensorrt.dynamo.lowering.passes` for displaying the currently-available lowering pass list, applying those passes to an arbitrary `torch.fx.GraphModule`, and removing the lowering pass at a specific index.
+
+.. code-block:: python
+
+    # Print all lowering passes in the list
+    print(dump_lowering_passes())
+
+    # Apply lowering passes to a GraphModule
+    apply_lowering_passes(graph_module, sample_inputs)
+
+    # Remove the lowering pass at index 1
+    _remove_lowering_pass(index=1)
+
+**Note:** The above APIs are subject to change, as the lowering pass system evolves.
diff --git a/docs/v2.2.0/_sources/dynamo/dynamo_export.rst.txt b/docs/v2.2.0/_sources/dynamo/dynamo_export.rst.txt
new file mode 100644
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+++ b/docs/v2.2.0/_sources/dynamo/dynamo_export.rst.txt
@@ -0,0 +1,75 @@
+.. _dynamo_export:
+
+Compiling Exported Programs with Torch-TensorRT
+=============================================
+.. currentmodule:: torch_tensorrt.dynamo
+
+.. automodule:: torch_tensorrt.dynamo
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Pytorch 2.1 introduced ``torch.export`` APIs which
+can export graphs from Pytorch programs into ``ExportedProgram`` objects. Torch-TensorRT dynamo
+frontend compiles these ``ExportedProgram`` objects and optimizes them using TensorRT. Here's a simple
+usage of the dynamo frontend
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = [torch.randn((1, 3, 224, 224), dtype=torch.float32).cuda()]
+    exp_program = torch.export.export(model, tuple(inputs))
+    trt_gm = torch_tensorrt.dynamo.compile(exp_program, inputs) # Output is a torch.fx.GraphModule
+    trt_gm(*inputs)
+
+.. note::  ``torch_tensorrt.dynamo.compile`` is the main API for users to interact with Torch-TensorRT dynamo frontend. The input type of the model should be ``ExportedProgram`` (ideally the output of ``torch.export.export`` or ``torch_tensorrt.dynamo.trace`` (discussed in the section below)) and output type is a ``torch.fx.GraphModule`` object.
+
+Customizeable Settings
+----------------------
+
+There are lot of options for users to customize their settings for optimizing with TensorRT.
+Some of the frequently used options are as follows:
+
+* ``inputs`` - For static shapes, this can be a list of torch tensors or `torch_tensorrt.Input` objects. For dynamic shapes, this should be a list of ``torch_tensorrt.Input`` objects.
+* ``enabled_precisions`` - Set of precisions that TensorRT builder can use during optimization.
+* ``truncate_long_and_double`` - Truncates long and double values to int and floats respectively.
+* ``torch_executed_ops`` - Operators which are forced to be executed by Torch.
+* ``min_block_size`` - Minimum number of consecutive operators required to be executed as a TensorRT segment.
+
+The complete list of options can be found `here <https://github.com/pytorch/TensorRT/blob/123a486d6644a5bbeeec33e2f32257349acc0b8f/py/torch_tensorrt/dynamo/compile.py#L51-L77>`_
+
+.. note:: We do not support INT precision currently in Dynamo. Support for this currently exists in our Torchscript IR. We plan to implement similar support for dynamo in our next release.
+
+Under the hood
+--------------
+
+Under the hood, ``torch_tensorrt.dynamo.compile`` performs the following on the graph.
+
+* Lowering - Applies lowering passes to add/remove operators for optimal conversion.
+* Partitioning - Partitions the graph into Pytorch and TensorRT segments based on the ``min_block_size`` and ``torch_executed_ops`` field.
+* Conversion - Pytorch ops get converted into TensorRT ops in this phase.
+* Optimization - Post conversion, we build the TensorRT engine and embed this inside the pytorch graph.
+
+Tracing
+-------
+
+``torch_tensorrt.dynamo.trace`` can be used to trace a Pytorch graphs and produce ``ExportedProgram``.
+This internally performs some decompositions of operators for downstream optimization.
+The ``ExportedProgram`` can then be used with ``torch_tensorrt.dynamo.compile`` API.
+If you have dynamic input shapes in your model, you can use this ``torch_tensorrt.dynamo.trace`` to export
+the model with dynamic shapes. Alternatively, you can use ``torch.export`` `with constraints <https://pytorch.org/docs/stable/export.html#expressing-dynamism>`_ directly as well.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    inputs = [torch_tensorrt.Input(min_shape=(1, 3, 224, 224),
+                                  opt_shape=(4, 3, 224, 224),
+                                  max_shape=(8, 3, 224, 224),
+                                  dtype=torch.float32)]
+    model = MyModel().eval()
+    exp_program = torch_tensorrt.dynamo.trace(model, inputs)
diff --git a/docs/v2.2.0/_sources/dynamo/torch_compile.rst.txt b/docs/v2.2.0/_sources/dynamo/torch_compile.rst.txt
new file mode 100644
index 0000000000..6e969092ae
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+++ b/docs/v2.2.0/_sources/dynamo/torch_compile.rst.txt
@@ -0,0 +1,110 @@
+.. _torch_compile:
+
+TensorRT Backend for ``torch.compile``
+======================================================
+.. currentmodule:: torch_tensorrt.dynamo
+
+.. automodule:: torch_tensorrt.dynamo
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+This guide presents the Torch-TensorRT `torch.compile` backend: a deep learning compiler which uses TensorRT to accelerate JIT-style workflows across a wide variety of models.
+
+Key Features
+--------------------------------------------
+
+The primary goal of the Torch-TensorRT `torch.compile` backend is to enable Just-In-Time compilation workflows by combining the simplicity of `torch.compile` API with the performance of TensorRT. Invoking the `torch.compile` backend is as simple as importing the `torch_tensorrt` package and specifying the backend:
+
+.. code-block:: python
+
+    import torch_tensorrt
+    ...
+    optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False)
+
+.. note:: Many additional customization options are available to the user. These will be discussed in further depth in this guide.
+
+The backend can handle a variety of challenging model structures and offers a simple-to-use interface for effective acceleration of models. Additionally, it has many customization options to ensure the compilation process is fitting to the specific use case.
+
+Customizeable Settings
+-----------------
+.. autoclass:: CompilationSettings
+
+Custom Setting Usage
+^^^^^^^^^^^^^^^^^
+.. code-block:: python
+
+    import torch_tensorrt
+    ...
+    optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False,
+                                    options={"truncate_long_and_double": True,
+                                             "precision": torch.half,
+                                             "debug": True,
+                                             "min_block_size": 2,
+                                             "torch_executed_ops": {"torch.ops.aten.sub.Tensor"},
+                                             "optimization_level": 4,
+                                             "use_python_runtime": False,})
+
+.. note:: Quantization/INT8 support is slated for a future release; currently, we support FP16 and FP32 precision layers.
+
+Compilation
+-----------------
+Compilation is triggered by passing inputs to the model, as so:
+
+.. code-block:: python
+
+    import torch_tensorrt
+    ...
+    # Causes model compilation to occur
+    first_outputs = optimized_model(*inputs)
+
+    # Subsequent inference runs with the same, or similar inputs will not cause recompilation
+    # For a full discussion of this, see "Recompilation Conditions" below
+    second_outputs = optimized_model(*inputs)
+
+After Compilation
+-----------------
+The compilation object can be used for inference within the Python session, and will recompile according to the recompilation conditions detailed below. In addition to general inference, the compilation process can be a helpful tool in determining model performance, current operator coverage, and feasibility of serialization. Each of these points will be covered in detail below.
+
+Model Performance
+^^^^^^^^^^^^^^^^^
+The optimized model returned from `torch.compile` is useful for model benchmarking since it can automatically handle changes in the compilation context, or differing inputs that could require recompilation. When benchmarking inputs of varying distributions, batch sizes, or other criteria, this can save time.
+
+Operator Coverage
+^^^^^^^^^^^^^^^^^
+Compilation is also a useful tool in determining operator coverage for a particular model. For instance, the following compilation command will display the operator coverage for each graph, but will not compile the model - effectively providing a "dryrun" mechanism:
+
+.. code-block:: python
+
+    import torch_tensorrt
+    ...
+    optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False,
+                                    options={"debug": True,
+                                             "min_block_size": float("inf"),})
+
+If key operators for your model are unsupported, see :ref:`dynamo_conversion` to contribute your own converters, or file an issue here: https://github.com/pytorch/TensorRT/issues.
+
+Feasibility of Serialization
+^^^^^^^^^^^^^^^^^
+Compilation can also be helpful in demonstrating graph breaks and the feasibility of serialization of a particular model. For instance, if a model has no graph breaks and compiles successfully with the Torch-TensorRT backend, then that model should be compileable and serializeable via the `torch_tensorrt` Dynamo IR, as discussed in :ref:`dynamic_shapes`. To determine the number of graph breaks in a model, the `torch._dynamo.explain` function is very useful:
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+    ...
+    explanation = torch._dynamo.explain(model)(*inputs)
+    print(f"Graph breaks: {explanation.graph_break_count}")
+    optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False, options={"truncate_long_and_double": True})
+
+Dynamic Shape Support
+-----------------
+
+The Torch-TensorRT `torch.compile` backend will currently require recompilation for each new batch size encountered, and it is preferred to use the `dynamic=False` argument when compiling with this backend. Full dynamic shape support is planned for a future release.
+
+Recompilation Conditions
+-----------------
+
+Once the model has been compiled, subsequent inference inputs with the same shape and data type, which traverse the graph in the same way will not require recompilation. Furthermore, each new recompilation will be cached for the duration of the Python session. For instance, if inputs of batch size 4 and 8 are provided to the model, causing two recompilations, no further recompilation would be necessary for future inputs with those batch sizes during inference within the same session. Support for engine cache serialization is planned for a future release.
+
+Recompilation is generally triggered by one of two events: encountering inputs of different sizes or inputs which traverse the model code differently. The latter scenario can occur when the model code includes conditional logic, complex loops, or data-dependent-shapes. `torch.compile` handles guarding in both of these scenario and determines when recompilation is necessary.
diff --git a/docs/v2.2.0/_sources/fx/getting_started_with_fx_path.rst.txt b/docs/v2.2.0/_sources/fx/getting_started_with_fx_path.rst.txt
new file mode 100644
index 0000000000..ca630b7a9b
--- /dev/null
+++ b/docs/v2.2.0/_sources/fx/getting_started_with_fx_path.rst.txt
@@ -0,0 +1,333 @@
+.. _getting_started_with_fx:
+
+Torch-TensorRT (FX Frontend) User Guide
+========================
+Torch-TensorRT (FX Frontend) is a tool that can convert a PyTorch model through ``torch.fx`` to an
+TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine
+developed by NVIDIA which composed of various kinds of optimization including kernel fusion,
+graph optimization, low precision, etc.. This tool is developed in Python environment which allows this
+workflow to be very accessible to researchers and engineers. There are a few stages that a
+user want to use this tool and we will introduce them here.
+
+> Torch-TensorRT (FX Frontend) is in ``Beta`` and currently it is recommended to work with PyTorch nightly.
+
+.. code-block:: shell
+
+    # Test an example by
+    $ python py/torch_tensorrt/fx/example/lower_example.py
+
+
+Converting a PyTorch Model to TensorRT Engine
+---------------------------------------------
+In general, users are welcome to use the ``compile()`` to finish the conversion from a model to tensorRT engine. It is a
+wrapper API that consists of the major steps needed to finish this converison. Please refer to an example usage in ``lower_example.py`` file under ``examples/fx``.
+
+.. code-block:: shell
+
+    def compile(
+        module: nn.Module,
+        input,
+        max_batch_size=2048,
+        max_workspace_size=33554432,
+        explicit_batch_dimension=False,
+        lower_precision=LowerPrecision.FP16,
+        verbose_log=False,
+        timing_cache_prefix="",
+        save_timing_cache=False,
+        cuda_graph_batch_size=-1,
+        dynamic_batch=True,
+    ) -> nn.Module:
+
+        """
+        Takes in original module, input and lowering setting, run lowering workflow to turn module
+        into lowered module, or so called TRTModule.
+
+        Args:
+            module: Original module for lowering.
+            input: Input for module.
+            max_batch_size: Maximum batch size (must be >= 1 to be set, 0 means not set)
+            max_workspace_size: Maximum size of workspace given to TensorRT.
+            explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.
+            lower_precision: lower_precision config given to TRTModule.
+            verbose_log: Enable verbose log for TensorRT if set True.
+            timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.
+            save_timing_cache: Update timing cache with current timing cache data if set to True.
+            cuda_graph_batch_size: Cuda graph batch size, default to be -1.
+            dynamic_batch: batch dimension (dim=0) is dynamic.
+        Returns:
+            A torch.nn.Module lowered by TensorRT.
+        """
+
+In this section, we will go through an example to illustrate the major steps that fx path uses.
+Users can refer to ``fx2trt_example.py`` file in ``examples/fx``.
+
+* **Step 1: Trace the model with acc_tracer**
+Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.
+
+.. code-block:: shell
+
+   import torch_tensorrt.fx.tracer.acc_tracer.acc_tracer as acc_tracer
+
+   # Build the model which needs to be a PyTorch nn.Module.
+   my_pytorch_model = build_model()
+
+   # Prepare inputs to the model. Inputs have to be a List of Tensors
+   inputs = [Tensor, Tensor, ...]
+
+   # Trace the model with acc_tracer.
+   acc_mod = acc_tracer.trace(my_pytorch_model, inputs)
+
+*Common Errors:*
+
+symbolically traced variables cannot be used as inputs to control flow
+This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in `FX guide <https://pytorch.org/docs/stable/fx.html#dynamic-control-flow>`_.
+
+* **Step 2: Build TensorRT engine**
+There are `two different modes <https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch>`_ for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set ``explicit_batch_dimension=False`` in ``compile()``. We do not recommend to use it since it will lack of support in future TensorRT versions.
+
+Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable ``dynamic_batch`` in ``compile()`` if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
+1. Shapes of inputs, outputs and activations are fixed except batch dimension.
+2. Inputs, outputs and activations have batch dimension as the major dimension.
+3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).
+
+For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.
+
+.. code-block:: shell
+
+    import deeplearning.trt.fx2trt.converter.converters
+    from torch.fx.experimental.fx2trt.fx2trt import InputTensorSpec, TRTInterpreter
+
+    # InputTensorSpec is a dataclass we use to store input information.
+    # There're two ways we can build input_specs.
+    # Option 1, build it manually.
+    input_specs = [
+      InputTensorSpec(shape=(1, 2, 3), dtype=torch.float32),
+      InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
+    ]
+    # Option 2, build it using sample_inputs where user provide a sample
+    inputs = [
+    torch.rand((1,2,3), dtype=torch.float32),
+    torch.rand((1,4,5), dtype=torch.float32),
+    ]
+    input_specs = InputTensorSpec.from_tensors(inputs)
+
+    # IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.
+    input_specs = [
+        InputTensorSpec(
+            shape=(-1, 2, 3),
+            dtype=torch.float32,
+            # Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models
+            # (min_shape, optimize_target_shape, max_shape)
+            # For more information refer to fx/input_tensor_spec.py
+            shape_ranges = [
+                ((1, 2, 3), (4, 2, 3), (100, 2, 3)),
+            ],
+        ),
+        InputTensorSpec(shape=(1, 4, 5), dtype=torch.float32),
+    ]
+
+    # Build a TRT interpreter. Set explicit_batch_dimension accordingly.
+    interpreter = TRTInterpreter(
+        acc_mod, input_specs, explicit_batch_dimension=True/False
+    )
+
+    # The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.
+    # The TRTInterpreterResult contains required parameter to build TRTModule,
+    # and other informational output from TRTInterpreter run.
+    class TRTInterpreterResult(NamedTuple):
+        engine: Any
+        input_names: Sequence[str]
+        output_names: Sequence[str]
+        serialized_cache: bytearray
+
+    #max_batch_size: set accordingly for maximum batch size you will use.
+    #max_workspace_size: set to the maximum size we can afford for temporary buffer
+    #lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).
+    #sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity
+    #force_fp32_output: force output to be fp32
+    #strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.
+    #algorithm_selector: set up algorithm selection for certain layer
+    #timing_cache: enable timing cache for TensorRT
+    #profiling_verbosity: TensorRT logging level
+    trt_interpreter_result = interpreter.run(
+        max_batch_size=64,
+        max_workspace_size=1 << 25,
+        sparse_weights=False,
+        force_fp32_output=False,
+        strict_type_constraints=False,
+        algorithm_selector=None,
+        timing_cache=None,
+        profiling_verbosity=None,
+    )
+
+
+*Common Errors:*
+
+RuntimeError: Conversion of function xxx not currently supported!
+- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.
+
+* **Step 3: Run the model**
+One way is using TRTModule, which is basically a PyTorch nn.Module.
+
+.. code-block:: shell
+
+    from torch_tensorrt.fx import TRTModule
+    mod = TRTModule(
+        trt_interpreter_result.engine,
+        trt_interpreter_result.input_names,
+        trt_interpreter_result.output_names)
+    # Just like all other PyTorch modules
+    outputs = mod(*inputs)
+    torch.save(mod, "trt.pt")
+    reload_trt_mod = torch.load("trt.pt")
+    reload_model_output = reload_trt_mod(*inputs)
+
+So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph's operation to corresponding TensorRT operation and build up the TensoRT engine for it.
+
+Acc Tracer
+---------
+
+Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:
+
+1. there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write.
+2. acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.
+
+FX2TRT
+--------
+After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).
+
+Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.
+
+.. code-block:: shell
+
+    @tensorrt_converter(acc_ops.sigmoid)
+    def acc_ops_sigmoid(network, target, args, kwargs, name):
+        """
+        network: TensorRT network. We'll be adding layers to it.
+
+        The rest arguments are attributes of fx node.
+        """
+        input_val = kwargs['input']
+
+        if not isinstance(input_val, trt.tensorrt.ITensor):
+            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
+                            'of the TensorRT region!')
+
+        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
+        layer.name = name
+        return layer.get_output(0)
+
+How to Add a Missing Op
+****************
+
+You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer.
+
+* **Step 1. Add a new acc op**
+
+TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.
+
+In `acc tracer <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py>`_, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.
+
+In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.
+
+Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py>`_. e.g. the following code adds an acc op named foo() which adds two given inputs.
+
+.. code-block:: shell
+
+    # NOTE: all acc ops should only take kwargs as inputs, therefore we need the "*"
+    # at the beginning.
+    @register_acc_op
+    def foo(*, input, other, alpha):
+        return input + alpha * other
+
+There’re two ways to register a mapping. One is `register_acc_op_mapping() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164>`_. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.
+
+.. code-block:: shell
+
+    this_arg_is_optional = True
+
+    @register_acc_op_mapping(
+        op_and_target=("call_function", torch.add),
+        arg_replacement_tuples=[
+            ("input", "input"),
+            ("other", "other"),
+            ("alpha", "alpha", this_arg_is_optional),
+        ],
+    )
+    @register_acc_op
+    def foo(*, input, other, alpha=1.0):
+        return input + alpha * other
+
+``op_and_target`` determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the ``op_and_target``, we will trigger the mapping. Since we want to map from ``torch.add``, then op would be call_function and target would be ``torch.add``. ``arg_replacement_tuples`` determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in ``arg_replacement_tuples`` represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in *original node*. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
+We don’t have to specify arg_replacement_tuples if none of the followings are true.
+
+1. kwargs of original nodes and acc op nodes have different name.
+2. there’re optional arguments.
+
+The other way to register a mapping is through `register_custom_acc_mapper_fn() <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206>`_. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.
+
+.. code-block:: shell
+
+    @register_acc_op
+    def foo(*, input, other, alpha=1.0):
+        return input + alpha * other
+
+    @register_custom_acc_mapper_fn(
+        op_and_target=("call_function", torch.add),
+        arg_replacement_tuples=[
+            ("input", "input"),
+            ("other", "other"),
+            ("alpha", "alpha", this_arg_is_optional),
+        ],
+    )
+    def custom_mapper(node: torch.fx.Node, _: nn.Module) -> torch.fx.Node:
+        """
+        `node` is original node, which is a call_function node with target
+        being torch.add.
+        """
+        alpha = 1
+        if "alpha" in node.kwargs:
+            alpha = node.kwargs["alpha"]
+        foo_kwargs = {"input": node["input"], "other": node["other"], "alpha": alpha}
+        with node.graph.inserting_before(node):
+            foo_node = node.graph.call_function(foo, kwargs=foo_kwargs)
+            foo_node.meta = node.meta.copy()
+            return foo_node
+
+
+In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes `acc_normalizer.py <https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379>`_.
+
+The last step would be *adding unit test* for the new acc op or mapper function we added. The place to add the unit test is here `test_acc_tracer.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py>`_.
+
+* **Step 2. Add a new converter**
+
+All the developed converters for acc ops are all in `acc_op_converter.py <https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py>`_. It could give you a good example of how the converter is added.
+
+Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using `TensorRT APIs <https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html>`_.
+
+.. code-block:: shell
+
+    @tensorrt_converter(acc_ops.sigmoid)
+    def acc_ops_sigmoid(network, target, args, kwargs, name):
+        """
+        network: TensorRT network. We'll be adding layers to it.
+
+        The rest arguments are attributes of fx node.
+        """
+        input_val = kwargs['input']
+
+        if not isinstance(input_val, trt.tensorrt.ITensor):
+            raise RuntimeError(f'Sigmoid received input {input_val} that is not part '
+                            'of the TensorRT region!')
+
+        layer = network.add_activation(input=input_val, type=trt.ActivationType.SIGMOID)
+        layer.name = name
+        return layer.get_output(0)
+
+We need to use ``tensorrt_converter`` decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is `acc_ops.sigmoid` which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.
+
+**What if we can not find corresponding layers in TensorRT that do the same thing as the node.**
+
+In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. *We have not implement this feature yet. We will update once it is enabled*.
+
+Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this `folder <https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op>`_.
diff --git a/docs/v2.2.0/_sources/getting_started/getting_started_with_windows.rst.txt b/docs/v2.2.0/_sources/getting_started/getting_started_with_windows.rst.txt
new file mode 100644
index 0000000000..edb3262d66
--- /dev/null
+++ b/docs/v2.2.0/_sources/getting_started/getting_started_with_windows.rst.txt
@@ -0,0 +1,147 @@
+.. _getting_started_windows:
+
+Building Torch-TensorRT on Windows
+====================================
+
+Torch-TensorRT has community support for Windows platform using CMake
+
+Prerequisite:
+
+* Microsoft Visual Studio
+* LibTorch
+* TensorRT
+* CUDA
+* cuDNN
+
+
+Build configuration
+-------------------
+
+* Open Microsoft Visual Studio
+* Open Torch-TensorRT source code folder
+* Open Manage configurations -> Edit JSON to open CMakeSettings.json file.
+* Configure the CMake build configurations. Following is an example configuration:
+
+.. code-block:: none
+
+    {
+      "configurations": [
+        {
+          "name": "x64-Debug",
+          "generator": "Ninja",
+          "configurationType": "Debug",
+          "inheritEnvironments": [ "msvc_x64_x64" ],
+          "buildRoot": "${projectDir}\\out\\build\\${name}",
+          "installRoot": "${projectDir}\\out\\install\\${name}",
+          "cmakeCommandArgs": "-S . -B out",
+          "buildCommandArgs": "cmake --build out",
+          "ctestCommandArgs": "",
+          "variables": [
+            {
+              "name": "CMAKE_MODULE_PATH",
+              "value": "$PWD\cmake\Modules",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "Torch_DIR",
+              "value": "<Path to libtorch>\share\cmake\Torch",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "TensorRT_ROOT",
+              "value": "<Path to TensorRT directory>",
+              "type": "FILEPATH"
+            },
+            {
+              "name": "CMAKE_BUILD_TYPE",
+              "value": "Release",
+              "type": " STRING"
+            }
+          ]
+        }
+      ]
+    }
+
+
+Compilation
+-----------
+
+* Click Build -> Build All or directly press Ctrl + Shift + B
+
+Note: After successful compilation, the build artifacts will be present at buildRoot path configured.
+
+Installation
+------------
+
+* Build -> Install Torch-TensorRT
+
+Note: After successful installation, the artifacts will be present at installRoot.
+
+
+Building With Visual Studio Code
+==================================
+
+1. Install Visual Studio Code
+2. Install Build Tools for Visual Studio 2022
+
+    - Select "Desktop Development with C++"
+      > Currently, this installs MSVC v143 - 2022. There are also options to install previous 2019/2017/2015 editions of MSVC
+      > License term "1b Build Tools additional use right" allows using Build Tools to compile Open Source Dependencies
+      > Also allows using Build Tools to develop and test Open Source Dependencies, to the minor extend of ensuring compatibility with Build Tools
+
+3. Install CUDA (e.g. 11.7.1)
+4. Install cuDNN (e.g. 8.5.0.96)
+
+    - Set ``cuDNN_ROOT_DIR``
+
+5. Install `TensorRT` (e.g 8.5.1.7)
+
+    - Set ``TensorRT_ROOT``
+    - Add ``TensorRT_ROOT\lib`` to ``PATH``
+
+6. Install "libtorch-win-shared-with-deps-latest.zip"
+
+    - Select build targeting the appropriate CUDA version
+    - Set ``Torch_DIR``
+    - Add ``Torch_DIR\lib`` to ``PATH``
+
+7. Clone TensorRT repo
+8. Install C++ and CMake Tools extensions from MS
+
+    - Change build to ``RelWithDebInfo``
+
+9. Update ``.vscode\settings.json``
+
+    - Clean, configure, build
+
+e.g. /.vscode/settings.json
+
+.. code-block:: json
+
+    {
+        "cmake.generator": "Ninja",
+        "cmake.configureSettings": {
+            "CMAKE_MODULE_PATH": {
+                "type": "FILEPATH",
+                "value": "$PWD\\cmake\\Modules"
+            },
+            "CMAKE_CXX_FLAGS": {
+                "type": "STRING",
+                "value": "-D_SILENCE_EXPERIMENTAL_FILESYSTEM_DEPRECATION_WARNING"
+            },
+            "Torch_DIR": {
+                "type": "FILEPATH",
+                "value": "X:\\libtorch\\share\\cmake\\Torch"
+            },
+            "TensorRT_ROOT": {
+                "type": "FILEPATH",
+                "value": "X:\\path\\to\\tensorrt"
+            },
+            "cuDNN_ROOT_DIR": {
+                "type": "FILEPATH",
+                "value": "X:\\path\\to\\cudnn"
+            },
+            "CMAKE_CUDA_FLAGS": "-allow-unsupported-compiler"
+        },
+        "cmake.buildDirectory": "${workspaceFolder}/torch_tensorrt_build"
+    }
diff --git a/docs/v2.2.0/_sources/getting_started/installation.rst.txt b/docs/v2.2.0/_sources/getting_started/installation.rst.txt
new file mode 100644
index 0000000000..9f0088c3b8
--- /dev/null
+++ b/docs/v2.2.0/_sources/getting_started/installation.rst.txt
@@ -0,0 +1,317 @@
+.. _installation:
+
+Installation
+=============
+
+Precompiled Binaries
+*********************
+
+Torch-TensorRT 2.x is centered primarily around Python. As such, precompiled releases can be found on pypi.org
+
+Dependencies
+---------------
+
+You need to have CUDA, PyTorch, and TensorRT (python package is sufficient) installed to use Torch-TensorRT
+
+    * https://developer.nvidia.com/cuda
+    * https://pytorch.org
+
+
+Installing Torch-TensorRT
+---------------------------
+
+You can install the python package using
+
+.. code-block:: sh
+
+    python -m pip install torch torch-tensorrt tensorrt
+
+Installing Torch-TensorRT for a specific CUDA version
+--------------------------------------------------------
+
+Similar to PyTorch, Torch-TensorRT has builds compiled for different versions of CUDA. These are distributed on PyTorch's package index
+
+For example CUDA 11.8
+
+.. code-block:: sh
+
+    python -m pip install torch torch-tensorrt tensorrt --extra-index-url https://download.pytorch.org/whl/cu118
+
+Installing Nightly Builds
+---------------------------
+
+Torch-TensorRT distributed nightlies targeting the PyTorch nightly. These can be installed from the PyTorch nightly package index (separated by CUDA version)
+
+.. code-block:: sh
+
+    python -m pip install --pre torch torch-tensorrt tensorrt --extra-index-url https://download.pytorch.org/whl/nightly/cu121
+
+
+
+.. _bin-dist:
+
+C++ Precompiled Binaries (TorchScript Only)
+--------------------------------------------------
+
+Precompiled tarballs for releases are provided here: https://github.com/pytorch/TensorRT/releases
+
+.. _compile-from-source:
+
+Compiling From Source
+******************************************
+
+.. _installing-deps:
+
+Dependencies for Compilation
+-------------------------------
+
+* Torch-TensorRT is built with **Bazel**, so begin by installing it.
+
+    * The easiest way is to install bazelisk using the method of your choosing https://github.com/bazelbuild/bazelisk
+    * Otherwise you can use the following instructions to install binaries https://docs.bazel.build/versions/master/install.html
+    * Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions
+
+    .. code-block:: shell
+
+        export BAZEL_VERSION=$(cat <PATH_TO_TORCHTRT_ROOT>/.bazelversion)
+        mkdir bazel
+        cd bazel
+        curl -fSsL -O https://github.com/bazelbuild/bazel/releases/download/$BAZEL_VERSION/bazel-$BAZEL_VERSION-dist.zip
+        unzip bazel-$BAZEL_VERSION-dist.zip
+        bash ./compile.sh
+        cp output/bazel /usr/local/bin/
+
+
+* You will also need to have **CUDA** installed on the system (or if running in a container, the system must have the CUDA driver installed and the container must have CUDA)
+
+    * Specify your CUDA version here if not the version used in the branch being built: https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L46
+
+
+* The correct **LibTorch** version will be pulled down for you by bazel.
+
+    NOTE: By default bazel will pull the latest nightly from pytorch.org. For building main, this is usually sufficient however if there is a specific PyTorch you are targeting,
+    edit these locations with updated URLs/paths:
+
+    * https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L53C1-L53C1
+
+
+* **cuDNN and TensorRT** are not required to be installed on the system to build Torch-TensorRT, in fact this is preferable to ensure reproducable builds. Download the tarballs for cuDNN and TensorRT from https://developer.nvidia.com and update the paths in the WORKSPACE file here https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L71
+
+    For example:
+
+    .. code-block:: python
+
+        http_archive(
+            name = "cudnn",
+            build_file = "@//third_party/cudnn/archive:BUILD",
+            sha256 = "79d77a769c7e7175abc7b5c2ed5c494148c0618a864138722c887f95c623777c",
+            strip_prefix = "cudnn-linux-x86_64-8.8.1.3_cuda12-archive",
+            urls = [
+                #"https://developer.nvidia.com/downloads/compute/cudnn/secure/8.8.1/local_installers/12.0/cudnn-linux-x86_64-8.8.1.3_cuda12-archive.tar.xz",
+                "file:///<ABSOLUTE PATH TO FILE>/cudnn-linux-x86_64-8.8.1.3_cuda12-archive.tar.xz"
+            ],
+        )
+
+        http_archive(
+            name = "tensorrt",
+            build_file = "@//third_party/tensorrt/archive:BUILD",
+            sha256 = "0f8157a5fc5329943b338b893591373350afa90ca81239cdadd7580cd1eba254",
+            strip_prefix = "TensorRT-8.6.1.6",
+            urls = [
+                #"https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/tars/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz",
+                "file:///<ABSOLUTE PATH TO FILE>/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz"
+            ],
+        )
+
+If you have a local version of cuDNN and TensorRT installed, this can be used as well by commenting out the above lines and uncommenting the following lines https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L114C1-L124C3
+
+
+Building the Package
+---------------------
+
+Once the WORKSPACE has been configured properly, all that is required to build torch-tensorrt is the following command
+
+    .. code-block:: sh
+
+        python -m pip install --pre . --extra-index-url https://download.pytorch.org/whl/nightly/cu121
+
+
+To build the wheel file
+
+    .. code-block:: sh
+
+        python -m pip wheel --no-deps --pre . --extra-index-url https://download.pytorch.org/whl/nightly/cu121 -w dist
+
+
+Building the C++ Library (TorchScript Only)
+------------------------------
+
+Release Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c opt
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+.. _build-from-archive-debug:
+
+Debug Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To build with debug symbols use the following command
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt -c dbg
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+Pre CXX11 ABI Build
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+To build using the pre-CXX11 ABI use the ``pre_cxx11_abi`` config
+
+.. code-block:: shell
+
+    bazel build //:libtorchtrt --config pre_cxx11_abi -c [dbg/opt]
+
+A tarball with the include files and library can then be found in ``bazel-bin``
+
+
+.. _abis:
+
+Choosing the Right ABI
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
+which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
+the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
+other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
+libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
+using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
+recommended commands:
+
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch Source                                              | Recommended Python Compilation Command                   | Recommended C++ Compilation Command                                |
++=============================================================+==========================================================+====================================================================+
+| PyTorch whl file from PyTorch.org                           | python -m pip install .                                  | bazel build //:libtorchtrt -c opt --config pre_cxx11_abi           |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| libtorch-shared-with-deps-*.zip from PyTorch.org            | python -m pip install .                                  | bazel build //:libtorchtrt -c opt --config pre_cxx11_abi           |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| libtorch-cxx11-abi-shared-with-deps-*.zip from PyTorch.org  | python setup.py bdist_wheel --use-cxx11-abi              | bazel build //:libtorchtrt -c opt                                  |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch preinstalled in an NGC container                    | python setup.py bdist_wheel --use-cxx11-abi              | bazel build //:libtorchtrt -c opt                                  |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch from the NVIDIA Forums for Jetson                   | python setup.py bdist_wheel --use-cxx11-abi              | bazel build //:libtorchtrt -c opt                                  |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+| PyTorch built from Source                                   | python setup.py bdist_wheel --use-cxx11-abi              | bazel build //:libtorchtrt -c opt                                  |
++-------------------------------------------------------------+----------------------------------------------------------+--------------------------------------------------------------------+
+
+    NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information
+
+**Building with CMake** (TorchScript Only)
+-------------------------------------------
+
+It is possible to build the API libraries (in cpp/) and the torchtrtc executable using CMake instead of Bazel.
+Currently, the python API and the tests cannot be built with CMake.
+Begin by installing CMake.
+
+    * Latest releases of CMake and instructions on how to install are available for different platforms
+      [on their website](https://cmake.org/download/).
+
+A few useful CMake options include:
+
+    * CMake finders for TensorRT and cuDNN are provided in `cmake/Modules`. In order for CMake to use them, pass
+      `-DCMAKE_MODULE_PATH=cmake/Modules` when configuring the project with CMake.
+    * Libtorch provides its own CMake finder. In case CMake doesn't find it, pass the path to your install of
+      libtorch with `-DTorch_DIR=<path to libtorch>/share/cmake/Torch`
+    * If TensorRT is not found with the provided cmake finder, specify `-DTensorRT_ROOT=<path to TensorRT>`
+    * Finally, configure and build the project in a build directory of your choice with the following command
+      from the root of Torch-TensorRT project:
+
+    .. code-block:: shell
+
+        cmake -S. -B<build directory> \
+            [-DCMAKE_MODULE_PATH=cmake/Module] \
+            [-DTorch_DIR=<path to libtorch>/share/cmake/Torch] \
+            [-DTensorRT_ROOT=<path to TensorRT>] \
+            [-DCMAKE_BUILD_TYPE=Debug|Release]
+        cmake --build <build directory>
+
+**Building Natively on aarch64 (Jetson)**
+-------------------------------------------
+
+Prerequisites
+^^^^^^^^^^^^^^
+
+Install or compile a build of PyTorch/LibTorch for aarch64
+
+NVIDIA hosts builds the latest release branch for Jetson here:
+
+    https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048
+
+
+Enviorment Setup
+^^^^^^^^^^^^^^^^^
+
+To build natively on aarch64-linux-gnu platform, configure the ``WORKSPACE`` with local available dependencies.
+
+1. Replace ``WORKSPACE`` with the corresponding WORKSPACE file in ``//toolchains/jp_workspaces``
+
+2. Configure the correct paths to directory roots containing local dependencies in the ``new_local_repository`` rules:
+
+    NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package.
+    In the case that you installed with ``sudo pip install`` this will be ``/usr/local/lib/python3.8/dist-packages/torch``.
+    In the case you installed with ``pip install --user`` this will be ``$HOME/.local/lib/python3.8/site-packages/torch``.
+
+In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
+PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to
+use that library, set the paths to the same path but when you compile make sure to add the flag ``--config=pre_cxx11_abi``
+
+.. code-block:: shell
+
+    new_local_repository(
+        name = "libtorch",
+        path = "/usr/local/lib/python3.8/dist-packages/torch",
+        build_file = "third_party/libtorch/BUILD"
+    )
+
+    new_local_repository(
+        name = "libtorch_pre_cxx11_abi",
+        path = "/usr/local/lib/python3.8/dist-packages/torch",
+        build_file = "third_party/libtorch/BUILD"
+    )
+
+
+Compile C++ Library and Compiler CLI
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+    NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version
+
+    .. code-block:: shell
+
+        --platforms //toolchains:jetpack_x.x
+
+
+Compile Torch-TensorRT library using bazel command:
+
+.. code-block:: shell
+
+   bazel build //:libtorchtrt --platforms //toolchains:jetpack_5.0
+
+Compile Python API
+^^^^^^^^^^^^^^^^^^^^
+
+    NOTE: Due to shifting dependencies locations between Jetpack 4.5 and newer Jetpack verisons there is now a flag for ``setup.py`` which sets the jetpack version (default: 5.0)
+
+Compile the Python API using the following command from the ``//py`` directory:
+
+.. code-block:: shell
+
+    python3 setup.py install --use-cxx11-abi
+
+If you have a build of PyTorch that uses Pre-CXX11 ABI drop the ``--use-cxx11-abi`` flag
+
+If you are building for Jetpack 4.5 add the ``--jetpack-version 5.0`` flag
diff --git a/docs/v2.2.0/_sources/index.rst.txt b/docs/v2.2.0/_sources/index.rst.txt
new file mode 100644
index 0000000000..455aeab8b3
--- /dev/null
+++ b/docs/v2.2.0/_sources/index.rst.txt
@@ -0,0 +1,210 @@
+.. Torch-TensorRT documentation master file, created by
+   sphinx-quickstart on Mon May  4 13:43:16 2020.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Torch-TensorRT
+==============
+
+In-framework compilation of PyTorch inference code for NVIDIA GPUs
+--------------------------------------------------------------------------
+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 ``torch.compile`` 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.
+
+More Information / System Architecture:
+
+* `Torch-TensorRT 2.0 <https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51714/>`_
+
+Getting Started
+----------------
+* :ref:`installation`
+
+.. toctree::
+   :caption: Getting Started
+   :maxdepth: 1
+   :hidden:
+
+   getting_started/installation
+   getting_started/getting_started_with_windows
+
+
+Dynamo Frontend
+----------------
+
+* :ref:`torch_compile`
+* :ref:`dynamo_export`
+
+.. toctree::
+   :caption: Dynamo Frontend
+   :maxdepth: 1
+   :hidden:
+
+   dynamo/torch_compile
+   dynamo/dynamo_export
+
+TorchScript Frontend
+-----------------------
+* :ref:`creating_a_ts_mod`
+* :ref:`getting_started_with_python_api`
+* :ref:`getting_started_cpp`
+* :ref:`use_from_pytorch`
+
+.. toctree::
+   :caption: TorchScript Frontend
+   :maxdepth: 1
+   :hidden:
+
+   ts/creating_torchscript_module_in_python
+   ts/getting_started_with_python_api
+   ts/getting_started_with_cpp_api
+   ts/use_from_pytorch
+
+FX Frontend
+------------
+
+* :ref:`getting_started_with_fx`
+
+.. toctree::
+   :caption: FX Frontend
+   :maxdepth: 1
+   :hidden:
+
+   fx/getting_started_with_fx_path
+
+
+User Guide
+------------
+
+* :ref:`dynamic_shapes`
+* :ref:`ptq`
+* :ref:`saving_models`
+* :ref:`runtime`
+* :ref:`using_dla`
+
+.. toctree::
+   :caption: User Guide
+   :maxdepth: 1
+   :hidden:
+
+
+   user_guide/dynamic_shapes
+   user_guide/ptq
+   user_guide/saving_models
+   user_guide/runtime
+   user_guide/using_dla
+
+Tutorials
+------------
+* :ref:`torch_tensorrt_tutorials`
+* :ref:`serving_torch_tensorrt_with_triton`
+* :ref:`notebooks`
+
+.. toctree::
+   :caption: Tutorials
+   :maxdepth: 3
+   :hidden:
+
+   tutorials/serving_torch_tensorrt_with_triton
+   tutorials/notebooks
+   tutorials/_rendered_examples/dynamo/torch_compile_resnet_example
+   tutorials/_rendered_examples/dynamo/torch_compile_transformers_example
+   tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage
+   tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion
+
+Python API Documenation
+------------------------
+* :ref:`torch_tensorrt_py`
+* :ref:`torch_tensorrt_logging_py`
+* :ref:`torch_tensorrt_ptq_py`
+* :ref:`torch_tensorrt_dynamo_py`
+* :ref:`torch_tensorrt_ts_py`
+* :ref:`torch_tensorrt_fx_py`
+
+.. toctree::
+   :caption: Python API Documenation
+   :maxdepth: 0
+   :hidden:
+
+   py_api/torch_tensorrt
+   py_api/logging
+   py_api/ptq
+   py_api/dynamo
+   py_api/ts
+   py_api/fx
+
+C++ API Documenation
+----------------------
+* :ref:`namespace_torch_tensorrt`
+* :ref:`namespace_torch_tensorrt__logging`
+* :ref:`namespace_torch_tensorrt__ptq`
+* :ref:`namespace_torch_tensorrt__torchscript`
+
+
+.. toctree::
+   :caption: C++ API Documenation
+   :maxdepth: 1
+   :hidden:
+
+   _cpp_api/torch_tensort_cpp
+   _cpp_api/namespace_torch_tensorrt
+   _cpp_api/namespace_torch_tensorrt__logging
+   _cpp_api/namespace_torch_tensorrt__torchscript
+   _cpp_api/namespace_torch_tensorrt__ptq
+
+CLI Documentation
+---------------------
+* :ref:`torchtrtc`
+
+.. toctree::
+   :caption: CLI Documenation
+   :maxdepth: 0
+   :hidden:
+
+   cli/torchtrtc
+
+
+Contributor Documentation
+--------------------------------
+* :ref:`system_overview`
+* :ref:`dynamo_converters`
+* :ref:`writing_dynamo_aten_lowering_passes`
+* :ref:`ts_converters`
+* :ref:`useful_links`
+
+.. toctree::
+   :caption: Contributor Documentation
+   :maxdepth: 1
+   :hidden:
+
+   contributors/system_overview
+   contributors/dynamo_converters
+   contributors/writing_dynamo_aten_lowering_passes
+   contributors/ts_converters
+   contributors/useful_links
+
+Indices
+----------------
+* :ref:`supported_ops`
+* :ref:`genindex`
+* :ref:`search`
+
+.. toctree::
+   :caption: Indices
+   :maxdepth: 1
+   :hidden:
+
+   indices/supported_ops
+
+
+Legacy Further Information (TorchScript)
+-------------------------------------------
+
+* `Introductory Blog Post <https://developer.nvidia.com/blog/accelerating-inference-up-to-6x-faster-in-pytorch-with-torch-tensorrt/>`_
+* `GTC 2020 Talk <https://www.nvidia.com/en-us/on-demand/session/gtcsj20-s21671/>`_
+* `GTC 2020 Fall Talk <https://www.nvidia.com/en-us/on-demand/session/gtcfall20-a21864/>`_
+* `GTC 2021 Talk <https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31864/>`_
+* `GTC 2021 Fall Talk <https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31107/>`_
+* `PyTorch Ecosystem Day 2021 <https://assets.pytorch.org/pted2021/posters/I6.png>`_
+* `PyTorch Developer Conference 2021 <https://s3.amazonaws.com/assets.pytorch.org/ptdd2021/posters/D2.png>`_
+* `PyTorch Developer Conference 2022 <https://pytorch.s3.amazonaws.com/posters/ptc2022/C04.pdf>`_
diff --git a/docs/v2.2.0/_sources/indices/supported_ops.rst.txt b/docs/v2.2.0/_sources/indices/supported_ops.rst.txt
new file mode 100644
index 0000000000..0367c02710
--- /dev/null
+++ b/docs/v2.2.0/_sources/indices/supported_ops.rst.txt
@@ -0,0 +1,285 @@
+
+.. _supported_ops:
+
+=================================
+Operators Supported
+=================================
+
+
+Operators Currently Supported Through Converters
+-------------------------------------------------
+
+- aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -> (Tensor)
+- aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -> (Tensor)
+- aten::abs(Tensor self) -> (Tensor)
+- aten::acos(Tensor self) -> (Tensor)
+- aten::acosh(Tensor self) -> (Tensor)
+- aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -> (Tensor)
+- aten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -> (Tensor)
+- aten::adaptive_avg_pool3d(Tensor self, int[3] output_size) -> (Tensor)
+- aten::adaptive_max_pool1d(Tensor self, int[2] output_size) -> (Tensor, Tensor)
+- aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -> (Tensor, Tensor)
+- aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -> (Tensor, Tensor)
+- aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
+- aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
+- aten::add_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> (Tensor(a!))
+- aten::argmax(Tensor self, int dim, bool keepdim=False) -> (Tensor)
+- aten::argmin(Tensor self, int dim, bool keepdim=False) -> (Tensor)
+- aten::asin(Tensor self) -> (Tensor)
+- aten::asinh(Tensor self) -> (Tensor)
+- aten::atan(Tensor self) -> (Tensor)
+- aten::atanh(Tensor self) -> (Tensor)
+- aten::avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[0], bool ceil_mode=False, bool count_include_pad=True) -> (Tensor)
+- aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> (Tensor)
+- aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -> (Tensor)
+- aten::batch_norm(Tensor input, Tensor? gamma, Tensor? beta, Tensor? mean, Tensor? var, bool training, float momentum, float eps, bool cudnn_enabled) -> (Tensor)
+- aten::bitwise_not(Tensor self) -> (Tensor)
+- aten::bmm(Tensor self, Tensor mat2) -> (Tensor)
+- aten::cat(Tensor[] tensors, int dim=0) -> (Tensor)
+- aten::ceil(Tensor self) -> (Tensor)
+- aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -> (Tensor)
+- aten::clamp_max(Tensor self, Scalar max) -> (Tensor)
+- aten::clamp_min(Tensor self, Scalar min) -> (Tensor)
+- aten::constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -> (Tensor)
+- aten::cos(Tensor self) -> (Tensor)
+- aten::cosh(Tensor self) -> (Tensor)
+- aten::cumsum(Tensor self, int dim, *, int? dtype=None) -> (Tensor)
+- aten::div.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::div.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::div.Tensor_mode(Tensor self, Tensor other, *, str? rounding_mode) -> (Tensor)
+- aten::div_.Scalar(Tensor(a!) self, Scalar other) -> (Tensor(a!))
+- aten::div_.Tensor(Tensor(a!) self, Tensor other) -> (Tensor(a!))
+- aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -> (Tensor)
+- aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -> (Tensor)
+- aten::eq.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::eq.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::erf(Tensor self) -> (Tensor)
+- aten::exp(Tensor self) -> (Tensor)
+- aten::expand(Tensor(a) self, int[] size, *, bool implicit=False) -> (Tensor(a))
+- aten::expand_as(Tensor(a) self, Tensor other) -> (Tensor(a))
+- aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> (Tensor)
+- aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -> (Tensor)
+- aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)
+- aten::floor(Tensor self) -> (Tensor)
+- aten::floor_divide(Tensor self, Tensor other) -> (Tensor)
+- aten::floor_divide.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::ge.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::ge.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor)
+- aten::gt.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::gt.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -> (Tensor)
+- aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -> (Tensor(a!))
+- aten::index.Tensor(Tensor self, Tensor?[] indices) -> (Tensor)
+- aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> (Tensor)
+- aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? gamma, Tensor? beta, float eps, bool cudnn_enabled) -> (Tensor)
+- aten::le.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::le.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> (Tensor)
+- aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -> (Tensor(a!))
+- aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> (Tensor)
+- aten::log(Tensor self) -> (Tensor)
+- aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)
+- aten::lt.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::lt.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -> (Tensor)
+- aten::matmul(Tensor self, Tensor other) -> (Tensor)
+- aten::max(Tensor self) -> (Tensor)
+- aten::max.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
+- aten::max.other(Tensor self, Tensor other) -> (Tensor)
+- aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[], int[1] dilation=[], bool ceil_mode=False) -> (Tensor)
+- aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], int[2] dilation=[1, 1], bool ceil_mode=False) -> (Tensor)
+- aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], int[3] dilation=[], bool ceil_mode=False) -> (Tensor)
+- aten::mean(Tensor self, *, int? dtype=None) -> (Tensor)
+- aten::mean.dim(Tensor self, int[] dim, bool keepdim=False, *, int? dtype=None) -> (Tensor)
+- aten::min(Tensor self) -> (Tensor)
+- aten::min.dim(Tensor self, int dim, bool keepdim=False) -> (Tensor values, Tensor indices)
+- aten::min.other(Tensor self, Tensor other) -> (Tensor)
+- aten::mul.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::mul.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::mul_.Tensor(Tensor(a!) self, Tensor other) -> (Tensor(a!))
+- aten::narrow(Tensor(a) self, int dim, int start, int length) -> (Tensor(a))
+- aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -> (Tensor(a))
+- aten::ne.Scalar(Tensor self, Scalar other) -> (Tensor)
+- aten::ne.Tensor(Tensor self, Tensor other) -> (Tensor)
+- aten::neg(Tensor self) -> (Tensor)
+- aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -> (Tensor)
+- aten::permute(Tensor(a) self, int[] dims) -> (Tensor(a))
+- aten::pixel_shuffle(Tensor self, int upscale_factor) -> (Tensor)
+- aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -> (Tensor)
+- aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -> (Tensor)
+- aten::prelu(Tensor self, Tensor weight) -> (Tensor)
+- aten::prod(Tensor self, *, int? dtype=None) -> (Tensor)
+- aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, int? dtype=None) -> (Tensor)
+- aten::reciprocal(Tensor self) -> (Tensor)
+- aten::reflection_pad1d(Tensor self, int[2] padding) -> (Tensor)
+- aten::reflection_pad2d(Tensor self, int[4] padding) -> (Tensor)
+- aten::relu(Tensor input) -> (Tensor)
+- aten::relu_(Tensor(a!) self) -> (Tensor(a!))
+- aten::repeat(Tensor self, int[] repeats) -> (Tensor)
+- aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, *, int? output_size=None) -> (Tensor)
+- aten::replication_pad1d(Tensor self, int[2] padding) -> (Tensor)
+- aten::replication_pad2d(Tensor self, int[4] padding) -> (Tensor)
+- aten::replication_pad3d(Tensor self, int[6] padding) -> (Tensor)
+- aten::reshape(Tensor self, int[] shape) -> (Tensor)
+- aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -> (Tensor)
+- aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
+- aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
+- aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -> (Tensor)
+- aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -> (Tensor)
+- aten::select.int(Tensor(a) self, int dim, int index) -> (Tensor(a))
+- aten::sigmoid(Tensor input) -> (Tensor)
+- aten::sigmoid_(Tensor(a!) self) -> (Tensor(a!))
+- aten::sin(Tensor self) -> (Tensor)
+- aten::sinh(Tensor self) -> (Tensor)
+- aten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -> (Tensor(a))
+- aten::softmax.int(Tensor self, int dim, int? dtype=None) -> (Tensor)
+- aten::split(Tensor self, int[] split_sizes, int dim=0) -> (Tensor[])
+- aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -> (Tensor[])
+- aten::split.sizes(Tensor(a -> *) self, int[] split_size, int dim=0) -> (Tensor[])
+- aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -> (Tensor[])
+- aten::sqrt(Tensor self) -> (Tensor)
+- aten::square(Tensor self) -> (Tensor)
+- aten::squeeze.dim(Tensor(a) self, int dim) -> (Tensor(a))
+- aten::stack(Tensor[] tensors, int dim=0) -> (Tensor)
+- aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -> (Tensor)
+- aten::sub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -> (Tensor)
+- aten::sub_.Tensor(Tensor(a!) self, Tensor other, *, Scalar alpha=1) -> (Tensor(a!))
+- aten::sum(Tensor self, *, int? dtype=None) -> (Tensor)
+- aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, int? dtype=None) -> (Tensor)
+- aten::t(Tensor self) -> (Tensor)
+- aten::tan(Tensor self) -> (Tensor)
+- aten::tanh(Tensor input) -> (Tensor)
+- aten::tanh_(Tensor(a!) self) -> (Tensor(a!))
+- aten::to.device(Tensor(a) self, Device device, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor(a))
+- aten::to.dtype(Tensor self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor)
+- aten::to.other(Tensor self, Tensor other, bool non_blocking=False, bool copy=False, int? memory_format=None) -> (Tensor)
+- aten::to.prim_Device(Tensor(a) self, Device? device, int? dtype=None, bool non_blocking=False, bool copy=False) -> (Tensor(a|b))
+- aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -> (Tensor values, Tensor indices)
+- aten::transpose.int(Tensor(a) self, int dim0, int dim1) -> (Tensor(a))
+- aten::unbind.int(Tensor(a -> *) self, int dim=0) -> (Tensor[])
+- aten::unsqueeze(Tensor(a) self, int dim) -> (Tensor(a))
+- aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -> (Tensor)
+- aten::upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> (Tensor)
+- aten::upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -> (Tensor)
+- aten::upsample_linear1d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> (Tensor)
+- aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -> (Tensor)
+- aten::upsample_nearest1d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)
+- aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> (Tensor)
+- aten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)
+- aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> (Tensor)
+- aten::upsample_nearest3d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -> (Tensor)
+- aten::upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> (Tensor)
+- aten::upsample_trilinear3d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -> (Tensor)
+- aten::view(Tensor(a) self, int[] size) -> (Tensor(a))
+- trt::const(Tensor self) -> (Tensor)
+
+Operators Currently Supported Through Evaluators
+-------------------------------------------------
+
+- aten::Bool.float(float b) -> (bool)
+- aten::Bool.int(int a) -> (bool)
+- aten::Float.Scalar(Scalar a) -> float
+- aten::Float.bool(bool a) -> float
+- aten::Float.int(int a) -> float
+- aten::Int.Scalar(Scalar a) -> int
+- aten::Int.bool(bool a) -> int
+- aten::Int.float(float a) -> int
+- aten::Int.int(int a) -> int
+- aten::__and__(int a, int b) -> (bool)
+- aten::__and__.bool(bool a, bool b) -> (bool)
+- aten::__derive_index(int idx, int start, int step) -> int
+- aten::__getitem__.t(t[](a) list, int idx) -> (t(*))
+- aten::__is__(t1 self, t2 obj) -> bool
+- aten::__isnot__(t1 self, t2 obj) -> bool
+- aten::__not__(bool self) -> bool
+- aten::__or__(int a, int b) -> (bool)
+- aten::__range_length(int lo, int hi, int step) -> int
+- aten::__round_to_zero_floordiv(int a, int b) -> (int)
+- aten::__xor__(int a, int b) -> (bool)
+- aten::add.float(float a, float b) -> (float)
+- aten::add.int(int a, int b) -> (int)
+- aten::add.str(str a, str b) -> (str)
+- aten::add_.t(t[](a!) self, t[] b) -> (t[])
+- aten::append.t(t[](a!) self, t(c -> *) el) -> (t[](a!))
+- aten::arange(Scalar end, *, int? dtype=None, int? layout=None,
+                            Device? device=None, bool? pin_memory=None) -> (Tensor)
+- aten::arange.start(Scalar start, Scalar end, *, ScalarType? dtype=None,
+                            Layout? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
+- aten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None,
+                        Layout? layout=None, Device? device=None, bool? pin_memory=None) -> (Tensor)
+- aten::clone(Tensor self, *, int? memory_format=None) -> (Tensor)
+- aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -> (Tensor(a!))
+- aten::dim(Tensor self) -> int
+- aten::div.float(float a, float b) -> (float)
+- aten::div.int(int a, int b) -> (float)
+- aten::eq.bool(bool a, bool b) -> (bool)
+- aten::eq.float(float a, float b) -> (bool)
+- aten::eq.float_int(float a, int b) -> (bool)
+- aten::eq.int(int a, int b) -> (bool)
+- aten::eq.int_float(int a, float b) -> (bool)
+- aten::eq.str(str a, str b) -> (bool)
+- aten::extend.t(t[](a!) self, t[] other) -> ()
+- aten::floor.float(float a) -> (int)
+- aten::floor.int(int a) -> (int)
+- aten::floordiv.float(float a, float b) -> (int)
+- aten::floordiv.int(int a, int b) -> (int)
+- aten::format(str self, ...) -> (str)
+- aten::ge.bool(bool a, bool b) -> (bool)
+- aten::ge.float(float a, float b) -> (bool)
+- aten::ge.float_int(float a, int b) -> (bool)
+- aten::ge.int(int a, int b) -> (bool)
+- aten::ge.int_float(int a, float b) -> (bool)
+- aten::gt.bool(bool a, bool b) -> (bool)
+- aten::gt.float(float a, float b) -> (bool)
+- aten::gt.float_int(float a, int b) -> (bool)
+- aten::gt.int(int a, int b) -> (bool)
+- aten::gt.int_float(int a, float b) -> (bool)
+- aten::is_floating_point(Tensor self) -> (bool)
+- aten::le.bool(bool a, bool b) -> (bool)
+- aten::le.float(float a, float b) -> (bool)
+- aten::le.float_int(float a, int b) -> (bool)
+- aten::le.int(int a, int b) -> (bool)
+- aten::le.int_float(int a, float b) -> (bool)
+- aten::len.t(t[] a) -> (int)
+- aten::lt.bool(bool a, bool b) -> (bool)
+- aten::lt.float(float a, float b) -> (bool)
+- aten::lt.float_int(float a, int b) -> (bool)
+- aten::lt.int(int a, int b) -> (bool)
+- aten::lt.int_float(int a, float b) -> (bool)
+- aten::mul.float(float a, float b) -> (float)
+- aten::mul.int(int a, int b) -> (int)
+- aten::ne.bool(bool a, bool b) -> (bool)
+- aten::ne.float(float a, float b) -> (bool)
+- aten::ne.float_int(float a, int b) -> (bool)
+- aten::ne.int(int a, int b) -> (bool)
+- aten::ne.int_float(int a, float b) -> (bool)
+- aten::neg.int(int a) -> (int)
+- aten::numel(Tensor self) -> int
+- aten::pow.float(float a, float b) -> (float)
+- aten::pow.float_int(float a, int b) -> (float)
+- aten::pow.int(int a, int b) -> (float)
+- aten::pow.int_float(int a, float b) -> (float)
+- aten::size(Tensor self) -> (int[])
+- aten::size.int(Tensor self, int dim) -> (int)
+- aten::slice.t(t[] l, int start, int end=9223372036854775807, int step=1) -> (t[])
+- aten::sqrt.float(float a) -> (float)
+- aten::sqrt.int(int a) -> (float)
+- aten::sub.float(float a, float b) -> (float)
+- aten::sub.int(int a, int b) -> (int)
+- aten::tensor(t[] data, *, int? dtype=None, Device? device=None, bool requires_grad=False) -> (Tensor)
+- prim::TupleIndex(Any tup, int i) -> (Any)
+- prim::dtype(Tensor a) -> (int)
+- prim::max.bool(bool a, bool b) -> (bool)
+- prim::max.float(float a, float b) -> (bool)
+- prim::max.float_int(float a, int b) -> (bool)
+- prim::max.int(int a, int b) -> (bool)
+- prim::max.int_float(int a, float b) -> (bool)
+- prim::max.self_int(int[] self) -> (int)
+- prim::min.bool(bool a, bool b) -> (bool)
+- prim::min.float(float a, float b) -> (bool)
+- prim::min.float_int(float a, int b) -> (bool)
+- prim::min.int(int a, int b) -> (bool)
+- prim::min.int_float(int a, float b) -> (bool)
+- prim::min.self_int(int[] self) -> (int)
+- prim::shape(Tensor a) -> (int[])
diff --git a/docs/v2.2.0/_sources/py_api/dynamo.rst.txt b/docs/v2.2.0/_sources/py_api/dynamo.rst.txt
new file mode 100644
index 0000000000..fce5372d0e
--- /dev/null
+++ b/docs/v2.2.0/_sources/py_api/dynamo.rst.txt
@@ -0,0 +1,36 @@
+.. _torch_tensorrt_dynamo_py:
+
+torch_tensorrt.dynamo
+===================
+
+.. currentmodule:: torch_tensorrt.dynamo
+
+.. automodule torch_tensorrt.ts
+   :undoc-members:
+
+.. automodule:: torch_tensorrt.dynamo
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Functions
+------------
+
+.. autofunction:: compile
+
+.. autofunction:: trace
+
+.. autofunction:: export
+
+
+
+Classes
+--------
+
+.. autoclass:: CompilationSettings
+
+.. autoclass:: SourceIR
+
+.. autoclass:: runtime.TorchTensorRTModule
+
+.. autoclass:: runtime.PythonTorchTensorRTModule
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/py_api/fx.rst.txt b/docs/v2.2.0/_sources/py_api/fx.rst.txt
new file mode 100644
index 0000000000..8ce591f5ee
--- /dev/null
+++ b/docs/v2.2.0/_sources/py_api/fx.rst.txt
@@ -0,0 +1,31 @@
+.. _torch_tensorrt_fx_py:
+
+torch_tensorrt.fx
+===================
+
+.. currentmodule:: torch_tensorrt.fx
+
+.. automodule torch_tensorrt.ts
+   :undoc-members:
+
+.. automodule:: torch_tensorrt.fx
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Functions
+------------
+
+.. autofunction:: compile
+
+
+Classes
+--------
+
+.. autoclass:: TRTModule
+
+.. autoclass:: InputTensorSpec
+
+.. autoclass:: TRTInterpreter
+
+.. autoclass:: TRTInterpreterResult
\ No newline at end of file
diff --git a/docs/v2.2.0/_sources/py_api/logging.rst.txt b/docs/v2.2.0/_sources/py_api/logging.rst.txt
new file mode 100644
index 0000000000..7918fe7f86
--- /dev/null
+++ b/docs/v2.2.0/_sources/py_api/logging.rst.txt
@@ -0,0 +1,13 @@
+.. _torch_tensorrt_logging_py:
+
+torch_tensorrt.logging
+----------------------
+
+.. currentmodule:: torch_tensorrt.logging
+
+.. automodule:: torch_tensorrt.logging
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. autoclass:: py torch_tensorrt.logging.Level
diff --git a/docs/v2.2.0/_sources/py_api/ptq.rst.txt b/docs/v2.2.0/_sources/py_api/ptq.rst.txt
new file mode 100644
index 0000000000..ec83662efb
--- /dev/null
+++ b/docs/v2.2.0/_sources/py_api/ptq.rst.txt
@@ -0,0 +1,27 @@
+.. _torch_tensorrt_ptq_py:
+
+torch_tensorrt.ptq
+===================
+
+.. currentmodule:: torch_tensorrt.ptq
+
+.. automodule:: torch_tensorrt.ptq
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Classes
+---------
+
+.. autoclass:: DataLoaderCalibrator
+   :members:
+   :special-members: __init__
+
+.. autoclass:: CacheCalibrator
+   :members:
+   :special-members: __init__
+
+Enums
+-------
+
+.. autoclass:: CalibrationAlgo
diff --git a/docs/v2.2.0/_sources/py_api/torch_tensorrt.rst.txt b/docs/v2.2.0/_sources/py_api/torch_tensorrt.rst.txt
new file mode 100644
index 0000000000..22fda13ba2
--- /dev/null
+++ b/docs/v2.2.0/_sources/py_api/torch_tensorrt.rst.txt
@@ -0,0 +1,65 @@
+.. _torch_tensorrt_py:
+
+torch_tensorrt
+===============
+
+.. automodule torch_tensorrt
+   :undoc-members:
+
+
+
+.. automodule:: torch_tensorrt
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Functions
+------------
+
+.. autofunction:: set_device
+
+.. autofunction:: compile
+
+.. autofunction:: convert_method_to_trt_engine
+
+.. autofunction:: get_build_info
+
+.. autofunction:: dump_build_info
+
+Classes
+---------
+
+.. autoclass:: Input
+   :members:
+   :special-members: __init__
+
+.. autoclass:: Device
+   :members:
+   :special-members: __init__
+
+.. autoclass:: TRTModuleNext
+   :members:
+   :special-members: __init__
+
+Enums
+-------
+
+.. autoclass:: dtype
+
+.. autoclass:: DeviceType
+
+.. autoclass:: EngineCapability
+
+.. autoclass:: TensorFormat
+
+Submodules
+----------
+
+.. toctree::
+   :maxdepth: 1
+
+   logging
+   ptq
+   ts
+   fx
+   dynamo
diff --git a/docs/v2.2.0/_sources/py_api/ts.rst.txt b/docs/v2.2.0/_sources/py_api/ts.rst.txt
new file mode 100644
index 0000000000..45f6418eb7
--- /dev/null
+++ b/docs/v2.2.0/_sources/py_api/ts.rst.txt
@@ -0,0 +1,27 @@
+.. _torch_tensorrt_ts_py:
+
+torch_tensorrt.ts
+===================
+
+.. currentmodule:: torch_tensorrt.ts
+
+.. automodule torch_tensorrt.ts
+   :undoc-members:
+
+.. automodule:: torch_tensorrt.ts
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Functions
+------------
+
+.. autofunction:: compile
+
+.. autofunction:: convert_method_to_trt_engine
+
+.. autofunction:: check_method_op_support
+
+.. autofunction:: embed_engine_in_new_module
+
+.. autofunction:: TensorRTCompileSpec
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/changelog.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/changelog.rst.txt
new file mode 100644
index 0000000000..d1ad81a0df
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/changelog.rst.txt
@@ -0,0 +1,6 @@
+
+*********
+Changelog
+*********
+
+v0.0.1
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/configuring.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/configuring.rst.txt
new file mode 100644
index 0000000000..3afcb7bc62
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/configuring.rst.txt
@@ -0,0 +1,108 @@
+
+*************
+Configuration
+*************
+
+You can configure different parts of the theme.
+
+Project-wide Configuration
+==========================
+
+HTML Theme Options
+------------------
+
+The theme's project-wide options are defined in the ``pytorch_sphinx_theme/theme.conf``
+file of this repository, and can be defined in your project's ``conf.py`` via
+``html_theme_options``. For example:
+
+.. code:: python
+
+    html_theme_options = {
+        'canonical_url': '',
+        'analytics_id': '',
+        'logo_only': False,
+        'display_version': True,
+        'prev_next_buttons_location': 'bottom',
+        'style_external_links': False,
+        'vcs_pageview_mode': '',
+        # Toc options
+        'collapse_navigation': True,
+        'sticky_navigation': True,
+        'navigation_depth': 4,
+        'includehidden': True,
+        'titles_only': False
+    }
+
+The following options are available:
+
+Base options
+~~~~~~~~~~~~
+
+* ``canonical_url`` String. This will specify a `canonical url <https://en.wikipedia.org/wiki/Canonical_link_element>`__
+  to let search engines know they should give higher ranking to latest version of the docs.
+  The url points to the root of the documentation and requires a trailing slash.
+* ``analytics_id`` String. Change the Google Analytics ID that is included on pages.
+* ``display_version`` Bool. With this disabled, the version number isn't shown at the top of the sidebar.
+* ``prev_next_buttons_location`` String. can take the value ``bottom``, ``top``, ``both`` , or ``None``
+  and will display the "Next" and "Previous" buttons accordingly.
+* ``style_external_links`` Bool. Add an icon next to external links. Defaults to ``False``.
+* ``vcs_pageview_mode`` String. Changes how to view files when using `display_github`, `display_gitlab`, etc.
+  When using Github or Gitlab this can be: `blob` (default), `edit`, or `raw`,
+  on Bitbucket, this can be either: `view` (default) or `edit`.
+
+TOC Options
+~~~~~~~~~~~
+
+These effect how we display the Table of Contents in the side bar. You can read more about them here: http://www.sphinx-doc.org/en/stable/templating.html#toctree
+
+* ``collapse_navigation`` Bool. With this enabled, you will lose the ``[+]`` drop downs next to each section in the sidebar.
+* ``sticky_navigation`` Bool. This causes the sidebar to scroll with the main page content as you scroll the page.
+* ``navigation_depth`` Int. Indicate the max depth of the tree; by default, 4 levels are included;
+  set it to -1 to allow unlimited depth.
+* ``includehidden`` Bool. Specifies if the sidebar includes toctrees marked with the ``:hidden:`` option
+* ``titles_only`` Bool. If True, removes headers within a page from the sidebar.
+
+.. note::
+
+   Setting ``collapse_navigation`` to False and using a high ``navigation_depth``
+   can cause projects with many files and a deep file structure to generate HTML files
+   that are significantly larger in file size and much longer compilation times.
+
+
+HTML Context Options
+--------------------
+
+TODO.
+
+
+Page-level Configuration
+========================
+
+Pages support metadata that changes how the theme renders.
+You can currently add the following:
+
+* ``:github_url:`` This will force the "Edit on GitHub" to the configured URL
+* ``:bitbucket_url:`` This will force the "Edit on Bitbucket" to the configured URL
+* ``:gitlab_url:`` This will force the "Edit on GitLab" to the configured URL
+
+
+How the Table of Contents builds
+================================
+
+Currently the left menu will build based upon any ``toctree(s)`` defined in your ``index.rst`` file.
+It outputs 2 levels of depth, which should give your visitors a high level of access to your
+docs. If no toctrees are set the theme reverts to sphinx's usual local toctree.
+
+It's important to note that if you don't follow the same styling for your rST headers across
+your documents, the toctree will misbuild, and the resulting menu might not show the correct
+depth when it renders.
+
+Also note that by default the table of contents is set with ``includehidden=True``. This allows you
+to set a hidden toc in your index file with the `:hidden: <hidden_>`_ property that will allow you
+to build a toc without it rendering in your index.
+
+By default, the navigation will "stick" to the screen as you scroll. However if your toc
+is vertically too large, it will revert to static positioning. To disable the sticky nav
+altogether change the setting in ``conf.py``.
+
+.. _hidden: http://sphinx-doc.org/markup/toctree.html
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/api.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/api.rst.txt
new file mode 100644
index 0000000000..b49bc248ac
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/api.rst.txt
@@ -0,0 +1,53 @@
+
+*********************
+:mod:`test_py_module`
+*********************
+
+.. contents:: Table of Contents
+
+.. automodule:: test_py_module.test
+    :members:
+    :private-members:
+    :special-members:
+
+Generated Index
+===============
+
+Part of the sphinx build process in generate and index file: :ref:`genindex`.
+
+
+Optional parameter args
+=======================
+
+At this point optional parameters `cannot be generated from code`_.
+However, some projects will manually do it, like so:
+
+This example comes from `django-payments module docs`_.
+
+.. class:: payments.dotpay.DotpayProvider(seller_id, pin[, channel=0[, lock=False], lang='pl'])
+
+   This backend implements payments using a popular Polish gateway, `Dotpay.pl <http://www.dotpay.pl>`_.
+
+   Due to API limitations there is no support for transferring purchased items.
+
+
+   :param seller_id: Seller ID assigned by Dotpay
+   :param pin: PIN assigned by Dotpay
+   :param channel: Default payment channel (consult reference guide)
+   :param lang: UI language
+   :param lock: Whether to disable channels other than the default selected above
+
+.. _cannot be generated from code: https://groups.google.com/forum/#!topic/sphinx-users/_qfsVT5Vxpw
+.. _django-payments module docs: http://django-payments.readthedocs.org/en/latest/modules.html#payments.authorizenet.AuthorizeNetProvide
+
+
+Data
+====
+
+.. data:: Data_item_1
+          Data_item_2
+          Data_item_3
+
+    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Fusce congue elit eu hendrerit mattis.
+
+Some data link :data:`Data_item_1`.
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/demo.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/demo.rst.txt
new file mode 100644
index 0000000000..3ad6e46b15
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/demo.rst.txt
@@ -0,0 +1,475 @@
+.. This is a comment. Note how any initial comments are moved by
+   transforms to after the document title, subtitle, and docinfo.
+
+.. demo.rst from: http://docutils.sourceforge.net/docs/user/rst/demo.txt
+
+.. |EXAMPLE| image:: static/yi_jing_01_chien.jpg
+   :width: 1em
+
+**********************
+Paragraph Level Markup
+**********************
+
+.. contents:: Table of Contents
+
+Inline Markup
+=============
+
+Paragraphs contain text and may contain inline markup: *emphasis*, **strong emphasis**, ``inline literals``,
+standalone hyperlinks (http://www.python.org), external hyperlinks (Python_), internal cross-references (example_),
+external hyperlinks with embedded URIs (`Python web site <http://www.python.org>`__), footnote references
+(manually numbered [1]_, anonymous auto-numbered [#]_, labeled auto-numbered [#label]_, or symbolic [*]_),
+citation references ([12]_), substitution references (|example|), and _`inline hyperlink targets`
+(see Targets_ below for a reference back to here). Character-level inline markup is also possible
+(although exceedingly ugly!) in *re*\ ``Structured``\ *Text*. Problems are indicated by |problematic|
+text (generated by processing errors; this one is intentional).
+
+Also with ``sphinx.ext.autodoc``, which I use in the demo, I can link to :class:`test_py_module.test.Foo`.
+It will link you right my code documentation for it.
+
+The default role for interpreted text is `Title Reference`.  Here are some explicit interpreted text roles:
+a PEP reference (:PEP:`287`); an RFC reference (:RFC:`2822`); a :sub:`subscript`; a :sup:`superscript`;
+and explicit roles for :emphasis:`standard` :strong:`inline` :literal:`markup`.
+
+GUI labels are a useful way to indicate that :guilabel:`Some action` is to be taken by the user.
+The GUI label should not run over ``line-height`` so as not to :guilabel:`interfere` with text from adjacent lines.
+
+Key-bindings indicate that the read is to press a button on the keyboard or mouse,
+for example :kbd:`MMB` and :kbd:`Shift-MMB`. Another useful markup to indicate a user action
+is to use ``menuselection`` this can be used to show short and long menus in software.
+For example, and ``menuselection`` can be seen here that breaks is too long to fit on this line.
+:menuselection:`My --> Software --> Some menu --> Some sub menu 1 --> sub menu 2`.
+
+.. DO NOT RE-WRAP THE FOLLOWING PARAGRAPH!
+
+Let's test wrapping and whitespace significance in inline literals:
+``This is an example of --inline-literal --text, --including some--
+strangely--hyphenated-words.  Adjust-the-width-of-your-browser-window
+to see how the text is wrapped.  -- ---- --------  Now note    the
+spacing    between the    words of    this sentence    (words
+should    be grouped    in pairs).``
+
+If the ``--pep-references`` option was supplied, there should be a live link to PEP 258 here.
+
+Math
+====
+
+This is a test. Here is an equation:
+:math:`X_{0:5} = (X_0, X_1, X_2, X_3, X_4)`.
+Here is another:
+
+.. math::
+    :label: This is a label
+
+    \nabla^2 f =
+    \frac{1}{r^2} \frac{\partial}{\partial r}
+    \left( r^2 \frac{\partial f}{\partial r} \right) +
+    \frac{1}{r^2 \sin \theta} \frac{\partial f}{\partial \theta}
+    \left( \sin \theta \, \frac{\partial f}{\partial \theta} \right) +
+    \frac{1}{r^2 \sin^2\theta} \frac{\partial^2 f}{\partial \phi^2}
+
+You can add a link to equations like the one above :eq:`This is a label` by using ``:eq:``.
+
+Meta
+====
+
+.. meta::
+   :keywords: reStructuredText, demonstration, demo, parser
+   :description lang=en: A demonstration of the reStructuredText
+       markup language, containing examples of all basic
+       constructs and many advanced constructs.
+       
+Blocks
+======
+
+Literal Blocks
+--------------
+
+Literal blocks are indicated with a double-colon ("::") at the end of
+the preceding paragraph (over there ``-->``).  They can be indented::
+
+    if literal_block:
+        text = 'is left as-is'
+        spaces_and_linebreaks = 'are preserved'
+        markup_processing = None
+
+Or they can be quoted without indentation::
+
+>> Great idea!
+>
+> Why didn't I think of that?
+
+Line Blocks
+-----------
+
+| This is a line block.  It ends with a blank line.
+|     Each new line begins with a vertical bar ("|").
+|     Line breaks and initial indents are preserved.
+| Continuation lines are wrapped portions of long lines;
+  they begin with a space in place of the vertical bar.
+|     The left edge of a continuation line need not be aligned with
+  the left edge of the text above it.
+
+| This is a second line block.
+|
+| Blank lines are permitted internally, but they must begin with a "|".
+
+Take it away, Eric the Orchestra Leader!
+
+    | A one, two, a one two three four
+    |
+    | Half a bee, philosophically,
+    |     must, *ipso facto*, half not be.
+    | But half the bee has got to be,
+    |     *vis a vis* its entity.  D'you see?
+    |
+    | But can a bee be said to be
+    |     or not to be an entire bee,
+    |         when half the bee is not a bee,
+    |             due to some ancient injury?
+    |
+    | Singing...
+
+Block Quotes
+------------
+
+Block quotes consist of indented body elements:
+
+    My theory by A. Elk.  Brackets Miss, brackets.  This theory goes
+    as follows and begins now.  All brontosauruses are thin at one
+    end, much much thicker in the middle and then thin again at the
+    far end.  That is my theory, it is mine, and belongs to me and I
+    own it, and what it is too.
+
+    -- Anne Elk (Miss)
+
+Doctest Blocks
+--------------
+
+>>> print 'Python-specific usage examples; begun with ">>>"'
+Python-specific usage examples; begun with ">>>"
+>>> print '(cut and pasted from interactive Python sessions)'
+(cut and pasted from interactive Python sessions)
+
+Code Blocks
+-----------
+
+.. parsed-literal::
+
+    # parsed-literal test
+    curl -O http://someurl/release-|version|.tar-gz
+
+
+.. code-block:: json
+    :caption: Code Blocks can have captions.
+
+    {
+    "windows": [
+        {
+        "panes": [
+            {
+            "shell_command": [
+                "echo 'did you know'",
+                "echo 'you can inline'"
+            ]
+            },
+            {
+            "shell_command": "echo 'single commands'"
+            },
+            "echo 'for panes'"
+        ],
+        "window_name": "long form"
+        }
+    ],
+    "session_name": "shorthands"
+    }
+
+Emphasized lines with line numbers
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: python
+   :linenos:
+   :emphasize-lines: 3,5
+
+   def some_function():
+       interesting = False
+       print 'This line is highlighted.'
+       print 'This one is not...'
+       print '...but this one is.'
+
+Sidebar
+=======
+
+.. sidebar:: Ch'ien / The Creative
+
+    .. image:: static/yi_jing_01_chien.jpg
+
+    *Above* CH'IEN THE CREATIVE, HEAVEN
+
+    *Below* CH'IEN THE CREATIVE, HEAVEN
+
+The first hexagram is made up of six unbroken lines. These unbroken lines stand for the primal power,
+which is light-giving, active, strong, and of the spirit. The hexagram is consistently strong in character,
+and since it is without weakness, its essence is power or energy. Its image is heaven.
+Its energy is represented as unrestricted by any fixed conditions in space and is therefore conceived of as motion.
+Time is regarded as the basis of this motion.
+Thus the hexagram includes also the power of time and the power of persisting in time, that is, duration.
+
+The power represented by the hexagram is to be interpreted in a dual sense in terms of its action
+on the universe and of its action on the world of men. In relation to the universe, the hexagram expresses the strong,
+creative action of the Deity. In relation to the human world, it denotes the creative action of the holy man or sage,
+of the ruler or leader of men, who through his power awakens and develops their higher nature.
+
+Code with Sidebar
+-----------------
+
+.. sidebar:: A code example
+
+    With a sidebar on the right.
+
+.. literalinclude:: test_py_module/test.py
+    :language: python
+    :caption: Literal includes can also have captions.
+    :linenos:
+    :lines: 1-40
+
+References
+==========
+
+Footnotes
+---------
+
+.. [1] A footnote contains body elements, consistently indented by at
+   least 3 spaces.
+
+   This is the footnote's second paragraph.
+
+.. [#label] Footnotes may be numbered, either manually (as in [1]_) or
+   automatically using a "#"-prefixed label.  This footnote has a
+   label so it can be referred to from multiple places, both as a
+   footnote reference ([#label]_) and as a hyperlink reference
+   (label_).
+
+.. [#] This footnote is numbered automatically and anonymously using a
+   label of "#" only.
+
+.. [*] Footnotes may also use symbols, specified with a "*" label.
+   Here's a reference to the next footnote: [*]_.
+
+.. [*] This footnote shows the next symbol in the sequence.
+
+.. [4] Here's an unreferenced footnote, with a reference to a
+   nonexistent footnote: [5]_.
+
+Citations
+---------
+
+.. [11] This is the citation I made, let's make this extremely long so that we can tell that it doesn't follow the normal responsive table stuff.
+
+.. [12] This citation has some ``code blocks`` in it, maybe some **bold** and
+       *italics* too. Heck, lets put a link to a meta citation [13]_ too.
+
+.. [13] This citation will have two backlinks.
+
+
+Here's a reference to the above, [12]_, and a [nonexistent]_ citation.
+
+Here is another type of citation: `citation`
+
+Glossary
+--------
+
+This is a glossary with definition terms for thing like :term:`Writing`:
+
+.. glossary::
+  
+  Documentation
+     Provides users with the knowledge they need to use something.
+
+  Reading
+     The process of taking information into ones mind through the use of eyes.
+
+  Writing
+     The process of putting thoughts into a medium for other people to :term:`read <Reading>`.
+
+Targets
+-------
+
+.. _example:
+
+This paragraph is pointed to by the explicit "example" target.
+A reference can be found under `Inline Markup`_, above. `Inline
+hyperlink targets`_ are also possible.
+
+Section headers are implicit targets, referred to by name. See
+Targets_, which is a subsection of `Body Elements`_.
+
+Explicit external targets are interpolated into references such as "Python_".
+
+.. _Python: http://www.python.org/
+
+Targets may be indirect and anonymous.  Thus `this phrase`__ may also
+refer to the Targets_ section.
+
+__ Targets_
+
+Here's a `hyperlink reference without a target`_, which generates an error.
+
+
+Directives
+==========
+
+Contents
+--------
+
+.. contents:: :local:
+
+These are just a sample of the many reStructuredText Directives. For others, please see:
+http://docutils.sourceforge.net/docs/ref/rst/directives.html.
+
+
+Centered text
+-------------
+
+You can create a statement with centered text with ``.. centered::``
+
+.. centered:: This is centered text!
+
+Images & Figures
+----------------
+
+Images
+^^^^^^
+
+An image directive (also clickable -- a hyperlink reference):
+
+.. image:: static/yi_jing_01_chien.jpg
+   :target: directives_
+
+Figures
+^^^^^^^
+
+.. figure:: static/yi_jing_01_chien.jpg
+   :alt: reStructuredText, the markup syntax
+
+   A figure is an image with a caption and/or a legend:
+
+   +------------+-----------------------------------------------+
+   | re         | Revised, revisited, based on 're' module.     |
+   +------------+-----------------------------------------------+
+   | Structured | Structure-enhanced text, structuredtext.      |
+   +------------+-----------------------------------------------+
+   | Text       | Well it is, isn't it?                         |
+   +------------+-----------------------------------------------+
+
+   This paragraph is also part of the legend.
+
+A figure directive with center alignment
+
+.. figure:: static/yi_jing_01_chien.jpg
+   :align: center
+
+   This caption should be centered.
+
+Admonitions
+-----------
+
+.. Attention:: Directives at large.
+
+.. Caution:: Don't take any wooden nickels.
+
+.. DANGER:: Mad scientist at work!
+
+.. Error:: Does not compute.
+
+.. Hint:: It's bigger than a bread box.
+
+.. Important::
+   - Wash behind your ears.
+   - Clean up your room.
+
+     - Including the closet.
+     - The bathroom too.
+
+       - Take the trash out of the bathroom.
+       - Clean the sink.
+   - Call your mother.
+   - Back up your data.
+
+.. Note:: This is a note.
+   Equations within a note:
+   :math:`G_{\mu\nu} = 8 \pi G (T_{\mu\nu}  + \rho_\Lambda g_{\mu\nu})`.
+
+.. Tip:: 15% if the service is good.
+
+    +---------+
+    | Example |
+    +=========+
+    | Thing1  |
+    +---------+
+    | Thing2  |
+    +---------+
+    | Thing3  |
+    +---------+
+
+.. WARNING:: Strong prose may provoke extreme mental exertion.
+   Reader discretion is strongly advised.
+
+.. admonition:: And, by the way...
+
+   You can make up your own admonition too.
+
+Topics, Sidebars, and Rubrics
+-----------------------------
+
+.. sidebar:: Sidebar Title
+   :subtitle: Optional Subtitle
+
+   This is a sidebar.  It is for text outside the flow of the main
+   text.
+
+   .. rubric:: This is a rubric inside a sidebar
+
+   Sidebars often appears beside the main text with a border and
+   background color.
+
+.. topic:: Topic Title
+
+   This is a topic.
+
+.. rubric:: This is a rubric
+
+Target Footnotes
+----------------
+
+.. target-notes::
+
+Replacement Text
+----------------
+
+I recommend you try |Python|_.
+
+.. |Python| replace:: Python, *the* best language around
+
+Compound Paragraph
+------------------
+
+.. compound::
+
+   This paragraph contains a literal block::
+
+       Connecting... OK
+       Transmitting data... OK
+       Disconnecting... OK
+
+   and thus consists of a simple paragraph, a literal block, and
+   another simple paragraph.  Nonetheless it is semantically *one*
+   paragraph.
+
+This construct is called a *compound paragraph* and can be produced
+with the "compound" directive.
+
+Download Links
+==============
+
+:download:`This long long long long long long long long long long long long long long long download link should be blue, normal weight text with a leading icon, and should wrap white-spaces <static/yi_jing_01_chien.jpg>`
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/lists_tables.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/lists_tables.rst.txt
new file mode 100644
index 0000000000..4c7642e5c5
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/lists_tables.rst.txt
@@ -0,0 +1,302 @@
+
+**************
+Lists & Tables
+**************
+
+.. contents:: Table of Contents
+
+Lists
+=====
+
+Enumerated Lists
+----------------
+
+1. Arabic numerals.
+
+   a) lower alpha)
+
+      (i) (lower roman)
+
+          A. upper alpha.
+
+             I) upper roman)
+
+2. Lists that don't start at 1:
+
+   3. Three
+
+   4. Four
+
+   C. C
+
+   D. D
+
+   iii. iii
+
+   iv. iv
+
+#. List items may also be auto-enumerated.
+
+Definition Lists
+----------------
+
+Term
+    Definition
+Term : classifier
+    Definition paragraph 1.
+
+    Definition paragraph 2.
+Term
+    Definition
+
+
+Option Lists
+------------
+
+For listing command-line options:
+
+-a            command-line option "a"
+-b file       options can have arguments
+              and long descriptions
+--long        options can be long also
+--input=file  long options can also have
+              arguments
+
+--very-long-option
+              The description can also start on the next line.
+
+              The description may contain multiple body elements,
+              regardless of where it starts.
+
+-x, -y, -z    Multiple options are an "option group".
+-v, --verbose  Commonly-seen: short & long options.
+-1 file, --one=file, --two file
+              Multiple options with arguments.
+/V            DOS/VMS-style options too
+
+There must be at least two spaces between the option and the description.
+
+Field list
+----------
+
+.. bibliographic fields (which also require a transform):
+
+:Author: David Goodger
+:Address: 123 Example Street
+          Example, EX  Canada
+          A1B 2C3
+:Contact: docutils-develop@lists.sourceforge.net
+:Authors: Me; Myself; I
+:organization: humankind
+:date: $Date: 2012-01-03 19:23:53 +0000 (Tue, 03 Jan 2012) $
+:status: This is a "work in progress"
+:revision: $Revision: 7302 $
+:version: 1
+:copyright: This document has been placed in the public domain. You
+            may do with it as you wish. You may copy, modify,
+            redistribute, reattribute, sell, buy, rent, lease,
+            destroy, or improve it, quote it at length, excerpt,
+            incorporate, collate, fold, staple, or mutilate it, or do
+            anything else to it that your or anyone else's heart
+            desires.
+:field name: This is a generic bibliographic field.
+:field name 2:
+    Generic bibliographic fields may contain multiple body elements.
+
+    Like this.
+
+:Dedication:
+
+    For Docutils users & co-developers.
+
+:abstract:
+
+    This document is a demonstration of the reStructuredText markup
+    language, containing examples of all basic reStructuredText
+    constructs and many advanced constructs.
+
+Bullet Lists
+------------
+
+- A bullet list
+
+  + Nested bullet list.
+  + Nested item 2.
+
+- Item 2.
+
+  Paragraph 2 of item 2.
+
+  * Nested bullet list.
+  * Nested item 2.
+
+    - Third level.
+    - Item 2.
+
+  * Nested item 3.
+
+- ``inline literall``
+- ``inline literall``
+- ``inline literall``
+
+Second list level
+^^^^^^^^^^^^^^^^^
+
+- here is a list in a second-level section.
+- `yahoo <http://www.yahoo.com>`_
+- `yahoo <http://www.yahoo.com>`_
+
+  - `yahoo <http://www.yahoo.com>`_
+  - here is an inner bullet ``oh``
+
+    - one more ``with an inline literally``. `yahoo <http://www.yahoo.com>`_
+      
+      heh heh. child. try to beat this embed:
+
+      .. literalinclude:: test_py_module/test.py
+          :language: python
+          :linenos:
+          :lines: 1-10
+  - and another. `yahoo <http://www.yahoo.com>`_
+  - `yahoo <http://www.yahoo.com>`_
+  - ``hi``
+- and hehe
+
+But deeper down the rabbit hole
+"""""""""""""""""""""""""""""""
+
+- I kept saying that, "deeper down the rabbit hole". `yahoo <http://www.yahoo.com>`_
+
+  - I cackle at night `yahoo <http://www.yahoo.com>`_.
+- I'm so lonely here in GZ ``guangzhou``
+- A man of python destiny, hopes and dreams. `yahoo <http://www.yahoo.com>`_
+
+  - `yahoo <http://www.yahoo.com>`_
+
+    - `yahoo <http://www.yahoo.com>`_ ``hi``
+    - ``destiny``
+
+Hlists
+------
+
+.. hlist::
+    :columns: 2
+
+    - First item
+    - Second item
+    - Third item
+    - Forth item
+    - Fifth item
+    - Sixths item
+
+.. rubric:: Hlist with images
+
+.. hlist::
+    :columns: 2
+
+    - .. figure:: static/yi_jing_01_chien.jpg
+
+         This is a short caption for a figure.
+
+    - .. figure:: static/yi_jing_01_chien.jpg
+
+         This is a long caption for a figure. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+         Donec porttitor dolor in odio posuere, vitae ornare libero mattis. In lobortis justo vestibulum nibh aliquet, non.
+
+Numbered List
+-------------
+
+#. One,
+#. Two.
+#. Three with long text. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+   Sed feugiat sagittis neque quis eleifend. Duis rutrum lectus sit amet mattis suscipit.
+
+- A) Using bullets and letters. (A)
+- B) Using bullets and letters. (B)
+- C) Using bullets and letters. (C)
+
+Tables
+======
+
+Grid Tables
+-----------
+
+Here's a grid table followed by a simple table:
+
++------------------------+------------+----------+----------+
+| Header row, column 1   | Header 2   | Header 3 | Header 4 |
+| (header rows optional) |            |          |          |
++========================+============+==========+==========+
+| body row 1, column 1   | column 2   | column 3 | column 4 |
++------------------------+------------+----------+----------+
+| body row 2             | Cells may span columns.          |
++------------------------+------------+---------------------+
+| body row 3             | Cells may  | - Table cells       |
++------------------------+ span rows. | - contain           |
+| body row 4             |            | - body elements.    |
++------------------------+------------+----------+----------+
+| body row 5             | Cells may also be     |          |
+|                        | empty: ``-->``        |          |
++------------------------+-----------------------+----------+
+
+=====  =====  ======
+   Inputs     Output
+------------  ------
+  A      B    A or B
+=====  =====  ======
+False  False  False
+True   False  True
+False  True   True
+True   True   True
+=====  =====  ======
+
+Giant Tables
+^^^^^^^^^^^^
+
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+| Header 1   | Header 2   | Header 3  | Header 1   | Header 2   | Header 3  | Header 1   | Header 2   | Header 3  | Header 1   | Header 2   | Header 3  |
++============+============+===========+============+============+===========+============+============+===========+============+============+===========+
+| body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  |
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+| body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  |
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+| body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  |
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+| body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  | body row 1 | column 2   | column 3  |
++------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+------------+------------+-----------+
+
+List Tables
+-----------
+
+.. list-table:: List tables can have captions like this one.
+    :widths: 10 5 10 50
+    :header-rows: 1
+    :stub-columns: 1
+
+    * - List table
+      - Header 1
+      - Header 2
+      - Header 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.
+    * - Stub Row 1
+      - Row 1
+      - Column 2
+      - Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.
+    * - Stub Row 2
+      - Row 2
+      - Column 2
+      - Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.
+    * - Stub Row 3
+      - Row 3
+      - Column 2
+      - Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.
+
+.. list-table:: This is a list table with images in it.
+
+    * - .. figure:: static/yi_jing_01_chien.jpg
+
+           This is a short caption for a figure.
+
+      - .. figure:: static/yi_jing_01_chien.jpg
+
+           This is a long caption for a figure. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+           Donec porttitor dolor in odio posuere, vitae ornare libero mattis. In lobortis justo vestibulum nibh aliquet, non.
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/long.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/long.rst.txt
new file mode 100644
index 0000000000..26b1add9d1
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/long.rst.txt
@@ -0,0 +1,214 @@
+
+***************
+Long Sticky Nav
+***************
+
+.. contents:: Table of Contents
+
+This section demonstrates how the 'sticky_navigation' setting behaves when the menu is very long.
+When this section is selected, it will make the menu and the main area scroll when you are at the top of the page.
+
+
+Example Menu 1
+==============
+
+Just a place holder...
+
+
+Example Menu 2
+==============
+
+Just a place holder...
+
+
+Example Menu 3
+==============
+
+Just a place holder...
+
+
+Example Menu 4
+==============
+
+Just a place holder...
+
+
+Example Menu 5
+==============
+
+Just a place holder...
+
+
+Example Menu 6
+==============
+
+Just a place holder...
+
+
+Example Menu 7
+==============
+
+Just a place holder...
+
+
+Example Menu 8
+==============
+
+Just a place holder...
+
+
+Example Menu 9
+==============
+
+Just a place holder...
+
+
+Example Menu 10
+===============
+
+Just a place holder...
+
+
+Example Menu 11
+===============
+
+Just a place holder...
+
+
+Example Menu 12
+===============
+
+Just a place holder...
+
+
+Example Menu 13
+===============
+
+Just a place holder...
+
+
+Example Menu 14
+===============
+
+Just a place holder...
+
+
+Example Menu 15
+===============
+
+Just a place holder...
+
+
+Example Menu 16
+===============
+
+Just a place holder...
+
+
+Example Menu 17
+===============
+
+Just a place holder...
+
+
+Example Menu 18
+===============
+
+Just a place holder...
+
+
+Example Menu 19
+===============
+
+Just a place holder...
+
+
+Example Menu 20
+===============
+
+Just a place holder...
+
+Example Submenu 1
+=================
+
+Just a place holder...
+
+Submenu 1
+---------
+
+Just a place holder...
+
+Subsubmenu 1
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Subsubmenu 2
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Submenu 2
+---------
+
+Just a place holder...
+
+Subsubmenu 1
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Submenu 3
+---------
+
+Just a place holder...
+
+Submenu 4
+---------
+
+Just a place holder...
+
+Submenu 5
+---------
+
+Just a place holder...
+
+Example Submenu 2
+=================
+
+Just a place holder...
+
+Submenu 1
+---------
+
+Just a place holder...
+
+Subsubmenu 1
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Submenu 2
+---------
+
+Just a place holder...
+
+Subsubmenu 1
+^^^^^^^^^^^^
+
+Just a place holder...
+
+Submenu 3
+---------
+
+Just a place holder...
+
+Submenu 4
+---------
+
+Just a place holder...
+
+Submenu 5
+---------
+
+Just a place holder...
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/structure.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/structure.rst.txt
new file mode 100644
index 0000000000..178ed63f07
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/demo/structure.rst.txt
@@ -0,0 +1,101 @@
+
+*******************
+Structural Elements
+*******************
+
+.. contents:: Table of Contents
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Donec lorem neque, interdum in ipsum nec,
+finibus dictum velit. Ut eu efficitur arcu, id aliquam erat. In sit amet diam gravida, imperdiet tellus eu,
+gravida nisl. Praesent aliquet odio eget libero elementum, quis rhoncus tellus tincidunt.
+Suspendisse quis volutpat ipsum. Sed lobortis scelerisque tristique. Aenean condimentum risus tellus,
+quis accumsan ipsum laoreet ut. Integer porttitor maximus suscipit. Mauris in posuere sapien.
+Aliquam accumsan feugiat ligula, nec fringilla libero commodo sed. Proin et erat pharetra.
+
+---------
+
+Etiam turpis ante, luctus sed velit tristique, finibus volutpat dui. Nam sagittis vel ante nec malesuada.
+Praesent dignissim mi nec ornare elementum. Nunc eu augue vel sem dignissim cursus sed et nulla.
+Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.
+Pellentesque dictum dui sem, non placerat tortor rhoncus in. Sed placerat nulla at rhoncus iaculis. 
+
+Document Section
+================
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed condimentum nulla vel neque venenatis,
+nec placerat lorem placerat. Cras purus eros, gravida vitae tincidunt id, vehicula nec nulla.
+Fusce aliquet auctor cursus. Phasellus ex neque, vestibulum non est vitae, viverra fringilla tortor.
+Donec vestibulum convallis justo, a faucibus lorem vulputate vel. Aliquam cursus odio eu felis sodales aliquet.
+Aliquam erat volutpat. Maecenas eget dictum mauris. Suspendisse arcu eros, condimentum eget risus sed,
+luctus efficitur arcu. Cras ut dictum mi. Nulla congue interdum lorem, semper semper enim commodo nec.
+
+Document Subsection
+-------------------
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam efficitur in eros et blandit. Nunc maximus,
+nisl at auctor vestibulum, justo ex sollicitudin ligula, id faucibus urna orci tristique nisl.
+Duis auctor rutrum orci, in ornare lacus condimentum quis. Quisque arcu velit, facilisis quis interdum ac,
+hendrerit auctor mauris. Curabitur urna nibh, porttitor at ante sit amet, vestibulum interdum dolor.
+Duis dictum elit orci, tincidunt imperdiet sem pellentesque et. In vehicula pellentesque varius.
+Phasellus a turpis sollicitudin, bibendum massa et, imperdiet neque. Integer quis sapien in magna rutrum bibendum.
+Integer cursus ex sed magna vehicula finibus. Proin tempus orci quis dolor tempus, nec condimentum odio vestibulum.
+Etiam efficitur sollicitudin libero, tincidunt volutpat ligula interdum sed.
+
+Document Subsubsection
+^^^^^^^^^^^^^^^^^^^^^^
+
+Donec non rutrum lorem. Aenean sagittis metus at pharetra fringilla. Nunc sapien dolor, cursus sed nisi at,
+pretium tristique lectus. Sed pellentesque leo lectus, et convallis ipsum euismod a.
+Integer at leo vitae felis pretium aliquam fringilla quis odio. Sed pharetra enim accumsan feugiat pretium.
+Maecenas at pharetra tortor. Morbi semper eget mi vel finibus. Cras rutrum nulla eros, id feugiat arcu pellentesque ut.
+Sed finibus tortor ac nisi ultrices viverra. Duis feugiat malesuada sapien, at commodo ante porttitor ac.
+Curabitur posuere mauris mi, vel ornare orci scelerisque sit amet. Suspendisse nec fringilla dui. 
+
+Document Paragraph
+""""""""""""""""""
+
+Pellentesque nec est in odio ultrices elementum. Vestibulum et hendrerit sapien, quis vulputate turpis.
+Suspendisse potenti. Curabitur tristique sit amet lectus non viverra. Phasellus rutrum dapibus turpis sed imperdiet.
+Mauris maximus viverra ante. Donec eu egestas mauris. Morbi vulputate tincidunt euismod. Integer vel porttitor neque.
+Donec at lacus suscipit, lacinia lectus vel, sagittis lectus.
+
+*********************
+Structural Elements 2
+*********************
+
+Etiam turpis ante, luctus sed velit tristique, finibus volutpat dui. Nam sagittis vel ante nec malesuada.
+Praesent dignissim mi nec ornare elementum. Nunc eu augue vel sem dignissim cursus sed et nulla.
+Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.
+Pellentesque dictum dui sem, non placerat tortor rhoncus in. Sed placerat nulla at rhoncus iaculis. 
+
+Document Section
+================
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed condimentum nulla vel neque venenatis,
+nec placerat lorem placerat. Cras purus eros, gravida vitae tincidunt id, vehicula nec nulla.
+Fusce aliquet auctor cursus. Phasellus ex neque, vestibulum non est vitae, viverra fringilla tortor.
+Donec vestibulum convallis justo, a faucibus lorem vulputate vel. Aliquam cursus odio eu felis sodales aliquet.
+Aliquam erat volutpat. Maecenas eget dictum mauris. Suspendisse arcu eros, condimentum eget risus sed,
+luctus efficitur arcu. Cras ut dictum mi. Nulla congue interdum lorem, semper semper enim commodo nec.
+
+Document Subsection
+-------------------
+
+.. figure:: static/yi_jing_01_chien.jpg
+    :align: right
+    :figwidth: 200px
+
+    This is a caption for a figure. Text should wrap around the caption.
+
+Lorem ipsum dolor sit amet, consectetur adipiscing elit. Etiam efficitur in eros et blandit. Nunc maximus,
+nisl at auctor vestibulum, justo ex sollicitudin ligula, id faucibus urna orci tristique nisl.
+Duis auctor rutrum orci, in ornare lacus condimentum quis. Quisque arcu velit, facilisis quis interdum ac,
+hendrerit auctor mauris. Curabitur urna nibh, porttitor at ante sit amet, vestibulum interdum dolor.
+Duis dictum elit orci, tincidunt imperdiet sem pellentesque et. In vehicula pellentesque varius.
+Phasellus a turpis sollicitudin, bibendum massa et, imperdiet neque. Integer quis sapien in magna rutrum bibendum.
+Integer cursus ex sed magna vehicula finibus. Proin tempus orci quis dolor tempus, nec condimentum odio vestibulum.
+Etiam efficitur sollicitudin libero, tincidunt volutpat ligula interdum sed. Praesent congue sagittis nisl et suscipit.
+Vivamus sagittis risus et egestas commodo.Cras venenatis arcu in pharetra interdum.
+Donec quis metus porttitor tellus cursus lobortis. Quisque et orci magna. Fusce rhoncus mi mi,
+at vehicula massa rhoncus quis. Mauris augue leo, pretium eget molestie vitae, efficitur nec nulla.
+In hac habitasse platea dictumst. Sed sit amet imperdiet purus. 
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/index.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/index.rst.txt
new file mode 100644
index 0000000000..ba42326fd7
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/index.rst.txt
@@ -0,0 +1,27 @@
+.. include:: ../README.rst
+
+.. toctree::
+   :caption: Theme Documentation
+   :maxdepth: 2
+
+   installing
+   configuring
+   changelog
+
+
+.. toctree::
+    :maxdepth: 2
+    :numbered:
+    :caption: Demo Documents
+
+    demo/structure
+    demo/demo
+    demo/lists_tables
+    demo/api
+
+.. toctree::
+    :maxdepth: 3
+    :numbered:
+    :caption: This is an incredibly long caption for a long menu
+
+    demo/long
diff --git a/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/installing.rst.txt b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/installing.rst.txt
new file mode 100644
index 0000000000..df406b999d
--- /dev/null
+++ b/docs/v2.2.0/_sources/src/pytorch-sphinx-theme/docs/installing.rst.txt
@@ -0,0 +1,17 @@
+
+************
+Installation
+************
+
+Via Git or Download
+===================
+
+Symlink or subtree the ``pytorch_sphinx_theme`` repository into your documentation at
+``docs/_themes/pytorch_sphinx_theme`` then add the following two settings to your Sphinx
+``conf.py`` file:
+
+.. code:: python
+
+    html_theme = "pytorch_sphinx_theme"
+    html_theme_path = ["_themes", ]
+
diff --git a/docs/v2.2.0/_sources/ts/creating_torchscript_module_in_python.rst.txt b/docs/v2.2.0/_sources/ts/creating_torchscript_module_in_python.rst.txt
new file mode 100644
index 0000000000..1d1cdba574
--- /dev/null
+++ b/docs/v2.2.0/_sources/ts/creating_torchscript_module_in_python.rst.txt
@@ -0,0 +1,140 @@
+.. _creating_a_ts_mod:
+
+Creating a TorchScript Module
+------------------------------
+TorchScript is a way to create serializable and optimizable models from PyTorch code.
+PyTorch has detailed documentation on how to do this https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html but briefly here is the
+here is key background information and the process:
+
+PyTorch programs are based around ``Module`` s which can be used to compose higher level modules. ``Modules`` contain a constructor to set up the modules, parameters and sub-modules
+and a forward function which describes how to use the parameters and submodules when the module is invoked.
+
+For example, we can define a LeNet module like this:
+
+.. code-block:: python
+    :linenos:
+
+    import torch.nn as nn
+    import torch.nn.functional as F
+
+
+    class LeNetFeatExtractor(nn.Module):
+        def __init__(self):
+            super(LeNetFeatExtractor, self).__init__()
+            self.conv1 = nn.Conv2d(1, 6, 3)
+            self.conv2 = nn.Conv2d(6, 16, 3)
+
+        def forward(self, x):
+            x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
+            x = F.max_pool2d(F.relu(self.conv2(x)), 2)
+            return x
+
+
+    class LeNetClassifier(nn.Module):
+        def __init__(self):
+            super(LeNetClassifier, self).__init__()
+            self.fc1 = nn.Linear(16 * 6 * 6, 120)
+            self.fc2 = nn.Linear(120, 84)
+            self.fc3 = nn.Linear(84, 10)
+
+        def forward(self, x):
+            x = torch.flatten(x, 1)
+            x = F.relu(self.fc1(x))
+            x = F.relu(self.fc2(x))
+            x = self.fc3(x)
+            return x
+
+
+    class LeNet(nn.Module):
+        def __init__(self):
+            super(LeNet, self).__init__()
+            self.feat = LeNetFeatExtractor()
+            self.classifer = LeNetClassifier()
+
+        def forward(self, x):
+            x = self.feat(x)
+            x = self.classifer(x)
+            return x
+
+.
+
+    Obviously you may want to consolidate such a simple model into a single module but we can see the composability of PyTorch here
+
+From here are two pathways for going from PyTorch Python code to TorchScript code: Tracing and Scripting.
+
+Tracing follows the path of execution when the module is called and records what happens.
+To trace an instance of our LeNet module, we can call ``torch.jit.trace`` with an example input.
+
+.. code-block:: python
+
+    import torch
+
+    model = LeNet()
+    input_data = torch.empty([1, 1, 32, 32])
+    traced_model = torch.jit.trace(model, input_data)
+
+Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing
+is following the execution of your module, it cannot pick up control flow for instance. By working from the Python code, the compiler can
+include these components. We can run the script compiler on our LeNet module by calling ``torch.jit.script``
+
+.. code-block:: python
+
+    import torch
+
+    model = LeNet()
+    script_model = torch.jit.script(model)
+
+There are reasons to use one path or another, the PyTorch documentation has information on how to choose. From a Torch-TensorRT prespective, there is
+better support (i.e your module is more likely to compile) for traced modules because it doesn't include all the complexities of a complete
+programming language, though both paths supported.
+
+After scripting or tracing your module, you are given back a TorchScript Module. This contains the code and parameters used to run the module stored
+in a intermediate representation that Torch-TensorRT can consume.
+
+Here is what the LeNet traced module IR looks like:
+
+.. code-block:: none
+
+    graph(%self.1 : __torch__.___torch_mangle_10.LeNet,
+        %input.1 : Float(1, 1, 32, 32)):
+        %129 : __torch__.___torch_mangle_9.LeNetClassifier = prim::GetAttr[name="classifer"](%self.1)
+        %119 : __torch__.___torch_mangle_5.LeNetFeatExtractor = prim::GetAttr[name="feat"](%self.1)
+        %137 : Tensor = prim::CallMethod[name="forward"](%119, %input.1)
+        %138 : Tensor = prim::CallMethod[name="forward"](%129, %137)
+        return (%138)
+
+and the LeNet scripted module IR:
+
+.. code-block:: none
+
+    graph(%self : __torch__.LeNet,
+        %x.1 : Tensor):
+        %2 : __torch__.LeNetFeatExtractor = prim::GetAttr[name="feat"](%self)
+        %x.3 : Tensor = prim::CallMethod[name="forward"](%2, %x.1) # x.py:38:12
+        %5 : __torch__.LeNetClassifier = prim::GetAttr[name="classifer"](%self)
+        %x.5 : Tensor = prim::CallMethod[name="forward"](%5, %x.3) # x.py:39:12
+        return (%x.5)
+
+You can see that the IR preserves the module structure we have in our python code.
+
+.. _ts_in_py:
+
+Working with TorchScript in Python
+-----------------------------------
+
+TorchScript Modules are run the same way you run normal PyTorch modules. You can run the forward pass using the
+``forward`` method or just calling the module ``torch_scirpt_module(in_tensor)`` The JIT compiler will compile
+and optimize the module on the fly and then returns the results.
+
+Saving TorchScript Module to Disk
+-----------------------------------
+
+For either traced or scripted modules, you can save the module to disk with the following command
+
+.. code-block:: python
+
+    import torch
+
+    model = LeNet()
+    script_model = torch.jit.script(model)
+    script_model.save("lenet_scripted.ts")
diff --git a/docs/v2.2.0/_sources/ts/getting_started_with_cpp_api.rst.txt b/docs/v2.2.0/_sources/ts/getting_started_with_cpp_api.rst.txt
new file mode 100644
index 0000000000..70f439ea6d
--- /dev/null
+++ b/docs/v2.2.0/_sources/ts/getting_started_with_cpp_api.rst.txt
@@ -0,0 +1,340 @@
+.. _getting_started_cpp:
+
+Using Torch-TensorRT in  C++
+==============================
+
+If you haven't already, acquire a tarball of the library by following the instructions in :ref:`Installation`
+
+Using Torch-TensorRT in C++
+***************************
+Torch-TensorRT C++ API accepts TorchScript modules (generated either from ``torch.jit.script`` or ``torch.jit.trace``) as an input and returns
+a Torchscript module (optimized using TensorRT), Dynamo compilation workflows will not be supported in the C++ API however, execution of
+torch.jit.trace'd compiled FX GraphModules is supported for FX and Dyanmo workflows.
+
+Please refer to `Creating TorchScript modules in Python <https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html>`_ section to generate torchscript graphs.
+
+
+.. _torch_tensorrt_quickstart:
+
+[Torch-TensorRT Quickstart] Compiling TorchScript Modules with ``torchtrtc``
+---------------------------------------------------------------------------------
+
+An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
+``torchtrtc``, which supports almost all features of the compiler from the command line including post training quantization
+(given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating
+precision and input size. This new TorchScript file can be loaded into Python (note: you need to ``import torch_tensorrt`` before loading
+these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).
+
+.. code-block:: shell
+
+    ❯ torchtrtc -p f16 lenet_scripted.ts trt_lenet_scripted.ts "(1,1,32,32)"
+
+    ❯ python3
+    Python 3.6.9 (default, Apr 18 2020, 01:56:04)
+    [GCC 8.4.0] on linux
+    Type "help", "copyright", "credits" or "license" for more information.
+    >>> import torch
+    >>> import torch_tensorrt
+    >>> ts_model = torch.jit.load(“trt_lenet_scripted.ts”)
+    >>> ts_model(torch.randn((1,1,32,32)).to(“cuda”).half())
+
+You can learn more about ``torchtrtc`` usage here: :ref:`torchtrtc`
+
+.. _ts_in_cc:
+
+Working with TorchScript in C++
+--------------------------------
+
+If we are developing an application to deploy with C++, we can save either our traced or scripted module using ``torch.jit.save``
+which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.
+
+.. code-block:: python
+
+    torch_script_module.save("lenet.jit.pt")
+
+From here we can now load our TorchScript module in C++
+
+.. code-block:: c++
+
+    #include <torch/script.h> // One-stop header.
+
+    #include <iostream>
+    #include <memory>
+
+    int main(int argc, const char* argv[]) {
+        torch::jit::Module module;
+        try {
+            // Deserialize the ScriptModule from a file using torch::jit::load().
+            module = torch::jit::load("<PATH TO SAVED TS MOD>");
+        }
+        catch (const c10::Error& e) {
+            std::cerr << "error loading the model\n";
+            return -1;
+        }
+
+        std::cout << "ok\n";
+
+
+You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
+have access to the same powerful tensor library that backs PyTorch. (For more information: https://pytorch.org/cppdocs/).
+For instance we can do inference with our LeNet module like this:
+
+.. code-block:: c++
+
+        mod.eval();
+        torch::Tensor in = torch::randn({1, 1, 32, 32});
+        auto out = mod.forward(in);
+
+and to run on the GPU:
+
+.. code-block:: c++
+
+        mod.eval();
+        mod.to(torch::kCUDA);
+        torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
+        auto out = mod.forward(in);
+
+As you can see it is pretty similar to the Python API. When you call the ``forward`` method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.
+
+.. _compile_cpp:
+
+Compiling with Torch-TensorRT in C++
+-------------------------------------
+We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
+since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.
+
+With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA});
+        auto trt_mod = torch_tensorrt::CompileGraph(mod, std::vector<torch_tensorrt::CompileSpec::InputRange>{{in.sizes()}});
+        auto out = trt_mod.forward({in});
+
+Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).
+
+We can also set settings like operating precision to run in FP16.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
+        auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
+        torch_tensorrt::CompileSpec info(input_sizes);
+        info.enable_precisions.insert(torch::kHALF);
+        auto trt_mod = torch_tensorrt::CompileGraph(mod, info);
+        auto out = trt_mod.forward({in});
+
+And now we are running the module in FP16 precision. You can then save the module to load later.
+
+.. code-block:: c++
+
+    trt_mod.save("<PATH TO SAVED TRT/TS MOD>")
+
+Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against ``libtorchtrt.so``
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+
+    int main(int argc, const char* argv[]) {
+        torch::jit::Module module;
+        try {
+            // Deserialize the ScriptModule from a file using torch::jit::load().
+            module = torch::jit::load("<PATH TO SAVED TRT/TS MOD>");
+        }
+        catch (const c10::Error& e) {
+            std::cerr << "error loading the model\n";
+            return -1;
+        }
+
+        torch::Tensor in = torch::randn({1, 1, 32, 32}, torch::kCUDA);
+        auto out = mod.forward(in);
+
+        std::cout << "ok\n";
+    }
+
+If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the ``ConvertGraphToTRTEngine`` API.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    ...
+
+        mod.to(at::kCUDA);
+        mod.eval();
+
+        auto in = torch::randn({1, 1, 32, 32}, {torch::kCUDA}).to(torch::kHALF);
+        auto input_sizes = std::vector<torch_tensorrt::CompileSpec::InputRange>({in.sizes()});
+        torch_tensorrt::CompileSpec info(input_sizes);
+        info.enabled_precisions.insert(torch::kHALF);
+        auto trt_mod = torch_tensorrt::ConvertGraphToTRTEngine(mod, "forward", info);
+        std::ofstream out("/tmp/engine_converted_from_jit.trt");
+        out << engine;
+        out.close();
+
+.. _under_the_hood:
+
+Under The Hood
+---------------
+
+When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:
+
+.. code-block:: none
+
+    graph(%input.2 : Tensor):
+        %2 : Float(84, 10) = prim::Constant[value=<Tensor>]()
+        %3 : Float(120, 84) = prim::Constant[value=<Tensor>]()
+        %4 : Float(576, 120) = prim::Constant[value=<Tensor>]()
+        %5 : int = prim::Constant[value=-1]() # x.py:25:0
+        %6 : int[] = prim::Constant[value=annotate(List[int], [])]()
+        %7 : int[] = prim::Constant[value=[2, 2]]()
+        %8 : int[] = prim::Constant[value=[0, 0]]()
+        %9 : int[] = prim::Constant[value=[1, 1]]()
+        %10 : bool = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %11 : int = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %12 : bool = prim::Constant[value=0]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %self.classifer.fc3.bias : Float(10) = prim::Constant[value= 0.0464  0.0383  0.0678  0.0932  0.1045 -0.0805 -0.0435 -0.0818  0.0208 -0.0358 [ CUDAFloatType{10} ]]()
+        %self.classifer.fc2.bias : Float(84) = prim::Constant[value=<Tensor>]()
+        %self.classifer.fc1.bias : Float(120) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv2.weight : Float(16, 6, 3, 3) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv2.bias : Float(16) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv1.weight : Float(6, 1, 3, 3) = prim::Constant[value=<Tensor>]()
+        %self.feat.conv1.bias : Float(6) = prim::Constant[value= 0.0530 -0.1691  0.2802  0.1502  0.1056 -0.1549 [ CUDAFloatType{6} ]]()
+        %input0.4 : Tensor = aten::_convolution(%input.2, %self.feat.conv1.weight, %self.feat.conv1.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %input0.5 : Tensor = aten::relu(%input0.4) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %input1.2 : Tensor = aten::max_pool2d(%input0.5, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %input0.6 : Tensor = aten::_convolution(%input1.2, %self.feat.conv2.weight, %self.feat.conv2.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+        %input2.1 : Tensor = aten::relu(%input0.6) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %x.1 : Tensor = aten::max_pool2d(%input2.1, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+        %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0
+        %27 : Tensor = aten::matmul(%input.1, %4)
+        %28 : Tensor = trt::const(%self.classifer.fc1.bias)
+        %29 : Tensor = aten::add_(%28, %27, %11)
+        %input0.2 : Tensor = aten::relu(%29) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %31 : Tensor = aten::matmul(%input0.2, %3)
+        %32 : Tensor = trt::const(%self.classifer.fc2.bias)
+        %33 : Tensor = aten::add_(%32, %31, %11)
+        %input1.1 : Tensor = aten::relu(%33) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+        %35 : Tensor = aten::matmul(%input1.1, %2)
+        %36 : Tensor = trt::const(%self.classifer.fc3.bias)
+        %37 : Tensor = aten::add_(%36, %35, %11)
+        return (%37)
+    (CompileGraph)
+
+The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
+into the graph and all of the operations laid out. Torch-TensorRT has also executed a number of optimizations and mappings to make the graph easier to translate to TensorRT.
+From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.
+
+When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
+to the user or moves into the graph construction phase. Here Torch-TensorRT creates a JIT Module to execute the TensorRT engine which will be instantiated and managed
+by the Torch-TensorRT runtime.
+
+Here is the graph that you get back after compilation is complete:
+
+.. code-block:: none
+
+    graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
+        %1 : ...trt.Engine = prim::GetAttr[name="lenet"](%self_1)
+        %3 : Tensor[] = prim::ListConstruct(%input_0)
+        %4 : Tensor[] = trt::execute_engine(%3, %1)
+        %5 : Tensor = prim::ListUnpack(%4)
+        return (%5)
+
+
+You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.
+
+.. _unsupported_ops:
+
+Working with Unsupported Operators
+-----------------------------------
+
+Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren't supported natively by the compiler. You can either use the composition techinques
+shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.
+
+    You can check support without going through the full compilation pipleine using the ``torch_tensorrt::CheckMethodOperatorSupport(const torch::jit::Module& module, std::string method_name)`` api
+    to see what operators are not supported. ``torchtrtc`` automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.
+
+.. _custom_converters:
+
+Registering Custom Converters
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT.
+Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a ``aten::relu(%input0.4)`` instruction will trigger
+the relu converter to be run on it, producing an activation layer in the TensorRT graph. But since this library is not exhaustive you may need to write your own to get Torch-TensorRT
+to support your module.
+
+Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.
+
+For example, if we try to compile a graph with a build of Torch-TensorRT that doesn't support the flatten operation (``aten::flatten``) you may see this error:
+
+.. code-block:: none
+
+    terminate called after throwing an instance of 'torch_tensorrt::Error'
+    what():  [enforce fail at core/conversion/conversion.cpp:109] Expected converter to be true but got false
+    Unable to convert node: %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0 (conversion.AddLayer)
+    Schema: aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)
+    Converter for aten::flatten requested, but no such converter was found.
+    If you need a converter for this operator, you can try implementing one yourself
+    or request a converter: https://www.github.com/NVIDIA/Torch-TensorRT/issues
+
+We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including ``torch_tensorrt/core/conversion/converters/converters.h``.
+We start by creating an instance of the self-registering class ``torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()`` which will register converters
+in the global converter registry, associating a function schema like ``aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)`` with a lambda that
+will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
+Arguments are passed as a vector of inspectable unions of TensorRT ``ITensors`` and Torch ``IValues`` in the order arguments are listed in the schema.
+
+Below is a implementation of a ``aten::flatten`` converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
+for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.
+
+.. code-block:: c++
+
+    #include "torch/script.h"
+    #include "torch_tensorrt/torch_tensorrt.h"
+    #include "torch_tensorrt/core/conversion/converters/converters.h"
+
+    static auto flatten_converter = torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()
+        .pattern({
+            "aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)",
+            [](torch_tensorrt::core::conversion::ConversionCtx* ctx,
+               const torch::jit::Node* n,
+               torch_tensorrt::core::conversion::converters::args& args) -> bool {
+                auto in = args[0].ITensor();
+                auto start_dim = args[1].unwrapToInt();
+                auto end_dim = args[2].unwrapToInt();
+                auto in_shape = torch_tensorrt::core::util::toVec(in->getDimensions());
+                auto out_shape = torch::flatten(torch::rand(in_shape), start_dim, end_dim).sizes();
+
+                auto shuffle = ctx->net->addShuffle(*in);
+                shuffle->setReshapeDimensions(torch_tensorrt::core::util::toDims(out_shape));
+                shuffle->setName(torch_tensorrt::core::util::node_info(n).c_str());
+
+                auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));
+                return true;
+            }
+        });
+
+    int main() {
+        ...
+
+To use this converter in Python, it is recommended to use PyTorch's `C++ / CUDA Extention <https://pytorch.org/tutorials/advanced/cpp_extension.html#custom-c-and-cuda-extensions>`_
+template to wrap your library of converters into a ``.so`` that you can load with ``ctypes.CDLL()`` in your Python application.
+
+You can find more information on all the details of writing converters in the contributors documentation (:ref:`writing_converters`).
+If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.
diff --git a/docs/v2.2.0/_sources/ts/getting_started_with_python_api.rst.txt b/docs/v2.2.0/_sources/ts/getting_started_with_python_api.rst.txt
new file mode 100644
index 0000000000..9ca679d602
--- /dev/null
+++ b/docs/v2.2.0/_sources/ts/getting_started_with_python_api.rst.txt
@@ -0,0 +1,82 @@
+.. _getting_started_with_python_api:
+
+Using Torch-TensorRT in Python
+*******************************
+
+The Torch-TensorRT Python API supports a number of unique usecases compared to the CLI and C++ APIs which solely support TorchScript compilation.
+
+Torch-TensorRT Python API can accept a ``torch.nn.Module``, ``torch.jit.ScriptModule``, or ``torch.fx.GraphModule`` as an input.
+Depending on what is provided one of the two frontends (TorchScript or FX) will be selected to compile the module. Provided the
+module type is supported, users may explicitly set which frontend they would like to use using the ``ir`` flag for ``compile``.
+If given a ``torch.nn.Module`` and the ``ir`` flag is set to either ``default`` or ``torchscript`` the module will be run through
+``torch.jit.script`` to convert the input module into a  TorchScript module.
+
+
+To compile your input ``torch.nn.Module`` with Torch-TensorRT, all you need to do is provide the module and inputs
+to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs
+is a list of ``torch_tensorrt.Input`` classes which define input Tensors' shape, datatype and memory format. Alternatively, if your input is a more complex data type, such as a tuple or list of Tensors, you can use the ``input_signature`` argument to specify a collection-based input, such as ``(List[Tensor], Tuple[Tensor, Tensor])``. See the second sample below for an example. You can also specify settings such as operating precision for the engine or target device. After compilation you can save the module just like any other module
+to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import ``torch_tensorrt``.
+
+.. code-block:: python
+
+    import torch_tensorrt
+
+    ...
+
+    model = MyModel().eval()  # torch module needs to be in eval (not training) mode
+
+    inputs = [
+        torch_tensorrt.Input(
+            min_shape=[1, 1, 16, 16],
+            opt_shape=[1, 1, 32, 32],
+            max_shape=[1, 1, 64, 64],
+            dtype=torch.half,
+        )
+    ]
+    enabled_precisions = {torch.float, torch.half}  # Run with fp16
+
+    trt_ts_module = torch_tensorrt.compile(
+        model, inputs=inputs, enabled_precisions=enabled_precisions
+    )
+
+    input_data = input_data.to("cuda").half()
+    result = trt_ts_module(input_data)
+    torch.jit.save(trt_ts_module, "trt_ts_module.ts")
+
+.. code-block:: python
+
+    # Sample using collection-based inputs via the input_signature argument
+    import torch_tensorrt
+
+    ...
+
+    model = MyModel().eval()
+
+    # input_signature expects a tuple of individual input arguments to the module
+    # The module below, for example, would have a docstring of the form:
+    # def forward(self, input0: List[torch.Tensor], input1: Tuple[torch.Tensor, torch.Tensor])
+    input_signature = (
+        [torch_tensorrt.Input(shape=[64, 64], dtype=torch.half), torch_tensorrt.Input(shape=[64, 64], dtype=torch.half)],
+        (torch_tensorrt.Input(shape=[64, 64], dtype=torch.half), torch_tensorrt.Input(shape=[64, 64], dtype=torch.half)),
+    )
+    enabled_precisions = {torch.float, torch.half}
+
+    trt_ts_module = torch_tensorrt.compile(
+        model, input_signature=input_signature, enabled_precisions=enabled_precisions
+    )
+
+    input_data = input_data.to("cuda").half()
+    result = trt_ts_module(input_data)
+    torch.jit.save(trt_ts_module, "trt_ts_module.ts")
+
+.. code-block:: python
+
+    # Deployment application
+    import torch
+    import torch_tensorrt
+
+    trt_ts_module = torch.jit.load("trt_ts_module.ts")
+    input_data = input_data.to("cuda").half()
+    result = trt_ts_module(input_data)
+
+Torch-TensorRT Python API also provides ``torch_tensorrt.ts.compile`` which accepts a TorchScript module as input and ``torch_tensorrt.fx.compile`` which accepts a FX GraphModule as input.
diff --git a/docs/v2.2.0/_sources/ts/torchscript_frontend_from_pytorch.rst.txt b/docs/v2.2.0/_sources/ts/torchscript_frontend_from_pytorch.rst.txt
new file mode 100644
index 0000000000..d0a403e93c
--- /dev/null
+++ b/docs/v2.2.0/_sources/ts/torchscript_frontend_from_pytorch.rst.txt
@@ -0,0 +1,64 @@
+.. _use_from_pytorch:
+
+Using Torch-TensorRT TorchScript Frontend Directly From PyTorch
+============================================
+
+You will now be able to directly access TensorRT from PyTorch APIs. The process to use this feature
+is very similar to the compilation workflow described in :ref:`getting_started_with_python_api`
+
+Start by loading ``torch_tensorrt`` into your application.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+
+Then given a TorchScript module, you can compile it with TensorRT using the ``torch._C._jit_to_backend("tensorrt", ...)`` API.
+
+.. code-block:: python
+
+    import torchvision.models as models
+
+    model = models.mobilenet_v2(pretrained=True)
+    script_model = torch.jit.script(model)
+
+Unlike the ``compile`` API in Torch-TensorRT which assumes you are trying to compile the ``forward`` function of a module
+or the ``convert_method_to_trt_engine`` which converts a specified function to a TensorRT engine, the backend API
+will take a dictionary which maps names of functions to compile to Compilation Spec objects which wrap the same
+sort of dictionary you would provide to ``compile``. For more information on the compile spec dictionary take a look
+at the documentation for the Torch-TensorRT ``TensorRTCompileSpec`` API.
+
+.. code-block:: python
+
+    spec = {
+        "forward": torch_tensorrt.ts.TensorRTCompileSpec(
+            **{
+                "inputs": [torch_tensorrt.Input([1, 3, 300, 300])],
+                "enabled_precisions": {torch.float, torch.half},
+                "refit": False,
+                "debug": False,
+                "device": {
+                    "device_type": torch_tensorrt.DeviceType.GPU,
+                    "gpu_id": 0,
+                    "dla_core": 0,
+                    "allow_gpu_fallback": True,
+                },
+                "capability": torch_tensorrt.EngineCapability.default,
+                "num_avg_timing_iters": 1,
+            }
+        )
+    }
+
+Now to compile with Torch-TensorRT, provide the target module objects and the spec dictionary to ``torch._C._jit_to_backend("tensorrt", ...)``
+
+.. code-block:: python
+
+    trt_model = torch._C._jit_to_backend("tensorrt", script_model, spec)
+
+To run explicitly call the function of the method you want to run (vs. how you can just call on the module itself in standard PyTorch)
+
+.. code-block:: python
+
+    input = torch.randn((1, 3, 300, 300)).to("cuda").to(torch.half)
+    print(trt_model.forward(input))
diff --git a/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/index.rst.txt b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/index.rst.txt
new file mode 100644
index 0000000000..d0e4d6630e
--- /dev/null
+++ b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/index.rst.txt
@@ -0,0 +1,105 @@
+
+
+.. _sphx_glr_tutorials__rendered_examples_dynamo:
+
+.. _torch_compile:
+
+Dynamo / ``torch.compile``
+----------------------------
+
+Torch-TensorRT provides a backend for the new ``torch.compile`` API released in PyTorch 2.0. In the following examples we describe
+a number of ways you can leverage this backend to accelerate inference.
+
+* :ref:`torch_compile_resnet`: Compiling a ResNet model using the Torch Compile Frontend for ``torch_tensorrt.compile``
+* :ref:`torch_compile_transformer`: Compiling a Transformer model using ``torch.compile``
+* :ref:`torch_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API
+* :ref:`torch_compile_stable_diffusion`: Compiling a Stable Diffusion model using ``torch.compile``
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi...">
+
+.. only:: html
+
+  .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_torch_compile_stable_diffusion_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_stable_diffusion.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Torch Compile Stable Diffusion</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi...">
+
+.. only:: html
+
+  .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_torch_compile_resnet_example_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_resnet_example.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Compiling ResNet using the Torch-TensorRT torch.compile Backend</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi...">
+
+.. only:: html
+
+  .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_torch_compile_transformers_example_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_transformers_example.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Compiling a Transformer using torch.compile and TensorRT</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as an overview of the process by which torch_tensorrt.compi...">
+
+.. only:: html
+
+  .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_torch_compile_advanced_usage_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_advanced_usage.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Torch Compile Advanced Usage</div>
+    </div>
+
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+
+   /tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion
+   /tutorials/_rendered_examples/dynamo/torch_compile_resnet_example
+   /tutorials/_rendered_examples/dynamo/torch_compile_transformers_example
+   /tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage
+
diff --git a/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.rst.txt b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.rst.txt
new file mode 100644
index 0000000000..9eaa970313
--- /dev/null
+++ b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.rst.txt
@@ -0,0 +1,197 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_tutorials__rendered_examples_dynamo_torch_compile_advanced_usage.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_advanced_usage.py:
+
+
+.. _torch_compile_advanced_usage:
+
+Torch Compile Advanced Usage
+======================================================
+
+This interactive script is intended as an overview of the process by which `torch_tensorrt.compile(..., ir="torch_compile", ...)` works, and how it integrates with the `torch.compile` API.
+
+.. GENERATED FROM PYTHON SOURCE LINES 10-12
+
+Imports and Model Definition
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-16
+
+.. code-block:: python
+
+
+    import torch
+    import torch_tensorrt
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-32
+
+.. code-block:: python
+
+
+
+    # We begin by defining a model
+    class Model(torch.nn.Module):
+        def __init__(self) -> None:
+            super().__init__()
+            self.relu = torch.nn.ReLU()
+
+        def forward(self, x: torch.Tensor, y: torch.Tensor):
+            x_out = self.relu(x)
+            y_out = self.relu(y)
+            x_y_out = x_out + y_out
+            return torch.mean(x_y_out)
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 33-35
+
+Compilation with `torch.compile` Using Default Settings
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 35-40
+
+.. code-block:: python
+
+
+    # Define sample float inputs and initialize model
+    sample_inputs = [torch.rand((5, 7)).cuda(), torch.rand((5, 7)).cuda()]
+    model = Model().eval().cuda()
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 41-49
+
+.. code-block:: python
+
+
+    # Next, we compile the model using torch.compile
+    # For the default settings, we can simply call torch.compile
+    # with the backend "torch_tensorrt", and run the model on an
+    # input to cause compilation, as so:
+    optimized_model = torch.compile(model, backend="torch_tensorrt", dynamic=False)
+    optimized_model(*sample_inputs)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 50-52
+
+Compilation with `torch.compile` Using Custom Settings
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 52-64
+
+.. code-block:: python
+
+
+    # First, we use Torch utilities to clean up the workspace
+    # after the previous compile invocation
+    torch._dynamo.reset()
+
+    # Define sample half inputs and initialize model
+    sample_inputs_half = [
+        torch.rand((5, 7)).half().cuda(),
+        torch.rand((5, 7)).half().cuda(),
+    ]
+    model_half = Model().eval().cuda()
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 65-91
+
+.. code-block:: python
+
+
+    # If we want to customize certain options in the backend,
+    # but still use the torch.compile call directly, we can provide
+    # custom options to the backend via the "options" keyword
+    # which takes in a dictionary mapping options to values.
+    #
+    # For accepted backend options, see the CompilationSettings dataclass:
+    # py/torch_tensorrt/dynamo/_settings.py
+    backend_kwargs = {
+        "enabled_precisions": {torch.half},
+        "debug": True,
+        "min_block_size": 2,
+        "torch_executed_ops": {"torch.ops.aten.sub.Tensor"},
+        "optimization_level": 4,
+        "use_python_runtime": False,
+    }
+
+    # Run the model on an input to cause compilation, as so:
+    optimized_model_custom = torch.compile(
+        model_half,
+        backend="torch_tensorrt",
+        options=backend_kwargs,
+        dynamic=False,
+    )
+    optimized_model_custom(*sample_inputs_half)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 92-94
+
+Cleanup
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-98
+
+.. code-block:: python
+
+
+    # Finally, we use Torch utilities to clean up the workspace
+    torch._dynamo.reset()
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 99-108
+
+Cuda Driver Error Note
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Occasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,
+one may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052
+and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::
+
+      if __name__ == '__main__':
+          compile_engine_and_infer()
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  0.000 seconds)
+
+
+.. _sphx_glr_download_tutorials__rendered_examples_dynamo_torch_compile_advanced_usage.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+
+
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: torch_compile_advanced_usage.py <torch_compile_advanced_usage.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: torch_compile_advanced_usage.ipynb <torch_compile_advanced_usage.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.rst.txt b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.rst.txt
new file mode 100644
index 0000000000..b0d1bb5fb2
--- /dev/null
+++ b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.rst.txt
@@ -0,0 +1,187 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_tutorials__rendered_examples_dynamo_torch_compile_resnet_example.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_resnet_example.py:
+
+
+.. _torch_compile_resnet:
+
+Compiling ResNet using the Torch-TensorRT `torch.compile` Backend
+==========================================================
+
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a ResNet model.
+
+.. GENERATED FROM PYTHON SOURCE LINES 10-12
+
+Imports and Model Definition
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-17
+
+.. code-block:: python
+
+
+    import torch
+    import torch_tensorrt
+    import torchvision.models as models
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-23
+
+.. code-block:: python
+
+
+    # Initialize model with half precision and sample inputs
+    model = models.resnet18(pretrained=True).half().eval().to("cuda")
+    inputs = [torch.randn((1, 3, 224, 224)).to("cuda").half()]
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 24-26
+
+Optional Input Arguments to `torch_tensorrt.compile`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 26-43
+
+.. code-block:: python
+
+
+    # Enabled precision for TensorRT optimization
+    enabled_precisions = {torch.half}
+
+    # Whether to print verbose logs
+    debug = True
+
+    # Workspace size for TensorRT
+    workspace_size = 20 << 30
+
+    # Maximum number of TRT Engines
+    # (Lower value allows more graph segmentation)
+    min_block_size = 7
+
+    # Operations to Run in Torch, regardless of converter support
+    torch_executed_ops = {}
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 44-46
+
+Compilation with `torch_tensorrt.compile`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 46-59
+
+.. code-block:: python
+
+
+    # Build and compile the model with torch.compile, using Torch-TensorRT backend
+    optimized_model = torch_tensorrt.compile(
+        model,
+        ir="torch_compile",
+        inputs=inputs,
+        enabled_precisions=enabled_precisions,
+        debug=debug,
+        workspace_size=workspace_size,
+        min_block_size=min_block_size,
+        torch_executed_ops=torch_executed_ops,
+    )
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 60-62
+
+Equivalently, we could have run the above via the torch.compile frontend, as so:
+`optimized_model = torch.compile(model, backend="torch_tensorrt", options={"enabled_precisions": enabled_precisions, ...}); optimized_model(*inputs)`
+
+.. GENERATED FROM PYTHON SOURCE LINES 64-66
+
+Inference
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 66-71
+
+.. code-block:: python
+
+
+    # Does not cause recompilation (same batch size as input)
+    new_inputs = [torch.randn((1, 3, 224, 224)).half().to("cuda")]
+    new_outputs = optimized_model(*new_inputs)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 72-77
+
+.. code-block:: python
+
+
+    # Does cause recompilation (new batch size)
+    new_batch_size_inputs = [torch.randn((8, 3, 224, 224)).half().to("cuda")]
+    new_batch_size_outputs = optimized_model(*new_batch_size_inputs)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 78-80
+
+Cleanup
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 80-84
+
+.. code-block:: python
+
+
+    # Finally, we use Torch utilities to clean up the workspace
+    torch._dynamo.reset()
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-94
+
+Cuda Driver Error Note
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Occasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,
+one may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052
+and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::
+
+      if __name__ == '__main__':
+          compile_engine_and_infer()
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  0.000 seconds)
+
+
+.. _sphx_glr_download_tutorials__rendered_examples_dynamo_torch_compile_resnet_example.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+
+
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: torch_compile_resnet_example.py <torch_compile_resnet_example.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: torch_compile_resnet_example.ipynb <torch_compile_resnet_example.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.rst.txt b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.rst.txt
new file mode 100644
index 0000000000..937eb5117e
--- /dev/null
+++ b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.rst.txt
@@ -0,0 +1,116 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_tutorials__rendered_examples_dynamo_torch_compile_stable_diffusion.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_stable_diffusion.py:
+
+
+.. _torch_compile_stable_diffusion:
+
+Torch Compile Stable Diffusion
+======================================================
+
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a Stable Diffusion model. A sample output is featured below:
+
+.. image:: /tutorials/images/majestic_castle.png
+   :width: 512px
+   :height: 512px
+   :scale: 50 %
+   :align: right
+
+.. GENERATED FROM PYTHON SOURCE LINES 17-19
+
+Imports and Model Definition
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 19-47
+
+.. code-block:: python
+
+
+    import torch
+    from diffusers import DiffusionPipeline
+
+    import torch_tensorrt
+
+    model_id = "CompVis/stable-diffusion-v1-4"
+    device = "cuda:0"
+
+    # Instantiate Stable Diffusion Pipeline with FP16 weights
+    pipe = DiffusionPipeline.from_pretrained(
+        model_id, revision="fp16", torch_dtype=torch.float16
+    )
+    pipe = pipe.to(device)
+
+    backend = "torch_tensorrt"
+
+    # Optimize the UNet portion with Torch-TensorRT
+    pipe.unet = torch.compile(
+        pipe.unet,
+        backend=backend,
+        options={
+            "truncate_long_and_double": True,
+            "precision": torch.float16,
+        },
+        dynamic=False,
+    )
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-50
+
+Inference
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 50-56
+
+.. code-block:: python
+
+
+    prompt = "a majestic castle in the clouds"
+    image = pipe(prompt).images[0]
+
+    image.save("images/majestic_castle.png")
+    image.show()
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  0.000 seconds)
+
+
+.. _sphx_glr_download_tutorials__rendered_examples_dynamo_torch_compile_stable_diffusion.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+
+
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: torch_compile_stable_diffusion.py <torch_compile_stable_diffusion.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: torch_compile_stable_diffusion.ipynb <torch_compile_stable_diffusion.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.rst.txt b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.rst.txt
new file mode 100644
index 0000000000..b362e42447
--- /dev/null
+++ b/docs/v2.2.0/_sources/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.rst.txt
@@ -0,0 +1,203 @@
+
+.. DO NOT EDIT.
+.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
+.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
+.. "tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.py"
+.. LINE NUMBERS ARE GIVEN BELOW.
+
+.. only:: html
+
+    .. note::
+        :class: sphx-glr-download-link-note
+
+        :ref:`Go to the end <sphx_glr_download_tutorials__rendered_examples_dynamo_torch_compile_transformers_example.py>`
+        to download the full example code
+
+.. rst-class:: sphx-glr-example-title
+
+.. _sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_transformers_example.py:
+
+
+.. _torch_compile_transformer:
+
+Compiling a Transformer using torch.compile and TensorRT
+==============================================================
+
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a transformer-based model.
+
+.. GENERATED FROM PYTHON SOURCE LINES 10-12
+
+Imports and Model Definition
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 12-17
+
+.. code-block:: python
+
+
+    import torch
+    import torch_tensorrt
+    from transformers import BertModel
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 18-27
+
+.. code-block:: python
+
+
+    # Initialize model with float precision and sample inputs
+    model = BertModel.from_pretrained("bert-base-uncased").eval().to("cuda")
+    inputs = [
+        torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+        torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+    ]
+
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 28-30
+
+Optional Input Arguments to `torch_tensorrt.compile`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 30-47
+
+.. code-block:: python
+
+
+    # Enabled precision for TensorRT optimization
+    enabled_precisions = {torch.float}
+
+    # Whether to print verbose logs
+    debug = True
+
+    # Workspace size for TensorRT
+    workspace_size = 20 << 30
+
+    # Maximum number of TRT Engines
+    # (Lower value allows more graph segmentation)
+    min_block_size = 7
+
+    # Operations to Run in Torch, regardless of converter support
+    torch_executed_ops = {}
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 48-50
+
+Compilation with `torch.compile`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 50-69
+
+.. code-block:: python
+
+
+    # Define backend compilation keyword arguments
+    compilation_kwargs = {
+        "enabled_precisions": enabled_precisions,
+        "debug": debug,
+        "workspace_size": workspace_size,
+        "min_block_size": min_block_size,
+        "torch_executed_ops": torch_executed_ops,
+    }
+
+    # Build and compile the model with torch.compile, using Torch-TensorRT backend
+    optimized_model = torch.compile(
+        model,
+        backend="torch_tensorrt",
+        dynamic=False,
+        options=compilation_kwargs,
+    )
+    optimized_model(*inputs)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 70-72
+
+Equivalently, we could have run the above via the convenience frontend, as so:
+`torch_tensorrt.compile(model, ir="torch_compile", inputs=inputs, **compilation_kwargs)`
+
+.. GENERATED FROM PYTHON SOURCE LINES 74-76
+
+Inference
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 76-84
+
+.. code-block:: python
+
+
+    # Does not cause recompilation (same batch size as input)
+    new_inputs = [
+        torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+        torch.randint(0, 2, (1, 14), dtype=torch.int32).to("cuda"),
+    ]
+    new_outputs = optimized_model(*new_inputs)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 85-93
+
+.. code-block:: python
+
+
+    # Does cause recompilation (new batch size)
+    new_inputs = [
+        torch.randint(0, 2, (4, 14), dtype=torch.int32).to("cuda"),
+        torch.randint(0, 2, (4, 14), dtype=torch.int32).to("cuda"),
+    ]
+    new_outputs = optimized_model(*new_inputs)
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 94-96
+
+Cleanup
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. GENERATED FROM PYTHON SOURCE LINES 96-100
+
+.. code-block:: python
+
+
+    # Finally, we use Torch utilities to clean up the workspace
+    torch._dynamo.reset()
+
+
+.. GENERATED FROM PYTHON SOURCE LINES 101-110
+
+Cuda Driver Error Note
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Occasionally, upon exiting the Python runtime after Dynamo compilation with `torch_tensorrt`,
+one may encounter a Cuda Driver Error. This issue is related to https://github.com/NVIDIA/TensorRT/issues/2052
+and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in::
+
+      if __name__ == '__main__':
+          compile_engine_and_infer()
+
+
+.. rst-class:: sphx-glr-timing
+
+   **Total running time of the script:** ( 0 minutes  0.000 seconds)
+
+
+.. _sphx_glr_download_tutorials__rendered_examples_dynamo_torch_compile_transformers_example.py:
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-example
+
+
+
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download Python source code: torch_compile_transformers_example.py <torch_compile_transformers_example.py>`
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download Jupyter notebook: torch_compile_transformers_example.ipynb <torch_compile_transformers_example.ipynb>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/v2.2.0/_sources/tutorials/_rendered_examples/index.rst.txt b/docs/v2.2.0/_sources/tutorials/_rendered_examples/index.rst.txt
new file mode 100644
index 0000000000..067d1b3e00
--- /dev/null
+++ b/docs/v2.2.0/_sources/tutorials/_rendered_examples/index.rst.txt
@@ -0,0 +1,139 @@
+:orphan:
+
+.. _torch_tensorrt_tutorials:
+
+Torch-TensorRT Tutorials
+===========================
+
+The user guide covers the basic concepts and usage of Torch-TensorRT.
+We also provide a number of tutorials to explore specific usecases and advanced concepts
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+
+.. raw:: html
+
+    </div>
+
+
+Dynamo / ``torch.compile``
+----------------------------
+
+Torch-TensorRT provides a backend for the new ``torch.compile`` API released in PyTorch 2.0. In the following examples we describe
+a number of ways you can leverage this backend to accelerate inference.
+
+* :ref:`torch_compile_resnet`: Compiling a ResNet model using the Torch Compile Frontend for ``torch_tensorrt.compile``
+* :ref:`torch_compile_transformer`: Compiling a Transformer model using ``torch.compile``
+* :ref:`torch_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API
+* :ref:`torch_compile_stable_diffusion`: Compiling a Stable Diffusion model using ``torch.compile``
+
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbnails">
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi...">
+
+.. only:: html
+
+  .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_torch_compile_stable_diffusion_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_stable_diffusion.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Torch Compile Stable Diffusion</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi...">
+
+.. only:: html
+
+  .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_torch_compile_resnet_example_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_resnet_example.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Compiling ResNet using the Torch-TensorRT torch.compile Backend</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi...">
+
+.. only:: html
+
+  .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_torch_compile_transformers_example_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_transformers_example.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Compiling a Transformer using torch.compile and TensorRT</div>
+    </div>
+
+
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as an overview of the process by which torch_tensorrt.compi...">
+
+.. only:: html
+
+  .. image:: /tutorials/_rendered_examples/dynamo/images/thumb/sphx_glr_torch_compile_advanced_usage_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_tutorials__rendered_examples_dynamo_torch_compile_advanced_usage.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Torch Compile Advanced Usage</div>
+    </div>
+
+
+.. raw:: html
+
+    </div>
+
+
+.. toctree::
+   :hidden:
+   :includehidden:
+
+
+   /tutorials/_rendered_examples//dynamo/index.rst
+
+
+.. only:: html
+
+  .. container:: sphx-glr-footer sphx-glr-footer-gallery
+
+    .. container:: sphx-glr-download sphx-glr-download-python
+
+      :download:`Download all examples in Python source code: _rendered_examples_python.zip </tutorials/_rendered_examples/_rendered_examples_python.zip>`
+
+    .. container:: sphx-glr-download sphx-glr-download-jupyter
+
+      :download:`Download all examples in Jupyter notebooks: _rendered_examples_jupyter.zip </tutorials/_rendered_examples/_rendered_examples_jupyter.zip>`
+
+
+.. only:: html
+
+ .. rst-class:: sphx-glr-signature
+
+    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
diff --git a/docs/v2.2.0/_sources/tutorials/notebooks.rst.txt b/docs/v2.2.0/_sources/tutorials/notebooks.rst.txt
new file mode 100644
index 0000000000..df903fc353
--- /dev/null
+++ b/docs/v2.2.0/_sources/tutorials/notebooks.rst.txt
@@ -0,0 +1,154 @@
+.. _notebooks:
+
+Example notebooks
+===================
+
+There exists a number of notebooks which cover specific using specific features and models
+with Torch-TensorRT
+
+Notebooks
+------------
+
+Compiling CitriNet with Torch-TensorRT
+********************************************
+
+Citrinet is an acoustic model used for the speech to text recognition task. It is a version
+of QuartzNet that extends ContextNet, utilizing subword encoding (via Word Piece tokenization)
+and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models. CitriNet
+models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.
+
+This notebook demonstrates the steps for optimizing a pretrained CitriNet model with Torch-TensorRT,
+and running it to test the speedup obtained.
+
+* `Torch-TensorRT Getting Started - CitriNet <https://github.com/pytorch/TensorRT/blob/master/notebooks/CitriNet-example.ipynb>`_
+
+
+Compiling EfficentNet with Torch-TensorRT
+********************************************
+
+EfficentNet is a feedforward CNN designed to achieve better performance and accuracy than alternative architectures
+by using a "scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient".
+
+This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
+and running it to test the speedup obtained.
+
+* `Torch-TensorRT Getting Started - EfficientNet-B0 <https://github.com/pytorch/TensorRT/blob/master/notebooks/EfficientNet-example.ipynb>`_
+
+
+Masked Language Modeling (MLM) with Hugging Face BERT Transformer accelerated by Torch-TensorRT
+*************************************************************************************************
+
+"BERT is a transformer model pretrained on a large corpus of English data in a self-supervised fashion.
+This way, the model learns an inner representation of the English language that can then be used to extract
+features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train
+a standard classifier using the features produced by the BERT model as inputs." (https://huggingface.co/bert-base-uncased)
+
+This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
+and running it to test the speedup obtained.
+
+* `Masked Language Modeling (MLM) with Hugging Face BERT Transformer <https://github.com/pytorch/TensorRT/blob/master/notebooks/Hugging-Face-BERT.ipynb>`_
+
+
+Serving a model in C++ using Torch-TensorRT
+**********************************************
+
+This example shows how you can load a pretrained ResNet-50 model, convert it to a Torch-TensorRT
+optimized model (via the Torch-TensorRT Python API), save the model as a torchscript module, and
+then finally load and serve the model with the PyTorch C++ API.
+
+* `ResNet C++ Serving Example <https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-CPP.ipynb>`_
+
+
+Compiling ResNet50 with Torch-TensorRT
+********************************************
+
+This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a
+pretrained ResNet-50 network, and running it to test the speedup obtained.
+
+* `Torch-TensorRT Getting Started - ResNet 50 <https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-example.ipynb>`_
+
+
+Using Dynamic Shapes with Torch-TensorRT
+********************************************
+
+Making use of Dynamic Shaped Tensors in Torch TensorRT is quite simple. Let's say you are
+using the ``torch_tensorrt.compile(...)`` function  to compile a torchscript module. One
+of the args in this function in this function is ``input``: which defines an input to a
+module in terms of expected shape, data type and tensor format: ``torch_tensorrt.Input.``
+
+For the purposes of this walkthrough we just need three kwargs: `min_shape`, `opt_shape`` and `max_shape`.
+
+.. code-block:: py
+
+    torch_tensorrt.Input(
+            min_shape=(1, 224, 224, 3),
+            opt_shape=(1, 512, 512, 3),
+            max_shape=(1, 1024, 1024, 3),
+            dtype=torch.int32
+            format=torch.channel_last
+        )
+    ...
+
+In this example, we are going to use a simple ResNet model to demonstrate the use of the API.
+
+* `Torch-TensorRT - Using Dynamic Shapes <https://github.com/pytorch/TensorRT/blob/master/notebooks/dynamic-shapes.ipynb>`_
+
+Using the FX Frontend with Torch-TensorRT
+********************************************
+
+The purpose of this example is to demostrate the overall flow of lowering a PyTorch model to TensorRT
+conveniently with using FX.
+
+* `Using the FX Frontend with Torch-TensorRT <https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb>`_
+
+
+Compiling a PyTorch model using FX Frontend with Torch-TensorRT
+*******************************************************************
+
+The purpose of this example is to demonstrate the overall flow of lowering a PyTorch
+model to TensorRT via FX with existing FX based tooling
+
+* `Compiling a PyTorch model using FX Frontend with Torch-TensorRT  <https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_module.ipynb>`_
+
+
+Compiling LeNet with Torch-TensorRT
+*******************************************************************
+
+This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network.
+
+* `Torch-TensorRT Getting Started - LeNet  <https://github.com/pytorch/TensorRT/blob/master/notebooks/lenet-getting-started.ipynb>`_
+
+
+Accelerate Deep Learning Models using Quantization in Torch-TensorRT
+*******************************************************************
+
+Model Quantization is a popular way of optimization which reduces the size of models thereby
+accelerating inference, also opening up the possibilities of deployments on devices with lower
+computation power such as Jetson. Simply put, quantization is a process of mapping input values
+ from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
+ that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
+ you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
+ called Imagenette which has 10 classes.
+
+* `Accelerate Deep Learning Models using Quantization in Torch-TensorRT <https://github.com/pytorch/TensorRT/blob/master/notebooks/qat-ptq-workflow.ipynb>`_
+
+
+Object Detection with Torch-TensorRT (SSD)
+*******************************************************************
+
+This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.
+
+* `Object Detection with Torch-TensorRT (SSD)  <https://github.com/pytorch/TensorRT/blob/master/notebooks/ssd-object-detection-demo.ipynb>`_
+
+
+Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT
+*****************************************************************************
+
+Quantization Aware training (QAT) simulates quantization during training by
+quantizing weights and activation layers. This will help to reduce the loss in
+accuracy when we convert the network trained in FP32 to INT8 for faster inference.
+QAT introduces additional nodes in the graph which will be used to learn the dynamic
+ranges of weights and activation layers. In this notebook, we illustrate the following
+steps from training to inference of a QAT model in Torch-TensorRT.
+
+* `Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT  <https://github.com/pytorch/TensorRT/blob/master/notebooks/vgg-qat.ipynb>`_
diff --git a/docs/v2.2.0/_sources/tutorials/serving_torch_tensorrt_with_triton.rst.txt b/docs/v2.2.0/_sources/tutorials/serving_torch_tensorrt_with_triton.rst.txt
new file mode 100644
index 0000000000..a713d563c2
--- /dev/null
+++ b/docs/v2.2.0/_sources/tutorials/serving_torch_tensorrt_with_triton.rst.txt
@@ -0,0 +1,216 @@
+.. _serving_torch_tensorrt_with_triton:
+
+Serving a Torch-TensorRT model with Triton
+==========================================
+
+Optimization and deployment go hand in hand in a discussion about Machine
+Learning infrastructure. Once network level optimzation are done
+to get the maximum performance, the next step would be to deploy it.
+
+However, serving this optimized model comes with it's own set of considerations
+and challenges like: building an infrastructure to support concorrent model
+executions, supporting clients over HTTP or gRPC and more.
+
+The `Triton Inference Server <https://github.com/triton-inference-server/server>`__
+solves the aforementioned and more. Let's discuss step-by-step, the process of
+optimizing a model with Torch-TensorRT, deploying it on Triton Inference
+Server, and building a client to query the model.
+
+Step 1: Optimize your model with Torch-TensorRT
+-----------------------------------------------
+
+Most Torch-TensorRT users will be familiar with this step. For the purpose of
+this demonstration, we will be using a ResNet50 model from Torchhub.
+
+Let’s first pull the `NGC PyTorch Docker container <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch>`__. You may need to create
+an account and get the API key from `here <https://ngc.nvidia.com/setup/>`__.
+Sign up and login with your key (follow the instructions
+`here <https://ngc.nvidia.com/setup/api-key>`__ after signing up).
+
+::
+
+   # <xx.xx> is the yy:mm for the publishing tag for NVIDIA's Pytorch
+   # container; eg. 22.04
+
+   docker run -it --gpus all -v ${PWD}:/scratch_space nvcr.io/nvidia/pytorch:<xx.xx>-py3
+   cd /scratch_space
+
+Once inside the container, we can proceed to download a ResNet model from
+Torchhub and optimize it with Torch-TensorRT.
+
+::
+
+   import torch
+   import torch_tensorrt
+   torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
+
+   # load model
+   model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True).eval().to("cuda")
+
+   # Compile with Torch TensorRT;
+   trt_model = torch_tensorrt.compile(model,
+       inputs= [torch_tensorrt.Input((1, 3, 224, 224))],
+       enabled_precisions= { torch.half} # Run with FP32
+   )
+
+   # Save the model
+   torch.jit.save(trt_model, "model.pt")
+
+After copying the model, exit the container. The next step in the process
+is to set up a Triton Inference Server.
+
+Step 2: Set Up Triton Inference Server
+--------------------------------------
+
+If you are new to the Triton Inference Server and want to learn more, we
+highly recommend to checking our `Github
+Repository <https://github.com/triton-inference-server>`__.
+
+To use Triton, we need to make a model repository. A model repository, as the
+name suggested, is a repository of the models the Inference server hosts. While
+Triton can serve models from multiple repositories, in this example, we will
+discuss the simplest possible form of the model repository.
+
+The structure of this repository should look something like this:
+
+::
+
+   model_repository
+   |
+   +-- resnet50
+       |
+       +-- config.pbtxt
+       +-- 1
+           |
+           +-- model.pt
+
+There are two files that Triton requires to serve the model: the model itself
+and a model configuration file which is typically provided in ``config.pbtxt``.
+For the model we prepared in step 1, the following configuration can be used:
+
+::
+
+   name: "resnet50"
+   platform: "pytorch_libtorch"
+   max_batch_size : 0
+   input [
+     {
+       name: "input__0"
+       data_type: TYPE_FP32
+       dims: [ 3, 224, 224 ]
+       reshape { shape: [ 1, 3, 224, 224 ] }
+     }
+   ]
+   output [
+     {
+       name: "output__0"
+       data_type: TYPE_FP32
+       dims: [ 1, 1000 ,1, 1]
+       reshape { shape: [ 1, 1000 ] }
+     }
+   ]
+
+The ``config.pbtxt`` file is used to describe the exact model configuration
+with details like the names and shapes of the input and output layer(s),
+datatypes, scheduling and batching details and more. If you are new to Triton,
+we highly encourage you to check out this `section of our
+documentation <https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md>`__
+for more details.
+
+With the model repository setup, we can proceed to launch the Triton server
+with the docker command below. Refer `this page <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver>`__ for the pull tag for the container.
+
+::
+
+   # Make sure that the TensorRT version in the Triton container
+   # and TensorRT version in the environment used to optimize the model
+   # are the same.
+
+   docker run --gpus all --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v /full/path/to/the_model_repository/model_repository:/models nvcr.io/nvidia/tritonserver:<xx.yy>-py3 tritonserver --model-repository=/models
+
+This should spin up a Triton Inference server. Next step, building a simple
+http client to query the server.
+
+Step 3: Building a Triton Client to Query the Server
+----------------------------------------------------
+
+Before proceeding, make sure to have a sample image on hand. If you don't
+have one, download an example image to test inference. In this section, we
+will be going over a very basic client. For a variety of more fleshed out
+examples, refer to the `Triton Client Repository <https://github.com/triton-inference-server/client/tree/main/src/python/examples>`__
+
+::
+
+   wget  -O img1.jpg "https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg"
+
+We then need to install dependencies for building a python client. These will
+change from client to client. For a full list of all languages supported by Triton,
+please refer to `Triton's client repository <https://github.com/triton-inference-server/client>`__.
+
+::
+
+   pip install torchvision
+   pip install attrdict
+   pip install nvidia-pyindex
+   pip install tritonclient[all]
+
+Let's jump into the client. Firstly, we write a small preprocessing function to
+resize and normalize the query image.
+
+::
+
+   import numpy as np
+   from torchvision import transforms
+   from PIL import Image
+   import tritonclient.http as httpclient
+   from tritonclient.utils import triton_to_np_dtype
+
+   # preprocessing function
+   def rn50_preprocess(img_path="img1.jpg"):
+       img = Image.open(img_path)
+       preprocess = transforms.Compose([
+           transforms.Resize(256),
+           transforms.CenterCrop(224),
+           transforms.ToTensor(),
+           transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+       ])
+       return preprocess(img).numpy()
+
+   transformed_img = rn50_preprocess()
+
+Building a client requires three basic points. Firstly, we setup a connection
+with the Triton Inference Server.
+
+::
+
+   # Setting up client
+   client = httpclient.InferenceServerClient(url="localhost:8000")
+
+Secondly, we specify the names of the input and output layer(s) of our model.
+
+::
+
+   inputs = httpclient.InferInput("input__0", transformed_img.shape, datatype="FP32")
+   inputs.set_data_from_numpy(transformed_img, binary_data=True)
+
+   outputs = httpclient.InferRequestedOutput("output__0", binary_data=True, class_count=1000)
+
+Lastly, we send an inference request to the Triton Inference Server.
+
+::
+
+   # Querying the server
+   results = client.infer(model_name="resnet50", inputs=[inputs], outputs=[outputs])
+   inference_output = results.as_numpy('output__0')
+   print(inference_output[:5])
+
+The output of the same should look like below:
+
+::
+
+   [b'12.468750:90' b'11.523438:92' b'9.664062:14' b'8.429688:136'
+    b'8.234375:11']
+
+The output format here is ``<confidence_score>:<classification_index>``.
+To learn how to map these to the label names and more, refer to Triton Inference Server's
+`documentation <https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_classification.md>`__.
diff --git a/docs/v2.2.0/_sources/user_guide/dynamic_shapes.rst.txt b/docs/v2.2.0/_sources/user_guide/dynamic_shapes.rst.txt
new file mode 100644
index 0000000000..73ed9b594a
--- /dev/null
+++ b/docs/v2.2.0/_sources/user_guide/dynamic_shapes.rst.txt
@@ -0,0 +1,209 @@
+.. _dynamic_shapes:
+
+Dynamic shapes with Torch-TensorRT
+====================================
+
+By default, you can run a pytorch model with varied input shapes and the output shapes are determined eagerly.
+However, Torch-TensorRT is an AOT compiler which requires some prior information about the input shapes to compile and optimize the model.
+In the case of dynamic input shapes, we must provide the (min_shape, opt_shape, max_shape) arguments so that the model can be optimized for
+these range of input shapes. An example usage of static and dynamic shapes is as follows.
+
+NOTE: The following code uses Dynamo Frontend. Incase of Torchscript Frontend, please swap out ``ir=dynamo`` with ``ir=ts`` and the behavior is exactly the same.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    # Compile with static shapes
+    inputs = torch_tensorrt.Input(shape=[1, 3, 224, 224], dtype=torch.float32)
+    # or compile with dynamic shapes
+    inputs = torch_tensorrt.Input(min_shape=[1, 3, 224, 224],
+                                  opt_shape=[4, 3, 224, 224],
+                                  max_shape=[8, 3, 224, 224],
+                                  dtype=torch.float32)
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+
+Under the hood
+--------------
+
+There are two phases of compilation when we use ``torch_tensorrt.compile`` API with ``ir=dynamo`` (default).
+
+- aten_tracer.trace (which uses torch.export to trace the graph with the given inputs)
+
+In the tracing phase, we use torch.export along with the constraints. In the case of
+dynamic shaped inputs, the range can be provided to the tracing via constraints. Please
+refer to this `docstring <https://github.com/pytorch/pytorch/blob/5dcee01c2b89f6bedeef9dd043fd8d6728286582/torch/export/__init__.py#L372-L434>`_
+for detailed information on how to set constraints. In short, we create new inputs for
+torch.export tracing and provide constraints on the min and max values(provided by the user), a particular dimension can take.
+Please take a look at ``aten_tracer.py`` file to understand how this works under the hood.
+
+- dynamo.compile (which compiles a torch.fx.GraphModule object using TensorRT)
+
+In the conversion to TensorRT, we use the user provided dynamic shape inputs.
+We perform shape analysis using dummy inputs (across min, opt and max shapes) and store the
+intermediate output shapes which can be used in case the graph has a mix of Pytorch
+and TensorRT submodules.
+
+Custom Constraints
+------------------
+
+Given an input ``x = torch_tensorrt.Input(min_shape, opt_shape, max_shape, dtype)``,
+Torch-TensorRT automatically sets the constraints during ``torch.export`` tracing as follows
+
+.. code-block:: python
+
+    for dim in constraint_dims:
+        if min_shape[dim] > 1:
+            constraints.append(min_shape[dim] <= dynamic_dim(trace_input, dim))
+        if max_shape[dim] > 1:
+            constraints.append(dynamic_dim(trace_input, dim) <= max_shape[dim])
+
+Sometimes, we might need to set additional constraints and Torchdynamo errors out if we don't specify them.
+For example, in the case of BERT model compilation, there are two inputs and a constraint has to be set involving the sequence length size of these two inputs.
+
+.. code-block:: python
+
+    constraints.append(dynamic_dim(trace_inputs[0], 0) == dynamic_dim(trace_inputs[1], 0))
+
+
+If you have to provide any custom constraints to your model, the overall workflow for model compilation using ``ir=dynamo`` would involve a few steps.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+    from torch_tensorrt.dynamo.lowering import apply_lowering_passes, get_decompositions
+    # Assume the model has two inputs
+    model = MyModel()
+    torch_input_1 = torch.randn((1, 14), dtype=torch.int32).cuda()
+    torch_input_2 = torch.randn((1, 14), dtype=torch.int32).cuda()
+
+    dynamic_inputs = [torch_tensorrt.Input(min_shape=[1, 14],
+                        opt_shape=[4, 14],
+                        max_shape=[8, 14],
+                        dtype=torch.int32),
+                      torch_tensorrt.Input(min_shape=[1, 14],
+                        opt_shape=[4, 14],
+                        max_shape=[8, 14],
+                        dtype=torch.int32)]
+
+    # Export the model with additional constraints
+    constraints = []
+    # The following constraints are automatically added by Torch-TensorRT in the
+    # general case when you call torch_tensorrt.compile directly on MyModel()
+    constraints.append(dynamic_dim(torch_input_1, 0) < 8)
+    constraints.append(dynamic_dim(torch_input_2, 0) < 8)
+    # This is an additional constraint as instructed by Torchdynamo
+    constraints.append(dynamic_dim(torch_input_1, 0) == dynamic_dim(torch_input_2, 0))
+    with unittest.mock.patch(
+        "torch._export.DECOMP_TABLE", get_decompositions(experimental_decompositions)
+    ):
+        graph_module = export(
+            model, (torch_input_1, torch_input_2), constraints=constraints
+        ).module()
+
+    # Use the dynamo.compile API
+    trt_mod = torch_tensorrt.dynamo.compile(graph_module, inputs=dynamic_inputs, **compile_spec)
+
+Limitations
+-----------
+
+If there are operations in the graph that use the dynamic dimension of the input, Pytorch
+introduces ``torch.ops.aten.sym_size.int`` ops in the graph. Currently, we cannot handle these operators and
+the compilation results in undefined behavior. We plan to add support for these operators and implement
+robust support for shape tensors in the next release. Here is an example of the limitation described above
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    class MyModule(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.avgpool = torch.nn.AdaptiveAvgPool2d((1, 1))
+
+        def forward(self, x):
+            x = self.avgpool(x)
+            out = torch.flatten(x, 1)
+            return out
+
+    model = MyModel().eval().cuda()
+    # Compile with dynamic shapes
+    inputs = torch_tensorrt.Input(min_shape=(1, 512, 1, 1),
+                         opt_shape=(4, 512, 1, 1),
+                         max_shape=(8, 512, 1, 1),
+                         dtype=torch.float32)
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs)
+
+
+The traced graph of `MyModule()` looks as follows
+
+.. code-block:: python
+
+    Post export graph: graph():
+    %arg0_1 : [num_users=2] = placeholder[target=arg0_1]
+    %mean : [num_users=1] = call_function[target=torch.ops.aten.mean.dim](args = (%arg0_1, [-1, -2], True), kwargs = {})
+    %sym_size : [num_users=1] = call_function[target=torch.ops.aten.sym_size.int](args = (%arg0_1, 0), kwargs = {})
+    %view : [num_users=1] = call_function[target=torch.ops.aten.view.default](args = (%mean, [%sym_size, 512]), kwargs = {})
+    return (view,)
+
+
+Here the ``%sym_size`` node captures the dynamic batch and uses it in the ``aten.view`` layer. This requires shape tensors support
+which would be a part of our next release.
+
+Workaround (BERT static compilation example)
+------------------------------------------
+
+In the case where you encounter the issues mentioned in the **Limitations** section,
+you can compile the model (static mode) with max input size that can be provided. In the cases of smaller inputs,
+we can pad them accordingly. This is only a workaround until we address the limitations.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+    from transformers.utils.fx import symbolic_trace as transformers_trace
+
+    model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
+
+    # Input sequence length is 20.
+    input1 = torch.randint(0, 5, (1, 20), dtype=torch.int32).to("cuda")
+    input2 = torch.randint(0, 5, (1, 20), dtype=torch.int32).to("cuda")
+
+    model = transformers_trace(model, input_names=["input_ids", "attention_mask"]).eval().cuda()
+    trt_mod = torch_tensorrt.compile(model, inputs=[input1, input2], **compile_spec)
+    model_outputs = model(input, input2)
+
+    # If you have a sequence of length 14, pad 6 zero tokens and run inference
+    # or recompile for sequence length of 14.
+    input1 = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
+    input2 = torch.randint(0, 5, (1, 14), dtype=torch.int32).to("cuda")
+    trt_mod = torch_tensorrt.compile(model, inputs=[input1, input2], **compile_spec)
+    model_outputs = model(input, input2)
+
+
+Dynamic shapes with ir=torch_compile
+------------------------------------
+
+``torch_tensorrt.compile(model, inputs, ir="torch_compile")`` returns a torch.compile boxed function with the backend
+configured to Tensorrt. In the case of ``ir=torch_compile``, when the input size changes, Dynamo will trigger a recompilation
+of the TensorRT engine automatically giving dynamic shape behavior similar to native PyTorch eager however with the cost of rebuilding
+TRT engine. This limitation will be addressed in future versions of Torch-TensorRT.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = torch.randn((1, 3, 224, 224), dtype=float32)
+    trt_gm = torch_tensorrt.compile(model, ir="torch_compile", inputs)
+    # Compilation happens when you call the model
+    trt_gm(inputs)
+
+    # Recompilation happens with modified batch size
+    inputs_bs2 = torch.randn((2, 3, 224, 224), dtype=torch.float32)
+    trt_gm = torch_tensorrt.compile(model, ir="torch_compile", inputs_bs2)
diff --git a/docs/v2.2.0/_sources/user_guide/ptq.rst.txt b/docs/v2.2.0/_sources/user_guide/ptq.rst.txt
new file mode 100644
index 0000000000..b62457109f
--- /dev/null
+++ b/docs/v2.2.0/_sources/user_guide/ptq.rst.txt
@@ -0,0 +1,205 @@
+.. _ptq:
+
+Post Training Quantization (PTQ)
+=================================
+
+Post Training Quantization (PTQ) is a technique to reduce the required computational resources for inference
+while still preserving the accuracy of your model by mapping the traditional FP32 activation space to a reduced
+INT8 space. TensorRT uses a calibration step which executes your model with sample data from the target domain
+and track the activations in FP32 to calibrate a mapping to INT8 that minimizes the information loss between
+FP32 inference and INT8 inference.
+
+Users writing TensorRT applications are required to setup a calibrator class which will provide sample data to
+the TensorRT calibrator. With Torch-TensorRT we look to leverage existing infrastructure in PyTorch to make implementing
+calibrators easier.
+
+LibTorch provides a ``DataLoader`` and ``Dataset`` API which steamlines preprocessing and batching input data.
+These APIs are exposed via both C++ and Python interface which makes it easier for the end user.
+For C++ interface, we use ``torch::Dataset`` and ``torch::data::make_data_loader`` objects to construct and perform pre-processing on datasets.
+The equivalent functionality in python interface uses ``torch.utils.data.Dataset`` and ``torch.utils.data.DataLoader``.
+This section of the PyTorch documentation has more information https://pytorch.org/tutorials/advanced/cpp_frontend.html#loading-data and https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html.
+Torch-TensorRT uses Dataloaders as the base of a generic calibrator implementation. So you will be able to reuse or quickly
+implement a ``torch::Dataset`` for your target domain, place it in a DataLoader and create a INT8 Calibrator
+which you can provide to Torch-TensorRT to run INT8 Calibration during compliation of your module.
+
+.. _writing_ptq_cpp:
+
+How to create your own PTQ application in C++
+-----------------------------------------------
+
+Here is an example interface of a ``torch::Dataset`` class for CIFAR10:
+
+.. code-block:: c++
+    :linenos:
+
+    //cpp/ptq/datasets/cifar10.h
+    #pragma once
+
+    #include "torch/data/datasets/base.h"
+    #include "torch/data/example.h"
+    #include "torch/types.h"
+
+    #include <cstddef>
+    #include <string>
+
+    namespace datasets {
+    // The CIFAR10 Dataset
+    class CIFAR10 : public torch::data::datasets::Dataset<CIFAR10> {
+    public:
+        // The mode in which the dataset is loaded
+        enum class Mode { kTrain, kTest };
+
+        // Loads CIFAR10 from un-tarred file
+        // Dataset can be found https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz
+        // Root path should be the directory that contains the content of tarball
+        explicit CIFAR10(const std::string& root, Mode mode = Mode::kTrain);
+
+        // Returns the pair at index in the dataset
+        torch::data::Example<> get(size_t index) override;
+
+        // The size of the dataset
+        c10::optional<size_t> size() const override;
+
+        // The mode the dataset is in
+        bool is_train() const noexcept;
+
+        // Returns all images stacked into a single tensor
+        const torch::Tensor& images() const;
+
+        // Returns all targets stacked into a single tensor
+        const torch::Tensor& targets() const;
+
+        // Trims the dataset to the first n pairs
+        CIFAR10&& use_subset(int64_t new_size);
+
+
+    private:
+        Mode mode_;
+        torch::Tensor images_, targets_;
+    };
+    } // namespace datasets
+
+
+This class's implementation reads from the binary distribution of the CIFAR10 dataset and builds two tensors which hold the images and labels.
+
+We use a subset of the dataset to use for calibration, since we don't need the the full dataset for effective calibration and calibration does
+some take time, then define the preprocessing to apply to the images in the dataset and create a DataLoader from the dataset which will batch the data:
+
+.. code-block:: c++
+
+    auto calibration_dataset = datasets::CIFAR10(data_dir, datasets::CIFAR10::Mode::kTest)
+                                        .use_subset(320)
+                                        .map(torch::data::transforms::Normalize<>({0.4914, 0.4822, 0.4465},
+                                                                                {0.2023, 0.1994, 0.2010}))
+                                        .map(torch::data::transforms::Stack<>());
+    auto calibration_dataloader = torch::data::make_data_loader(std::move(calibration_dataset),
+                                                                torch::data::DataLoaderOptions().batch_size(32)
+                                                                                                .workers(2));
+
+
+Next we create a calibrator from the ``calibration_dataloader`` using the calibrator factory (found in ``torch_tensorrt/ptq.h``):
+
+.. code-block:: c++
+
+    #include "torch_tensorrt/ptq.h"
+    ...
+
+    auto calibrator = torch_tensorrt::ptq::make_int8_calibrator(std::move(calibration_dataloader), calibration_cache_file, true);
+
+Here we also define a location to write a calibration cache file to which we can use to reuse the calibration data without needing the dataset and whether or not
+we should use the cache file if it exists. There also exists a ``torch_tensorrt::ptq::make_int8_cache_calibrator`` factory which creates a calibrator that uses the cache
+only for cases where you may do engine building on a machine that has limited storage (i.e. no space for a full dataset) or to have a simpiler deployment application.
+
+The calibrator factories create a calibrator that inherits from a ``nvinfer1::IInt8Calibrator`` virtual class (``nvinfer1::IInt8EntropyCalibrator2`` by default) which
+defines the calibration algorithm used when calibrating. You can explicitly make the selection of calibration algorithm like this:
+
+.. code-block:: c++
+
+    // MinMax Calibrator is geared more towards NLP tasks
+    auto calibrator = torch_tensorrt::ptq::make_int8_calibrator<nvinfer1::IInt8MinMaxCalibrator>(std::move(calibration_dataloader), calibration_cache_file, true);
+
+Then all thats required to setup the module for INT8 calibration is to set the following compile settings in the `torch_tensorrt::CompileSpec` struct and compiling the module:
+
+.. code-block:: c++
+
+    std::vector<std::vector<int64_t>> input_shape = {{32, 3, 32, 32}};
+    /// Configure settings for compilation
+    auto compile_spec = torch_tensorrt::CompileSpec({input_shape});
+    /// Set operating precision to INT8
+    compile_spec.enabled_precisions.insert(torch::kF16);
+    compile_spec.enabled_precisions.insert(torch::kI8);
+    /// Use the TensorRT Entropy Calibrator
+    compile_spec.ptq_calibrator = calibrator;
+
+    auto trt_mod = torch_tensorrt::CompileGraph(mod, compile_spec);
+
+If you have an existing Calibrator implementation for TensorRT you may directly set the ``ptq_calibrator`` field with a pointer to your calibrator and it will work as well.
+From here not much changes in terms of how to execution works. You are still able to fully use LibTorch as the sole interface for inference. Data should remain
+in FP32 precision when it's passed into `trt_mod.forward`. There exists an example application in the Torch-TensorRT demo that takes you from training a VGG16 network on
+CIFAR10 to deploying in INT8 with Torch-TensorRT here: https://github.com/pytorch/TensorRT/tree/master/cpp/ptq
+
+.. _writing_ptq_python:
+
+How to create your own PTQ application in Python
+--------------------------------------------------
+
+Torch-TensorRT Python API provides an easy and convenient way to use pytorch dataloaders with TensorRT calibrators. ``DataLoaderCalibrator`` class can be used to create
+a TensorRT calibrator by providing desired configuration. The following code demonstrates an example on how to use it
+
+.. code-block:: python
+
+    testing_dataset = torchvision.datasets.CIFAR10(
+        root="./data",
+        train=False,
+        download=True,
+        transform=transforms.Compose(
+            [
+                transforms.ToTensor(),
+                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+            ]
+        ),
+    )
+
+    testing_dataloader = torch.utils.data.DataLoader(
+        testing_dataset, batch_size=1, shuffle=False, num_workers=1
+    )
+    calibrator = torch_tensorrt.ptq.DataLoaderCalibrator(
+        testing_dataloader,
+        cache_file="./calibration.cache",
+        use_cache=False,
+        algo_type=torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2,
+        device=torch.device("cuda:0"),
+    )
+
+    trt_mod = torch_tensorrt.compile(model, inputs=[torch_tensorrt.Input((1, 3, 32, 32))],
+                                        enabled_precisions={torch.float, torch.half, torch.int8},
+                                        calibrator=calibrator,
+                                        device={
+                                             "device_type": torch_tensorrt.DeviceType.GPU,
+                                             "gpu_id": 0,
+                                             "dla_core": 0,
+                                             "allow_gpu_fallback": False,
+                                             "disable_tf32": False
+                                         })
+
+In the cases where there is a pre-existing calibration cache file that users want to use, ``CacheCalibrator`` can be used without any dataloaders. The following example demonstrates how
+to use ``CacheCalibrator`` to use in INT8 mode.
+
+.. code-block:: python
+
+  calibrator = torch_tensorrt.ptq.CacheCalibrator("./calibration.cache")
+
+  trt_mod = torch_tensorrt.compile(model, inputs=[torch_tensorrt.Input([1, 3, 32, 32])],
+                                        enabled_precisions={torch.float, torch.half, torch.int8},
+                                        calibrator=calibrator)
+
+If you already have an existing calibrator class (implemented directly using TensorRT API), you can directly set the calibrator field to your class which can be very convenient.
+For a demo on how PTQ can be performed on a VGG network using Torch-TensorRT API, you can refer to https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_dataloader_calibrator.py
+and https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_trt_calibrator.py
+
+Citations
+^^^^^^^^^^^
+
+Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images.
+
+Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
diff --git a/docs/v2.2.0/_sources/user_guide/runtime.rst.txt b/docs/v2.2.0/_sources/user_guide/runtime.rst.txt
new file mode 100644
index 0000000000..8264abdd32
--- /dev/null
+++ b/docs/v2.2.0/_sources/user_guide/runtime.rst.txt
@@ -0,0 +1,70 @@
+.. _runtime:
+
+Deploying Torch-TensorRT Programs
+====================================
+
+After compiling and saving Torch-TensorRT programs there is no longer a strict dependency on the full
+Torch-TensorRT library. All that is required to run a compiled program is the runtime. There are therfore a couple
+options to deploy your programs other than shipping the full Torch-TensorRT compiler with your applications.
+
+Torch-TensorRT package / libtorchtrt.so
+--------------------------------------------
+
+Once a program is compiled, you run it using the standard PyTorch APIs. All that is required is that the package
+must be imported in python or linked in C++.
+
+Runtime Library
+-----------------
+
+Distributed with the C++ distribution is ``libtorchtrt_runtime.so``. This library only contains the components
+necessary to run Torch-TensorRT programs. Instead of linking ``libtorchtrt.so`` or importing ``torch_tensorrt`` you can
+link ``libtorchtrt_runtime.so`` in your deployment programs or use ``DL_OPEN`` or ``LD_PRELOAD``. For python
+you can load the runtime with ``torch.ops.load_library("libtorchtrt_runtime.so")``. You can then continue to use
+programs just as you would otherwise via PyTorch API.
+
+.. note:: If you are using the standard distribution of PyTorch in Python on x86, likely you will need the pre-cxx11-abi variant of ``libtorchtrt_runtime.so``, check :ref:`Installation` documentation for more details.
+
+.. note:: If you are linking ``libtorchtrt_runtime.so``, likely using the following flags will help ``-Wl,--no-as-needed -ltorchtrt -Wl,--as-needed`` as theres no direct symbol dependency to anything in the Torch-TensorRT runtime for most Torch-TensorRT runtime applications
+
+An example of how to use ``libtorchtrt_runtime.so`` can be found here: https://github.com/pytorch/TensorRT/tree/master/examples/torchtrt_runtime_example
+
+Plugin Library
+---------------
+
+In the case you use Torch-TensorRT as a converter to a TensorRT engine and your engine uses plugins provided by Torch-TensorRT, Torch-TensorRT
+ships the library ``libtorchtrt_plugins.so`` which contains the implementation of the TensorRT plugins used by Torch-TensorRT during
+compilation. This library can be ``DL_OPEN`` or ``LD_PRELOAD`` similar to other TensorRT plugin libraries.
+
+Multi Device Safe Mode
+---------------
+
+Multi-device safe mode is a setting in Torch-TensorRT which allows the user to determine whether
+the runtime checks for device consistency prior to every inference call.
+
+There is a non-negligible, fixed cost per-inference call when multi-device safe mode is enabled, which is why
+it is now disabled by default. It can be controlled via the following convenience function which
+doubles as a context manager.
+
+.. code-block:: python
+
+    # Enables Multi Device Safe Mode
+    torch_tensorrt.runtime.set_multi_device_safe_mode(True)
+
+    # Disables Multi Device Safe Mode [Default Behavior]
+    torch_tensorrt.runtime.set_multi_device_safe_mode(False)
+
+    # Enables Multi Device Safe Mode, then resets the safe mode to its prior setting
+    with torch_tensorrt.runtime.set_multi_device_safe_mode(True):
+        ...
+
+TensorRT requires that each engine be associated with the CUDA context in the active thread from which it is invoked.
+Therefore, if the device were to change in the active thread, which may be the case when invoking
+engines on multiple GPUs from the same Python process, safe mode will cause Torch-TensorRT to display
+an alert and switch GPUs accordingly. If safe mode were not enabled, there could be a mismatch in the engine
+device and CUDA context device, which could lead the program to crash.
+
+One technique for managing multiple TRT engines on different GPUs while not sacrificing performance for
+multi-device safe mode is to use Python threads. Each thread is responsible for all of the TRT engines
+on a single GPU, and the default CUDA device on each thread corresponds to the GPU for which it is
+responsible (can be set via ``torch.cuda.set_device(...)``). In this way, multiple threads can be used in the same
+Python script without needing to switch CUDA contexts and incur performance overhead.
diff --git a/docs/v2.2.0/_sources/user_guide/saving_models.rst.txt b/docs/v2.2.0/_sources/user_guide/saving_models.rst.txt
new file mode 100644
index 0000000000..8379b44f0f
--- /dev/null
+++ b/docs/v2.2.0/_sources/user_guide/saving_models.rst.txt
@@ -0,0 +1,101 @@
+.. _saving_models:
+
+Saving models compiled with Torch-TensorRT
+====================================
+.. currentmodule:: torch_tensorrt.dynamo
+
+.. automodule:: torch_tensorrt.dynamo
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Saving models compiled with Torch-TensorRT varies slightly with the `ir` that has been used for compilation.
+
+Dynamo IR
+-------------
+
+The output type of `ir=dynamo` compilation of Torch-TensorRT is `torch.export.ExportedProgram` object by default. 
+In addition, we provide a new parameter `output_format` in the `CompilationSetting` object provided before compilation.
+The `output_format` can take the following options 
+
+* `exported_program` (or) `ep` : This is the default. Returns an ExportedProgram 
+* `torchscript` (or) `ts` : This returns a TorchScript module
+* `graph_module` (or) `fx` : This returns a torch.fx.GraphModule which can be traced into Torchscript to save to disk.
+
+a) Torchscript
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you set the `output_format="torchscript"`, this will return a `ScriptModule` which can be serialized via torch.jit.save
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = [torch.randn((1, 3, 224, 224)).cuda()]
+    # trt_ts is a torch.jit.ScriptModule object
+    trt_ts = torch_tensorrt.compile(model, ir="dynamo", inputs, output_format="torchscript")
+    torch.jit.save(trt_ts, "trt_model.ts")
+
+    # Later, you can load it and run inference
+    model = torch.jit.load("trt_model.ts").cuda()
+    model(*inputs)
+
+b) ExportedProgram
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+`torch.export.ExportedProgram`, a new format introduced in Pytorch 2.X is the default return type of Torch-TensorRT compilation.
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = [torch.randn((1, 3, 224, 224)).cuda()]
+    # trt_ep is a torch.export.ExportedProgram object
+    trt_ep = torch_tensorrt.compile(model, ir="dynamo", inputs) 
+    torch.export.save(trt_ep, "trt_model.ep")
+
+    # Later, you can load it and run inference
+    model = torch.export.load("trt_model.ep")
+    model(*inputs)
+
+c) GraphModule
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We can also return a `torch.fx.GraphModule` object as the output of Torch-TensorRT compilation by setting `output_format="graph_module"`.
+Internally, partitioning, lowering, conversion phases operate using GraphModule objects. These can be either traced into a Torchscript modules or 
+exported into `ExportedProgram` objects
+
+.. code-block:: python
+
+    import torch
+    import torch_tensorrt
+
+    model = MyModel().eval().cuda()
+    inputs = [torch.randn((1, 3, 224, 224)).cuda()]
+    # trt_gm is a torch.fx.GraphModule object
+    trt_gm = torch_tensorrt.compile(model, ir="dynamo", inputs, output_format="graph_module") 
+
+Torchscript IR
+-------------
+
+In Torch-TensorRT 1.X versions, the primary way to compile and run inference with Torch-TensorRT is using Torchscript IR.
+For `ir=ts`, this behavior stays the same in 2.X versions as well.
+
+.. code-block:: python
+
+  import torch
+  import torch_tensorrt
+
+  model = MyModel().eval().cuda()
+  inputs = [torch.randn((1, 3, 224, 224)).cuda()]
+  trt_ts = torch_tensorrt.compile(model, ir="ts", inputs) # Output is a ScriptModule object
+  torch.jit.save(trt_ts, "trt_model.ts")
+
+  # Later, you can load it and run inference
+  model = torch.jit.load("trt_model.ts").cuda()
+  model(*inputs)
+
diff --git a/docs/v2.2.0/_sources/user_guide/using_dla.rst.txt b/docs/v2.2.0/_sources/user_guide/using_dla.rst.txt
new file mode 100644
index 0000000000..9b062f7053
--- /dev/null
+++ b/docs/v2.2.0/_sources/user_guide/using_dla.rst.txt
@@ -0,0 +1,47 @@
+.. _using_dla:
+
+DLA
+=================================
+
+``DLA`` NVIDIA Deep Learning Accelerator is a fixed-function accelerator engine targeted for deep learning operations. DLA is designed to do full hardware acceleration of convolutional neural networks. DLA supports various layers such as convolution, deconvolution, fully-connected, activation, pooling, batch normalization, etc. ``torch_tensorrt`` supports compilation of TorchScript Module and deployment pipeline on the DLA hardware available on NVIDIA embedded platforms.
+
+NOTE: DLA supports fp16 and int8 precision only.
+
+Using DLA with torchtrtc
+
+.. code-block:: shell
+
+    torchtrtc [input_file_path] [output_file_path] [input_shapes...] -p f16 -d dla {OPTIONS}
+
+Using DLA in a C++ application
+
+.. code-block:: c++
+
+    std::vector<std::vector<int64_t>> input_shape = {{32, 3, 32, 32}};
+    auto compile_spec = torch_tensorrt::CompileSpec({input_shape});
+
+    # Set a precision. DLA supports fp16 or int8 only
+    compile_spec.enabled_precisions = {torch::kF16};
+    compile_spec.device.device_type = torch_tensorrt::CompileSpec::DeviceType::kDLA;
+
+    # Make sure the gpu id is set to Xavier id for DLA
+    compile_spec.device.gpu_id = 0;
+
+    # Set the DLA core id
+    compile_spec.device.dla_core = 1;
+
+    # If a layer fails to run on DLA it will fallback to GPU
+    compile_spec.device.allow_gpu_fallback = true;
+
+
+Using DLA in a python application
+
+.. code-block:: python
+
+    compile_spec = {
+        "inputs": [torch_tensorrt.Input(self.input.shape)],
+        "device": torch_tensorrt.Device("dla:0", allow_gpu_fallback=True),
+        "enalbed_precisions": {torch.half},
+    }
+
+    trt_mod = torch_tensorrt.compile(self.scripted_model, compile_spec)
diff --git a/docs/v2.2.0/_static/basic.css b/docs/v2.2.0/_static/basic.css
new file mode 100644
index 0000000000..bf18350b65
--- /dev/null
+++ b/docs/v2.2.0/_static/basic.css
@@ -0,0 +1,906 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: 230px;
+    margin-left: -100%;
+    font-size: 90%;
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+
+div.sphinxsidebar ul {
+    list-style: none;
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+div.sphinxsidebar ul ul,
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+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
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+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
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+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
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+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
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+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
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+
+ul.search li a {
+    font-weight: bold;
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+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
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+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: 450px;
+    max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
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+}
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+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
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+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
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+}
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+}
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+    margin-top: 10px;
+}
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+    margin-bottom: 10px;
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+}
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+}
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+    margin-bottom: 10px;
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+}
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+    border-bottom: 1px solid #aaa;
+}
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+}
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+    margin-left: 1px;
+}
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+}
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+}
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+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
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+    padding: 0.3em;
+}
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+}
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+}
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+}
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+}
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+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
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+    margin: 1em 0;
+}
+
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+}
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+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
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+    margin-bottom: 10px;
+    margin-left: 30px;
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+}
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+    margin-top: 1em;
+    margin-bottom: 1em;
+}
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+    margin-bottom: 0;
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+}
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+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
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+
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+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
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+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
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+}
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+}
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+  -webkit-user-select: none; /* Chrome/Safari */
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+}
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new file mode 100644
index 0000000000..ef81a64535
--- /dev/null
+++ b/docs/v2.2.0/_static/collapsible-lists/LICENSE.md
@@ -0,0 +1,7 @@
+This code is the fruit of Kate Morley's labor, taken from here:
+
+- http://code.iamkate.com/javascript/collapsible-lists/
+
+She includes a generous CC0 1.0 license for all materials on her site:
+
+- http://code.iamkate.com/
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diff --git a/docs/v2.2.0/_static/collapsible-lists/css/tree_view.css b/docs/v2.2.0/_static/collapsible-lists/css/tree_view.css
new file mode 100644
index 0000000000..fa21ac4e84
--- /dev/null
+++ b/docs/v2.2.0/_static/collapsible-lists/css/tree_view.css
@@ -0,0 +1,61 @@
+/* Source taken directly from:
+ *     view-source:http://code.iamkate.com/javascript/collapsible-lists/
+ *
+ * Kate Morley's license for this code is CC0:
+ *     Created by [Kate Morley](http://iamkate.com/). Except where explicitly
+ *     stated otherwise, all content is released under the terms of the
+ *     [CC0 1.0 Universal legal code](http://creativecommons.org/publicdomain/zero/1.0/legalcode).
+ */
+.treeView{
+  -moz-user-select:none;
+  position:relative;
+}
+
+.treeView ul{
+  margin:0 0 0 -1.5em ! important;
+  padding:0 0 0 1.5em ! important;
+}
+
+.treeView ul ul{
+  background:url('list-item-contents.png') repeat-y left ! important;
+}
+
+.treeView li.lastChild > ul{
+  background-image:none ! important;
+}
+
+.treeView li{
+  margin:0 ! important;
+  padding:0 ! important;
+  background:url('list-item-root.png') no-repeat top left ! important;
+  list-style-position:inside ! important;
+  list-style-image:url('button.png') ! important;
+  cursor:auto;
+}
+
+.treeView li.collapsibleListOpen{
+  list-style-image:url('button-open.png') ! important;
+  cursor:pointer;
+}
+
+.treeView li.collapsibleListClosed{
+  list-style-image:url('button-closed.png') ! important;
+  cursor:pointer;
+}
+
+.treeView li li{
+  background-image:url('list-item.png') ! important;
+  padding-left:1.5em ! important;
+}
+
+.treeView li.lastChild{
+  background-image:url('list-item-last.png') ! important;
+}
+
+.treeView li.collapsibleListOpen{
+  background-image:url('list-item-open.png') ! important;
+}
+
+.treeView li.collapsibleListOpen.lastChild{
+  background-image:url('list-item-last-open.png') ! important;
+}
diff --git a/docs/v2.2.0/_static/collapsible-lists/js/CollapsibleLists.compressed.js b/docs/v2.2.0/_static/collapsible-lists/js/CollapsibleLists.compressed.js
new file mode 100644
index 0000000000..429406cf39
--- /dev/null
+++ b/docs/v2.2.0/_static/collapsible-lists/js/CollapsibleLists.compressed.js
@@ -0,0 +1,83 @@
+/*
+
+CollapsibleLists.js
+
+An object allowing lists to dynamically expand and collapse
+
+Created by Kate Morley - http://code.iamkate.com/ - and released under
+the terms of the CC0 1.0 Universal legal code:
+
+http://creativecommons.org/publicdomain/zero/1.0/legalcode
+
+*/
+
+var CollapsibleLists=new function(){
+this.apply=function(_1){
+var _2=document.getElementsByTagName("ul");
+for(var _3=0;_3<_2.length;_3++){
+if(_2[_3].className.match(/(^| )collapsibleList( |$)/)){
+this.applyTo(_2[_3],true);
+if(!_1){
+var _4=_2[_3].getElementsByTagName("ul");
+for(var _5=0;_5<_4.length;_5++){
+_4[_5].className+=" collapsibleList";
+}
+}
+}
+}
+};
+this.applyTo=function(_6,_7){
+var _8=_6.getElementsByTagName("li");
+for(var _9=0;_9<_8.length;_9++){
+if(!_7||_6==_8[_9].parentNode){
+if(_8[_9].addEventListener){
+_8[_9].addEventListener("mousedown",function(e){
+e.preventDefault();
+},false);
+}else{
+_8[_9].attachEvent("onselectstart",function(){
+event.returnValue=false;
+});
+}
+if(_8[_9].addEventListener){
+_8[_9].addEventListener("click",_a(_8[_9]),false);
+}else{
+_8[_9].attachEvent("onclick",_a(_8[_9]));
+}
+_b(_8[_9]);
+}
+}
+};
+function _a(_c){
+return function(e){
+if(!e){
+e=window.event;
+}
+var _d=(e.target?e.target:e.srcElement);
+while(_d.nodeName!="LI"){
+_d=_d.parentNode;
+}
+if(_d==_c){
+_b(_c);
+}
+};
+};
+function _b(_e){
+var _f=_e.className.match(/(^| )collapsibleListClosed( |$)/);
+var uls=_e.getElementsByTagName("ul");
+for(var _10=0;_10<uls.length;_10++){
+var li=uls[_10];
+while(li.nodeName!="LI"){
+li=li.parentNode;
+}
+if(li==_e){
+uls[_10].style.display=(_f?"block":"none");
+}
+}
+_e.className=_e.className.replace(/(^| )collapsibleList(Open|Closed)( |$)/,"");
+if(uls.length>0){
+_e.className+=" collapsibleList"+(_f?"Open":"Closed");
+}
+};
+}();
+
diff --git a/docs/v2.2.0/_static/collapsible-lists/js/apply-collapsible-lists.js b/docs/v2.2.0/_static/collapsible-lists/js/apply-collapsible-lists.js
new file mode 100644
index 0000000000..e848bb9811
--- /dev/null
+++ b/docs/v2.2.0/_static/collapsible-lists/js/apply-collapsible-lists.js
@@ -0,0 +1,3 @@
+$(document).ready(function() {
+    CollapsibleLists.apply();
+});
diff --git a/docs/v2.2.0/_static/css/custom.css b/docs/v2.2.0/_static/css/custom.css
new file mode 100644
index 0000000000..2523d4e541
--- /dev/null
+++ b/docs/v2.2.0/_static/css/custom.css
@@ -0,0 +1,8 @@
+/* sphinx-design styles for cards/tabs
+*/
+
+.sphx-glr-thumbcontainer {
+    padding: 50%;
+    display: flex;
+    align-content: center;
+}
\ No newline at end of file
diff --git a/docs/v2.2.0/_static/css/pytorch_theme.css b/docs/v2.2.0/_static/css/pytorch_theme.css
new file mode 100644
index 0000000000..153f4889c0
--- /dev/null
+++ b/docs/v2.2.0/_static/css/pytorch_theme.css
@@ -0,0 +1,127 @@
+body {
+    font-family: "Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;
+}
+
+/* Default header fonts are ugly */
+h1, h2, .rst-content .toctree-wrapper p.caption, h3, h4, h5, h6, legend, p.caption {
+    font-family: "Lato","proxima-nova","Helvetica Neue",Arial,sans-serif;
+}
+
+/* Use white for docs background */
+.wy-side-nav-search {
+    background-color: #fff;
+}
+
+.wy-nav-content-wrap, .wy-menu li.current > a  {
+    background-color: #fff;
+}
+
+@media screen and (min-width: 1400px) {
+    .wy-nav-content-wrap {
+        background-color: rgba(0, 0, 0, 0.0470588);
+    }
+
+    .wy-nav-content {
+        background-color: #fff;
+    }
+}
+
+/* Fixes for mobile */
+.wy-nav-top {
+    background-color: #fff;
+    background-image: url('../img/pytorch-logo-dark.svg');
+    background-repeat: no-repeat;
+    background-position: center;
+    padding: 0;
+    margin: 0.4045em 0.809em;
+    color: #333;
+}
+
+.wy-nav-top > a {
+    display: none;
+}
+
+@media screen and (max-width: 768px) {
+    .wy-side-nav-search>a img.logo {
+        height: 60px;
+    }
+}
+
+/* This is needed to ensure that logo above search scales properly */
+.wy-side-nav-search a {
+    display: block;
+}
+
+/* This ensures that multiple constructors will remain in separate lines. */
+.rst-content dl:not(.docutils) dt {
+    display: table;
+}
+
+/* Use our red for literals (it's very similar to the original color) */
+.rst-content tt.literal, .rst-content tt.literal, .rst-content code.literal {
+    color: #F05732;
+}
+
+.rst-content tt.xref, a .rst-content tt, .rst-content tt.xref,
+.rst-content code.xref, a .rst-content tt, a .rst-content code {
+    color: #404040;
+}
+
+/* Change link colors (except for the menu) */
+
+a {
+    color: #F05732;
+}
+
+a:hover {
+    color: #F05732;
+}
+
+
+a:visited {
+    color: #D44D2C;
+}
+
+.wy-menu a {
+    color: #b3b3b3;
+}
+
+.wy-menu a:hover {
+    color: #b3b3b3;
+}
+
+a.icon.icon-home {
+    color: #D44D2C;
+}
+
+.version{
+    color: #D44D2C !important;
+}
+
+/* Default footer text is quite big */
+footer {
+    font-size: 80%;
+}
+
+footer .rst-footer-buttons {
+    font-size: 125%; /* revert footer settings - 1/80% = 125% */
+}
+
+footer p {
+    font-size: 100%;
+}
+
+/* For hidden headers that appear in TOC tree */
+/* see https://stackoverflow.com/a/32363545/3343043 */
+.rst-content .hidden-section {
+    display: none;
+}
+
+nav .hidden-section {
+    display: inherit;
+}
+
+/* Make code blocks have a background */
+.codeblock,pre.literal-block,.rst-content .literal-block,.rst-content pre.literal-block,div[class^='highlight'] {
+        background: rgba(0, 0, 0, 0.0470588);
+}
diff --git a/docs/v2.2.0/_static/css/theme.css b/docs/v2.2.0/_static/css/theme.css
new file mode 100644
index 0000000000..b8d69c23b0
--- /dev/null
+++ b/docs/v2.2.0/_static/css/theme.css
@@ -0,0 +1,12483 @@
+@charset "UTF-8";
+/*!
+ * Bootstrap v4.0.0 (https://getbootstrap.com)
+ * Copyright 2011-2018 The Bootstrap Authors
+ * Copyright 2011-2018 Twitter, Inc.
+ * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE)
+ */
+:root {
+  --blue: #007bff;
+  --indigo: #6610f2;
+  --purple: #6f42c1;
+  --pink: #e83e8c;
+  --red: #dc3545;
+  --orange: #fd7e14;
+  --yellow: #ffc107;
+  --green: #28a745;
+  --teal: #20c997;
+  --cyan: #17a2b8;
+  --white: #fff;
+  --gray: #6c757d;
+  --gray-dark: #343a40;
+  --primary: #007bff;
+  --secondary: #6c757d;
+  --success: #28a745;
+  --info: #17a2b8;
+  --warning: #ffc107;
+  --danger: #dc3545;
+  --light: #f8f9fa;
+  --dark: #343a40;
+  --breakpoint-xs: 0;
+  --breakpoint-sm: 576px;
+  --breakpoint-md: 768px;
+  --breakpoint-lg: 992px;
+  --breakpoint-xl: 1200px;
+  --font-family-sans-serif: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  --font-family-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+}
+
+*,
+*::before,
+*::after {
+  -webkit-box-sizing: border-box;
+          box-sizing: border-box;
+}
+
+html {
+  font-family: sans-serif;
+  line-height: 1.15;
+  -webkit-text-size-adjust: 100%;
+  -ms-text-size-adjust: 100%;
+  -ms-overflow-style: scrollbar;
+  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
+}
+
+@-ms-viewport {
+  width: device-width;
+}
+article, aside, dialog, figcaption, figure, footer, header, hgroup, main, nav, section {
+  display: block;
+}
+
+body {
+  margin: 0;
+  font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  font-size: 1rem;
+  font-weight: 400;
+  line-height: 1.5;
+  color: #212529;
+  text-align: left;
+  background-color: #fff;
+}
+
+[tabindex="-1"]:focus {
+  outline: 0 !important;
+}
+
+hr {
+  -webkit-box-sizing: content-box;
+          box-sizing: content-box;
+  height: 0;
+  overflow: visible;
+}
+
+h1, h2, h3, h4, h5, h6 {
+  margin-top: 0;
+  margin-bottom: 0.5rem;
+}
+
+p {
+  margin-top: 0;
+  margin-bottom: 1rem;
+}
+
+abbr[title],
+abbr[data-original-title] {
+  text-decoration: underline;
+  -webkit-text-decoration: underline dotted;
+          text-decoration: underline dotted;
+  cursor: help;
+  border-bottom: 0;
+}
+
+address {
+  margin-bottom: 1rem;
+  font-style: normal;
+  line-height: inherit;
+}
+
+ol,
+ul,
+dl {
+  margin-top: 0;
+  margin-bottom: 1rem;
+}
+
+ol ol,
+ul ul,
+ol ul,
+ul ol {
+  margin-bottom: 0;
+}
+
+dt {
+  font-weight: 700;
+}
+
+dd {
+  margin-bottom: .5rem;
+  margin-left: 0;
+}
+
+blockquote {
+  margin: 0 0 1rem;
+}
+
+dfn {
+  font-style: italic;
+}
+
+b,
+strong {
+  font-weight: bolder;
+}
+
+small {
+  font-size: 80%;
+}
+
+sub,
+sup {
+  position: relative;
+  font-size: 75%;
+  line-height: 0;
+  vertical-align: baseline;
+}
+
+sub {
+  bottom: -.25em;
+}
+
+sup {
+  top: -.5em;
+}
+
+a {
+  color: #007bff;
+  text-decoration: none;
+  background-color: transparent;
+  -webkit-text-decoration-skip: objects;
+}
+a:hover {
+  color: #0056b3;
+  text-decoration: underline;
+}
+
+a:not([href]):not([tabindex]) {
+  color: inherit;
+  text-decoration: none;
+}
+a:not([href]):not([tabindex]):hover, a:not([href]):not([tabindex]):focus {
+  color: inherit;
+  text-decoration: none;
+}
+a:not([href]):not([tabindex]):focus {
+  outline: 0;
+}
+
+pre,
+code,
+kbd,
+samp {
+  font-family: monospace, monospace;
+  font-size: 1em;
+}
+
+pre {
+  margin-top: 0;
+  margin-bottom: 1rem;
+  overflow: auto;
+  -ms-overflow-style: scrollbar;
+}
+
+figure {
+  margin: 0 0 1rem;
+}
+
+img {
+  vertical-align: middle;
+  border-style: none;
+}
+
+svg:not(:root) {
+  overflow: hidden;
+}
+
+table {
+  border-collapse: collapse;
+}
+
+caption {
+  padding-top: 0.75rem;
+  padding-bottom: 0.75rem;
+  color: #6c757d;
+  text-align: left;
+  caption-side: bottom;
+}
+
+th {
+  text-align: inherit;
+}
+
+label {
+  display: inline-block;
+  margin-bottom: .5rem;
+}
+
+button {
+  border-radius: 0;
+}
+
+button:focus {
+  outline: 1px dotted;
+  outline: 5px auto -webkit-focus-ring-color;
+}
+
+input,
+button,
+select,
+optgroup,
+textarea {
+  margin: 0;
+  font-family: inherit;
+  font-size: inherit;
+  line-height: inherit;
+}
+
+button,
+input {
+  overflow: visible;
+}
+
+button,
+select {
+  text-transform: none;
+}
+
+button,
+html [type="button"],
+[type="reset"],
+[type="submit"] {
+  -webkit-appearance: button;
+}
+
+button::-moz-focus-inner,
+[type="button"]::-moz-focus-inner,
+[type="reset"]::-moz-focus-inner,
+[type="submit"]::-moz-focus-inner {
+  padding: 0;
+  border-style: none;
+}
+
+input[type="radio"],
+input[type="checkbox"] {
+  -webkit-box-sizing: border-box;
+          box-sizing: border-box;
+  padding: 0;
+}
+
+input[type="date"],
+input[type="time"],
+input[type="datetime-local"],
+input[type="month"] {
+  -webkit-appearance: listbox;
+}
+
+textarea {
+  overflow: auto;
+  resize: vertical;
+}
+
+fieldset {
+  min-width: 0;
+  padding: 0;
+  margin: 0;
+  border: 0;
+}
+
+legend {
+  display: block;
+  width: 100%;
+  max-width: 100%;
+  padding: 0;
+  margin-bottom: .5rem;
+  font-size: 1.5rem;
+  line-height: inherit;
+  color: inherit;
+  white-space: normal;
+}
+
+progress {
+  vertical-align: baseline;
+}
+
+[type="number"]::-webkit-inner-spin-button,
+[type="number"]::-webkit-outer-spin-button {
+  height: auto;
+}
+
+[type="search"] {
+  outline-offset: -2px;
+  -webkit-appearance: none;
+}
+
+[type="search"]::-webkit-search-cancel-button,
+[type="search"]::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+
+::-webkit-file-upload-button {
+  font: inherit;
+  -webkit-appearance: button;
+}
+
+output {
+  display: inline-block;
+}
+
+summary {
+  display: list-item;
+  cursor: pointer;
+}
+
+template {
+  display: none;
+}
+
+[hidden] {
+  display: none !important;
+}
+
+h1, h2, h3, h4, h5, h6,
+.h1, .h2, .h3, .h4, .h5, .h6 {
+  margin-bottom: 0.5rem;
+  font-family: inherit;
+  font-weight: 500;
+  line-height: 1.2;
+  color: inherit;
+}
+
+h1, .h1 {
+  font-size: 2.5rem;
+}
+
+h2, .h2 {
+  font-size: 2rem;
+}
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+h5, .h5 {
+  font-size: 1.25rem;
+}
+
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+  font-size: 1rem;
+}
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+  font-size: 1.25rem;
+  font-weight: 300;
+}
+
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+  font-size: 6rem;
+  font-weight: 300;
+  line-height: 1.2;
+}
+
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+  font-size: 5.5rem;
+  font-weight: 300;
+  line-height: 1.2;
+}
+
+.display-3 {
+  font-size: 4.5rem;
+  font-weight: 300;
+  line-height: 1.2;
+}
+
+.display-4 {
+  font-size: 3.5rem;
+  font-weight: 300;
+  line-height: 1.2;
+}
+
+hr {
+  margin-top: 1rem;
+  margin-bottom: 1rem;
+  border: 0;
+  border-top: 1px solid rgba(0, 0, 0, 0.1);
+}
+
+small,
+.small {
+  font-size: 80%;
+  font-weight: 400;
+}
+
+mark,
+.mark {
+  padding: 0.2em;
+  background-color: #fcf8e3;
+}
+
+.list-unstyled {
+  padding-left: 0;
+  list-style: none;
+}
+
+.list-inline {
+  padding-left: 0;
+  list-style: none;
+}
+
+.list-inline-item {
+  display: inline-block;
+}
+.list-inline-item:not(:last-child) {
+  margin-right: 0.5rem;
+}
+
+.initialism {
+  font-size: 90%;
+  text-transform: uppercase;
+}
+
+.blockquote {
+  margin-bottom: 1rem;
+  font-size: 1.25rem;
+}
+
+.blockquote-footer {
+  display: block;
+  font-size: 80%;
+  color: #6c757d;
+}
+.blockquote-footer::before {
+  content: "\2014 \00A0";
+}
+
+.img-fluid {
+  max-width: 100%;
+  height: auto;
+}
+
+.img-thumbnail {
+  padding: 0.25rem;
+  background-color: #fff;
+  border: 1px solid #dee2e6;
+  border-radius: 0.25rem;
+  max-width: 100%;
+  height: auto;
+}
+
+.figure {
+  display: inline-block;
+}
+
+.figure-img {
+  margin-bottom: 0.5rem;
+  line-height: 1;
+}
+
+.figure-caption {
+  font-size: 90%;
+  color: #6c757d;
+}
+
+code,
+kbd,
+pre,
+samp {
+  font-family: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+}
+
+code {
+  font-size: 87.5%;
+  color: #e83e8c;
+  word-break: break-word;
+}
+a > code {
+  color: inherit;
+}
+
+kbd {
+  padding: 0.2rem 0.4rem;
+  font-size: 87.5%;
+  color: #fff;
+  background-color: #212529;
+  border-radius: 0.2rem;
+}
+kbd kbd {
+  padding: 0;
+  font-size: 100%;
+  font-weight: 700;
+}
+
+pre {
+  display: block;
+  font-size: 87.5%;
+  color: #212529;
+}
+pre code {
+  font-size: inherit;
+  color: inherit;
+  word-break: normal;
+}
+
+.pre-scrollable {
+  max-height: 340px;
+  overflow-y: scroll;
+}
+
+.container {
+  width: 100%;
+  padding-right: 15px;
+  padding-left: 15px;
+  margin-right: auto;
+  margin-left: auto;
+}
+@media (min-width: 576px) {
+  .container {
+    max-width: 540px;
+  }
+}
+@media (min-width: 768px) {
+  .container {
+    max-width: 720px;
+  }
+}
+@media (min-width: 992px) {
+  .container {
+    max-width: 960px;
+  }
+}
+@media (min-width: 1200px) {
+  .container {
+    max-width: 1140px;
+  }
+}
+
+.container-fluid {
+  width: 100%;
+  padding-right: 15px;
+  padding-left: 15px;
+  margin-right: auto;
+  margin-left: auto;
+}
+
+.row {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  margin-right: -15px;
+  margin-left: -15px;
+}
+
+.no-gutters {
+  margin-right: 0;
+  margin-left: 0;
+}
+.no-gutters > .col,
+.no-gutters > [class*="col-"] {
+  padding-right: 0;
+  padding-left: 0;
+}
+
+.col-1, .col-2, .col-3, .col-4, .col-5, .col-6, .col-7, .col-8, .col-9, .col-10, .col-11, .col-12, .col,
+.col-auto, .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12, .col-sm,
+.col-sm-auto, .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12, .col-md,
+.col-md-auto, .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12, .col-lg,
+.col-lg-auto, .col-xl-1, .col-xl-2, .col-xl-3, .col-xl-4, .col-xl-5, .col-xl-6, .col-xl-7, .col-xl-8, .col-xl-9, .col-xl-10, .col-xl-11, .col-xl-12, .col-xl,
+.col-xl-auto {
+  position: relative;
+  width: 100%;
+  min-height: 1px;
+  padding-right: 15px;
+  padding-left: 15px;
+}
+
+.col {
+  -ms-flex-preferred-size: 0;
+      flex-basis: 0;
+  -webkit-box-flex: 1;
+      -ms-flex-positive: 1;
+          flex-grow: 1;
+  max-width: 100%;
+}
+
+.col-auto {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 auto;
+          flex: 0 0 auto;
+  width: auto;
+  max-width: none;
+}
+
+.col-1 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 8.3333333333%;
+          flex: 0 0 8.3333333333%;
+  max-width: 8.3333333333%;
+}
+
+.col-2 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 16.6666666667%;
+          flex: 0 0 16.6666666667%;
+  max-width: 16.6666666667%;
+}
+
+.col-3 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 25%;
+          flex: 0 0 25%;
+  max-width: 25%;
+}
+
+.col-4 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 33.3333333333%;
+          flex: 0 0 33.3333333333%;
+  max-width: 33.3333333333%;
+}
+
+.col-5 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 41.6666666667%;
+          flex: 0 0 41.6666666667%;
+  max-width: 41.6666666667%;
+}
+
+.col-6 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 50%;
+          flex: 0 0 50%;
+  max-width: 50%;
+}
+
+.col-7 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 58.3333333333%;
+          flex: 0 0 58.3333333333%;
+  max-width: 58.3333333333%;
+}
+
+.col-8 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 66.6666666667%;
+          flex: 0 0 66.6666666667%;
+  max-width: 66.6666666667%;
+}
+
+.col-9 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 75%;
+          flex: 0 0 75%;
+  max-width: 75%;
+}
+
+.col-10 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 83.3333333333%;
+          flex: 0 0 83.3333333333%;
+  max-width: 83.3333333333%;
+}
+
+.col-11 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 91.6666666667%;
+          flex: 0 0 91.6666666667%;
+  max-width: 91.6666666667%;
+}
+
+.col-12 {
+  -webkit-box-flex: 0;
+      -ms-flex: 0 0 100%;
+          flex: 0 0 100%;
+  max-width: 100%;
+}
+
+.order-first {
+  -webkit-box-ordinal-group: 0;
+      -ms-flex-order: -1;
+          order: -1;
+}
+
+.order-last {
+  -webkit-box-ordinal-group: 14;
+      -ms-flex-order: 13;
+          order: 13;
+}
+
+.order-0 {
+  -webkit-box-ordinal-group: 1;
+      -ms-flex-order: 0;
+          order: 0;
+}
+
+.order-1 {
+  -webkit-box-ordinal-group: 2;
+      -ms-flex-order: 1;
+          order: 1;
+}
+
+.order-2 {
+  -webkit-box-ordinal-group: 3;
+      -ms-flex-order: 2;
+          order: 2;
+}
+
+.order-3 {
+  -webkit-box-ordinal-group: 4;
+      -ms-flex-order: 3;
+          order: 3;
+}
+
+.order-4 {
+  -webkit-box-ordinal-group: 5;
+      -ms-flex-order: 4;
+          order: 4;
+}
+
+.order-5 {
+  -webkit-box-ordinal-group: 6;
+      -ms-flex-order: 5;
+          order: 5;
+}
+
+.order-6 {
+  -webkit-box-ordinal-group: 7;
+      -ms-flex-order: 6;
+          order: 6;
+}
+
+.order-7 {
+  -webkit-box-ordinal-group: 8;
+      -ms-flex-order: 7;
+          order: 7;
+}
+
+.order-8 {
+  -webkit-box-ordinal-group: 9;
+      -ms-flex-order: 8;
+          order: 8;
+}
+
+.order-9 {
+  -webkit-box-ordinal-group: 10;
+      -ms-flex-order: 9;
+          order: 9;
+}
+
+.order-10 {
+  -webkit-box-ordinal-group: 11;
+      -ms-flex-order: 10;
+          order: 10;
+}
+
+.order-11 {
+  -webkit-box-ordinal-group: 12;
+      -ms-flex-order: 11;
+          order: 11;
+}
+
+.order-12 {
+  -webkit-box-ordinal-group: 13;
+      -ms-flex-order: 12;
+          order: 12;
+}
+
+.offset-1 {
+  margin-left: 8.3333333333%;
+}
+
+.offset-2 {
+  margin-left: 16.6666666667%;
+}
+
+.offset-3 {
+  margin-left: 25%;
+}
+
+.offset-4 {
+  margin-left: 33.3333333333%;
+}
+
+.offset-5 {
+  margin-left: 41.6666666667%;
+}
+
+.offset-6 {
+  margin-left: 50%;
+}
+
+.offset-7 {
+  margin-left: 58.3333333333%;
+}
+
+.offset-8 {
+  margin-left: 66.6666666667%;
+}
+
+.offset-9 {
+  margin-left: 75%;
+}
+
+.offset-10 {
+  margin-left: 83.3333333333%;
+}
+
+.offset-11 {
+  margin-left: 91.6666666667%;
+}
+
+@media (min-width: 576px) {
+  .col-sm {
+    -ms-flex-preferred-size: 0;
+        flex-basis: 0;
+    -webkit-box-flex: 1;
+        -ms-flex-positive: 1;
+            flex-grow: 1;
+    max-width: 100%;
+  }
+
+  .col-sm-auto {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    width: auto;
+    max-width: none;
+  }
+
+  .col-sm-1 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 8.3333333333%;
+            flex: 0 0 8.3333333333%;
+    max-width: 8.3333333333%;
+  }
+
+  .col-sm-2 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 16.6666666667%;
+            flex: 0 0 16.6666666667%;
+    max-width: 16.6666666667%;
+  }
+
+  .col-sm-3 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 25%;
+            flex: 0 0 25%;
+    max-width: 25%;
+  }
+
+  .col-sm-4 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 33.3333333333%;
+            flex: 0 0 33.3333333333%;
+    max-width: 33.3333333333%;
+  }
+
+  .col-sm-5 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 41.6666666667%;
+            flex: 0 0 41.6666666667%;
+    max-width: 41.6666666667%;
+  }
+
+  .col-sm-6 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 50%;
+            flex: 0 0 50%;
+    max-width: 50%;
+  }
+
+  .col-sm-7 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 58.3333333333%;
+            flex: 0 0 58.3333333333%;
+    max-width: 58.3333333333%;
+  }
+
+  .col-sm-8 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 66.6666666667%;
+            flex: 0 0 66.6666666667%;
+    max-width: 66.6666666667%;
+  }
+
+  .col-sm-9 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 75%;
+            flex: 0 0 75%;
+    max-width: 75%;
+  }
+
+  .col-sm-10 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 83.3333333333%;
+            flex: 0 0 83.3333333333%;
+    max-width: 83.3333333333%;
+  }
+
+  .col-sm-11 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 91.6666666667%;
+            flex: 0 0 91.6666666667%;
+    max-width: 91.6666666667%;
+  }
+
+  .col-sm-12 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+    max-width: 100%;
+  }
+
+  .order-sm-first {
+    -webkit-box-ordinal-group: 0;
+        -ms-flex-order: -1;
+            order: -1;
+  }
+
+  .order-sm-last {
+    -webkit-box-ordinal-group: 14;
+        -ms-flex-order: 13;
+            order: 13;
+  }
+
+  .order-sm-0 {
+    -webkit-box-ordinal-group: 1;
+        -ms-flex-order: 0;
+            order: 0;
+  }
+
+  .order-sm-1 {
+    -webkit-box-ordinal-group: 2;
+        -ms-flex-order: 1;
+            order: 1;
+  }
+
+  .order-sm-2 {
+    -webkit-box-ordinal-group: 3;
+        -ms-flex-order: 2;
+            order: 2;
+  }
+
+  .order-sm-3 {
+    -webkit-box-ordinal-group: 4;
+        -ms-flex-order: 3;
+            order: 3;
+  }
+
+  .order-sm-4 {
+    -webkit-box-ordinal-group: 5;
+        -ms-flex-order: 4;
+            order: 4;
+  }
+
+  .order-sm-5 {
+    -webkit-box-ordinal-group: 6;
+        -ms-flex-order: 5;
+            order: 5;
+  }
+
+  .order-sm-6 {
+    -webkit-box-ordinal-group: 7;
+        -ms-flex-order: 6;
+            order: 6;
+  }
+
+  .order-sm-7 {
+    -webkit-box-ordinal-group: 8;
+        -ms-flex-order: 7;
+            order: 7;
+  }
+
+  .order-sm-8 {
+    -webkit-box-ordinal-group: 9;
+        -ms-flex-order: 8;
+            order: 8;
+  }
+
+  .order-sm-9 {
+    -webkit-box-ordinal-group: 10;
+        -ms-flex-order: 9;
+            order: 9;
+  }
+
+  .order-sm-10 {
+    -webkit-box-ordinal-group: 11;
+        -ms-flex-order: 10;
+            order: 10;
+  }
+
+  .order-sm-11 {
+    -webkit-box-ordinal-group: 12;
+        -ms-flex-order: 11;
+            order: 11;
+  }
+
+  .order-sm-12 {
+    -webkit-box-ordinal-group: 13;
+        -ms-flex-order: 12;
+            order: 12;
+  }
+
+  .offset-sm-0 {
+    margin-left: 0;
+  }
+
+  .offset-sm-1 {
+    margin-left: 8.3333333333%;
+  }
+
+  .offset-sm-2 {
+    margin-left: 16.6666666667%;
+  }
+
+  .offset-sm-3 {
+    margin-left: 25%;
+  }
+
+  .offset-sm-4 {
+    margin-left: 33.3333333333%;
+  }
+
+  .offset-sm-5 {
+    margin-left: 41.6666666667%;
+  }
+
+  .offset-sm-6 {
+    margin-left: 50%;
+  }
+
+  .offset-sm-7 {
+    margin-left: 58.3333333333%;
+  }
+
+  .offset-sm-8 {
+    margin-left: 66.6666666667%;
+  }
+
+  .offset-sm-9 {
+    margin-left: 75%;
+  }
+
+  .offset-sm-10 {
+    margin-left: 83.3333333333%;
+  }
+
+  .offset-sm-11 {
+    margin-left: 91.6666666667%;
+  }
+}
+@media (min-width: 768px) {
+  .col-md {
+    -ms-flex-preferred-size: 0;
+        flex-basis: 0;
+    -webkit-box-flex: 1;
+        -ms-flex-positive: 1;
+            flex-grow: 1;
+    max-width: 100%;
+  }
+
+  .col-md-auto {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    width: auto;
+    max-width: none;
+  }
+
+  .col-md-1 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 8.3333333333%;
+            flex: 0 0 8.3333333333%;
+    max-width: 8.3333333333%;
+  }
+
+  .col-md-2 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 16.6666666667%;
+            flex: 0 0 16.6666666667%;
+    max-width: 16.6666666667%;
+  }
+
+  .col-md-3 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 25%;
+            flex: 0 0 25%;
+    max-width: 25%;
+  }
+
+  .col-md-4 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 33.3333333333%;
+            flex: 0 0 33.3333333333%;
+    max-width: 33.3333333333%;
+  }
+
+  .col-md-5 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 41.6666666667%;
+            flex: 0 0 41.6666666667%;
+    max-width: 41.6666666667%;
+  }
+
+  .col-md-6 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 50%;
+            flex: 0 0 50%;
+    max-width: 50%;
+  }
+
+  .col-md-7 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 58.3333333333%;
+            flex: 0 0 58.3333333333%;
+    max-width: 58.3333333333%;
+  }
+
+  .col-md-8 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 66.6666666667%;
+            flex: 0 0 66.6666666667%;
+    max-width: 66.6666666667%;
+  }
+
+  .col-md-9 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 75%;
+            flex: 0 0 75%;
+    max-width: 75%;
+  }
+
+  .col-md-10 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 83.3333333333%;
+            flex: 0 0 83.3333333333%;
+    max-width: 83.3333333333%;
+  }
+
+  .col-md-11 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 91.6666666667%;
+            flex: 0 0 91.6666666667%;
+    max-width: 91.6666666667%;
+  }
+
+  .col-md-12 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+    max-width: 100%;
+  }
+
+  .order-md-first {
+    -webkit-box-ordinal-group: 0;
+        -ms-flex-order: -1;
+            order: -1;
+  }
+
+  .order-md-last {
+    -webkit-box-ordinal-group: 14;
+        -ms-flex-order: 13;
+            order: 13;
+  }
+
+  .order-md-0 {
+    -webkit-box-ordinal-group: 1;
+        -ms-flex-order: 0;
+            order: 0;
+  }
+
+  .order-md-1 {
+    -webkit-box-ordinal-group: 2;
+        -ms-flex-order: 1;
+            order: 1;
+  }
+
+  .order-md-2 {
+    -webkit-box-ordinal-group: 3;
+        -ms-flex-order: 2;
+            order: 2;
+  }
+
+  .order-md-3 {
+    -webkit-box-ordinal-group: 4;
+        -ms-flex-order: 3;
+            order: 3;
+  }
+
+  .order-md-4 {
+    -webkit-box-ordinal-group: 5;
+        -ms-flex-order: 4;
+            order: 4;
+  }
+
+  .order-md-5 {
+    -webkit-box-ordinal-group: 6;
+        -ms-flex-order: 5;
+            order: 5;
+  }
+
+  .order-md-6 {
+    -webkit-box-ordinal-group: 7;
+        -ms-flex-order: 6;
+            order: 6;
+  }
+
+  .order-md-7 {
+    -webkit-box-ordinal-group: 8;
+        -ms-flex-order: 7;
+            order: 7;
+  }
+
+  .order-md-8 {
+    -webkit-box-ordinal-group: 9;
+        -ms-flex-order: 8;
+            order: 8;
+  }
+
+  .order-md-9 {
+    -webkit-box-ordinal-group: 10;
+        -ms-flex-order: 9;
+            order: 9;
+  }
+
+  .order-md-10 {
+    -webkit-box-ordinal-group: 11;
+        -ms-flex-order: 10;
+            order: 10;
+  }
+
+  .order-md-11 {
+    -webkit-box-ordinal-group: 12;
+        -ms-flex-order: 11;
+            order: 11;
+  }
+
+  .order-md-12 {
+    -webkit-box-ordinal-group: 13;
+        -ms-flex-order: 12;
+            order: 12;
+  }
+
+  .offset-md-0 {
+    margin-left: 0;
+  }
+
+  .offset-md-1 {
+    margin-left: 8.3333333333%;
+  }
+
+  .offset-md-2 {
+    margin-left: 16.6666666667%;
+  }
+
+  .offset-md-3 {
+    margin-left: 25%;
+  }
+
+  .offset-md-4 {
+    margin-left: 33.3333333333%;
+  }
+
+  .offset-md-5 {
+    margin-left: 41.6666666667%;
+  }
+
+  .offset-md-6 {
+    margin-left: 50%;
+  }
+
+  .offset-md-7 {
+    margin-left: 58.3333333333%;
+  }
+
+  .offset-md-8 {
+    margin-left: 66.6666666667%;
+  }
+
+  .offset-md-9 {
+    margin-left: 75%;
+  }
+
+  .offset-md-10 {
+    margin-left: 83.3333333333%;
+  }
+
+  .offset-md-11 {
+    margin-left: 91.6666666667%;
+  }
+}
+@media (min-width: 992px) {
+  .col-lg {
+    -ms-flex-preferred-size: 0;
+        flex-basis: 0;
+    -webkit-box-flex: 1;
+        -ms-flex-positive: 1;
+            flex-grow: 1;
+    max-width: 100%;
+  }
+
+  .col-lg-auto {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    width: auto;
+    max-width: none;
+  }
+
+  .col-lg-1 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 8.3333333333%;
+            flex: 0 0 8.3333333333%;
+    max-width: 8.3333333333%;
+  }
+
+  .col-lg-2 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 16.6666666667%;
+            flex: 0 0 16.6666666667%;
+    max-width: 16.6666666667%;
+  }
+
+  .col-lg-3 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 25%;
+            flex: 0 0 25%;
+    max-width: 25%;
+  }
+
+  .col-lg-4 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 33.3333333333%;
+            flex: 0 0 33.3333333333%;
+    max-width: 33.3333333333%;
+  }
+
+  .col-lg-5 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 41.6666666667%;
+            flex: 0 0 41.6666666667%;
+    max-width: 41.6666666667%;
+  }
+
+  .col-lg-6 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 50%;
+            flex: 0 0 50%;
+    max-width: 50%;
+  }
+
+  .col-lg-7 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 58.3333333333%;
+            flex: 0 0 58.3333333333%;
+    max-width: 58.3333333333%;
+  }
+
+  .col-lg-8 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 66.6666666667%;
+            flex: 0 0 66.6666666667%;
+    max-width: 66.6666666667%;
+  }
+
+  .col-lg-9 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 75%;
+            flex: 0 0 75%;
+    max-width: 75%;
+  }
+
+  .col-lg-10 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 83.3333333333%;
+            flex: 0 0 83.3333333333%;
+    max-width: 83.3333333333%;
+  }
+
+  .col-lg-11 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 91.6666666667%;
+            flex: 0 0 91.6666666667%;
+    max-width: 91.6666666667%;
+  }
+
+  .col-lg-12 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+    max-width: 100%;
+  }
+
+  .order-lg-first {
+    -webkit-box-ordinal-group: 0;
+        -ms-flex-order: -1;
+            order: -1;
+  }
+
+  .order-lg-last {
+    -webkit-box-ordinal-group: 14;
+        -ms-flex-order: 13;
+            order: 13;
+  }
+
+  .order-lg-0 {
+    -webkit-box-ordinal-group: 1;
+        -ms-flex-order: 0;
+            order: 0;
+  }
+
+  .order-lg-1 {
+    -webkit-box-ordinal-group: 2;
+        -ms-flex-order: 1;
+            order: 1;
+  }
+
+  .order-lg-2 {
+    -webkit-box-ordinal-group: 3;
+        -ms-flex-order: 2;
+            order: 2;
+  }
+
+  .order-lg-3 {
+    -webkit-box-ordinal-group: 4;
+        -ms-flex-order: 3;
+            order: 3;
+  }
+
+  .order-lg-4 {
+    -webkit-box-ordinal-group: 5;
+        -ms-flex-order: 4;
+            order: 4;
+  }
+
+  .order-lg-5 {
+    -webkit-box-ordinal-group: 6;
+        -ms-flex-order: 5;
+            order: 5;
+  }
+
+  .order-lg-6 {
+    -webkit-box-ordinal-group: 7;
+        -ms-flex-order: 6;
+            order: 6;
+  }
+
+  .order-lg-7 {
+    -webkit-box-ordinal-group: 8;
+        -ms-flex-order: 7;
+            order: 7;
+  }
+
+  .order-lg-8 {
+    -webkit-box-ordinal-group: 9;
+        -ms-flex-order: 8;
+            order: 8;
+  }
+
+  .order-lg-9 {
+    -webkit-box-ordinal-group: 10;
+        -ms-flex-order: 9;
+            order: 9;
+  }
+
+  .order-lg-10 {
+    -webkit-box-ordinal-group: 11;
+        -ms-flex-order: 10;
+            order: 10;
+  }
+
+  .order-lg-11 {
+    -webkit-box-ordinal-group: 12;
+        -ms-flex-order: 11;
+            order: 11;
+  }
+
+  .order-lg-12 {
+    -webkit-box-ordinal-group: 13;
+        -ms-flex-order: 12;
+            order: 12;
+  }
+
+  .offset-lg-0 {
+    margin-left: 0;
+  }
+
+  .offset-lg-1 {
+    margin-left: 8.3333333333%;
+  }
+
+  .offset-lg-2 {
+    margin-left: 16.6666666667%;
+  }
+
+  .offset-lg-3 {
+    margin-left: 25%;
+  }
+
+  .offset-lg-4 {
+    margin-left: 33.3333333333%;
+  }
+
+  .offset-lg-5 {
+    margin-left: 41.6666666667%;
+  }
+
+  .offset-lg-6 {
+    margin-left: 50%;
+  }
+
+  .offset-lg-7 {
+    margin-left: 58.3333333333%;
+  }
+
+  .offset-lg-8 {
+    margin-left: 66.6666666667%;
+  }
+
+  .offset-lg-9 {
+    margin-left: 75%;
+  }
+
+  .offset-lg-10 {
+    margin-left: 83.3333333333%;
+  }
+
+  .offset-lg-11 {
+    margin-left: 91.6666666667%;
+  }
+}
+@media (min-width: 1200px) {
+  .col-xl {
+    -ms-flex-preferred-size: 0;
+        flex-basis: 0;
+    -webkit-box-flex: 1;
+        -ms-flex-positive: 1;
+            flex-grow: 1;
+    max-width: 100%;
+  }
+
+  .col-xl-auto {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    width: auto;
+    max-width: none;
+  }
+
+  .col-xl-1 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 8.3333333333%;
+            flex: 0 0 8.3333333333%;
+    max-width: 8.3333333333%;
+  }
+
+  .col-xl-2 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 16.6666666667%;
+            flex: 0 0 16.6666666667%;
+    max-width: 16.6666666667%;
+  }
+
+  .col-xl-3 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 25%;
+            flex: 0 0 25%;
+    max-width: 25%;
+  }
+
+  .col-xl-4 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 33.3333333333%;
+            flex: 0 0 33.3333333333%;
+    max-width: 33.3333333333%;
+  }
+
+  .col-xl-5 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 41.6666666667%;
+            flex: 0 0 41.6666666667%;
+    max-width: 41.6666666667%;
+  }
+
+  .col-xl-6 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 50%;
+            flex: 0 0 50%;
+    max-width: 50%;
+  }
+
+  .col-xl-7 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 58.3333333333%;
+            flex: 0 0 58.3333333333%;
+    max-width: 58.3333333333%;
+  }
+
+  .col-xl-8 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 66.6666666667%;
+            flex: 0 0 66.6666666667%;
+    max-width: 66.6666666667%;
+  }
+
+  .col-xl-9 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 75%;
+            flex: 0 0 75%;
+    max-width: 75%;
+  }
+
+  .col-xl-10 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 83.3333333333%;
+            flex: 0 0 83.3333333333%;
+    max-width: 83.3333333333%;
+  }
+
+  .col-xl-11 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 91.6666666667%;
+            flex: 0 0 91.6666666667%;
+    max-width: 91.6666666667%;
+  }
+
+  .col-xl-12 {
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 100%;
+            flex: 0 0 100%;
+    max-width: 100%;
+  }
+
+  .order-xl-first {
+    -webkit-box-ordinal-group: 0;
+        -ms-flex-order: -1;
+            order: -1;
+  }
+
+  .order-xl-last {
+    -webkit-box-ordinal-group: 14;
+        -ms-flex-order: 13;
+            order: 13;
+  }
+
+  .order-xl-0 {
+    -webkit-box-ordinal-group: 1;
+        -ms-flex-order: 0;
+            order: 0;
+  }
+
+  .order-xl-1 {
+    -webkit-box-ordinal-group: 2;
+        -ms-flex-order: 1;
+            order: 1;
+  }
+
+  .order-xl-2 {
+    -webkit-box-ordinal-group: 3;
+        -ms-flex-order: 2;
+            order: 2;
+  }
+
+  .order-xl-3 {
+    -webkit-box-ordinal-group: 4;
+        -ms-flex-order: 3;
+            order: 3;
+  }
+
+  .order-xl-4 {
+    -webkit-box-ordinal-group: 5;
+        -ms-flex-order: 4;
+            order: 4;
+  }
+
+  .order-xl-5 {
+    -webkit-box-ordinal-group: 6;
+        -ms-flex-order: 5;
+            order: 5;
+  }
+
+  .order-xl-6 {
+    -webkit-box-ordinal-group: 7;
+        -ms-flex-order: 6;
+            order: 6;
+  }
+
+  .order-xl-7 {
+    -webkit-box-ordinal-group: 8;
+        -ms-flex-order: 7;
+            order: 7;
+  }
+
+  .order-xl-8 {
+    -webkit-box-ordinal-group: 9;
+        -ms-flex-order: 8;
+            order: 8;
+  }
+
+  .order-xl-9 {
+    -webkit-box-ordinal-group: 10;
+        -ms-flex-order: 9;
+            order: 9;
+  }
+
+  .order-xl-10 {
+    -webkit-box-ordinal-group: 11;
+        -ms-flex-order: 10;
+            order: 10;
+  }
+
+  .order-xl-11 {
+    -webkit-box-ordinal-group: 12;
+        -ms-flex-order: 11;
+            order: 11;
+  }
+
+  .order-xl-12 {
+    -webkit-box-ordinal-group: 13;
+        -ms-flex-order: 12;
+            order: 12;
+  }
+
+  .offset-xl-0 {
+    margin-left: 0;
+  }
+
+  .offset-xl-1 {
+    margin-left: 8.3333333333%;
+  }
+
+  .offset-xl-2 {
+    margin-left: 16.6666666667%;
+  }
+
+  .offset-xl-3 {
+    margin-left: 25%;
+  }
+
+  .offset-xl-4 {
+    margin-left: 33.3333333333%;
+  }
+
+  .offset-xl-5 {
+    margin-left: 41.6666666667%;
+  }
+
+  .offset-xl-6 {
+    margin-left: 50%;
+  }
+
+  .offset-xl-7 {
+    margin-left: 58.3333333333%;
+  }
+
+  .offset-xl-8 {
+    margin-left: 66.6666666667%;
+  }
+
+  .offset-xl-9 {
+    margin-left: 75%;
+  }
+
+  .offset-xl-10 {
+    margin-left: 83.3333333333%;
+  }
+
+  .offset-xl-11 {
+    margin-left: 91.6666666667%;
+  }
+}
+.table {
+  width: 100%;
+  max-width: 100%;
+  margin-bottom: 1rem;
+  background-color: transparent;
+}
+.table th,
+.table td {
+  padding: 0.75rem;
+  vertical-align: top;
+  border-top: 1px solid #dee2e6;
+}
+.table thead th {
+  vertical-align: bottom;
+  border-bottom: 2px solid #dee2e6;
+}
+.table tbody + tbody {
+  border-top: 2px solid #dee2e6;
+}
+.table .table {
+  background-color: #fff;
+}
+
+.table-sm th,
+.table-sm td {
+  padding: 0.3rem;
+}
+
+.table-bordered {
+  border: 1px solid #dee2e6;
+}
+.table-bordered th,
+.table-bordered td {
+  border: 1px solid #dee2e6;
+}
+.table-bordered thead th,
+.table-bordered thead td {
+  border-bottom-width: 2px;
+}
+
+.table-striped tbody tr:nth-of-type(odd) {
+  background-color: rgba(0, 0, 0, 0.05);
+}
+
+.table-hover tbody tr:hover {
+  background-color: rgba(0, 0, 0, 0.075);
+}
+
+.table-primary,
+.table-primary > th,
+.table-primary > td {
+  background-color: #b8daff;
+}
+
+.table-hover .table-primary:hover {
+  background-color: #9fcdff;
+}
+.table-hover .table-primary:hover > td,
+.table-hover .table-primary:hover > th {
+  background-color: #9fcdff;
+}
+
+.table-secondary,
+.table-secondary > th,
+.table-secondary > td {
+  background-color: #d6d8db;
+}
+
+.table-hover .table-secondary:hover {
+  background-color: #c8cbcf;
+}
+.table-hover .table-secondary:hover > td,
+.table-hover .table-secondary:hover > th {
+  background-color: #c8cbcf;
+}
+
+.table-success,
+.table-success > th,
+.table-success > td {
+  background-color: #c3e6cb;
+}
+
+.table-hover .table-success:hover {
+  background-color: #b1dfbb;
+}
+.table-hover .table-success:hover > td,
+.table-hover .table-success:hover > th {
+  background-color: #b1dfbb;
+}
+
+.table-info,
+.table-info > th,
+.table-info > td {
+  background-color: #bee5eb;
+}
+
+.table-hover .table-info:hover {
+  background-color: #abdde5;
+}
+.table-hover .table-info:hover > td,
+.table-hover .table-info:hover > th {
+  background-color: #abdde5;
+}
+
+.table-warning,
+.table-warning > th,
+.table-warning > td {
+  background-color: #ffeeba;
+}
+
+.table-hover .table-warning:hover {
+  background-color: #ffe8a1;
+}
+.table-hover .table-warning:hover > td,
+.table-hover .table-warning:hover > th {
+  background-color: #ffe8a1;
+}
+
+.table-danger,
+.table-danger > th,
+.table-danger > td {
+  background-color: #f5c6cb;
+}
+
+.table-hover .table-danger:hover {
+  background-color: #f1b0b7;
+}
+.table-hover .table-danger:hover > td,
+.table-hover .table-danger:hover > th {
+  background-color: #f1b0b7;
+}
+
+.table-light,
+.table-light > th,
+.table-light > td {
+  background-color: #fdfdfe;
+}
+
+.table-hover .table-light:hover {
+  background-color: #ececf6;
+}
+.table-hover .table-light:hover > td,
+.table-hover .table-light:hover > th {
+  background-color: #ececf6;
+}
+
+.table-dark,
+.table-dark > th,
+.table-dark > td {
+  background-color: #c6c8ca;
+}
+
+.table-hover .table-dark:hover {
+  background-color: #b9bbbe;
+}
+.table-hover .table-dark:hover > td,
+.table-hover .table-dark:hover > th {
+  background-color: #b9bbbe;
+}
+
+.table-active,
+.table-active > th,
+.table-active > td {
+  background-color: rgba(0, 0, 0, 0.075);
+}
+
+.table-hover .table-active:hover {
+  background-color: rgba(0, 0, 0, 0.075);
+}
+.table-hover .table-active:hover > td,
+.table-hover .table-active:hover > th {
+  background-color: rgba(0, 0, 0, 0.075);
+}
+
+.table .thead-dark th {
+  color: #fff;
+  background-color: #212529;
+  border-color: #32383e;
+}
+.table .thead-light th {
+  color: #495057;
+  background-color: #e9ecef;
+  border-color: #dee2e6;
+}
+
+.table-dark {
+  color: #fff;
+  background-color: #212529;
+}
+.table-dark th,
+.table-dark td,
+.table-dark thead th {
+  border-color: #32383e;
+}
+.table-dark.table-bordered {
+  border: 0;
+}
+.table-dark.table-striped tbody tr:nth-of-type(odd) {
+  background-color: rgba(255, 255, 255, 0.05);
+}
+.table-dark.table-hover tbody tr:hover {
+  background-color: rgba(255, 255, 255, 0.075);
+}
+
+@media (max-width: 575.98px) {
+  .table-responsive-sm {
+    display: block;
+    width: 100%;
+    overflow-x: auto;
+    -webkit-overflow-scrolling: touch;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+  }
+  .table-responsive-sm > .table-bordered {
+    border: 0;
+  }
+}
+@media (max-width: 767.98px) {
+  .table-responsive-md {
+    display: block;
+    width: 100%;
+    overflow-x: auto;
+    -webkit-overflow-scrolling: touch;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+  }
+  .table-responsive-md > .table-bordered {
+    border: 0;
+  }
+}
+@media (max-width: 991.98px) {
+  .table-responsive-lg {
+    display: block;
+    width: 100%;
+    overflow-x: auto;
+    -webkit-overflow-scrolling: touch;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+  }
+  .table-responsive-lg > .table-bordered {
+    border: 0;
+  }
+}
+@media (max-width: 1199.98px) {
+  .table-responsive-xl {
+    display: block;
+    width: 100%;
+    overflow-x: auto;
+    -webkit-overflow-scrolling: touch;
+    -ms-overflow-style: -ms-autohiding-scrollbar;
+  }
+  .table-responsive-xl > .table-bordered {
+    border: 0;
+  }
+}
+.table-responsive {
+  display: block;
+  width: 100%;
+  overflow-x: auto;
+  -webkit-overflow-scrolling: touch;
+  -ms-overflow-style: -ms-autohiding-scrollbar;
+}
+.table-responsive > .table-bordered {
+  border: 0;
+}
+
+.form-control {
+  display: block;
+  width: 100%;
+  padding: 0.375rem 0.75rem;
+  font-size: 1rem;
+  line-height: 1.5;
+  color: #495057;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid #ced4da;
+  border-radius: 0.25rem;
+  -webkit-transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+  transition: border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+  transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;
+  transition: border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+}
+.form-control::-ms-expand {
+  background-color: transparent;
+  border: 0;
+}
+.form-control:focus {
+  color: #495057;
+  background-color: #fff;
+  border-color: #80bdff;
+  outline: 0;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.form-control::-webkit-input-placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control::-moz-placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control:-ms-input-placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control::-ms-input-placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control::placeholder {
+  color: #6c757d;
+  opacity: 1;
+}
+.form-control:disabled, .form-control[readonly] {
+  background-color: #e9ecef;
+  opacity: 1;
+}
+
+select.form-control:not([size]):not([multiple]) {
+  height: calc(2.25rem + 2px);
+}
+select.form-control:focus::-ms-value {
+  color: #495057;
+  background-color: #fff;
+}
+
+.form-control-file,
+.form-control-range {
+  display: block;
+  width: 100%;
+}
+
+.col-form-label {
+  padding-top: calc(0.375rem + 1px);
+  padding-bottom: calc(0.375rem + 1px);
+  margin-bottom: 0;
+  font-size: inherit;
+  line-height: 1.5;
+}
+
+.col-form-label-lg {
+  padding-top: calc(0.5rem + 1px);
+  padding-bottom: calc(0.5rem + 1px);
+  font-size: 1.25rem;
+  line-height: 1.5;
+}
+
+.col-form-label-sm {
+  padding-top: calc(0.25rem + 1px);
+  padding-bottom: calc(0.25rem + 1px);
+  font-size: 0.875rem;
+  line-height: 1.5;
+}
+
+.form-control-plaintext {
+  display: block;
+  width: 100%;
+  padding-top: 0.375rem;
+  padding-bottom: 0.375rem;
+  margin-bottom: 0;
+  line-height: 1.5;
+  background-color: transparent;
+  border: solid transparent;
+  border-width: 1px 0;
+}
+.form-control-plaintext.form-control-sm, .input-group-sm > .form-control-plaintext.form-control,
+.input-group-sm > .input-group-prepend > .form-control-plaintext.input-group-text,
+.input-group-sm > .input-group-append > .form-control-plaintext.input-group-text,
+.input-group-sm > .input-group-prepend > .form-control-plaintext.btn,
+.input-group-sm > .input-group-append > .form-control-plaintext.btn, .form-control-plaintext.form-control-lg, .input-group-lg > .form-control-plaintext.form-control,
+.input-group-lg > .input-group-prepend > .form-control-plaintext.input-group-text,
+.input-group-lg > .input-group-append > .form-control-plaintext.input-group-text,
+.input-group-lg > .input-group-prepend > .form-control-plaintext.btn,
+.input-group-lg > .input-group-append > .form-control-plaintext.btn {
+  padding-right: 0;
+  padding-left: 0;
+}
+
+.form-control-sm, .input-group-sm > .form-control,
+.input-group-sm > .input-group-prepend > .input-group-text,
+.input-group-sm > .input-group-append > .input-group-text,
+.input-group-sm > .input-group-prepend > .btn,
+.input-group-sm > .input-group-append > .btn {
+  padding: 0.25rem 0.5rem;
+  font-size: 0.875rem;
+  line-height: 1.5;
+  border-radius: 0.2rem;
+}
+
+select.form-control-sm:not([size]):not([multiple]), .input-group-sm > select.form-control:not([size]):not([multiple]),
+.input-group-sm > .input-group-prepend > select.input-group-text:not([size]):not([multiple]),
+.input-group-sm > .input-group-append > select.input-group-text:not([size]):not([multiple]),
+.input-group-sm > .input-group-prepend > select.btn:not([size]):not([multiple]),
+.input-group-sm > .input-group-append > select.btn:not([size]):not([multiple]) {
+  height: calc(1.8125rem + 2px);
+}
+
+.form-control-lg, .input-group-lg > .form-control,
+.input-group-lg > .input-group-prepend > .input-group-text,
+.input-group-lg > .input-group-append > .input-group-text,
+.input-group-lg > .input-group-prepend > .btn,
+.input-group-lg > .input-group-append > .btn {
+  padding: 0.5rem 1rem;
+  font-size: 1.25rem;
+  line-height: 1.5;
+  border-radius: 0.3rem;
+}
+
+select.form-control-lg:not([size]):not([multiple]), .input-group-lg > select.form-control:not([size]):not([multiple]),
+.input-group-lg > .input-group-prepend > select.input-group-text:not([size]):not([multiple]),
+.input-group-lg > .input-group-append > select.input-group-text:not([size]):not([multiple]),
+.input-group-lg > .input-group-prepend > select.btn:not([size]):not([multiple]),
+.input-group-lg > .input-group-append > select.btn:not([size]):not([multiple]) {
+  height: calc(2.875rem + 2px);
+}
+
+.form-group {
+  margin-bottom: 1rem;
+}
+
+.form-text {
+  display: block;
+  margin-top: 0.25rem;
+}
+
+.form-row {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  margin-right: -5px;
+  margin-left: -5px;
+}
+.form-row > .col,
+.form-row > [class*="col-"] {
+  padding-right: 5px;
+  padding-left: 5px;
+}
+
+.form-check {
+  position: relative;
+  display: block;
+  padding-left: 1.25rem;
+}
+
+.form-check-input {
+  position: absolute;
+  margin-top: 0.3rem;
+  margin-left: -1.25rem;
+}
+.form-check-input:disabled ~ .form-check-label {
+  color: #6c757d;
+}
+
+.form-check-label {
+  margin-bottom: 0;
+}
+
+.form-check-inline {
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  padding-left: 0;
+  margin-right: 0.75rem;
+}
+.form-check-inline .form-check-input {
+  position: static;
+  margin-top: 0;
+  margin-right: 0.3125rem;
+  margin-left: 0;
+}
+
+.valid-feedback {
+  display: none;
+  width: 100%;
+  margin-top: 0.25rem;
+  font-size: 80%;
+  color: #28a745;
+}
+
+.valid-tooltip {
+  position: absolute;
+  top: 100%;
+  z-index: 5;
+  display: none;
+  max-width: 100%;
+  padding: .5rem;
+  margin-top: .1rem;
+  font-size: .875rem;
+  line-height: 1;
+  color: #fff;
+  background-color: rgba(40, 167, 69, 0.8);
+  border-radius: .2rem;
+}
+
+.was-validated .form-control:valid, .form-control.is-valid,
+.was-validated .custom-select:valid,
+.custom-select.is-valid {
+  border-color: #28a745;
+}
+.was-validated .form-control:valid:focus, .form-control.is-valid:focus,
+.was-validated .custom-select:valid:focus,
+.custom-select.is-valid:focus {
+  border-color: #28a745;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+}
+.was-validated .form-control:valid ~ .valid-feedback,
+.was-validated .form-control:valid ~ .valid-tooltip, .form-control.is-valid ~ .valid-feedback,
+.form-control.is-valid ~ .valid-tooltip,
+.was-validated .custom-select:valid ~ .valid-feedback,
+.was-validated .custom-select:valid ~ .valid-tooltip,
+.custom-select.is-valid ~ .valid-feedback,
+.custom-select.is-valid ~ .valid-tooltip {
+  display: block;
+}
+
+.was-validated .form-check-input:valid ~ .form-check-label, .form-check-input.is-valid ~ .form-check-label {
+  color: #28a745;
+}
+.was-validated .form-check-input:valid ~ .valid-feedback,
+.was-validated .form-check-input:valid ~ .valid-tooltip, .form-check-input.is-valid ~ .valid-feedback,
+.form-check-input.is-valid ~ .valid-tooltip {
+  display: block;
+}
+
+.was-validated .custom-control-input:valid ~ .custom-control-label, .custom-control-input.is-valid ~ .custom-control-label {
+  color: #28a745;
+}
+.was-validated .custom-control-input:valid ~ .custom-control-label::before, .custom-control-input.is-valid ~ .custom-control-label::before {
+  background-color: #71dd8a;
+}
+.was-validated .custom-control-input:valid ~ .valid-feedback,
+.was-validated .custom-control-input:valid ~ .valid-tooltip, .custom-control-input.is-valid ~ .valid-feedback,
+.custom-control-input.is-valid ~ .valid-tooltip {
+  display: block;
+}
+.was-validated .custom-control-input:valid:checked ~ .custom-control-label::before, .custom-control-input.is-valid:checked ~ .custom-control-label::before {
+  background-color: #34ce57;
+}
+.was-validated .custom-control-input:valid:focus ~ .custom-control-label::before, .custom-control-input.is-valid:focus ~ .custom-control-label::before {
+  -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+          box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+}
+
+.was-validated .custom-file-input:valid ~ .custom-file-label, .custom-file-input.is-valid ~ .custom-file-label {
+  border-color: #28a745;
+}
+.was-validated .custom-file-input:valid ~ .custom-file-label::before, .custom-file-input.is-valid ~ .custom-file-label::before {
+  border-color: inherit;
+}
+.was-validated .custom-file-input:valid ~ .valid-feedback,
+.was-validated .custom-file-input:valid ~ .valid-tooltip, .custom-file-input.is-valid ~ .valid-feedback,
+.custom-file-input.is-valid ~ .valid-tooltip {
+  display: block;
+}
+.was-validated .custom-file-input:valid:focus ~ .custom-file-label, .custom-file-input.is-valid:focus ~ .custom-file-label {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.25);
+}
+
+.invalid-feedback {
+  display: none;
+  width: 100%;
+  margin-top: 0.25rem;
+  font-size: 80%;
+  color: #dc3545;
+}
+
+.invalid-tooltip {
+  position: absolute;
+  top: 100%;
+  z-index: 5;
+  display: none;
+  max-width: 100%;
+  padding: .5rem;
+  margin-top: .1rem;
+  font-size: .875rem;
+  line-height: 1;
+  color: #fff;
+  background-color: rgba(220, 53, 69, 0.8);
+  border-radius: .2rem;
+}
+
+.was-validated .form-control:invalid, .form-control.is-invalid,
+.was-validated .custom-select:invalid,
+.custom-select.is-invalid {
+  border-color: #dc3545;
+}
+.was-validated .form-control:invalid:focus, .form-control.is-invalid:focus,
+.was-validated .custom-select:invalid:focus,
+.custom-select.is-invalid:focus {
+  border-color: #dc3545;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+}
+.was-validated .form-control:invalid ~ .invalid-feedback,
+.was-validated .form-control:invalid ~ .invalid-tooltip, .form-control.is-invalid ~ .invalid-feedback,
+.form-control.is-invalid ~ .invalid-tooltip,
+.was-validated .custom-select:invalid ~ .invalid-feedback,
+.was-validated .custom-select:invalid ~ .invalid-tooltip,
+.custom-select.is-invalid ~ .invalid-feedback,
+.custom-select.is-invalid ~ .invalid-tooltip {
+  display: block;
+}
+
+.was-validated .form-check-input:invalid ~ .form-check-label, .form-check-input.is-invalid ~ .form-check-label {
+  color: #dc3545;
+}
+.was-validated .form-check-input:invalid ~ .invalid-feedback,
+.was-validated .form-check-input:invalid ~ .invalid-tooltip, .form-check-input.is-invalid ~ .invalid-feedback,
+.form-check-input.is-invalid ~ .invalid-tooltip {
+  display: block;
+}
+
+.was-validated .custom-control-input:invalid ~ .custom-control-label, .custom-control-input.is-invalid ~ .custom-control-label {
+  color: #dc3545;
+}
+.was-validated .custom-control-input:invalid ~ .custom-control-label::before, .custom-control-input.is-invalid ~ .custom-control-label::before {
+  background-color: #efa2a9;
+}
+.was-validated .custom-control-input:invalid ~ .invalid-feedback,
+.was-validated .custom-control-input:invalid ~ .invalid-tooltip, .custom-control-input.is-invalid ~ .invalid-feedback,
+.custom-control-input.is-invalid ~ .invalid-tooltip {
+  display: block;
+}
+.was-validated .custom-control-input:invalid:checked ~ .custom-control-label::before, .custom-control-input.is-invalid:checked ~ .custom-control-label::before {
+  background-color: #e4606d;
+}
+.was-validated .custom-control-input:invalid:focus ~ .custom-control-label::before, .custom-control-input.is-invalid:focus ~ .custom-control-label::before {
+  -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+          box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+}
+
+.was-validated .custom-file-input:invalid ~ .custom-file-label, .custom-file-input.is-invalid ~ .custom-file-label {
+  border-color: #dc3545;
+}
+.was-validated .custom-file-input:invalid ~ .custom-file-label::before, .custom-file-input.is-invalid ~ .custom-file-label::before {
+  border-color: inherit;
+}
+.was-validated .custom-file-input:invalid ~ .invalid-feedback,
+.was-validated .custom-file-input:invalid ~ .invalid-tooltip, .custom-file-input.is-invalid ~ .invalid-feedback,
+.custom-file-input.is-invalid ~ .invalid-tooltip {
+  display: block;
+}
+.was-validated .custom-file-input:invalid:focus ~ .custom-file-label, .custom-file-input.is-invalid:focus ~ .custom-file-label {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.25);
+}
+
+.form-inline {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: horizontal;
+  -webkit-box-direction: normal;
+      -ms-flex-flow: row wrap;
+          flex-flow: row wrap;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+.form-inline .form-check {
+  width: 100%;
+}
+@media (min-width: 576px) {
+  .form-inline label {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    -webkit-box-pack: center;
+        -ms-flex-pack: center;
+            justify-content: center;
+    margin-bottom: 0;
+  }
+  .form-inline .form-group {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-flex: 0;
+        -ms-flex: 0 0 auto;
+            flex: 0 0 auto;
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row wrap;
+            flex-flow: row wrap;
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    margin-bottom: 0;
+  }
+  .form-inline .form-control {
+    display: inline-block;
+    width: auto;
+    vertical-align: middle;
+  }
+  .form-inline .form-control-plaintext {
+    display: inline-block;
+  }
+  .form-inline .input-group {
+    width: auto;
+  }
+  .form-inline .form-check {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    -webkit-box-pack: center;
+        -ms-flex-pack: center;
+            justify-content: center;
+    width: auto;
+    padding-left: 0;
+  }
+  .form-inline .form-check-input {
+    position: relative;
+    margin-top: 0;
+    margin-right: 0.25rem;
+    margin-left: 0;
+  }
+  .form-inline .custom-control {
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    -webkit-box-pack: center;
+        -ms-flex-pack: center;
+            justify-content: center;
+  }
+  .form-inline .custom-control-label {
+    margin-bottom: 0;
+  }
+}
+
+.btn {
+  display: inline-block;
+  font-weight: 400;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: middle;
+  -webkit-user-select: none;
+     -moz-user-select: none;
+      -ms-user-select: none;
+          user-select: none;
+  border: 1px solid transparent;
+  padding: 0.375rem 0.75rem;
+  font-size: 1rem;
+  line-height: 1.5;
+  border-radius: 0.25rem;
+  -webkit-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out;
+  transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, -webkit-box-shadow 0.15s ease-in-out;
+}
+.btn:hover, .btn:focus {
+  text-decoration: none;
+}
+.btn:focus, .btn.focus {
+  outline: 0;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.btn.disabled, .btn:disabled {
+  opacity: 0.65;
+}
+.btn:not(:disabled):not(.disabled) {
+  cursor: pointer;
+}
+.btn:not(:disabled):not(.disabled):active, .btn:not(:disabled):not(.disabled).active {
+  background-image: none;
+}
+
+a.btn.disabled,
+fieldset:disabled a.btn {
+  pointer-events: none;
+}
+
+.btn-primary {
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.btn-primary:hover {
+  color: #fff;
+  background-color: #0069d9;
+  border-color: #0062cc;
+}
+.btn-primary:focus, .btn-primary.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+}
+.btn-primary.disabled, .btn-primary:disabled {
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.btn-primary:not(:disabled):not(.disabled):active, .btn-primary:not(:disabled):not(.disabled).active, .show > .btn-primary.dropdown-toggle {
+  color: #fff;
+  background-color: #0062cc;
+  border-color: #005cbf;
+}
+.btn-primary:not(:disabled):not(.disabled):active:focus, .btn-primary:not(:disabled):not(.disabled).active:focus, .show > .btn-primary.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+}
+
+.btn-secondary {
+  color: #fff;
+  background-color: #6c757d;
+  border-color: #6c757d;
+}
+.btn-secondary:hover {
+  color: #fff;
+  background-color: #5a6268;
+  border-color: #545b62;
+}
+.btn-secondary:focus, .btn-secondary.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+}
+.btn-secondary.disabled, .btn-secondary:disabled {
+  color: #fff;
+  background-color: #6c757d;
+  border-color: #6c757d;
+}
+.btn-secondary:not(:disabled):not(.disabled):active, .btn-secondary:not(:disabled):not(.disabled).active, .show > .btn-secondary.dropdown-toggle {
+  color: #fff;
+  background-color: #545b62;
+  border-color: #4e555b;
+}
+.btn-secondary:not(:disabled):not(.disabled):active:focus, .btn-secondary:not(:disabled):not(.disabled).active:focus, .show > .btn-secondary.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+}
+
+.btn-success {
+  color: #fff;
+  background-color: #28a745;
+  border-color: #28a745;
+}
+.btn-success:hover {
+  color: #fff;
+  background-color: #218838;
+  border-color: #1e7e34;
+}
+.btn-success:focus, .btn-success.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+}
+.btn-success.disabled, .btn-success:disabled {
+  color: #fff;
+  background-color: #28a745;
+  border-color: #28a745;
+}
+.btn-success:not(:disabled):not(.disabled):active, .btn-success:not(:disabled):not(.disabled).active, .show > .btn-success.dropdown-toggle {
+  color: #fff;
+  background-color: #1e7e34;
+  border-color: #1c7430;
+}
+.btn-success:not(:disabled):not(.disabled):active:focus, .btn-success:not(:disabled):not(.disabled).active:focus, .show > .btn-success.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+}
+
+.btn-info {
+  color: #fff;
+  background-color: #17a2b8;
+  border-color: #17a2b8;
+}
+.btn-info:hover {
+  color: #fff;
+  background-color: #138496;
+  border-color: #117a8b;
+}
+.btn-info:focus, .btn-info.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+}
+.btn-info.disabled, .btn-info:disabled {
+  color: #fff;
+  background-color: #17a2b8;
+  border-color: #17a2b8;
+}
+.btn-info:not(:disabled):not(.disabled):active, .btn-info:not(:disabled):not(.disabled).active, .show > .btn-info.dropdown-toggle {
+  color: #fff;
+  background-color: #117a8b;
+  border-color: #10707f;
+}
+.btn-info:not(:disabled):not(.disabled):active:focus, .btn-info:not(:disabled):not(.disabled).active:focus, .show > .btn-info.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+}
+
+.btn-warning {
+  color: #212529;
+  background-color: #ffc107;
+  border-color: #ffc107;
+}
+.btn-warning:hover {
+  color: #212529;
+  background-color: #e0a800;
+  border-color: #d39e00;
+}
+.btn-warning:focus, .btn-warning.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+}
+.btn-warning.disabled, .btn-warning:disabled {
+  color: #212529;
+  background-color: #ffc107;
+  border-color: #ffc107;
+}
+.btn-warning:not(:disabled):not(.disabled):active, .btn-warning:not(:disabled):not(.disabled).active, .show > .btn-warning.dropdown-toggle {
+  color: #212529;
+  background-color: #d39e00;
+  border-color: #c69500;
+}
+.btn-warning:not(:disabled):not(.disabled):active:focus, .btn-warning:not(:disabled):not(.disabled).active:focus, .show > .btn-warning.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+}
+
+.btn-danger {
+  color: #fff;
+  background-color: #dc3545;
+  border-color: #dc3545;
+}
+.btn-danger:hover {
+  color: #fff;
+  background-color: #c82333;
+  border-color: #bd2130;
+}
+.btn-danger:focus, .btn-danger.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+}
+.btn-danger.disabled, .btn-danger:disabled {
+  color: #fff;
+  background-color: #dc3545;
+  border-color: #dc3545;
+}
+.btn-danger:not(:disabled):not(.disabled):active, .btn-danger:not(:disabled):not(.disabled).active, .show > .btn-danger.dropdown-toggle {
+  color: #fff;
+  background-color: #bd2130;
+  border-color: #b21f2d;
+}
+.btn-danger:not(:disabled):not(.disabled):active:focus, .btn-danger:not(:disabled):not(.disabled).active:focus, .show > .btn-danger.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+}
+
+.btn-light {
+  color: #212529;
+  background-color: #f8f9fa;
+  border-color: #f8f9fa;
+}
+.btn-light:hover {
+  color: #212529;
+  background-color: #e2e6ea;
+  border-color: #dae0e5;
+}
+.btn-light:focus, .btn-light.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+}
+.btn-light.disabled, .btn-light:disabled {
+  color: #212529;
+  background-color: #f8f9fa;
+  border-color: #f8f9fa;
+}
+.btn-light:not(:disabled):not(.disabled):active, .btn-light:not(:disabled):not(.disabled).active, .show > .btn-light.dropdown-toggle {
+  color: #212529;
+  background-color: #dae0e5;
+  border-color: #d3d9df;
+}
+.btn-light:not(:disabled):not(.disabled):active:focus, .btn-light:not(:disabled):not(.disabled).active:focus, .show > .btn-light.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+}
+
+.btn-dark {
+  color: #fff;
+  background-color: #343a40;
+  border-color: #343a40;
+}
+.btn-dark:hover {
+  color: #fff;
+  background-color: #23272b;
+  border-color: #1d2124;
+}
+.btn-dark:focus, .btn-dark.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+}
+.btn-dark.disabled, .btn-dark:disabled {
+  color: #fff;
+  background-color: #343a40;
+  border-color: #343a40;
+}
+.btn-dark:not(:disabled):not(.disabled):active, .btn-dark:not(:disabled):not(.disabled).active, .show > .btn-dark.dropdown-toggle {
+  color: #fff;
+  background-color: #1d2124;
+  border-color: #171a1d;
+}
+.btn-dark:not(:disabled):not(.disabled):active:focus, .btn-dark:not(:disabled):not(.disabled).active:focus, .show > .btn-dark.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+}
+
+.btn-outline-primary {
+  color: #007bff;
+  background-color: transparent;
+  background-image: none;
+  border-color: #007bff;
+}
+.btn-outline-primary:hover {
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.btn-outline-primary:focus, .btn-outline-primary.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+}
+.btn-outline-primary.disabled, .btn-outline-primary:disabled {
+  color: #007bff;
+  background-color: transparent;
+}
+.btn-outline-primary:not(:disabled):not(.disabled):active, .btn-outline-primary:not(:disabled):not(.disabled).active, .show > .btn-outline-primary.dropdown-toggle {
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.btn-outline-primary:not(:disabled):not(.disabled):active:focus, .btn-outline-primary:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-primary.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.5);
+}
+
+.btn-outline-secondary {
+  color: #6c757d;
+  background-color: transparent;
+  background-image: none;
+  border-color: #6c757d;
+}
+.btn-outline-secondary:hover {
+  color: #fff;
+  background-color: #6c757d;
+  border-color: #6c757d;
+}
+.btn-outline-secondary:focus, .btn-outline-secondary.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+}
+.btn-outline-secondary.disabled, .btn-outline-secondary:disabled {
+  color: #6c757d;
+  background-color: transparent;
+}
+.btn-outline-secondary:not(:disabled):not(.disabled):active, .btn-outline-secondary:not(:disabled):not(.disabled).active, .show > .btn-outline-secondary.dropdown-toggle {
+  color: #fff;
+  background-color: #6c757d;
+  border-color: #6c757d;
+}
+.btn-outline-secondary:not(:disabled):not(.disabled):active:focus, .btn-outline-secondary:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-secondary.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(108, 117, 125, 0.5);
+}
+
+.btn-outline-success {
+  color: #28a745;
+  background-color: transparent;
+  background-image: none;
+  border-color: #28a745;
+}
+.btn-outline-success:hover {
+  color: #fff;
+  background-color: #28a745;
+  border-color: #28a745;
+}
+.btn-outline-success:focus, .btn-outline-success.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+}
+.btn-outline-success.disabled, .btn-outline-success:disabled {
+  color: #28a745;
+  background-color: transparent;
+}
+.btn-outline-success:not(:disabled):not(.disabled):active, .btn-outline-success:not(:disabled):not(.disabled).active, .show > .btn-outline-success.dropdown-toggle {
+  color: #fff;
+  background-color: #28a745;
+  border-color: #28a745;
+}
+.btn-outline-success:not(:disabled):not(.disabled):active:focus, .btn-outline-success:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-success.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(40, 167, 69, 0.5);
+}
+
+.btn-outline-info {
+  color: #17a2b8;
+  background-color: transparent;
+  background-image: none;
+  border-color: #17a2b8;
+}
+.btn-outline-info:hover {
+  color: #fff;
+  background-color: #17a2b8;
+  border-color: #17a2b8;
+}
+.btn-outline-info:focus, .btn-outline-info.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+}
+.btn-outline-info.disabled, .btn-outline-info:disabled {
+  color: #17a2b8;
+  background-color: transparent;
+}
+.btn-outline-info:not(:disabled):not(.disabled):active, .btn-outline-info:not(:disabled):not(.disabled).active, .show > .btn-outline-info.dropdown-toggle {
+  color: #fff;
+  background-color: #17a2b8;
+  border-color: #17a2b8;
+}
+.btn-outline-info:not(:disabled):not(.disabled):active:focus, .btn-outline-info:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-info.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(23, 162, 184, 0.5);
+}
+
+.btn-outline-warning {
+  color: #ffc107;
+  background-color: transparent;
+  background-image: none;
+  border-color: #ffc107;
+}
+.btn-outline-warning:hover {
+  color: #212529;
+  background-color: #ffc107;
+  border-color: #ffc107;
+}
+.btn-outline-warning:focus, .btn-outline-warning.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+}
+.btn-outline-warning.disabled, .btn-outline-warning:disabled {
+  color: #ffc107;
+  background-color: transparent;
+}
+.btn-outline-warning:not(:disabled):not(.disabled):active, .btn-outline-warning:not(:disabled):not(.disabled).active, .show > .btn-outline-warning.dropdown-toggle {
+  color: #212529;
+  background-color: #ffc107;
+  border-color: #ffc107;
+}
+.btn-outline-warning:not(:disabled):not(.disabled):active:focus, .btn-outline-warning:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-warning.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(255, 193, 7, 0.5);
+}
+
+.btn-outline-danger {
+  color: #dc3545;
+  background-color: transparent;
+  background-image: none;
+  border-color: #dc3545;
+}
+.btn-outline-danger:hover {
+  color: #fff;
+  background-color: #dc3545;
+  border-color: #dc3545;
+}
+.btn-outline-danger:focus, .btn-outline-danger.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+}
+.btn-outline-danger.disabled, .btn-outline-danger:disabled {
+  color: #dc3545;
+  background-color: transparent;
+}
+.btn-outline-danger:not(:disabled):not(.disabled):active, .btn-outline-danger:not(:disabled):not(.disabled).active, .show > .btn-outline-danger.dropdown-toggle {
+  color: #fff;
+  background-color: #dc3545;
+  border-color: #dc3545;
+}
+.btn-outline-danger:not(:disabled):not(.disabled):active:focus, .btn-outline-danger:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-danger.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(220, 53, 69, 0.5);
+}
+
+.btn-outline-light {
+  color: #f8f9fa;
+  background-color: transparent;
+  background-image: none;
+  border-color: #f8f9fa;
+}
+.btn-outline-light:hover {
+  color: #212529;
+  background-color: #f8f9fa;
+  border-color: #f8f9fa;
+}
+.btn-outline-light:focus, .btn-outline-light.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+}
+.btn-outline-light.disabled, .btn-outline-light:disabled {
+  color: #f8f9fa;
+  background-color: transparent;
+}
+.btn-outline-light:not(:disabled):not(.disabled):active, .btn-outline-light:not(:disabled):not(.disabled).active, .show > .btn-outline-light.dropdown-toggle {
+  color: #212529;
+  background-color: #f8f9fa;
+  border-color: #f8f9fa;
+}
+.btn-outline-light:not(:disabled):not(.disabled):active:focus, .btn-outline-light:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-light.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(248, 249, 250, 0.5);
+}
+
+.btn-outline-dark {
+  color: #343a40;
+  background-color: transparent;
+  background-image: none;
+  border-color: #343a40;
+}
+.btn-outline-dark:hover {
+  color: #fff;
+  background-color: #343a40;
+  border-color: #343a40;
+}
+.btn-outline-dark:focus, .btn-outline-dark.focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+}
+.btn-outline-dark.disabled, .btn-outline-dark:disabled {
+  color: #343a40;
+  background-color: transparent;
+}
+.btn-outline-dark:not(:disabled):not(.disabled):active, .btn-outline-dark:not(:disabled):not(.disabled).active, .show > .btn-outline-dark.dropdown-toggle {
+  color: #fff;
+  background-color: #343a40;
+  border-color: #343a40;
+}
+.btn-outline-dark:not(:disabled):not(.disabled):active:focus, .btn-outline-dark:not(:disabled):not(.disabled).active:focus, .show > .btn-outline-dark.dropdown-toggle:focus {
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+          box-shadow: 0 0 0 0.2rem rgba(52, 58, 64, 0.5);
+}
+
+.btn-link {
+  font-weight: 400;
+  color: #007bff;
+  background-color: transparent;
+}
+.btn-link:hover {
+  color: #0056b3;
+  text-decoration: underline;
+  background-color: transparent;
+  border-color: transparent;
+}
+.btn-link:focus, .btn-link.focus {
+  text-decoration: underline;
+  border-color: transparent;
+  -webkit-box-shadow: none;
+          box-shadow: none;
+}
+.btn-link:disabled, .btn-link.disabled {
+  color: #6c757d;
+}
+
+.btn-lg, .btn-group-lg > .btn {
+  padding: 0.5rem 1rem;
+  font-size: 1.25rem;
+  line-height: 1.5;
+  border-radius: 0.3rem;
+}
+
+.btn-sm, .btn-group-sm > .btn {
+  padding: 0.25rem 0.5rem;
+  font-size: 0.875rem;
+  line-height: 1.5;
+  border-radius: 0.2rem;
+}
+
+.btn-block {
+  display: block;
+  width: 100%;
+}
+.btn-block + .btn-block {
+  margin-top: 0.5rem;
+}
+
+input[type="submit"].btn-block,
+input[type="reset"].btn-block,
+input[type="button"].btn-block {
+  width: 100%;
+}
+
+.fade {
+  opacity: 0;
+  -webkit-transition: opacity 0.15s linear;
+  transition: opacity 0.15s linear;
+}
+.fade.show {
+  opacity: 1;
+}
+
+.collapse {
+  display: none;
+}
+.collapse.show {
+  display: block;
+}
+
+tr.collapse.show {
+  display: table-row;
+}
+
+tbody.collapse.show {
+  display: table-row-group;
+}
+
+.collapsing {
+  position: relative;
+  height: 0;
+  overflow: hidden;
+  -webkit-transition: height 0.35s ease;
+  transition: height 0.35s ease;
+}
+
+.dropup,
+.dropdown {
+  position: relative;
+}
+
+.dropdown-toggle::after {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+  border-top: 0.3em solid;
+  border-right: 0.3em solid transparent;
+  border-bottom: 0;
+  border-left: 0.3em solid transparent;
+}
+.dropdown-toggle:empty::after {
+  margin-left: 0;
+}
+
+.dropdown-menu {
+  position: absolute;
+  top: 100%;
+  left: 0;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 10rem;
+  padding: 0.5rem 0;
+  margin: 0.125rem 0 0;
+  font-size: 1rem;
+  color: #212529;
+  text-align: left;
+  list-style: none;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 0.25rem;
+}
+
+.dropup .dropdown-menu {
+  margin-top: 0;
+  margin-bottom: 0.125rem;
+}
+.dropup .dropdown-toggle::after {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+  border-top: 0;
+  border-right: 0.3em solid transparent;
+  border-bottom: 0.3em solid;
+  border-left: 0.3em solid transparent;
+}
+.dropup .dropdown-toggle:empty::after {
+  margin-left: 0;
+}
+
+.dropright .dropdown-menu {
+  margin-top: 0;
+  margin-left: 0.125rem;
+}
+.dropright .dropdown-toggle::after {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+  border-top: 0.3em solid transparent;
+  border-bottom: 0.3em solid transparent;
+  border-left: 0.3em solid;
+}
+.dropright .dropdown-toggle:empty::after {
+  margin-left: 0;
+}
+.dropright .dropdown-toggle::after {
+  vertical-align: 0;
+}
+
+.dropleft .dropdown-menu {
+  margin-top: 0;
+  margin-right: 0.125rem;
+}
+.dropleft .dropdown-toggle::after {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-left: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+}
+.dropleft .dropdown-toggle::after {
+  display: none;
+}
+.dropleft .dropdown-toggle::before {
+  display: inline-block;
+  width: 0;
+  height: 0;
+  margin-right: 0.255em;
+  vertical-align: 0.255em;
+  content: "";
+  border-top: 0.3em solid transparent;
+  border-right: 0.3em solid;
+  border-bottom: 0.3em solid transparent;
+}
+.dropleft .dropdown-toggle:empty::after {
+  margin-left: 0;
+}
+.dropleft .dropdown-toggle::before {
+  vertical-align: 0;
+}
+
+.dropdown-divider {
+  height: 0;
+  margin: 0.5rem 0;
+  overflow: hidden;
+  border-top: 1px solid #e9ecef;
+}
+
+.dropdown-item {
+  display: block;
+  width: 100%;
+  padding: 0.25rem 1.5rem;
+  clear: both;
+  font-weight: 400;
+  color: #212529;
+  text-align: inherit;
+  white-space: nowrap;
+  background-color: transparent;
+  border: 0;
+}
+.dropdown-item:hover, .dropdown-item:focus {
+  color: #16181b;
+  text-decoration: none;
+  background-color: #f8f9fa;
+}
+.dropdown-item.active, .dropdown-item:active {
+  color: #fff;
+  text-decoration: none;
+  background-color: #007bff;
+}
+.dropdown-item.disabled, .dropdown-item:disabled {
+  color: #6c757d;
+  background-color: transparent;
+}
+
+.dropdown-menu.show {
+  display: block;
+}
+
+.dropdown-header {
+  display: block;
+  padding: 0.5rem 1.5rem;
+  margin-bottom: 0;
+  font-size: 0.875rem;
+  color: #6c757d;
+  white-space: nowrap;
+}
+
+.btn-group,
+.btn-group-vertical {
+  position: relative;
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+  vertical-align: middle;
+}
+.btn-group > .btn,
+.btn-group-vertical > .btn {
+  position: relative;
+  -webkit-box-flex: 0;
+      -ms-flex: 0 1 auto;
+          flex: 0 1 auto;
+}
+.btn-group > .btn:hover,
+.btn-group-vertical > .btn:hover {
+  z-index: 1;
+}
+.btn-group > .btn:focus, .btn-group > .btn:active, .btn-group > .btn.active,
+.btn-group-vertical > .btn:focus,
+.btn-group-vertical > .btn:active,
+.btn-group-vertical > .btn.active {
+  z-index: 1;
+}
+.btn-group .btn + .btn,
+.btn-group .btn + .btn-group,
+.btn-group .btn-group + .btn,
+.btn-group .btn-group + .btn-group,
+.btn-group-vertical .btn + .btn,
+.btn-group-vertical .btn + .btn-group,
+.btn-group-vertical .btn-group + .btn,
+.btn-group-vertical .btn-group + .btn-group {
+  margin-left: -1px;
+}
+
+.btn-toolbar {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  -webkit-box-pack: start;
+      -ms-flex-pack: start;
+          justify-content: flex-start;
+}
+.btn-toolbar .input-group {
+  width: auto;
+}
+
+.btn-group > .btn:first-child {
+  margin-left: 0;
+}
+.btn-group > .btn:not(:last-child):not(.dropdown-toggle),
+.btn-group > .btn-group:not(:last-child) > .btn {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.btn-group > .btn:not(:first-child),
+.btn-group > .btn-group:not(:first-child) > .btn {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.dropdown-toggle-split {
+  padding-right: 0.5625rem;
+  padding-left: 0.5625rem;
+}
+.dropdown-toggle-split::after {
+  margin-left: 0;
+}
+
+.btn-sm + .dropdown-toggle-split, .btn-group-sm > .btn + .dropdown-toggle-split {
+  padding-right: 0.375rem;
+  padding-left: 0.375rem;
+}
+
+.btn-lg + .dropdown-toggle-split, .btn-group-lg > .btn + .dropdown-toggle-split {
+  padding-right: 0.75rem;
+  padding-left: 0.75rem;
+}
+
+.btn-group-vertical {
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  -webkit-box-align: start;
+      -ms-flex-align: start;
+          align-items: flex-start;
+  -webkit-box-pack: center;
+      -ms-flex-pack: center;
+          justify-content: center;
+}
+.btn-group-vertical .btn,
+.btn-group-vertical .btn-group {
+  width: 100%;
+}
+.btn-group-vertical > .btn + .btn,
+.btn-group-vertical > .btn + .btn-group,
+.btn-group-vertical > .btn-group + .btn,
+.btn-group-vertical > .btn-group + .btn-group {
+  margin-top: -1px;
+  margin-left: 0;
+}
+.btn-group-vertical > .btn:not(:last-child):not(.dropdown-toggle),
+.btn-group-vertical > .btn-group:not(:last-child) > .btn {
+  border-bottom-right-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.btn-group-vertical > .btn:not(:first-child),
+.btn-group-vertical > .btn-group:not(:first-child) > .btn {
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+
+.btn-group-toggle > .btn,
+.btn-group-toggle > .btn-group > .btn {
+  margin-bottom: 0;
+}
+.btn-group-toggle > .btn input[type="radio"],
+.btn-group-toggle > .btn input[type="checkbox"],
+.btn-group-toggle > .btn-group > .btn input[type="radio"],
+.btn-group-toggle > .btn-group > .btn input[type="checkbox"] {
+  position: absolute;
+  clip: rect(0, 0, 0, 0);
+  pointer-events: none;
+}
+
+.input-group {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  -webkit-box-align: stretch;
+      -ms-flex-align: stretch;
+          align-items: stretch;
+  width: 100%;
+}
+.input-group > .form-control,
+.input-group > .custom-select,
+.input-group > .custom-file {
+  position: relative;
+  -webkit-box-flex: 1;
+      -ms-flex: 1 1 auto;
+          flex: 1 1 auto;
+  width: 1%;
+  margin-bottom: 0;
+}
+.input-group > .form-control:focus,
+.input-group > .custom-select:focus,
+.input-group > .custom-file:focus {
+  z-index: 3;
+}
+.input-group > .form-control + .form-control,
+.input-group > .form-control + .custom-select,
+.input-group > .form-control + .custom-file,
+.input-group > .custom-select + .form-control,
+.input-group > .custom-select + .custom-select,
+.input-group > .custom-select + .custom-file,
+.input-group > .custom-file + .form-control,
+.input-group > .custom-file + .custom-select,
+.input-group > .custom-file + .custom-file {
+  margin-left: -1px;
+}
+.input-group > .form-control:not(:last-child),
+.input-group > .custom-select:not(:last-child) {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.input-group > .form-control:not(:first-child),
+.input-group > .custom-select:not(:first-child) {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+.input-group > .custom-file {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+.input-group > .custom-file:not(:last-child) .custom-file-label, .input-group > .custom-file:not(:last-child) .custom-file-label::before {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+.input-group > .custom-file:not(:first-child) .custom-file-label, .input-group > .custom-file:not(:first-child) .custom-file-label::before {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.input-group-prepend,
+.input-group-append {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+}
+.input-group-prepend .btn,
+.input-group-append .btn {
+  position: relative;
+  z-index: 2;
+}
+.input-group-prepend .btn + .btn,
+.input-group-prepend .btn + .input-group-text,
+.input-group-prepend .input-group-text + .input-group-text,
+.input-group-prepend .input-group-text + .btn,
+.input-group-append .btn + .btn,
+.input-group-append .btn + .input-group-text,
+.input-group-append .input-group-text + .input-group-text,
+.input-group-append .input-group-text + .btn {
+  margin-left: -1px;
+}
+
+.input-group-prepend {
+  margin-right: -1px;
+}
+
+.input-group-append {
+  margin-left: -1px;
+}
+
+.input-group-text {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  padding: 0.375rem 0.75rem;
+  margin-bottom: 0;
+  font-size: 1rem;
+  font-weight: 400;
+  line-height: 1.5;
+  color: #495057;
+  text-align: center;
+  white-space: nowrap;
+  background-color: #e9ecef;
+  border: 1px solid #ced4da;
+  border-radius: 0.25rem;
+}
+.input-group-text input[type="radio"],
+.input-group-text input[type="checkbox"] {
+  margin-top: 0;
+}
+
+.input-group > .input-group-prepend > .btn,
+.input-group > .input-group-prepend > .input-group-text,
+.input-group > .input-group-append:not(:last-child) > .btn,
+.input-group > .input-group-append:not(:last-child) > .input-group-text,
+.input-group > .input-group-append:last-child > .btn:not(:last-child):not(.dropdown-toggle),
+.input-group > .input-group-append:last-child > .input-group-text:not(:last-child) {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+.input-group > .input-group-append > .btn,
+.input-group > .input-group-append > .input-group-text,
+.input-group > .input-group-prepend:not(:first-child) > .btn,
+.input-group > .input-group-prepend:not(:first-child) > .input-group-text,
+.input-group > .input-group-prepend:first-child > .btn:not(:first-child),
+.input-group > .input-group-prepend:first-child > .input-group-text:not(:first-child) {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
+}
+
+.custom-control {
+  position: relative;
+  display: block;
+  min-height: 1.5rem;
+  padding-left: 1.5rem;
+}
+
+.custom-control-inline {
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+  margin-right: 1rem;
+}
+
+.custom-control-input {
+  position: absolute;
+  z-index: -1;
+  opacity: 0;
+}
+.custom-control-input:checked ~ .custom-control-label::before {
+  color: #fff;
+  background-color: #007bff;
+}
+.custom-control-input:focus ~ .custom-control-label::before {
+  -webkit-box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 1px #fff, 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.custom-control-input:active ~ .custom-control-label::before {
+  color: #fff;
+  background-color: #b3d7ff;
+}
+.custom-control-input:disabled ~ .custom-control-label {
+  color: #6c757d;
+}
+.custom-control-input:disabled ~ .custom-control-label::before {
+  background-color: #e9ecef;
+}
+
+.custom-control-label {
+  margin-bottom: 0;
+}
+.custom-control-label::before {
+  position: absolute;
+  top: 0.25rem;
+  left: 0;
+  display: block;
+  width: 1rem;
+  height: 1rem;
+  pointer-events: none;
+  content: "";
+  -webkit-user-select: none;
+     -moz-user-select: none;
+      -ms-user-select: none;
+          user-select: none;
+  background-color: #dee2e6;
+}
+.custom-control-label::after {
+  position: absolute;
+  top: 0.25rem;
+  left: 0;
+  display: block;
+  width: 1rem;
+  height: 1rem;
+  content: "";
+  background-repeat: no-repeat;
+  background-position: center center;
+  background-size: 50% 50%;
+}
+
+.custom-checkbox .custom-control-label::before {
+  border-radius: 0.25rem;
+}
+.custom-checkbox .custom-control-input:checked ~ .custom-control-label::before {
+  background-color: #007bff;
+}
+.custom-checkbox .custom-control-input:checked ~ .custom-control-label::after {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 8 8'%3E%3Cpath fill='%23fff' d='M6.564.75l-3.59 3.612-1.538-1.55L0 4.26 2.974 7.25 8 2.193z'/%3E%3C/svg%3E");
+}
+.custom-checkbox .custom-control-input:indeterminate ~ .custom-control-label::before {
+  background-color: #007bff;
+}
+.custom-checkbox .custom-control-input:indeterminate ~ .custom-control-label::after {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 4'%3E%3Cpath stroke='%23fff' d='M0 2h4'/%3E%3C/svg%3E");
+}
+.custom-checkbox .custom-control-input:disabled:checked ~ .custom-control-label::before {
+  background-color: rgba(0, 123, 255, 0.5);
+}
+.custom-checkbox .custom-control-input:disabled:indeterminate ~ .custom-control-label::before {
+  background-color: rgba(0, 123, 255, 0.5);
+}
+
+.custom-radio .custom-control-label::before {
+  border-radius: 50%;
+}
+.custom-radio .custom-control-input:checked ~ .custom-control-label::before {
+  background-color: #007bff;
+}
+.custom-radio .custom-control-input:checked ~ .custom-control-label::after {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='-4 -4 8 8'%3E%3Ccircle r='3' fill='%23fff'/%3E%3C/svg%3E");
+}
+.custom-radio .custom-control-input:disabled:checked ~ .custom-control-label::before {
+  background-color: rgba(0, 123, 255, 0.5);
+}
+
+.custom-select {
+  display: inline-block;
+  width: 100%;
+  height: calc(2.25rem + 2px);
+  padding: 0.375rem 1.75rem 0.375rem 0.75rem;
+  line-height: 1.5;
+  color: #495057;
+  vertical-align: middle;
+  background: #fff url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 4 5'%3E%3Cpath fill='%23343a40' d='M2 0L0 2h4zm0 5L0 3h4z'/%3E%3C/svg%3E") no-repeat right 0.75rem center;
+  background-size: 8px 10px;
+  border: 1px solid #ced4da;
+  border-radius: 0.25rem;
+  -webkit-appearance: none;
+     -moz-appearance: none;
+          appearance: none;
+}
+.custom-select:focus {
+  border-color: #80bdff;
+  outline: 0;
+  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075), 0 0 5px rgba(128, 189, 255, 0.5);
+          box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075), 0 0 5px rgba(128, 189, 255, 0.5);
+}
+.custom-select:focus::-ms-value {
+  color: #495057;
+  background-color: #fff;
+}
+.custom-select[multiple], .custom-select[size]:not([size="1"]) {
+  height: auto;
+  padding-right: 0.75rem;
+  background-image: none;
+}
+.custom-select:disabled {
+  color: #6c757d;
+  background-color: #e9ecef;
+}
+.custom-select::-ms-expand {
+  opacity: 0;
+}
+
+.custom-select-sm {
+  height: calc(1.8125rem + 2px);
+  padding-top: 0.375rem;
+  padding-bottom: 0.375rem;
+  font-size: 75%;
+}
+
+.custom-select-lg {
+  height: calc(2.875rem + 2px);
+  padding-top: 0.375rem;
+  padding-bottom: 0.375rem;
+  font-size: 125%;
+}
+
+.custom-file {
+  position: relative;
+  display: inline-block;
+  width: 100%;
+  height: calc(2.25rem + 2px);
+  margin-bottom: 0;
+}
+
+.custom-file-input {
+  position: relative;
+  z-index: 2;
+  width: 100%;
+  height: calc(2.25rem + 2px);
+  margin: 0;
+  opacity: 0;
+}
+.custom-file-input:focus ~ .custom-file-control {
+  border-color: #80bdff;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.custom-file-input:focus ~ .custom-file-control::before {
+  border-color: #80bdff;
+}
+.custom-file-input:lang(en) ~ .custom-file-label::after {
+  content: "Browse";
+}
+
+.custom-file-label {
+  position: absolute;
+  top: 0;
+  right: 0;
+  left: 0;
+  z-index: 1;
+  height: calc(2.25rem + 2px);
+  padding: 0.375rem 0.75rem;
+  line-height: 1.5;
+  color: #495057;
+  background-color: #fff;
+  border: 1px solid #ced4da;
+  border-radius: 0.25rem;
+}
+.custom-file-label::after {
+  position: absolute;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  z-index: 3;
+  display: block;
+  height: calc(calc(2.25rem + 2px) - 1px * 2);
+  padding: 0.375rem 0.75rem;
+  line-height: 1.5;
+  color: #495057;
+  content: "Browse";
+  background-color: #e9ecef;
+  border-left: 1px solid #ced4da;
+  border-radius: 0 0.25rem 0.25rem 0;
+}
+
+.nav {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  padding-left: 0;
+  margin-bottom: 0;
+  list-style: none;
+}
+
+.nav-link {
+  display: block;
+  padding: 0.5rem 1rem;
+}
+.nav-link:hover, .nav-link:focus {
+  text-decoration: none;
+}
+.nav-link.disabled {
+  color: #6c757d;
+}
+
+.nav-tabs {
+  border-bottom: 1px solid #dee2e6;
+}
+.nav-tabs .nav-item {
+  margin-bottom: -1px;
+}
+.nav-tabs .nav-link {
+  border: 1px solid transparent;
+  border-top-left-radius: 0.25rem;
+  border-top-right-radius: 0.25rem;
+}
+.nav-tabs .nav-link:hover, .nav-tabs .nav-link:focus {
+  border-color: #e9ecef #e9ecef #dee2e6;
+}
+.nav-tabs .nav-link.disabled {
+  color: #6c757d;
+  background-color: transparent;
+  border-color: transparent;
+}
+.nav-tabs .nav-link.active,
+.nav-tabs .nav-item.show .nav-link {
+  color: #495057;
+  background-color: #fff;
+  border-color: #dee2e6 #dee2e6 #fff;
+}
+.nav-tabs .dropdown-menu {
+  margin-top: -1px;
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+}
+
+.nav-pills .nav-link {
+  border-radius: 0.25rem;
+}
+.nav-pills .nav-link.active,
+.nav-pills .show > .nav-link {
+  color: #fff;
+  background-color: #007bff;
+}
+
+.nav-fill .nav-item {
+  -webkit-box-flex: 1;
+      -ms-flex: 1 1 auto;
+          flex: 1 1 auto;
+  text-align: center;
+}
+
+.nav-justified .nav-item {
+  -ms-flex-preferred-size: 0;
+      flex-basis: 0;
+  -webkit-box-flex: 1;
+      -ms-flex-positive: 1;
+          flex-grow: 1;
+  text-align: center;
+}
+
+.tab-content > .tab-pane {
+  display: none;
+}
+.tab-content > .active {
+  display: block;
+}
+
+.navbar {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  -webkit-box-pack: justify;
+      -ms-flex-pack: justify;
+          justify-content: space-between;
+  padding: 0.5rem 1rem;
+}
+.navbar > .container,
+.navbar > .container-fluid {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  -webkit-box-pack: justify;
+      -ms-flex-pack: justify;
+          justify-content: space-between;
+}
+
+.navbar-brand {
+  display: inline-block;
+  padding-top: 0.3125rem;
+  padding-bottom: 0.3125rem;
+  margin-right: 1rem;
+  font-size: 1.25rem;
+  line-height: inherit;
+  white-space: nowrap;
+}
+.navbar-brand:hover, .navbar-brand:focus {
+  text-decoration: none;
+}
+
+.navbar-nav {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  padding-left: 0;
+  margin-bottom: 0;
+  list-style: none;
+}
+.navbar-nav .nav-link {
+  padding-right: 0;
+  padding-left: 0;
+}
+.navbar-nav .dropdown-menu {
+  position: static;
+  float: none;
+}
+
+.navbar-text {
+  display: inline-block;
+  padding-top: 0.5rem;
+  padding-bottom: 0.5rem;
+}
+
+.navbar-collapse {
+  -ms-flex-preferred-size: 100%;
+      flex-basis: 100%;
+  -webkit-box-flex: 1;
+      -ms-flex-positive: 1;
+          flex-grow: 1;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+
+.navbar-toggler {
+  padding: 0.25rem 0.75rem;
+  font-size: 1.25rem;
+  line-height: 1;
+  background-color: transparent;
+  border: 1px solid transparent;
+  border-radius: 0.25rem;
+}
+.navbar-toggler:hover, .navbar-toggler:focus {
+  text-decoration: none;
+}
+.navbar-toggler:not(:disabled):not(.disabled) {
+  cursor: pointer;
+}
+
+.navbar-toggler-icon {
+  display: inline-block;
+  width: 1.5em;
+  height: 1.5em;
+  vertical-align: middle;
+  content: "";
+  background: no-repeat center center;
+  background-size: 100% 100%;
+}
+
+@media (max-width: 575.98px) {
+  .navbar-expand-sm > .container,
+  .navbar-expand-sm > .container-fluid {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+@media (min-width: 576px) {
+  .navbar-expand-sm {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row nowrap;
+            flex-flow: row nowrap;
+    -webkit-box-pack: start;
+        -ms-flex-pack: start;
+            justify-content: flex-start;
+  }
+  .navbar-expand-sm .navbar-nav {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: row;
+            flex-direction: row;
+  }
+  .navbar-expand-sm .navbar-nav .dropdown-menu {
+    position: absolute;
+  }
+  .navbar-expand-sm .navbar-nav .dropdown-menu-right {
+    right: 0;
+    left: auto;
+  }
+  .navbar-expand-sm .navbar-nav .nav-link {
+    padding-right: 0.5rem;
+    padding-left: 0.5rem;
+  }
+  .navbar-expand-sm > .container,
+  .navbar-expand-sm > .container-fluid {
+    -ms-flex-wrap: nowrap;
+        flex-wrap: nowrap;
+  }
+  .navbar-expand-sm .navbar-collapse {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+    -ms-flex-preferred-size: auto;
+        flex-basis: auto;
+  }
+  .navbar-expand-sm .navbar-toggler {
+    display: none;
+  }
+  .navbar-expand-sm .dropup .dropdown-menu {
+    top: auto;
+    bottom: 100%;
+  }
+}
+@media (max-width: 767.98px) {
+  .navbar-expand-md > .container,
+  .navbar-expand-md > .container-fluid {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+@media (min-width: 768px) {
+  .navbar-expand-md {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row nowrap;
+            flex-flow: row nowrap;
+    -webkit-box-pack: start;
+        -ms-flex-pack: start;
+            justify-content: flex-start;
+  }
+  .navbar-expand-md .navbar-nav {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: row;
+            flex-direction: row;
+  }
+  .navbar-expand-md .navbar-nav .dropdown-menu {
+    position: absolute;
+  }
+  .navbar-expand-md .navbar-nav .dropdown-menu-right {
+    right: 0;
+    left: auto;
+  }
+  .navbar-expand-md .navbar-nav .nav-link {
+    padding-right: 0.5rem;
+    padding-left: 0.5rem;
+  }
+  .navbar-expand-md > .container,
+  .navbar-expand-md > .container-fluid {
+    -ms-flex-wrap: nowrap;
+        flex-wrap: nowrap;
+  }
+  .navbar-expand-md .navbar-collapse {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+    -ms-flex-preferred-size: auto;
+        flex-basis: auto;
+  }
+  .navbar-expand-md .navbar-toggler {
+    display: none;
+  }
+  .navbar-expand-md .dropup .dropdown-menu {
+    top: auto;
+    bottom: 100%;
+  }
+}
+@media (max-width: 991.98px) {
+  .navbar-expand-lg > .container,
+  .navbar-expand-lg > .container-fluid {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+@media (min-width: 992px) {
+  .navbar-expand-lg {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row nowrap;
+            flex-flow: row nowrap;
+    -webkit-box-pack: start;
+        -ms-flex-pack: start;
+            justify-content: flex-start;
+  }
+  .navbar-expand-lg .navbar-nav {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: row;
+            flex-direction: row;
+  }
+  .navbar-expand-lg .navbar-nav .dropdown-menu {
+    position: absolute;
+  }
+  .navbar-expand-lg .navbar-nav .dropdown-menu-right {
+    right: 0;
+    left: auto;
+  }
+  .navbar-expand-lg .navbar-nav .nav-link {
+    padding-right: 0.5rem;
+    padding-left: 0.5rem;
+  }
+  .navbar-expand-lg > .container,
+  .navbar-expand-lg > .container-fluid {
+    -ms-flex-wrap: nowrap;
+        flex-wrap: nowrap;
+  }
+  .navbar-expand-lg .navbar-collapse {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+    -ms-flex-preferred-size: auto;
+        flex-basis: auto;
+  }
+  .navbar-expand-lg .navbar-toggler {
+    display: none;
+  }
+  .navbar-expand-lg .dropup .dropdown-menu {
+    top: auto;
+    bottom: 100%;
+  }
+}
+@media (max-width: 1199.98px) {
+  .navbar-expand-xl > .container,
+  .navbar-expand-xl > .container-fluid {
+    padding-right: 0;
+    padding-left: 0;
+  }
+}
+@media (min-width: 1200px) {
+  .navbar-expand-xl {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row nowrap;
+            flex-flow: row nowrap;
+    -webkit-box-pack: start;
+        -ms-flex-pack: start;
+            justify-content: flex-start;
+  }
+  .navbar-expand-xl .navbar-nav {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: row;
+            flex-direction: row;
+  }
+  .navbar-expand-xl .navbar-nav .dropdown-menu {
+    position: absolute;
+  }
+  .navbar-expand-xl .navbar-nav .dropdown-menu-right {
+    right: 0;
+    left: auto;
+  }
+  .navbar-expand-xl .navbar-nav .nav-link {
+    padding-right: 0.5rem;
+    padding-left: 0.5rem;
+  }
+  .navbar-expand-xl > .container,
+  .navbar-expand-xl > .container-fluid {
+    -ms-flex-wrap: nowrap;
+        flex-wrap: nowrap;
+  }
+  .navbar-expand-xl .navbar-collapse {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+    -ms-flex-preferred-size: auto;
+        flex-basis: auto;
+  }
+  .navbar-expand-xl .navbar-toggler {
+    display: none;
+  }
+  .navbar-expand-xl .dropup .dropdown-menu {
+    top: auto;
+    bottom: 100%;
+  }
+}
+.navbar-expand {
+  -webkit-box-orient: horizontal;
+  -webkit-box-direction: normal;
+      -ms-flex-flow: row nowrap;
+          flex-flow: row nowrap;
+  -webkit-box-pack: start;
+      -ms-flex-pack: start;
+          justify-content: flex-start;
+}
+.navbar-expand > .container,
+.navbar-expand > .container-fluid {
+  padding-right: 0;
+  padding-left: 0;
+}
+.navbar-expand .navbar-nav {
+  -webkit-box-orient: horizontal;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: row;
+          flex-direction: row;
+}
+.navbar-expand .navbar-nav .dropdown-menu {
+  position: absolute;
+}
+.navbar-expand .navbar-nav .dropdown-menu-right {
+  right: 0;
+  left: auto;
+}
+.navbar-expand .navbar-nav .nav-link {
+  padding-right: 0.5rem;
+  padding-left: 0.5rem;
+}
+.navbar-expand > .container,
+.navbar-expand > .container-fluid {
+  -ms-flex-wrap: nowrap;
+      flex-wrap: nowrap;
+}
+.navbar-expand .navbar-collapse {
+  display: -webkit-box !important;
+  display: -ms-flexbox !important;
+  display: flex !important;
+  -ms-flex-preferred-size: auto;
+      flex-basis: auto;
+}
+.navbar-expand .navbar-toggler {
+  display: none;
+}
+.navbar-expand .dropup .dropdown-menu {
+  top: auto;
+  bottom: 100%;
+}
+
+.navbar-light .navbar-brand {
+  color: rgba(0, 0, 0, 0.9);
+}
+.navbar-light .navbar-brand:hover, .navbar-light .navbar-brand:focus {
+  color: rgba(0, 0, 0, 0.9);
+}
+.navbar-light .navbar-nav .nav-link {
+  color: rgba(0, 0, 0, 0.5);
+}
+.navbar-light .navbar-nav .nav-link:hover, .navbar-light .navbar-nav .nav-link:focus {
+  color: rgba(0, 0, 0, 0.7);
+}
+.navbar-light .navbar-nav .nav-link.disabled {
+  color: rgba(0, 0, 0, 0.3);
+}
+.navbar-light .navbar-nav .show > .nav-link,
+.navbar-light .navbar-nav .active > .nav-link,
+.navbar-light .navbar-nav .nav-link.show,
+.navbar-light .navbar-nav .nav-link.active {
+  color: rgba(0, 0, 0, 0.9);
+}
+.navbar-light .navbar-toggler {
+  color: rgba(0, 0, 0, 0.5);
+  border-color: rgba(0, 0, 0, 0.1);
+}
+.navbar-light .navbar-toggler-icon {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='rgba(0, 0, 0, 0.5)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3E%3C/svg%3E");
+}
+.navbar-light .navbar-text {
+  color: rgba(0, 0, 0, 0.5);
+}
+.navbar-light .navbar-text a {
+  color: rgba(0, 0, 0, 0.9);
+}
+.navbar-light .navbar-text a:hover, .navbar-light .navbar-text a:focus {
+  color: rgba(0, 0, 0, 0.9);
+}
+
+.navbar-dark .navbar-brand {
+  color: #fff;
+}
+.navbar-dark .navbar-brand:hover, .navbar-dark .navbar-brand:focus {
+  color: #fff;
+}
+.navbar-dark .navbar-nav .nav-link {
+  color: rgba(255, 255, 255, 0.5);
+}
+.navbar-dark .navbar-nav .nav-link:hover, .navbar-dark .navbar-nav .nav-link:focus {
+  color: rgba(255, 255, 255, 0.75);
+}
+.navbar-dark .navbar-nav .nav-link.disabled {
+  color: rgba(255, 255, 255, 0.25);
+}
+.navbar-dark .navbar-nav .show > .nav-link,
+.navbar-dark .navbar-nav .active > .nav-link,
+.navbar-dark .navbar-nav .nav-link.show,
+.navbar-dark .navbar-nav .nav-link.active {
+  color: #fff;
+}
+.navbar-dark .navbar-toggler {
+  color: rgba(255, 255, 255, 0.5);
+  border-color: rgba(255, 255, 255, 0.1);
+}
+.navbar-dark .navbar-toggler-icon {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg viewBox='0 0 30 30' xmlns='http://www.w3.org/2000/svg'%3E%3Cpath stroke='rgba(255, 255, 255, 0.5)' stroke-width='2' stroke-linecap='round' stroke-miterlimit='10' d='M4 7h22M4 15h22M4 23h22'/%3E%3C/svg%3E");
+}
+.navbar-dark .navbar-text {
+  color: rgba(255, 255, 255, 0.5);
+}
+.navbar-dark .navbar-text a {
+  color: #fff;
+}
+.navbar-dark .navbar-text a:hover, .navbar-dark .navbar-text a:focus {
+  color: #fff;
+}
+
+.card {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  min-width: 0;
+  word-wrap: break-word;
+  background-color: #fff;
+  background-clip: border-box;
+  border: 1px solid rgba(0, 0, 0, 0.125);
+  border-radius: 0.25rem;
+}
+.card > hr {
+  margin-right: 0;
+  margin-left: 0;
+}
+.card > .list-group:first-child .list-group-item:first-child {
+  border-top-left-radius: 0.25rem;
+  border-top-right-radius: 0.25rem;
+}
+.card > .list-group:last-child .list-group-item:last-child {
+  border-bottom-right-radius: 0.25rem;
+  border-bottom-left-radius: 0.25rem;
+}
+
+.card-body {
+  -webkit-box-flex: 1;
+      -ms-flex: 1 1 auto;
+          flex: 1 1 auto;
+  padding: 1.25rem;
+}
+
+.card-title {
+  margin-bottom: 0.75rem;
+}
+
+.card-subtitle {
+  margin-top: -0.375rem;
+  margin-bottom: 0;
+}
+
+.card-text:last-child {
+  margin-bottom: 0;
+}
+
+.card-link:hover {
+  text-decoration: none;
+}
+.card-link + .card-link {
+  margin-left: 1.25rem;
+}
+
+.card-header {
+  padding: 0.75rem 1.25rem;
+  margin-bottom: 0;
+  background-color: rgba(0, 0, 0, 0.03);
+  border-bottom: 1px solid rgba(0, 0, 0, 0.125);
+}
+.card-header:first-child {
+  border-radius: calc(0.25rem - 1px) calc(0.25rem - 1px) 0 0;
+}
+.card-header + .list-group .list-group-item:first-child {
+  border-top: 0;
+}
+
+.card-footer {
+  padding: 0.75rem 1.25rem;
+  background-color: rgba(0, 0, 0, 0.03);
+  border-top: 1px solid rgba(0, 0, 0, 0.125);
+}
+.card-footer:last-child {
+  border-radius: 0 0 calc(0.25rem - 1px) calc(0.25rem - 1px);
+}
+
+.card-header-tabs {
+  margin-right: -0.625rem;
+  margin-bottom: -0.75rem;
+  margin-left: -0.625rem;
+  border-bottom: 0;
+}
+
+.card-header-pills {
+  margin-right: -0.625rem;
+  margin-left: -0.625rem;
+}
+
+.card-img-overlay {
+  position: absolute;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  padding: 1.25rem;
+}
+
+.card-img {
+  width: 100%;
+  border-radius: calc(0.25rem - 1px);
+}
+
+.card-img-top {
+  width: 100%;
+  border-top-left-radius: calc(0.25rem - 1px);
+  border-top-right-radius: calc(0.25rem - 1px);
+}
+
+.card-img-bottom {
+  width: 100%;
+  border-bottom-right-radius: calc(0.25rem - 1px);
+  border-bottom-left-radius: calc(0.25rem - 1px);
+}
+
+.card-deck {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+}
+.card-deck .card {
+  margin-bottom: 15px;
+}
+@media (min-width: 576px) {
+  .card-deck {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row wrap;
+            flex-flow: row wrap;
+    margin-right: -15px;
+    margin-left: -15px;
+  }
+  .card-deck .card {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-flex: 1;
+        -ms-flex: 1 0 0%;
+            flex: 1 0 0%;
+    -webkit-box-orient: vertical;
+    -webkit-box-direction: normal;
+        -ms-flex-direction: column;
+            flex-direction: column;
+    margin-right: 15px;
+    margin-bottom: 0;
+    margin-left: 15px;
+  }
+}
+
+.card-group {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+}
+.card-group > .card {
+  margin-bottom: 15px;
+}
+@media (min-width: 576px) {
+  .card-group {
+    -webkit-box-orient: horizontal;
+    -webkit-box-direction: normal;
+        -ms-flex-flow: row wrap;
+            flex-flow: row wrap;
+  }
+  .card-group > .card {
+    -webkit-box-flex: 1;
+        -ms-flex: 1 0 0%;
+            flex: 1 0 0%;
+    margin-bottom: 0;
+  }
+  .card-group > .card + .card {
+    margin-left: 0;
+    border-left: 0;
+  }
+  .card-group > .card:first-child {
+    border-top-right-radius: 0;
+    border-bottom-right-radius: 0;
+  }
+  .card-group > .card:first-child .card-img-top,
+  .card-group > .card:first-child .card-header {
+    border-top-right-radius: 0;
+  }
+  .card-group > .card:first-child .card-img-bottom,
+  .card-group > .card:first-child .card-footer {
+    border-bottom-right-radius: 0;
+  }
+  .card-group > .card:last-child {
+    border-top-left-radius: 0;
+    border-bottom-left-radius: 0;
+  }
+  .card-group > .card:last-child .card-img-top,
+  .card-group > .card:last-child .card-header {
+    border-top-left-radius: 0;
+  }
+  .card-group > .card:last-child .card-img-bottom,
+  .card-group > .card:last-child .card-footer {
+    border-bottom-left-radius: 0;
+  }
+  .card-group > .card:only-child {
+    border-radius: 0.25rem;
+  }
+  .card-group > .card:only-child .card-img-top,
+  .card-group > .card:only-child .card-header {
+    border-top-left-radius: 0.25rem;
+    border-top-right-radius: 0.25rem;
+  }
+  .card-group > .card:only-child .card-img-bottom,
+  .card-group > .card:only-child .card-footer {
+    border-bottom-right-radius: 0.25rem;
+    border-bottom-left-radius: 0.25rem;
+  }
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) {
+    border-radius: 0;
+  }
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-img-top,
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-img-bottom,
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-header,
+  .card-group > .card:not(:first-child):not(:last-child):not(:only-child) .card-footer {
+    border-radius: 0;
+  }
+}
+
+.card-columns .card {
+  margin-bottom: 0.75rem;
+}
+@media (min-width: 576px) {
+  .card-columns {
+    -webkit-column-count: 3;
+       -moz-column-count: 3;
+            column-count: 3;
+    -webkit-column-gap: 1.25rem;
+       -moz-column-gap: 1.25rem;
+            column-gap: 1.25rem;
+  }
+  .card-columns .card {
+    display: inline-block;
+    width: 100%;
+  }
+}
+
+.breadcrumb {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+  padding: 0.75rem 1rem;
+  margin-bottom: 1rem;
+  list-style: none;
+  background-color: #e9ecef;
+  border-radius: 0.25rem;
+}
+
+.breadcrumb-item + .breadcrumb-item::before {
+  display: inline-block;
+  padding-right: 0.5rem;
+  padding-left: 0.5rem;
+  color: #6c757d;
+  content: "/";
+}
+.breadcrumb-item + .breadcrumb-item:hover::before {
+  text-decoration: underline;
+}
+.breadcrumb-item + .breadcrumb-item:hover::before {
+  text-decoration: none;
+}
+.breadcrumb-item.active {
+  color: #6c757d;
+}
+
+.pagination {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  padding-left: 0;
+  list-style: none;
+  border-radius: 0.25rem;
+}
+
+.page-link {
+  position: relative;
+  display: block;
+  padding: 0.5rem 0.75rem;
+  margin-left: -1px;
+  line-height: 1.25;
+  color: #007bff;
+  background-color: #fff;
+  border: 1px solid #dee2e6;
+}
+.page-link:hover {
+  color: #0056b3;
+  text-decoration: none;
+  background-color: #e9ecef;
+  border-color: #dee2e6;
+}
+.page-link:focus {
+  z-index: 2;
+  outline: 0;
+  -webkit-box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+          box-shadow: 0 0 0 0.2rem rgba(0, 123, 255, 0.25);
+}
+.page-link:not(:disabled):not(.disabled) {
+  cursor: pointer;
+}
+
+.page-item:first-child .page-link {
+  margin-left: 0;
+  border-top-left-radius: 0.25rem;
+  border-bottom-left-radius: 0.25rem;
+}
+.page-item:last-child .page-link {
+  border-top-right-radius: 0.25rem;
+  border-bottom-right-radius: 0.25rem;
+}
+.page-item.active .page-link {
+  z-index: 1;
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+.page-item.disabled .page-link {
+  color: #6c757d;
+  pointer-events: none;
+  cursor: auto;
+  background-color: #fff;
+  border-color: #dee2e6;
+}
+
+.pagination-lg .page-link {
+  padding: 0.75rem 1.5rem;
+  font-size: 1.25rem;
+  line-height: 1.5;
+}
+.pagination-lg .page-item:first-child .page-link {
+  border-top-left-radius: 0.3rem;
+  border-bottom-left-radius: 0.3rem;
+}
+.pagination-lg .page-item:last-child .page-link {
+  border-top-right-radius: 0.3rem;
+  border-bottom-right-radius: 0.3rem;
+}
+
+.pagination-sm .page-link {
+  padding: 0.25rem 0.5rem;
+  font-size: 0.875rem;
+  line-height: 1.5;
+}
+.pagination-sm .page-item:first-child .page-link {
+  border-top-left-radius: 0.2rem;
+  border-bottom-left-radius: 0.2rem;
+}
+.pagination-sm .page-item:last-child .page-link {
+  border-top-right-radius: 0.2rem;
+  border-bottom-right-radius: 0.2rem;
+}
+
+.badge {
+  display: inline-block;
+  padding: 0.25em 0.4em;
+  font-size: 75%;
+  font-weight: 700;
+  line-height: 1;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: baseline;
+  border-radius: 0.25rem;
+}
+.badge:empty {
+  display: none;
+}
+
+.btn .badge {
+  position: relative;
+  top: -1px;
+}
+
+.badge-pill {
+  padding-right: 0.6em;
+  padding-left: 0.6em;
+  border-radius: 10rem;
+}
+
+.badge-primary {
+  color: #fff;
+  background-color: #007bff;
+}
+.badge-primary[href]:hover, .badge-primary[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #0062cc;
+}
+
+.badge-secondary {
+  color: #fff;
+  background-color: #6c757d;
+}
+.badge-secondary[href]:hover, .badge-secondary[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #545b62;
+}
+
+.badge-success {
+  color: #fff;
+  background-color: #28a745;
+}
+.badge-success[href]:hover, .badge-success[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #1e7e34;
+}
+
+.badge-info {
+  color: #fff;
+  background-color: #17a2b8;
+}
+.badge-info[href]:hover, .badge-info[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #117a8b;
+}
+
+.badge-warning {
+  color: #212529;
+  background-color: #ffc107;
+}
+.badge-warning[href]:hover, .badge-warning[href]:focus {
+  color: #212529;
+  text-decoration: none;
+  background-color: #d39e00;
+}
+
+.badge-danger {
+  color: #fff;
+  background-color: #dc3545;
+}
+.badge-danger[href]:hover, .badge-danger[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #bd2130;
+}
+
+.badge-light {
+  color: #212529;
+  background-color: #f8f9fa;
+}
+.badge-light[href]:hover, .badge-light[href]:focus {
+  color: #212529;
+  text-decoration: none;
+  background-color: #dae0e5;
+}
+
+.badge-dark {
+  color: #fff;
+  background-color: #343a40;
+}
+.badge-dark[href]:hover, .badge-dark[href]:focus {
+  color: #fff;
+  text-decoration: none;
+  background-color: #1d2124;
+}
+
+.jumbotron {
+  padding: 2rem 1rem;
+  margin-bottom: 2rem;
+  background-color: #e9ecef;
+  border-radius: 0.3rem;
+}
+@media (min-width: 576px) {
+  .jumbotron {
+    padding: 4rem 2rem;
+  }
+}
+
+.jumbotron-fluid {
+  padding-right: 0;
+  padding-left: 0;
+  border-radius: 0;
+}
+
+.alert {
+  position: relative;
+  padding: 0.75rem 1.25rem;
+  margin-bottom: 1rem;
+  border: 1px solid transparent;
+  border-radius: 0.25rem;
+}
+
+.alert-heading {
+  color: inherit;
+}
+
+.alert-link {
+  font-weight: 700;
+}
+
+.alert-dismissible {
+  padding-right: 4rem;
+}
+.alert-dismissible .close {
+  position: absolute;
+  top: 0;
+  right: 0;
+  padding: 0.75rem 1.25rem;
+  color: inherit;
+}
+
+.alert-primary {
+  color: #004085;
+  background-color: #cce5ff;
+  border-color: #b8daff;
+}
+.alert-primary hr {
+  border-top-color: #9fcdff;
+}
+.alert-primary .alert-link {
+  color: #002752;
+}
+
+.alert-secondary {
+  color: #383d41;
+  background-color: #e2e3e5;
+  border-color: #d6d8db;
+}
+.alert-secondary hr {
+  border-top-color: #c8cbcf;
+}
+.alert-secondary .alert-link {
+  color: #202326;
+}
+
+.alert-success {
+  color: #155724;
+  background-color: #d4edda;
+  border-color: #c3e6cb;
+}
+.alert-success hr {
+  border-top-color: #b1dfbb;
+}
+.alert-success .alert-link {
+  color: #0b2e13;
+}
+
+.alert-info {
+  color: #0c5460;
+  background-color: #d1ecf1;
+  border-color: #bee5eb;
+}
+.alert-info hr {
+  border-top-color: #abdde5;
+}
+.alert-info .alert-link {
+  color: #062c33;
+}
+
+.alert-warning {
+  color: #856404;
+  background-color: #fff3cd;
+  border-color: #ffeeba;
+}
+.alert-warning hr {
+  border-top-color: #ffe8a1;
+}
+.alert-warning .alert-link {
+  color: #533f03;
+}
+
+.alert-danger {
+  color: #721c24;
+  background-color: #f8d7da;
+  border-color: #f5c6cb;
+}
+.alert-danger hr {
+  border-top-color: #f1b0b7;
+}
+.alert-danger .alert-link {
+  color: #491217;
+}
+
+.alert-light {
+  color: #818182;
+  background-color: #fefefe;
+  border-color: #fdfdfe;
+}
+.alert-light hr {
+  border-top-color: #ececf6;
+}
+.alert-light .alert-link {
+  color: #686868;
+}
+
+.alert-dark {
+  color: #1b1e21;
+  background-color: #d6d8d9;
+  border-color: #c6c8ca;
+}
+.alert-dark hr {
+  border-top-color: #b9bbbe;
+}
+.alert-dark .alert-link {
+  color: #040505;
+}
+
+@-webkit-keyframes progress-bar-stripes {
+  from {
+    background-position: 1rem 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+
+@keyframes progress-bar-stripes {
+  from {
+    background-position: 1rem 0;
+  }
+  to {
+    background-position: 0 0;
+  }
+}
+.progress {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  height: 1rem;
+  overflow: hidden;
+  font-size: 0.75rem;
+  background-color: #e9ecef;
+  border-radius: 0.25rem;
+}
+
+.progress-bar {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  -webkit-box-pack: center;
+      -ms-flex-pack: center;
+          justify-content: center;
+  color: #fff;
+  text-align: center;
+  background-color: #007bff;
+  -webkit-transition: width 0.6s ease;
+  transition: width 0.6s ease;
+}
+
+.progress-bar-striped {
+  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
+  background-size: 1rem 1rem;
+}
+
+.progress-bar-animated {
+  -webkit-animation: progress-bar-stripes 1s linear infinite;
+          animation: progress-bar-stripes 1s linear infinite;
+}
+
+.media {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: start;
+      -ms-flex-align: start;
+          align-items: flex-start;
+}
+
+.media-body {
+  -webkit-box-flex: 1;
+      -ms-flex: 1;
+          flex: 1;
+}
+
+.list-group {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  padding-left: 0;
+  margin-bottom: 0;
+}
+
+.list-group-item-action {
+  width: 100%;
+  color: #495057;
+  text-align: inherit;
+}
+.list-group-item-action:hover, .list-group-item-action:focus {
+  color: #495057;
+  text-decoration: none;
+  background-color: #f8f9fa;
+}
+.list-group-item-action:active {
+  color: #212529;
+  background-color: #e9ecef;
+}
+
+.list-group-item {
+  position: relative;
+  display: block;
+  padding: 0.75rem 1.25rem;
+  margin-bottom: -1px;
+  background-color: #fff;
+  border: 1px solid rgba(0, 0, 0, 0.125);
+}
+.list-group-item:first-child {
+  border-top-left-radius: 0.25rem;
+  border-top-right-radius: 0.25rem;
+}
+.list-group-item:last-child {
+  margin-bottom: 0;
+  border-bottom-right-radius: 0.25rem;
+  border-bottom-left-radius: 0.25rem;
+}
+.list-group-item:hover, .list-group-item:focus {
+  z-index: 1;
+  text-decoration: none;
+}
+.list-group-item.disabled, .list-group-item:disabled {
+  color: #6c757d;
+  background-color: #fff;
+}
+.list-group-item.active {
+  z-index: 2;
+  color: #fff;
+  background-color: #007bff;
+  border-color: #007bff;
+}
+
+.list-group-flush .list-group-item {
+  border-right: 0;
+  border-left: 0;
+  border-radius: 0;
+}
+.list-group-flush:first-child .list-group-item:first-child {
+  border-top: 0;
+}
+.list-group-flush:last-child .list-group-item:last-child {
+  border-bottom: 0;
+}
+
+.list-group-item-primary {
+  color: #004085;
+  background-color: #b8daff;
+}
+.list-group-item-primary.list-group-item-action:hover, .list-group-item-primary.list-group-item-action:focus {
+  color: #004085;
+  background-color: #9fcdff;
+}
+.list-group-item-primary.list-group-item-action.active {
+  color: #fff;
+  background-color: #004085;
+  border-color: #004085;
+}
+
+.list-group-item-secondary {
+  color: #383d41;
+  background-color: #d6d8db;
+}
+.list-group-item-secondary.list-group-item-action:hover, .list-group-item-secondary.list-group-item-action:focus {
+  color: #383d41;
+  background-color: #c8cbcf;
+}
+.list-group-item-secondary.list-group-item-action.active {
+  color: #fff;
+  background-color: #383d41;
+  border-color: #383d41;
+}
+
+.list-group-item-success {
+  color: #155724;
+  background-color: #c3e6cb;
+}
+.list-group-item-success.list-group-item-action:hover, .list-group-item-success.list-group-item-action:focus {
+  color: #155724;
+  background-color: #b1dfbb;
+}
+.list-group-item-success.list-group-item-action.active {
+  color: #fff;
+  background-color: #155724;
+  border-color: #155724;
+}
+
+.list-group-item-info {
+  color: #0c5460;
+  background-color: #bee5eb;
+}
+.list-group-item-info.list-group-item-action:hover, .list-group-item-info.list-group-item-action:focus {
+  color: #0c5460;
+  background-color: #abdde5;
+}
+.list-group-item-info.list-group-item-action.active {
+  color: #fff;
+  background-color: #0c5460;
+  border-color: #0c5460;
+}
+
+.list-group-item-warning {
+  color: #856404;
+  background-color: #ffeeba;
+}
+.list-group-item-warning.list-group-item-action:hover, .list-group-item-warning.list-group-item-action:focus {
+  color: #856404;
+  background-color: #ffe8a1;
+}
+.list-group-item-warning.list-group-item-action.active {
+  color: #fff;
+  background-color: #856404;
+  border-color: #856404;
+}
+
+.list-group-item-danger {
+  color: #721c24;
+  background-color: #f5c6cb;
+}
+.list-group-item-danger.list-group-item-action:hover, .list-group-item-danger.list-group-item-action:focus {
+  color: #721c24;
+  background-color: #f1b0b7;
+}
+.list-group-item-danger.list-group-item-action.active {
+  color: #fff;
+  background-color: #721c24;
+  border-color: #721c24;
+}
+
+.list-group-item-light {
+  color: #818182;
+  background-color: #fdfdfe;
+}
+.list-group-item-light.list-group-item-action:hover, .list-group-item-light.list-group-item-action:focus {
+  color: #818182;
+  background-color: #ececf6;
+}
+.list-group-item-light.list-group-item-action.active {
+  color: #fff;
+  background-color: #818182;
+  border-color: #818182;
+}
+
+.list-group-item-dark {
+  color: #1b1e21;
+  background-color: #c6c8ca;
+}
+.list-group-item-dark.list-group-item-action:hover, .list-group-item-dark.list-group-item-action:focus {
+  color: #1b1e21;
+  background-color: #b9bbbe;
+}
+.list-group-item-dark.list-group-item-action.active {
+  color: #fff;
+  background-color: #1b1e21;
+  border-color: #1b1e21;
+}
+
+.close {
+  float: right;
+  font-size: 1.5rem;
+  font-weight: 700;
+  line-height: 1;
+  color: #000;
+  text-shadow: 0 1px 0 #fff;
+  opacity: .5;
+}
+.close:hover, .close:focus {
+  color: #000;
+  text-decoration: none;
+  opacity: .75;
+}
+.close:not(:disabled):not(.disabled) {
+  cursor: pointer;
+}
+
+button.close {
+  padding: 0;
+  background-color: transparent;
+  border: 0;
+  -webkit-appearance: none;
+}
+
+.modal-open {
+  overflow: hidden;
+}
+
+.modal {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1050;
+  display: none;
+  overflow: hidden;
+  outline: 0;
+}
+.modal-open .modal {
+  overflow-x: hidden;
+  overflow-y: auto;
+}
+
+.modal-dialog {
+  position: relative;
+  width: auto;
+  margin: 0.5rem;
+  pointer-events: none;
+}
+.modal.fade .modal-dialog {
+  -webkit-transition: -webkit-transform 0.3s ease-out;
+  transition: -webkit-transform 0.3s ease-out;
+  transition: transform 0.3s ease-out;
+  transition: transform 0.3s ease-out, -webkit-transform 0.3s ease-out;
+  -webkit-transform: translate(0, -25%);
+          transform: translate(0, -25%);
+}
+.modal.show .modal-dialog {
+  -webkit-transform: translate(0, 0);
+          transform: translate(0, 0);
+}
+
+.modal-dialog-centered {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  min-height: calc(100% - (0.5rem * 2));
+}
+
+.modal-content {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-orient: vertical;
+  -webkit-box-direction: normal;
+      -ms-flex-direction: column;
+          flex-direction: column;
+  width: 100%;
+  pointer-events: auto;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 0.3rem;
+  outline: 0;
+}
+
+.modal-backdrop {
+  position: fixed;
+  top: 0;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1040;
+  background-color: #000;
+}
+.modal-backdrop.fade {
+  opacity: 0;
+}
+.modal-backdrop.show {
+  opacity: 0.5;
+}
+
+.modal-header {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: start;
+      -ms-flex-align: start;
+          align-items: flex-start;
+  -webkit-box-pack: justify;
+      -ms-flex-pack: justify;
+          justify-content: space-between;
+  padding: 1rem;
+  border-bottom: 1px solid #e9ecef;
+  border-top-left-radius: 0.3rem;
+  border-top-right-radius: 0.3rem;
+}
+.modal-header .close {
+  padding: 1rem;
+  margin: -1rem -1rem -1rem auto;
+}
+
+.modal-title {
+  margin-bottom: 0;
+  line-height: 1.5;
+}
+
+.modal-body {
+  position: relative;
+  -webkit-box-flex: 1;
+      -ms-flex: 1 1 auto;
+          flex: 1 1 auto;
+  padding: 1rem;
+}
+
+.modal-footer {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  -webkit-box-pack: end;
+      -ms-flex-pack: end;
+          justify-content: flex-end;
+  padding: 1rem;
+  border-top: 1px solid #e9ecef;
+}
+.modal-footer > :not(:first-child) {
+  margin-left: .25rem;
+}
+.modal-footer > :not(:last-child) {
+  margin-right: .25rem;
+}
+
+.modal-scrollbar-measure {
+  position: absolute;
+  top: -9999px;
+  width: 50px;
+  height: 50px;
+  overflow: scroll;
+}
+
+@media (min-width: 576px) {
+  .modal-dialog {
+    max-width: 500px;
+    margin: 1.75rem auto;
+  }
+
+  .modal-dialog-centered {
+    min-height: calc(100% - (1.75rem * 2));
+  }
+
+  .modal-sm {
+    max-width: 300px;
+  }
+}
+@media (min-width: 992px) {
+  .modal-lg {
+    max-width: 800px;
+  }
+}
+.tooltip {
+  position: absolute;
+  z-index: 1070;
+  display: block;
+  margin: 0;
+  font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  font-style: normal;
+  font-weight: 400;
+  line-height: 1.5;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  letter-spacing: normal;
+  word-break: normal;
+  word-spacing: normal;
+  white-space: normal;
+  line-break: auto;
+  font-size: 0.875rem;
+  word-wrap: break-word;
+  opacity: 0;
+}
+.tooltip.show {
+  opacity: 0.9;
+}
+.tooltip .arrow {
+  position: absolute;
+  display: block;
+  width: 0.8rem;
+  height: 0.4rem;
+}
+.tooltip .arrow::before {
+  position: absolute;
+  content: "";
+  border-color: transparent;
+  border-style: solid;
+}
+
+.bs-tooltip-top, .bs-tooltip-auto[x-placement^="top"] {
+  padding: 0.4rem 0;
+}
+.bs-tooltip-top .arrow, .bs-tooltip-auto[x-placement^="top"] .arrow {
+  bottom: 0;
+}
+.bs-tooltip-top .arrow::before, .bs-tooltip-auto[x-placement^="top"] .arrow::before {
+  top: 0;
+  border-width: 0.4rem 0.4rem 0;
+  border-top-color: #000;
+}
+
+.bs-tooltip-right, .bs-tooltip-auto[x-placement^="right"] {
+  padding: 0 0.4rem;
+}
+.bs-tooltip-right .arrow, .bs-tooltip-auto[x-placement^="right"] .arrow {
+  left: 0;
+  width: 0.4rem;
+  height: 0.8rem;
+}
+.bs-tooltip-right .arrow::before, .bs-tooltip-auto[x-placement^="right"] .arrow::before {
+  right: 0;
+  border-width: 0.4rem 0.4rem 0.4rem 0;
+  border-right-color: #000;
+}
+
+.bs-tooltip-bottom, .bs-tooltip-auto[x-placement^="bottom"] {
+  padding: 0.4rem 0;
+}
+.bs-tooltip-bottom .arrow, .bs-tooltip-auto[x-placement^="bottom"] .arrow {
+  top: 0;
+}
+.bs-tooltip-bottom .arrow::before, .bs-tooltip-auto[x-placement^="bottom"] .arrow::before {
+  bottom: 0;
+  border-width: 0 0.4rem 0.4rem;
+  border-bottom-color: #000;
+}
+
+.bs-tooltip-left, .bs-tooltip-auto[x-placement^="left"] {
+  padding: 0 0.4rem;
+}
+.bs-tooltip-left .arrow, .bs-tooltip-auto[x-placement^="left"] .arrow {
+  right: 0;
+  width: 0.4rem;
+  height: 0.8rem;
+}
+.bs-tooltip-left .arrow::before, .bs-tooltip-auto[x-placement^="left"] .arrow::before {
+  left: 0;
+  border-width: 0.4rem 0 0.4rem 0.4rem;
+  border-left-color: #000;
+}
+
+.tooltip-inner {
+  max-width: 200px;
+  padding: 0.25rem 0.5rem;
+  color: #fff;
+  text-align: center;
+  background-color: #000;
+  border-radius: 0.25rem;
+}
+
+.popover {
+  position: absolute;
+  top: 0;
+  left: 0;
+  z-index: 1060;
+  display: block;
+  max-width: 276px;
+  font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
+  font-style: normal;
+  font-weight: 400;
+  line-height: 1.5;
+  text-align: left;
+  text-align: start;
+  text-decoration: none;
+  text-shadow: none;
+  text-transform: none;
+  letter-spacing: normal;
+  word-break: normal;
+  word-spacing: normal;
+  white-space: normal;
+  line-break: auto;
+  font-size: 0.875rem;
+  word-wrap: break-word;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, 0.2);
+  border-radius: 0.3rem;
+}
+.popover .arrow {
+  position: absolute;
+  display: block;
+  width: 1rem;
+  height: 0.5rem;
+  margin: 0 0.3rem;
+}
+.popover .arrow::before, .popover .arrow::after {
+  position: absolute;
+  display: block;
+  content: "";
+  border-color: transparent;
+  border-style: solid;
+}
+
+.bs-popover-top, .bs-popover-auto[x-placement^="top"] {
+  margin-bottom: 0.5rem;
+}
+.bs-popover-top .arrow, .bs-popover-auto[x-placement^="top"] .arrow {
+  bottom: calc((0.5rem + 1px) * -1);
+}
+.bs-popover-top .arrow::before, .bs-popover-auto[x-placement^="top"] .arrow::before,
+.bs-popover-top .arrow::after,
+.bs-popover-auto[x-placement^="top"] .arrow::after {
+  border-width: 0.5rem 0.5rem 0;
+}
+.bs-popover-top .arrow::before, .bs-popover-auto[x-placement^="top"] .arrow::before {
+  bottom: 0;
+  border-top-color: rgba(0, 0, 0, 0.25);
+}
+.bs-popover-top .arrow::after, .bs-popover-auto[x-placement^="top"] .arrow::after {
+  bottom: 1px;
+  border-top-color: #fff;
+}
+
+.bs-popover-right, .bs-popover-auto[x-placement^="right"] {
+  margin-left: 0.5rem;
+}
+.bs-popover-right .arrow, .bs-popover-auto[x-placement^="right"] .arrow {
+  left: calc((0.5rem + 1px) * -1);
+  width: 0.5rem;
+  height: 1rem;
+  margin: 0.3rem 0;
+}
+.bs-popover-right .arrow::before, .bs-popover-auto[x-placement^="right"] .arrow::before,
+.bs-popover-right .arrow::after,
+.bs-popover-auto[x-placement^="right"] .arrow::after {
+  border-width: 0.5rem 0.5rem 0.5rem 0;
+}
+.bs-popover-right .arrow::before, .bs-popover-auto[x-placement^="right"] .arrow::before {
+  left: 0;
+  border-right-color: rgba(0, 0, 0, 0.25);
+}
+.bs-popover-right .arrow::after, .bs-popover-auto[x-placement^="right"] .arrow::after {
+  left: 1px;
+  border-right-color: #fff;
+}
+
+.bs-popover-bottom, .bs-popover-auto[x-placement^="bottom"] {
+  margin-top: 0.5rem;
+}
+.bs-popover-bottom .arrow, .bs-popover-auto[x-placement^="bottom"] .arrow {
+  top: calc((0.5rem + 1px) * -1);
+}
+.bs-popover-bottom .arrow::before, .bs-popover-auto[x-placement^="bottom"] .arrow::before,
+.bs-popover-bottom .arrow::after,
+.bs-popover-auto[x-placement^="bottom"] .arrow::after {
+  border-width: 0 0.5rem 0.5rem 0.5rem;
+}
+.bs-popover-bottom .arrow::before, .bs-popover-auto[x-placement^="bottom"] .arrow::before {
+  top: 0;
+  border-bottom-color: rgba(0, 0, 0, 0.25);
+}
+.bs-popover-bottom .arrow::after, .bs-popover-auto[x-placement^="bottom"] .arrow::after {
+  top: 1px;
+  border-bottom-color: #fff;
+}
+.bs-popover-bottom .popover-header::before, .bs-popover-auto[x-placement^="bottom"] .popover-header::before {
+  position: absolute;
+  top: 0;
+  left: 50%;
+  display: block;
+  width: 1rem;
+  margin-left: -0.5rem;
+  content: "";
+  border-bottom: 1px solid #f7f7f7;
+}
+
+.bs-popover-left, .bs-popover-auto[x-placement^="left"] {
+  margin-right: 0.5rem;
+}
+.bs-popover-left .arrow, .bs-popover-auto[x-placement^="left"] .arrow {
+  right: calc((0.5rem + 1px) * -1);
+  width: 0.5rem;
+  height: 1rem;
+  margin: 0.3rem 0;
+}
+.bs-popover-left .arrow::before, .bs-popover-auto[x-placement^="left"] .arrow::before,
+.bs-popover-left .arrow::after,
+.bs-popover-auto[x-placement^="left"] .arrow::after {
+  border-width: 0.5rem 0 0.5rem 0.5rem;
+}
+.bs-popover-left .arrow::before, .bs-popover-auto[x-placement^="left"] .arrow::before {
+  right: 0;
+  border-left-color: rgba(0, 0, 0, 0.25);
+}
+.bs-popover-left .arrow::after, .bs-popover-auto[x-placement^="left"] .arrow::after {
+  right: 1px;
+  border-left-color: #fff;
+}
+
+.popover-header {
+  padding: 0.5rem 0.75rem;
+  margin-bottom: 0;
+  font-size: 1rem;
+  color: inherit;
+  background-color: #f7f7f7;
+  border-bottom: 1px solid #ebebeb;
+  border-top-left-radius: calc(0.3rem - 1px);
+  border-top-right-radius: calc(0.3rem - 1px);
+}
+.popover-header:empty {
+  display: none;
+}
+
+.popover-body {
+  padding: 0.5rem 0.75rem;
+  color: #212529;
+}
+
+.carousel {
+  position: relative;
+}
+
+.carousel-inner {
+  position: relative;
+  width: 100%;
+  overflow: hidden;
+}
+
+.carousel-item {
+  position: relative;
+  display: none;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  width: 100%;
+  -webkit-transition: -webkit-transform 0.6s ease;
+  transition: -webkit-transform 0.6s ease;
+  transition: transform 0.6s ease;
+  transition: transform 0.6s ease, -webkit-transform 0.6s ease;
+  -webkit-backface-visibility: hidden;
+          backface-visibility: hidden;
+  -webkit-perspective: 1000px;
+          perspective: 1000px;
+}
+
+.carousel-item.active,
+.carousel-item-next,
+.carousel-item-prev {
+  display: block;
+}
+
+.carousel-item-next,
+.carousel-item-prev {
+  position: absolute;
+  top: 0;
+}
+
+.carousel-item-next.carousel-item-left,
+.carousel-item-prev.carousel-item-right {
+  -webkit-transform: translateX(0);
+          transform: translateX(0);
+}
+@supports (transform-style: preserve-3d) {
+  .carousel-item-next.carousel-item-left,
+  .carousel-item-prev.carousel-item-right {
+    -webkit-transform: translate3d(0, 0, 0);
+            transform: translate3d(0, 0, 0);
+  }
+}
+
+.carousel-item-next,
+.active.carousel-item-right {
+  -webkit-transform: translateX(100%);
+          transform: translateX(100%);
+}
+@supports (transform-style: preserve-3d) {
+  .carousel-item-next,
+  .active.carousel-item-right {
+    -webkit-transform: translate3d(100%, 0, 0);
+            transform: translate3d(100%, 0, 0);
+  }
+}
+
+.carousel-item-prev,
+.active.carousel-item-left {
+  -webkit-transform: translateX(-100%);
+          transform: translateX(-100%);
+}
+@supports (transform-style: preserve-3d) {
+  .carousel-item-prev,
+  .active.carousel-item-left {
+    -webkit-transform: translate3d(-100%, 0, 0);
+            transform: translate3d(-100%, 0, 0);
+  }
+}
+
+.carousel-control-prev,
+.carousel-control-next {
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  -webkit-box-pack: center;
+      -ms-flex-pack: center;
+          justify-content: center;
+  width: 15%;
+  color: #fff;
+  text-align: center;
+  opacity: 0.5;
+}
+.carousel-control-prev:hover, .carousel-control-prev:focus,
+.carousel-control-next:hover,
+.carousel-control-next:focus {
+  color: #fff;
+  text-decoration: none;
+  outline: 0;
+  opacity: .9;
+}
+
+.carousel-control-prev {
+  left: 0;
+}
+
+.carousel-control-next {
+  right: 0;
+}
+
+.carousel-control-prev-icon,
+.carousel-control-next-icon {
+  display: inline-block;
+  width: 20px;
+  height: 20px;
+  background: transparent no-repeat center center;
+  background-size: 100% 100%;
+}
+
+.carousel-control-prev-icon {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='%23fff' viewBox='0 0 8 8'%3E%3Cpath d='M5.25 0l-4 4 4 4 1.5-1.5-2.5-2.5 2.5-2.5-1.5-1.5z'/%3E%3C/svg%3E");
+}
+
+.carousel-control-next-icon {
+  background-image: url("data:image/svg+xml;charset=utf8,%3Csvg xmlns='http://www.w3.org/2000/svg' fill='%23fff' viewBox='0 0 8 8'%3E%3Cpath d='M2.75 0l-1.5 1.5 2.5 2.5-2.5 2.5 1.5 1.5 4-4-4-4z'/%3E%3C/svg%3E");
+}
+
+.carousel-indicators {
+  position: absolute;
+  right: 0;
+  bottom: 10px;
+  left: 0;
+  z-index: 15;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-pack: center;
+      -ms-flex-pack: center;
+          justify-content: center;
+  padding-left: 0;
+  margin-right: 15%;
+  margin-left: 15%;
+  list-style: none;
+}
+.carousel-indicators li {
+  position: relative;
+  -webkit-box-flex: 0;
+      -ms-flex: 0 1 auto;
+          flex: 0 1 auto;
+  width: 30px;
+  height: 3px;
+  margin-right: 3px;
+  margin-left: 3px;
+  text-indent: -999px;
+  background-color: rgba(255, 255, 255, 0.5);
+}
+.carousel-indicators li::before {
+  position: absolute;
+  top: -10px;
+  left: 0;
+  display: inline-block;
+  width: 100%;
+  height: 10px;
+  content: "";
+}
+.carousel-indicators li::after {
+  position: absolute;
+  bottom: -10px;
+  left: 0;
+  display: inline-block;
+  width: 100%;
+  height: 10px;
+  content: "";
+}
+.carousel-indicators .active {
+  background-color: #fff;
+}
+
+.carousel-caption {
+  position: absolute;
+  right: 15%;
+  bottom: 20px;
+  left: 15%;
+  z-index: 10;
+  padding-top: 20px;
+  padding-bottom: 20px;
+  color: #fff;
+  text-align: center;
+}
+
+.align-baseline {
+  vertical-align: baseline !important;
+}
+
+.align-top {
+  vertical-align: top !important;
+}
+
+.align-middle {
+  vertical-align: middle !important;
+}
+
+.align-bottom {
+  vertical-align: bottom !important;
+}
+
+.align-text-bottom {
+  vertical-align: text-bottom !important;
+}
+
+.align-text-top {
+  vertical-align: text-top !important;
+}
+
+.bg-primary {
+  background-color: #007bff !important;
+}
+
+a.bg-primary:hover, a.bg-primary:focus,
+button.bg-primary:hover,
+button.bg-primary:focus {
+  background-color: #0062cc !important;
+}
+
+.bg-secondary {
+  background-color: #6c757d !important;
+}
+
+a.bg-secondary:hover, a.bg-secondary:focus,
+button.bg-secondary:hover,
+button.bg-secondary:focus {
+  background-color: #545b62 !important;
+}
+
+.bg-success {
+  background-color: #28a745 !important;
+}
+
+a.bg-success:hover, a.bg-success:focus,
+button.bg-success:hover,
+button.bg-success:focus {
+  background-color: #1e7e34 !important;
+}
+
+.bg-info {
+  background-color: #17a2b8 !important;
+}
+
+a.bg-info:hover, a.bg-info:focus,
+button.bg-info:hover,
+button.bg-info:focus {
+  background-color: #117a8b !important;
+}
+
+.bg-warning {
+  background-color: #ffc107 !important;
+}
+
+a.bg-warning:hover, a.bg-warning:focus,
+button.bg-warning:hover,
+button.bg-warning:focus {
+  background-color: #d39e00 !important;
+}
+
+.bg-danger {
+  background-color: #dc3545 !important;
+}
+
+a.bg-danger:hover, a.bg-danger:focus,
+button.bg-danger:hover,
+button.bg-danger:focus {
+  background-color: #bd2130 !important;
+}
+
+.bg-light {
+  background-color: #f8f9fa !important;
+}
+
+a.bg-light:hover, a.bg-light:focus,
+button.bg-light:hover,
+button.bg-light:focus {
+  background-color: #dae0e5 !important;
+}
+
+.bg-dark {
+  background-color: #343a40 !important;
+}
+
+a.bg-dark:hover, a.bg-dark:focus,
+button.bg-dark:hover,
+button.bg-dark:focus {
+  background-color: #1d2124 !important;
+}
+
+.bg-white {
+  background-color: #fff !important;
+}
+
+.bg-transparent {
+  background-color: transparent !important;
+}
+
+.border {
+  border: 1px solid #dee2e6 !important;
+}
+
+.border-top {
+  border-top: 1px solid #dee2e6 !important;
+}
+
+.border-right {
+  border-right: 1px solid #dee2e6 !important;
+}
+
+.border-bottom {
+  border-bottom: 1px solid #dee2e6 !important;
+}
+
+.border-left {
+  border-left: 1px solid #dee2e6 !important;
+}
+
+.border-0 {
+  border: 0 !important;
+}
+
+.border-top-0 {
+  border-top: 0 !important;
+}
+
+.border-right-0 {
+  border-right: 0 !important;
+}
+
+.border-bottom-0 {
+  border-bottom: 0 !important;
+}
+
+.border-left-0 {
+  border-left: 0 !important;
+}
+
+.border-primary {
+  border-color: #007bff !important;
+}
+
+.border-secondary {
+  border-color: #6c757d !important;
+}
+
+.border-success {
+  border-color: #28a745 !important;
+}
+
+.border-info {
+  border-color: #17a2b8 !important;
+}
+
+.border-warning {
+  border-color: #ffc107 !important;
+}
+
+.border-danger {
+  border-color: #dc3545 !important;
+}
+
+.border-light {
+  border-color: #f8f9fa !important;
+}
+
+.border-dark {
+  border-color: #343a40 !important;
+}
+
+.border-white {
+  border-color: #fff !important;
+}
+
+.rounded {
+  border-radius: 0.25rem !important;
+}
+
+.rounded-top {
+  border-top-left-radius: 0.25rem !important;
+  border-top-right-radius: 0.25rem !important;
+}
+
+.rounded-right {
+  border-top-right-radius: 0.25rem !important;
+  border-bottom-right-radius: 0.25rem !important;
+}
+
+.rounded-bottom {
+  border-bottom-right-radius: 0.25rem !important;
+  border-bottom-left-radius: 0.25rem !important;
+}
+
+.rounded-left {
+  border-top-left-radius: 0.25rem !important;
+  border-bottom-left-radius: 0.25rem !important;
+}
+
+.rounded-circle {
+  border-radius: 50% !important;
+}
+
+.rounded-0 {
+  border-radius: 0 !important;
+}
+
+.clearfix::after {
+  display: block;
+  clear: both;
+  content: "";
+}
+
+.d-none {
+  display: none !important;
+}
+
+.d-inline {
+  display: inline !important;
+}
+
+.d-inline-block {
+  display: inline-block !important;
+}
+
+.d-block {
+  display: block !important;
+}
+
+.d-table {
+  display: table !important;
+}
+
+.d-table-row {
+  display: table-row !important;
+}
+
+.d-table-cell {
+  display: table-cell !important;
+}
+
+.d-flex {
+  display: -webkit-box !important;
+  display: -ms-flexbox !important;
+  display: flex !important;
+}
+
+.d-inline-flex {
+  display: -webkit-inline-box !important;
+  display: -ms-inline-flexbox !important;
+  display: inline-flex !important;
+}
+
+@media (min-width: 576px) {
+  .d-sm-none {
+    display: none !important;
+  }
+
+  .d-sm-inline {
+    display: inline !important;
+  }
+
+  .d-sm-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-sm-block {
+    display: block !important;
+  }
+
+  .d-sm-table {
+    display: table !important;
+  }
+
+  .d-sm-table-row {
+    display: table-row !important;
+  }
+
+  .d-sm-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-sm-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-sm-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+@media (min-width: 768px) {
+  .d-md-none {
+    display: none !important;
+  }
+
+  .d-md-inline {
+    display: inline !important;
+  }
+
+  .d-md-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-md-block {
+    display: block !important;
+  }
+
+  .d-md-table {
+    display: table !important;
+  }
+
+  .d-md-table-row {
+    display: table-row !important;
+  }
+
+  .d-md-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-md-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-md-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+@media (min-width: 992px) {
+  .d-lg-none {
+    display: none !important;
+  }
+
+  .d-lg-inline {
+    display: inline !important;
+  }
+
+  .d-lg-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-lg-block {
+    display: block !important;
+  }
+
+  .d-lg-table {
+    display: table !important;
+  }
+
+  .d-lg-table-row {
+    display: table-row !important;
+  }
+
+  .d-lg-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-lg-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-lg-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+@media (min-width: 1200px) {
+  .d-xl-none {
+    display: none !important;
+  }
+
+  .d-xl-inline {
+    display: inline !important;
+  }
+
+  .d-xl-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-xl-block {
+    display: block !important;
+  }
+
+  .d-xl-table {
+    display: table !important;
+  }
+
+  .d-xl-table-row {
+    display: table-row !important;
+  }
+
+  .d-xl-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-xl-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-xl-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+@media print {
+  .d-print-none {
+    display: none !important;
+  }
+
+  .d-print-inline {
+    display: inline !important;
+  }
+
+  .d-print-inline-block {
+    display: inline-block !important;
+  }
+
+  .d-print-block {
+    display: block !important;
+  }
+
+  .d-print-table {
+    display: table !important;
+  }
+
+  .d-print-table-row {
+    display: table-row !important;
+  }
+
+  .d-print-table-cell {
+    display: table-cell !important;
+  }
+
+  .d-print-flex {
+    display: -webkit-box !important;
+    display: -ms-flexbox !important;
+    display: flex !important;
+  }
+
+  .d-print-inline-flex {
+    display: -webkit-inline-box !important;
+    display: -ms-inline-flexbox !important;
+    display: inline-flex !important;
+  }
+}
+.embed-responsive {
+  position: relative;
+  display: block;
+  width: 100%;
+  padding: 0;
+  overflow: hidden;
+}
+.embed-responsive::before {
+  display: block;
+  content: "";
+}
+.embed-responsive .embed-responsive-item,
+.embed-responsive iframe,
+.embed-responsive embed,
+.embed-responsive object,
+.embed-responsive video {
+  position: absolute;
+  top: 0;
+  bottom: 0;
+  left: 0;
+  width: 100%;
+  height: 100%;
+  border: 0;
+}
+
+.embed-responsive-21by9::before {
+  padding-top: 42.8571428571%;
+}
+
+.embed-responsive-16by9::before {
+  padding-top: 56.25%;
+}
+
+.embed-responsive-4by3::before {
+  padding-top: 75%;
+}
+
+.embed-responsive-1by1::before {
+  padding-top: 100%;
+}
+
+.flex-row {
+  -webkit-box-orient: horizontal !important;
+  -webkit-box-direction: normal !important;
+      -ms-flex-direction: row !important;
+          flex-direction: row !important;
+}
+
+.flex-column {
+  -webkit-box-orient: vertical !important;
+  -webkit-box-direction: normal !important;
+      -ms-flex-direction: column !important;
+          flex-direction: column !important;
+}
+
+.flex-row-reverse {
+  -webkit-box-orient: horizontal !important;
+  -webkit-box-direction: reverse !important;
+      -ms-flex-direction: row-reverse !important;
+          flex-direction: row-reverse !important;
+}
+
+.flex-column-reverse {
+  -webkit-box-orient: vertical !important;
+  -webkit-box-direction: reverse !important;
+      -ms-flex-direction: column-reverse !important;
+          flex-direction: column-reverse !important;
+}
+
+.flex-wrap {
+  -ms-flex-wrap: wrap !important;
+      flex-wrap: wrap !important;
+}
+
+.flex-nowrap {
+  -ms-flex-wrap: nowrap !important;
+      flex-wrap: nowrap !important;
+}
+
+.flex-wrap-reverse {
+  -ms-flex-wrap: wrap-reverse !important;
+      flex-wrap: wrap-reverse !important;
+}
+
+.justify-content-start {
+  -webkit-box-pack: start !important;
+      -ms-flex-pack: start !important;
+          justify-content: flex-start !important;
+}
+
+.justify-content-end {
+  -webkit-box-pack: end !important;
+      -ms-flex-pack: end !important;
+          justify-content: flex-end !important;
+}
+
+.justify-content-center {
+  -webkit-box-pack: center !important;
+      -ms-flex-pack: center !important;
+          justify-content: center !important;
+}
+
+.justify-content-between {
+  -webkit-box-pack: justify !important;
+      -ms-flex-pack: justify !important;
+          justify-content: space-between !important;
+}
+
+.justify-content-around {
+  -ms-flex-pack: distribute !important;
+      justify-content: space-around !important;
+}
+
+.align-items-start {
+  -webkit-box-align: start !important;
+      -ms-flex-align: start !important;
+          align-items: flex-start !important;
+}
+
+.align-items-end {
+  -webkit-box-align: end !important;
+      -ms-flex-align: end !important;
+          align-items: flex-end !important;
+}
+
+.align-items-center {
+  -webkit-box-align: center !important;
+      -ms-flex-align: center !important;
+          align-items: center !important;
+}
+
+.align-items-baseline {
+  -webkit-box-align: baseline !important;
+      -ms-flex-align: baseline !important;
+          align-items: baseline !important;
+}
+
+.align-items-stretch {
+  -webkit-box-align: stretch !important;
+      -ms-flex-align: stretch !important;
+          align-items: stretch !important;
+}
+
+.align-content-start {
+  -ms-flex-line-pack: start !important;
+      align-content: flex-start !important;
+}
+
+.align-content-end {
+  -ms-flex-line-pack: end !important;
+      align-content: flex-end !important;
+}
+
+.align-content-center {
+  -ms-flex-line-pack: center !important;
+      align-content: center !important;
+}
+
+.align-content-between {
+  -ms-flex-line-pack: justify !important;
+      align-content: space-between !important;
+}
+
+.align-content-around {
+  -ms-flex-line-pack: distribute !important;
+      align-content: space-around !important;
+}
+
+.align-content-stretch {
+  -ms-flex-line-pack: stretch !important;
+      align-content: stretch !important;
+}
+
+.align-self-auto {
+  -ms-flex-item-align: auto !important;
+      align-self: auto !important;
+}
+
+.align-self-start {
+  -ms-flex-item-align: start !important;
+      align-self: flex-start !important;
+}
+
+.align-self-end {
+  -ms-flex-item-align: end !important;
+      align-self: flex-end !important;
+}
+
+.align-self-center {
+  -ms-flex-item-align: center !important;
+      align-self: center !important;
+}
+
+.align-self-baseline {
+  -ms-flex-item-align: baseline !important;
+      align-self: baseline !important;
+}
+
+.align-self-stretch {
+  -ms-flex-item-align: stretch !important;
+      align-self: stretch !important;
+}
+
+@media (min-width: 576px) {
+  .flex-sm-row {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: row !important;
+            flex-direction: row !important;
+  }
+
+  .flex-sm-column {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: column !important;
+            flex-direction: column !important;
+  }
+
+  .flex-sm-row-reverse {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: row-reverse !important;
+            flex-direction: row-reverse !important;
+  }
+
+  .flex-sm-column-reverse {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: column-reverse !important;
+            flex-direction: column-reverse !important;
+  }
+
+  .flex-sm-wrap {
+    -ms-flex-wrap: wrap !important;
+        flex-wrap: wrap !important;
+  }
+
+  .flex-sm-nowrap {
+    -ms-flex-wrap: nowrap !important;
+        flex-wrap: nowrap !important;
+  }
+
+  .flex-sm-wrap-reverse {
+    -ms-flex-wrap: wrap-reverse !important;
+        flex-wrap: wrap-reverse !important;
+  }
+
+  .justify-content-sm-start {
+    -webkit-box-pack: start !important;
+        -ms-flex-pack: start !important;
+            justify-content: flex-start !important;
+  }
+
+  .justify-content-sm-end {
+    -webkit-box-pack: end !important;
+        -ms-flex-pack: end !important;
+            justify-content: flex-end !important;
+  }
+
+  .justify-content-sm-center {
+    -webkit-box-pack: center !important;
+        -ms-flex-pack: center !important;
+            justify-content: center !important;
+  }
+
+  .justify-content-sm-between {
+    -webkit-box-pack: justify !important;
+        -ms-flex-pack: justify !important;
+            justify-content: space-between !important;
+  }
+
+  .justify-content-sm-around {
+    -ms-flex-pack: distribute !important;
+        justify-content: space-around !important;
+  }
+
+  .align-items-sm-start {
+    -webkit-box-align: start !important;
+        -ms-flex-align: start !important;
+            align-items: flex-start !important;
+  }
+
+  .align-items-sm-end {
+    -webkit-box-align: end !important;
+        -ms-flex-align: end !important;
+            align-items: flex-end !important;
+  }
+
+  .align-items-sm-center {
+    -webkit-box-align: center !important;
+        -ms-flex-align: center !important;
+            align-items: center !important;
+  }
+
+  .align-items-sm-baseline {
+    -webkit-box-align: baseline !important;
+        -ms-flex-align: baseline !important;
+            align-items: baseline !important;
+  }
+
+  .align-items-sm-stretch {
+    -webkit-box-align: stretch !important;
+        -ms-flex-align: stretch !important;
+            align-items: stretch !important;
+  }
+
+  .align-content-sm-start {
+    -ms-flex-line-pack: start !important;
+        align-content: flex-start !important;
+  }
+
+  .align-content-sm-end {
+    -ms-flex-line-pack: end !important;
+        align-content: flex-end !important;
+  }
+
+  .align-content-sm-center {
+    -ms-flex-line-pack: center !important;
+        align-content: center !important;
+  }
+
+  .align-content-sm-between {
+    -ms-flex-line-pack: justify !important;
+        align-content: space-between !important;
+  }
+
+  .align-content-sm-around {
+    -ms-flex-line-pack: distribute !important;
+        align-content: space-around !important;
+  }
+
+  .align-content-sm-stretch {
+    -ms-flex-line-pack: stretch !important;
+        align-content: stretch !important;
+  }
+
+  .align-self-sm-auto {
+    -ms-flex-item-align: auto !important;
+        align-self: auto !important;
+  }
+
+  .align-self-sm-start {
+    -ms-flex-item-align: start !important;
+        align-self: flex-start !important;
+  }
+
+  .align-self-sm-end {
+    -ms-flex-item-align: end !important;
+        align-self: flex-end !important;
+  }
+
+  .align-self-sm-center {
+    -ms-flex-item-align: center !important;
+        align-self: center !important;
+  }
+
+  .align-self-sm-baseline {
+    -ms-flex-item-align: baseline !important;
+        align-self: baseline !important;
+  }
+
+  .align-self-sm-stretch {
+    -ms-flex-item-align: stretch !important;
+        align-self: stretch !important;
+  }
+}
+@media (min-width: 768px) {
+  .flex-md-row {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: row !important;
+            flex-direction: row !important;
+  }
+
+  .flex-md-column {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: column !important;
+            flex-direction: column !important;
+  }
+
+  .flex-md-row-reverse {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: row-reverse !important;
+            flex-direction: row-reverse !important;
+  }
+
+  .flex-md-column-reverse {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: column-reverse !important;
+            flex-direction: column-reverse !important;
+  }
+
+  .flex-md-wrap {
+    -ms-flex-wrap: wrap !important;
+        flex-wrap: wrap !important;
+  }
+
+  .flex-md-nowrap {
+    -ms-flex-wrap: nowrap !important;
+        flex-wrap: nowrap !important;
+  }
+
+  .flex-md-wrap-reverse {
+    -ms-flex-wrap: wrap-reverse !important;
+        flex-wrap: wrap-reverse !important;
+  }
+
+  .justify-content-md-start {
+    -webkit-box-pack: start !important;
+        -ms-flex-pack: start !important;
+            justify-content: flex-start !important;
+  }
+
+  .justify-content-md-end {
+    -webkit-box-pack: end !important;
+        -ms-flex-pack: end !important;
+            justify-content: flex-end !important;
+  }
+
+  .justify-content-md-center {
+    -webkit-box-pack: center !important;
+        -ms-flex-pack: center !important;
+            justify-content: center !important;
+  }
+
+  .justify-content-md-between {
+    -webkit-box-pack: justify !important;
+        -ms-flex-pack: justify !important;
+            justify-content: space-between !important;
+  }
+
+  .justify-content-md-around {
+    -ms-flex-pack: distribute !important;
+        justify-content: space-around !important;
+  }
+
+  .align-items-md-start {
+    -webkit-box-align: start !important;
+        -ms-flex-align: start !important;
+            align-items: flex-start !important;
+  }
+
+  .align-items-md-end {
+    -webkit-box-align: end !important;
+        -ms-flex-align: end !important;
+            align-items: flex-end !important;
+  }
+
+  .align-items-md-center {
+    -webkit-box-align: center !important;
+        -ms-flex-align: center !important;
+            align-items: center !important;
+  }
+
+  .align-items-md-baseline {
+    -webkit-box-align: baseline !important;
+        -ms-flex-align: baseline !important;
+            align-items: baseline !important;
+  }
+
+  .align-items-md-stretch {
+    -webkit-box-align: stretch !important;
+        -ms-flex-align: stretch !important;
+            align-items: stretch !important;
+  }
+
+  .align-content-md-start {
+    -ms-flex-line-pack: start !important;
+        align-content: flex-start !important;
+  }
+
+  .align-content-md-end {
+    -ms-flex-line-pack: end !important;
+        align-content: flex-end !important;
+  }
+
+  .align-content-md-center {
+    -ms-flex-line-pack: center !important;
+        align-content: center !important;
+  }
+
+  .align-content-md-between {
+    -ms-flex-line-pack: justify !important;
+        align-content: space-between !important;
+  }
+
+  .align-content-md-around {
+    -ms-flex-line-pack: distribute !important;
+        align-content: space-around !important;
+  }
+
+  .align-content-md-stretch {
+    -ms-flex-line-pack: stretch !important;
+        align-content: stretch !important;
+  }
+
+  .align-self-md-auto {
+    -ms-flex-item-align: auto !important;
+        align-self: auto !important;
+  }
+
+  .align-self-md-start {
+    -ms-flex-item-align: start !important;
+        align-self: flex-start !important;
+  }
+
+  .align-self-md-end {
+    -ms-flex-item-align: end !important;
+        align-self: flex-end !important;
+  }
+
+  .align-self-md-center {
+    -ms-flex-item-align: center !important;
+        align-self: center !important;
+  }
+
+  .align-self-md-baseline {
+    -ms-flex-item-align: baseline !important;
+        align-self: baseline !important;
+  }
+
+  .align-self-md-stretch {
+    -ms-flex-item-align: stretch !important;
+        align-self: stretch !important;
+  }
+}
+@media (min-width: 992px) {
+  .flex-lg-row {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: row !important;
+            flex-direction: row !important;
+  }
+
+  .flex-lg-column {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: column !important;
+            flex-direction: column !important;
+  }
+
+  .flex-lg-row-reverse {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: row-reverse !important;
+            flex-direction: row-reverse !important;
+  }
+
+  .flex-lg-column-reverse {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: column-reverse !important;
+            flex-direction: column-reverse !important;
+  }
+
+  .flex-lg-wrap {
+    -ms-flex-wrap: wrap !important;
+        flex-wrap: wrap !important;
+  }
+
+  .flex-lg-nowrap {
+    -ms-flex-wrap: nowrap !important;
+        flex-wrap: nowrap !important;
+  }
+
+  .flex-lg-wrap-reverse {
+    -ms-flex-wrap: wrap-reverse !important;
+        flex-wrap: wrap-reverse !important;
+  }
+
+  .justify-content-lg-start {
+    -webkit-box-pack: start !important;
+        -ms-flex-pack: start !important;
+            justify-content: flex-start !important;
+  }
+
+  .justify-content-lg-end {
+    -webkit-box-pack: end !important;
+        -ms-flex-pack: end !important;
+            justify-content: flex-end !important;
+  }
+
+  .justify-content-lg-center {
+    -webkit-box-pack: center !important;
+        -ms-flex-pack: center !important;
+            justify-content: center !important;
+  }
+
+  .justify-content-lg-between {
+    -webkit-box-pack: justify !important;
+        -ms-flex-pack: justify !important;
+            justify-content: space-between !important;
+  }
+
+  .justify-content-lg-around {
+    -ms-flex-pack: distribute !important;
+        justify-content: space-around !important;
+  }
+
+  .align-items-lg-start {
+    -webkit-box-align: start !important;
+        -ms-flex-align: start !important;
+            align-items: flex-start !important;
+  }
+
+  .align-items-lg-end {
+    -webkit-box-align: end !important;
+        -ms-flex-align: end !important;
+            align-items: flex-end !important;
+  }
+
+  .align-items-lg-center {
+    -webkit-box-align: center !important;
+        -ms-flex-align: center !important;
+            align-items: center !important;
+  }
+
+  .align-items-lg-baseline {
+    -webkit-box-align: baseline !important;
+        -ms-flex-align: baseline !important;
+            align-items: baseline !important;
+  }
+
+  .align-items-lg-stretch {
+    -webkit-box-align: stretch !important;
+        -ms-flex-align: stretch !important;
+            align-items: stretch !important;
+  }
+
+  .align-content-lg-start {
+    -ms-flex-line-pack: start !important;
+        align-content: flex-start !important;
+  }
+
+  .align-content-lg-end {
+    -ms-flex-line-pack: end !important;
+        align-content: flex-end !important;
+  }
+
+  .align-content-lg-center {
+    -ms-flex-line-pack: center !important;
+        align-content: center !important;
+  }
+
+  .align-content-lg-between {
+    -ms-flex-line-pack: justify !important;
+        align-content: space-between !important;
+  }
+
+  .align-content-lg-around {
+    -ms-flex-line-pack: distribute !important;
+        align-content: space-around !important;
+  }
+
+  .align-content-lg-stretch {
+    -ms-flex-line-pack: stretch !important;
+        align-content: stretch !important;
+  }
+
+  .align-self-lg-auto {
+    -ms-flex-item-align: auto !important;
+        align-self: auto !important;
+  }
+
+  .align-self-lg-start {
+    -ms-flex-item-align: start !important;
+        align-self: flex-start !important;
+  }
+
+  .align-self-lg-end {
+    -ms-flex-item-align: end !important;
+        align-self: flex-end !important;
+  }
+
+  .align-self-lg-center {
+    -ms-flex-item-align: center !important;
+        align-self: center !important;
+  }
+
+  .align-self-lg-baseline {
+    -ms-flex-item-align: baseline !important;
+        align-self: baseline !important;
+  }
+
+  .align-self-lg-stretch {
+    -ms-flex-item-align: stretch !important;
+        align-self: stretch !important;
+  }
+}
+@media (min-width: 1200px) {
+  .flex-xl-row {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: row !important;
+            flex-direction: row !important;
+  }
+
+  .flex-xl-column {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: normal !important;
+        -ms-flex-direction: column !important;
+            flex-direction: column !important;
+  }
+
+  .flex-xl-row-reverse {
+    -webkit-box-orient: horizontal !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: row-reverse !important;
+            flex-direction: row-reverse !important;
+  }
+
+  .flex-xl-column-reverse {
+    -webkit-box-orient: vertical !important;
+    -webkit-box-direction: reverse !important;
+        -ms-flex-direction: column-reverse !important;
+            flex-direction: column-reverse !important;
+  }
+
+  .flex-xl-wrap {
+    -ms-flex-wrap: wrap !important;
+        flex-wrap: wrap !important;
+  }
+
+  .flex-xl-nowrap {
+    -ms-flex-wrap: nowrap !important;
+        flex-wrap: nowrap !important;
+  }
+
+  .flex-xl-wrap-reverse {
+    -ms-flex-wrap: wrap-reverse !important;
+        flex-wrap: wrap-reverse !important;
+  }
+
+  .justify-content-xl-start {
+    -webkit-box-pack: start !important;
+        -ms-flex-pack: start !important;
+            justify-content: flex-start !important;
+  }
+
+  .justify-content-xl-end {
+    -webkit-box-pack: end !important;
+        -ms-flex-pack: end !important;
+            justify-content: flex-end !important;
+  }
+
+  .justify-content-xl-center {
+    -webkit-box-pack: center !important;
+        -ms-flex-pack: center !important;
+            justify-content: center !important;
+  }
+
+  .justify-content-xl-between {
+    -webkit-box-pack: justify !important;
+        -ms-flex-pack: justify !important;
+            justify-content: space-between !important;
+  }
+
+  .justify-content-xl-around {
+    -ms-flex-pack: distribute !important;
+        justify-content: space-around !important;
+  }
+
+  .align-items-xl-start {
+    -webkit-box-align: start !important;
+        -ms-flex-align: start !important;
+            align-items: flex-start !important;
+  }
+
+  .align-items-xl-end {
+    -webkit-box-align: end !important;
+        -ms-flex-align: end !important;
+            align-items: flex-end !important;
+  }
+
+  .align-items-xl-center {
+    -webkit-box-align: center !important;
+        -ms-flex-align: center !important;
+            align-items: center !important;
+  }
+
+  .align-items-xl-baseline {
+    -webkit-box-align: baseline !important;
+        -ms-flex-align: baseline !important;
+            align-items: baseline !important;
+  }
+
+  .align-items-xl-stretch {
+    -webkit-box-align: stretch !important;
+        -ms-flex-align: stretch !important;
+            align-items: stretch !important;
+  }
+
+  .align-content-xl-start {
+    -ms-flex-line-pack: start !important;
+        align-content: flex-start !important;
+  }
+
+  .align-content-xl-end {
+    -ms-flex-line-pack: end !important;
+        align-content: flex-end !important;
+  }
+
+  .align-content-xl-center {
+    -ms-flex-line-pack: center !important;
+        align-content: center !important;
+  }
+
+  .align-content-xl-between {
+    -ms-flex-line-pack: justify !important;
+        align-content: space-between !important;
+  }
+
+  .align-content-xl-around {
+    -ms-flex-line-pack: distribute !important;
+        align-content: space-around !important;
+  }
+
+  .align-content-xl-stretch {
+    -ms-flex-line-pack: stretch !important;
+        align-content: stretch !important;
+  }
+
+  .align-self-xl-auto {
+    -ms-flex-item-align: auto !important;
+        align-self: auto !important;
+  }
+
+  .align-self-xl-start {
+    -ms-flex-item-align: start !important;
+        align-self: flex-start !important;
+  }
+
+  .align-self-xl-end {
+    -ms-flex-item-align: end !important;
+        align-self: flex-end !important;
+  }
+
+  .align-self-xl-center {
+    -ms-flex-item-align: center !important;
+        align-self: center !important;
+  }
+
+  .align-self-xl-baseline {
+    -ms-flex-item-align: baseline !important;
+        align-self: baseline !important;
+  }
+
+  .align-self-xl-stretch {
+    -ms-flex-item-align: stretch !important;
+        align-self: stretch !important;
+  }
+}
+.float-left {
+  float: left !important;
+}
+
+.float-right {
+  float: right !important;
+}
+
+.float-none {
+  float: none !important;
+}
+
+@media (min-width: 576px) {
+  .float-sm-left {
+    float: left !important;
+  }
+
+  .float-sm-right {
+    float: right !important;
+  }
+
+  .float-sm-none {
+    float: none !important;
+  }
+}
+@media (min-width: 768px) {
+  .float-md-left {
+    float: left !important;
+  }
+
+  .float-md-right {
+    float: right !important;
+  }
+
+  .float-md-none {
+    float: none !important;
+  }
+}
+@media (min-width: 992px) {
+  .float-lg-left {
+    float: left !important;
+  }
+
+  .float-lg-right {
+    float: right !important;
+  }
+
+  .float-lg-none {
+    float: none !important;
+  }
+}
+@media (min-width: 1200px) {
+  .float-xl-left {
+    float: left !important;
+  }
+
+  .float-xl-right {
+    float: right !important;
+  }
+
+  .float-xl-none {
+    float: none !important;
+  }
+}
+.position-static {
+  position: static !important;
+}
+
+.position-relative {
+  position: relative !important;
+}
+
+.position-absolute {
+  position: absolute !important;
+}
+
+.position-fixed {
+  position: fixed !important;
+}
+
+.position-sticky {
+  position: sticky !important;
+}
+
+.fixed-top {
+  position: fixed;
+  top: 0;
+  right: 0;
+  left: 0;
+  z-index: 1030;
+}
+
+.fixed-bottom {
+  position: fixed;
+  right: 0;
+  bottom: 0;
+  left: 0;
+  z-index: 1030;
+}
+
+@supports (position: sticky) {
+  .sticky-top {
+    position: sticky;
+    top: 0;
+    z-index: 1020;
+  }
+}
+
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  -webkit-clip-path: inset(50%);
+          clip-path: inset(50%);
+  border: 0;
+}
+
+.sr-only-focusable:active, .sr-only-focusable:focus {
+  position: static;
+  width: auto;
+  height: auto;
+  overflow: visible;
+  clip: auto;
+  white-space: normal;
+  -webkit-clip-path: none;
+          clip-path: none;
+}
+
+.w-25 {
+  width: 25% !important;
+}
+
+.w-50 {
+  width: 50% !important;
+}
+
+.w-75 {
+  width: 75% !important;
+}
+
+.w-100 {
+  width: 100% !important;
+}
+
+.h-25 {
+  height: 25% !important;
+}
+
+.h-50 {
+  height: 50% !important;
+}
+
+.h-75 {
+  height: 75% !important;
+}
+
+.h-100 {
+  height: 100% !important;
+}
+
+.mw-100 {
+  max-width: 100% !important;
+}
+
+.mh-100 {
+  max-height: 100% !important;
+}
+
+.m-0 {
+  margin: 0 !important;
+}
+
+.mt-0,
+.my-0 {
+  margin-top: 0 !important;
+}
+
+.mr-0,
+.mx-0 {
+  margin-right: 0 !important;
+}
+
+.mb-0,
+.my-0 {
+  margin-bottom: 0 !important;
+}
+
+.ml-0,
+.mx-0 {
+  margin-left: 0 !important;
+}
+
+.m-1 {
+  margin: 0.25rem !important;
+}
+
+.mt-1,
+.my-1 {
+  margin-top: 0.25rem !important;
+}
+
+.mr-1,
+.mx-1 {
+  margin-right: 0.25rem !important;
+}
+
+.mb-1,
+.my-1 {
+  margin-bottom: 0.25rem !important;
+}
+
+.ml-1,
+.mx-1 {
+  margin-left: 0.25rem !important;
+}
+
+.m-2 {
+  margin: 0.5rem !important;
+}
+
+.mt-2,
+.my-2 {
+  margin-top: 0.5rem !important;
+}
+
+.mr-2,
+.mx-2 {
+  margin-right: 0.5rem !important;
+}
+
+.mb-2,
+.my-2 {
+  margin-bottom: 0.5rem !important;
+}
+
+.ml-2,
+.mx-2 {
+  margin-left: 0.5rem !important;
+}
+
+.m-3 {
+  margin: 1rem !important;
+}
+
+.mt-3,
+.my-3 {
+  margin-top: 1rem !important;
+}
+
+.mr-3,
+.mx-3 {
+  margin-right: 1rem !important;
+}
+
+.mb-3,
+.my-3 {
+  margin-bottom: 1rem !important;
+}
+
+.ml-3,
+.mx-3 {
+  margin-left: 1rem !important;
+}
+
+.m-4 {
+  margin: 1.5rem !important;
+}
+
+.mt-4,
+.my-4 {
+  margin-top: 1.5rem !important;
+}
+
+.mr-4,
+.mx-4 {
+  margin-right: 1.5rem !important;
+}
+
+.mb-4,
+.my-4 {
+  margin-bottom: 1.5rem !important;
+}
+
+.ml-4,
+.mx-4 {
+  margin-left: 1.5rem !important;
+}
+
+.m-5 {
+  margin: 3rem !important;
+}
+
+.mt-5,
+.my-5 {
+  margin-top: 3rem !important;
+}
+
+.mr-5,
+.mx-5 {
+  margin-right: 3rem !important;
+}
+
+.mb-5,
+.my-5 {
+  margin-bottom: 3rem !important;
+}
+
+.ml-5,
+.mx-5 {
+  margin-left: 3rem !important;
+}
+
+.p-0 {
+  padding: 0 !important;
+}
+
+.pt-0,
+.py-0 {
+  padding-top: 0 !important;
+}
+
+.pr-0,
+.px-0 {
+  padding-right: 0 !important;
+}
+
+.pb-0,
+.py-0 {
+  padding-bottom: 0 !important;
+}
+
+.pl-0,
+.px-0 {
+  padding-left: 0 !important;
+}
+
+.p-1 {
+  padding: 0.25rem !important;
+}
+
+.pt-1,
+.py-1 {
+  padding-top: 0.25rem !important;
+}
+
+.pr-1,
+.px-1 {
+  padding-right: 0.25rem !important;
+}
+
+.pb-1,
+.py-1 {
+  padding-bottom: 0.25rem !important;
+}
+
+.pl-1,
+.px-1 {
+  padding-left: 0.25rem !important;
+}
+
+.p-2 {
+  padding: 0.5rem !important;
+}
+
+.pt-2,
+.py-2 {
+  padding-top: 0.5rem !important;
+}
+
+.pr-2,
+.px-2 {
+  padding-right: 0.5rem !important;
+}
+
+.pb-2,
+.py-2 {
+  padding-bottom: 0.5rem !important;
+}
+
+.pl-2,
+.px-2 {
+  padding-left: 0.5rem !important;
+}
+
+.p-3 {
+  padding: 1rem !important;
+}
+
+.pt-3,
+.py-3 {
+  padding-top: 1rem !important;
+}
+
+.pr-3,
+.px-3 {
+  padding-right: 1rem !important;
+}
+
+.pb-3,
+.py-3 {
+  padding-bottom: 1rem !important;
+}
+
+.pl-3,
+.px-3 {
+  padding-left: 1rem !important;
+}
+
+.p-4 {
+  padding: 1.5rem !important;
+}
+
+.pt-4,
+.py-4 {
+  padding-top: 1.5rem !important;
+}
+
+.pr-4,
+.px-4 {
+  padding-right: 1.5rem !important;
+}
+
+.pb-4,
+.py-4 {
+  padding-bottom: 1.5rem !important;
+}
+
+.pl-4,
+.px-4 {
+  padding-left: 1.5rem !important;
+}
+
+.p-5 {
+  padding: 3rem !important;
+}
+
+.pt-5,
+.py-5 {
+  padding-top: 3rem !important;
+}
+
+.pr-5,
+.px-5 {
+  padding-right: 3rem !important;
+}
+
+.pb-5,
+.py-5 {
+  padding-bottom: 3rem !important;
+}
+
+.pl-5,
+.px-5 {
+  padding-left: 3rem !important;
+}
+
+.m-auto {
+  margin: auto !important;
+}
+
+.mt-auto,
+.my-auto {
+  margin-top: auto !important;
+}
+
+.mr-auto,
+.mx-auto {
+  margin-right: auto !important;
+}
+
+.mb-auto,
+.my-auto {
+  margin-bottom: auto !important;
+}
+
+.ml-auto,
+.mx-auto {
+  margin-left: auto !important;
+}
+
+@media (min-width: 576px) {
+  .m-sm-0 {
+    margin: 0 !important;
+  }
+
+  .mt-sm-0,
+  .my-sm-0 {
+    margin-top: 0 !important;
+  }
+
+  .mr-sm-0,
+  .mx-sm-0 {
+    margin-right: 0 !important;
+  }
+
+  .mb-sm-0,
+  .my-sm-0 {
+    margin-bottom: 0 !important;
+  }
+
+  .ml-sm-0,
+  .mx-sm-0 {
+    margin-left: 0 !important;
+  }
+
+  .m-sm-1 {
+    margin: 0.25rem !important;
+  }
+
+  .mt-sm-1,
+  .my-sm-1 {
+    margin-top: 0.25rem !important;
+  }
+
+  .mr-sm-1,
+  .mx-sm-1 {
+    margin-right: 0.25rem !important;
+  }
+
+  .mb-sm-1,
+  .my-sm-1 {
+    margin-bottom: 0.25rem !important;
+  }
+
+  .ml-sm-1,
+  .mx-sm-1 {
+    margin-left: 0.25rem !important;
+  }
+
+  .m-sm-2 {
+    margin: 0.5rem !important;
+  }
+
+  .mt-sm-2,
+  .my-sm-2 {
+    margin-top: 0.5rem !important;
+  }
+
+  .mr-sm-2,
+  .mx-sm-2 {
+    margin-right: 0.5rem !important;
+  }
+
+  .mb-sm-2,
+  .my-sm-2 {
+    margin-bottom: 0.5rem !important;
+  }
+
+  .ml-sm-2,
+  .mx-sm-2 {
+    margin-left: 0.5rem !important;
+  }
+
+  .m-sm-3 {
+    margin: 1rem !important;
+  }
+
+  .mt-sm-3,
+  .my-sm-3 {
+    margin-top: 1rem !important;
+  }
+
+  .mr-sm-3,
+  .mx-sm-3 {
+    margin-right: 1rem !important;
+  }
+
+  .mb-sm-3,
+  .my-sm-3 {
+    margin-bottom: 1rem !important;
+  }
+
+  .ml-sm-3,
+  .mx-sm-3 {
+    margin-left: 1rem !important;
+  }
+
+  .m-sm-4 {
+    margin: 1.5rem !important;
+  }
+
+  .mt-sm-4,
+  .my-sm-4 {
+    margin-top: 1.5rem !important;
+  }
+
+  .mr-sm-4,
+  .mx-sm-4 {
+    margin-right: 1.5rem !important;
+  }
+
+  .mb-sm-4,
+  .my-sm-4 {
+    margin-bottom: 1.5rem !important;
+  }
+
+  .ml-sm-4,
+  .mx-sm-4 {
+    margin-left: 1.5rem !important;
+  }
+
+  .m-sm-5 {
+    margin: 3rem !important;
+  }
+
+  .mt-sm-5,
+  .my-sm-5 {
+    margin-top: 3rem !important;
+  }
+
+  .mr-sm-5,
+  .mx-sm-5 {
+    margin-right: 3rem !important;
+  }
+
+  .mb-sm-5,
+  .my-sm-5 {
+    margin-bottom: 3rem !important;
+  }
+
+  .ml-sm-5,
+  .mx-sm-5 {
+    margin-left: 3rem !important;
+  }
+
+  .p-sm-0 {
+    padding: 0 !important;
+  }
+
+  .pt-sm-0,
+  .py-sm-0 {
+    padding-top: 0 !important;
+  }
+
+  .pr-sm-0,
+  .px-sm-0 {
+    padding-right: 0 !important;
+  }
+
+  .pb-sm-0,
+  .py-sm-0 {
+    padding-bottom: 0 !important;
+  }
+
+  .pl-sm-0,
+  .px-sm-0 {
+    padding-left: 0 !important;
+  }
+
+  .p-sm-1 {
+    padding: 0.25rem !important;
+  }
+
+  .pt-sm-1,
+  .py-sm-1 {
+    padding-top: 0.25rem !important;
+  }
+
+  .pr-sm-1,
+  .px-sm-1 {
+    padding-right: 0.25rem !important;
+  }
+
+  .pb-sm-1,
+  .py-sm-1 {
+    padding-bottom: 0.25rem !important;
+  }
+
+  .pl-sm-1,
+  .px-sm-1 {
+    padding-left: 0.25rem !important;
+  }
+
+  .p-sm-2 {
+    padding: 0.5rem !important;
+  }
+
+  .pt-sm-2,
+  .py-sm-2 {
+    padding-top: 0.5rem !important;
+  }
+
+  .pr-sm-2,
+  .px-sm-2 {
+    padding-right: 0.5rem !important;
+  }
+
+  .pb-sm-2,
+  .py-sm-2 {
+    padding-bottom: 0.5rem !important;
+  }
+
+  .pl-sm-2,
+  .px-sm-2 {
+    padding-left: 0.5rem !important;
+  }
+
+  .p-sm-3 {
+    padding: 1rem !important;
+  }
+
+  .pt-sm-3,
+  .py-sm-3 {
+    padding-top: 1rem !important;
+  }
+
+  .pr-sm-3,
+  .px-sm-3 {
+    padding-right: 1rem !important;
+  }
+
+  .pb-sm-3,
+  .py-sm-3 {
+    padding-bottom: 1rem !important;
+  }
+
+  .pl-sm-3,
+  .px-sm-3 {
+    padding-left: 1rem !important;
+  }
+
+  .p-sm-4 {
+    padding: 1.5rem !important;
+  }
+
+  .pt-sm-4,
+  .py-sm-4 {
+    padding-top: 1.5rem !important;
+  }
+
+  .pr-sm-4,
+  .px-sm-4 {
+    padding-right: 1.5rem !important;
+  }
+
+  .pb-sm-4,
+  .py-sm-4 {
+    padding-bottom: 1.5rem !important;
+  }
+
+  .pl-sm-4,
+  .px-sm-4 {
+    padding-left: 1.5rem !important;
+  }
+
+  .p-sm-5 {
+    padding: 3rem !important;
+  }
+
+  .pt-sm-5,
+  .py-sm-5 {
+    padding-top: 3rem !important;
+  }
+
+  .pr-sm-5,
+  .px-sm-5 {
+    padding-right: 3rem !important;
+  }
+
+  .pb-sm-5,
+  .py-sm-5 {
+    padding-bottom: 3rem !important;
+  }
+
+  .pl-sm-5,
+  .px-sm-5 {
+    padding-left: 3rem !important;
+  }
+
+  .m-sm-auto {
+    margin: auto !important;
+  }
+
+  .mt-sm-auto,
+  .my-sm-auto {
+    margin-top: auto !important;
+  }
+
+  .mr-sm-auto,
+  .mx-sm-auto {
+    margin-right: auto !important;
+  }
+
+  .mb-sm-auto,
+  .my-sm-auto {
+    margin-bottom: auto !important;
+  }
+
+  .ml-sm-auto,
+  .mx-sm-auto {
+    margin-left: auto !important;
+  }
+}
+@media (min-width: 768px) {
+  .m-md-0 {
+    margin: 0 !important;
+  }
+
+  .mt-md-0,
+  .my-md-0 {
+    margin-top: 0 !important;
+  }
+
+  .mr-md-0,
+  .mx-md-0 {
+    margin-right: 0 !important;
+  }
+
+  .mb-md-0,
+  .my-md-0 {
+    margin-bottom: 0 !important;
+  }
+
+  .ml-md-0,
+  .mx-md-0 {
+    margin-left: 0 !important;
+  }
+
+  .m-md-1 {
+    margin: 0.25rem !important;
+  }
+
+  .mt-md-1,
+  .my-md-1 {
+    margin-top: 0.25rem !important;
+  }
+
+  .mr-md-1,
+  .mx-md-1 {
+    margin-right: 0.25rem !important;
+  }
+
+  .mb-md-1,
+  .my-md-1 {
+    margin-bottom: 0.25rem !important;
+  }
+
+  .ml-md-1,
+  .mx-md-1 {
+    margin-left: 0.25rem !important;
+  }
+
+  .m-md-2 {
+    margin: 0.5rem !important;
+  }
+
+  .mt-md-2,
+  .my-md-2 {
+    margin-top: 0.5rem !important;
+  }
+
+  .mr-md-2,
+  .mx-md-2 {
+    margin-right: 0.5rem !important;
+  }
+
+  .mb-md-2,
+  .my-md-2 {
+    margin-bottom: 0.5rem !important;
+  }
+
+  .ml-md-2,
+  .mx-md-2 {
+    margin-left: 0.5rem !important;
+  }
+
+  .m-md-3 {
+    margin: 1rem !important;
+  }
+
+  .mt-md-3,
+  .my-md-3 {
+    margin-top: 1rem !important;
+  }
+
+  .mr-md-3,
+  .mx-md-3 {
+    margin-right: 1rem !important;
+  }
+
+  .mb-md-3,
+  .my-md-3 {
+    margin-bottom: 1rem !important;
+  }
+
+  .ml-md-3,
+  .mx-md-3 {
+    margin-left: 1rem !important;
+  }
+
+  .m-md-4 {
+    margin: 1.5rem !important;
+  }
+
+  .mt-md-4,
+  .my-md-4 {
+    margin-top: 1.5rem !important;
+  }
+
+  .mr-md-4,
+  .mx-md-4 {
+    margin-right: 1.5rem !important;
+  }
+
+  .mb-md-4,
+  .my-md-4 {
+    margin-bottom: 1.5rem !important;
+  }
+
+  .ml-md-4,
+  .mx-md-4 {
+    margin-left: 1.5rem !important;
+  }
+
+  .m-md-5 {
+    margin: 3rem !important;
+  }
+
+  .mt-md-5,
+  .my-md-5 {
+    margin-top: 3rem !important;
+  }
+
+  .mr-md-5,
+  .mx-md-5 {
+    margin-right: 3rem !important;
+  }
+
+  .mb-md-5,
+  .my-md-5 {
+    margin-bottom: 3rem !important;
+  }
+
+  .ml-md-5,
+  .mx-md-5 {
+    margin-left: 3rem !important;
+  }
+
+  .p-md-0 {
+    padding: 0 !important;
+  }
+
+  .pt-md-0,
+  .py-md-0 {
+    padding-top: 0 !important;
+  }
+
+  .pr-md-0,
+  .px-md-0 {
+    padding-right: 0 !important;
+  }
+
+  .pb-md-0,
+  .py-md-0 {
+    padding-bottom: 0 !important;
+  }
+
+  .pl-md-0,
+  .px-md-0 {
+    padding-left: 0 !important;
+  }
+
+  .p-md-1 {
+    padding: 0.25rem !important;
+  }
+
+  .pt-md-1,
+  .py-md-1 {
+    padding-top: 0.25rem !important;
+  }
+
+  .pr-md-1,
+  .px-md-1 {
+    padding-right: 0.25rem !important;
+  }
+
+  .pb-md-1,
+  .py-md-1 {
+    padding-bottom: 0.25rem !important;
+  }
+
+  .pl-md-1,
+  .px-md-1 {
+    padding-left: 0.25rem !important;
+  }
+
+  .p-md-2 {
+    padding: 0.5rem !important;
+  }
+
+  .pt-md-2,
+  .py-md-2 {
+    padding-top: 0.5rem !important;
+  }
+
+  .pr-md-2,
+  .px-md-2 {
+    padding-right: 0.5rem !important;
+  }
+
+  .pb-md-2,
+  .py-md-2 {
+    padding-bottom: 0.5rem !important;
+  }
+
+  .pl-md-2,
+  .px-md-2 {
+    padding-left: 0.5rem !important;
+  }
+
+  .p-md-3 {
+    padding: 1rem !important;
+  }
+
+  .pt-md-3,
+  .py-md-3 {
+    padding-top: 1rem !important;
+  }
+
+  .pr-md-3,
+  .px-md-3 {
+    padding-right: 1rem !important;
+  }
+
+  .pb-md-3,
+  .py-md-3 {
+    padding-bottom: 1rem !important;
+  }
+
+  .pl-md-3,
+  .px-md-3 {
+    padding-left: 1rem !important;
+  }
+
+  .p-md-4 {
+    padding: 1.5rem !important;
+  }
+
+  .pt-md-4,
+  .py-md-4 {
+    padding-top: 1.5rem !important;
+  }
+
+  .pr-md-4,
+  .px-md-4 {
+    padding-right: 1.5rem !important;
+  }
+
+  .pb-md-4,
+  .py-md-4 {
+    padding-bottom: 1.5rem !important;
+  }
+
+  .pl-md-4,
+  .px-md-4 {
+    padding-left: 1.5rem !important;
+  }
+
+  .p-md-5 {
+    padding: 3rem !important;
+  }
+
+  .pt-md-5,
+  .py-md-5 {
+    padding-top: 3rem !important;
+  }
+
+  .pr-md-5,
+  .px-md-5 {
+    padding-right: 3rem !important;
+  }
+
+  .pb-md-5,
+  .py-md-5 {
+    padding-bottom: 3rem !important;
+  }
+
+  .pl-md-5,
+  .px-md-5 {
+    padding-left: 3rem !important;
+  }
+
+  .m-md-auto {
+    margin: auto !important;
+  }
+
+  .mt-md-auto,
+  .my-md-auto {
+    margin-top: auto !important;
+  }
+
+  .mr-md-auto,
+  .mx-md-auto {
+    margin-right: auto !important;
+  }
+
+  .mb-md-auto,
+  .my-md-auto {
+    margin-bottom: auto !important;
+  }
+
+  .ml-md-auto,
+  .mx-md-auto {
+    margin-left: auto !important;
+  }
+}
+@media (min-width: 992px) {
+  .m-lg-0 {
+    margin: 0 !important;
+  }
+
+  .mt-lg-0,
+  .my-lg-0 {
+    margin-top: 0 !important;
+  }
+
+  .mr-lg-0,
+  .mx-lg-0 {
+    margin-right: 0 !important;
+  }
+
+  .mb-lg-0,
+  .my-lg-0 {
+    margin-bottom: 0 !important;
+  }
+
+  .ml-lg-0,
+  .mx-lg-0 {
+    margin-left: 0 !important;
+  }
+
+  .m-lg-1 {
+    margin: 0.25rem !important;
+  }
+
+  .mt-lg-1,
+  .my-lg-1 {
+    margin-top: 0.25rem !important;
+  }
+
+  .mr-lg-1,
+  .mx-lg-1 {
+    margin-right: 0.25rem !important;
+  }
+
+  .mb-lg-1,
+  .my-lg-1 {
+    margin-bottom: 0.25rem !important;
+  }
+
+  .ml-lg-1,
+  .mx-lg-1 {
+    margin-left: 0.25rem !important;
+  }
+
+  .m-lg-2 {
+    margin: 0.5rem !important;
+  }
+
+  .mt-lg-2,
+  .my-lg-2 {
+    margin-top: 0.5rem !important;
+  }
+
+  .mr-lg-2,
+  .mx-lg-2 {
+    margin-right: 0.5rem !important;
+  }
+
+  .mb-lg-2,
+  .my-lg-2 {
+    margin-bottom: 0.5rem !important;
+  }
+
+  .ml-lg-2,
+  .mx-lg-2 {
+    margin-left: 0.5rem !important;
+  }
+
+  .m-lg-3 {
+    margin: 1rem !important;
+  }
+
+  .mt-lg-3,
+  .my-lg-3 {
+    margin-top: 1rem !important;
+  }
+
+  .mr-lg-3,
+  .mx-lg-3 {
+    margin-right: 1rem !important;
+  }
+
+  .mb-lg-3,
+  .my-lg-3 {
+    margin-bottom: 1rem !important;
+  }
+
+  .ml-lg-3,
+  .mx-lg-3 {
+    margin-left: 1rem !important;
+  }
+
+  .m-lg-4 {
+    margin: 1.5rem !important;
+  }
+
+  .mt-lg-4,
+  .my-lg-4 {
+    margin-top: 1.5rem !important;
+  }
+
+  .mr-lg-4,
+  .mx-lg-4 {
+    margin-right: 1.5rem !important;
+  }
+
+  .mb-lg-4,
+  .my-lg-4 {
+    margin-bottom: 1.5rem !important;
+  }
+
+  .ml-lg-4,
+  .mx-lg-4 {
+    margin-left: 1.5rem !important;
+  }
+
+  .m-lg-5 {
+    margin: 3rem !important;
+  }
+
+  .mt-lg-5,
+  .my-lg-5 {
+    margin-top: 3rem !important;
+  }
+
+  .mr-lg-5,
+  .mx-lg-5 {
+    margin-right: 3rem !important;
+  }
+
+  .mb-lg-5,
+  .my-lg-5 {
+    margin-bottom: 3rem !important;
+  }
+
+  .ml-lg-5,
+  .mx-lg-5 {
+    margin-left: 3rem !important;
+  }
+
+  .p-lg-0 {
+    padding: 0 !important;
+  }
+
+  .pt-lg-0,
+  .py-lg-0 {
+    padding-top: 0 !important;
+  }
+
+  .pr-lg-0,
+  .px-lg-0 {
+    padding-right: 0 !important;
+  }
+
+  .pb-lg-0,
+  .py-lg-0 {
+    padding-bottom: 0 !important;
+  }
+
+  .pl-lg-0,
+  .px-lg-0 {
+    padding-left: 0 !important;
+  }
+
+  .p-lg-1 {
+    padding: 0.25rem !important;
+  }
+
+  .pt-lg-1,
+  .py-lg-1 {
+    padding-top: 0.25rem !important;
+  }
+
+  .pr-lg-1,
+  .px-lg-1 {
+    padding-right: 0.25rem !important;
+  }
+
+  .pb-lg-1,
+  .py-lg-1 {
+    padding-bottom: 0.25rem !important;
+  }
+
+  .pl-lg-1,
+  .px-lg-1 {
+    padding-left: 0.25rem !important;
+  }
+
+  .p-lg-2 {
+    padding: 0.5rem !important;
+  }
+
+  .pt-lg-2,
+  .py-lg-2 {
+    padding-top: 0.5rem !important;
+  }
+
+  .pr-lg-2,
+  .px-lg-2 {
+    padding-right: 0.5rem !important;
+  }
+
+  .pb-lg-2,
+  .py-lg-2 {
+    padding-bottom: 0.5rem !important;
+  }
+
+  .pl-lg-2,
+  .px-lg-2 {
+    padding-left: 0.5rem !important;
+  }
+
+  .p-lg-3 {
+    padding: 1rem !important;
+  }
+
+  .pt-lg-3,
+  .py-lg-3 {
+    padding-top: 1rem !important;
+  }
+
+  .pr-lg-3,
+  .px-lg-3 {
+    padding-right: 1rem !important;
+  }
+
+  .pb-lg-3,
+  .py-lg-3 {
+    padding-bottom: 1rem !important;
+  }
+
+  .pl-lg-3,
+  .px-lg-3 {
+    padding-left: 1rem !important;
+  }
+
+  .p-lg-4 {
+    padding: 1.5rem !important;
+  }
+
+  .pt-lg-4,
+  .py-lg-4 {
+    padding-top: 1.5rem !important;
+  }
+
+  .pr-lg-4,
+  .px-lg-4 {
+    padding-right: 1.5rem !important;
+  }
+
+  .pb-lg-4,
+  .py-lg-4 {
+    padding-bottom: 1.5rem !important;
+  }
+
+  .pl-lg-4,
+  .px-lg-4 {
+    padding-left: 1.5rem !important;
+  }
+
+  .p-lg-5 {
+    padding: 3rem !important;
+  }
+
+  .pt-lg-5,
+  .py-lg-5 {
+    padding-top: 3rem !important;
+  }
+
+  .pr-lg-5,
+  .px-lg-5 {
+    padding-right: 3rem !important;
+  }
+
+  .pb-lg-5,
+  .py-lg-5 {
+    padding-bottom: 3rem !important;
+  }
+
+  .pl-lg-5,
+  .px-lg-5 {
+    padding-left: 3rem !important;
+  }
+
+  .m-lg-auto {
+    margin: auto !important;
+  }
+
+  .mt-lg-auto,
+  .my-lg-auto {
+    margin-top: auto !important;
+  }
+
+  .mr-lg-auto,
+  .mx-lg-auto {
+    margin-right: auto !important;
+  }
+
+  .mb-lg-auto,
+  .my-lg-auto {
+    margin-bottom: auto !important;
+  }
+
+  .ml-lg-auto,
+  .mx-lg-auto {
+    margin-left: auto !important;
+  }
+}
+@media (min-width: 1200px) {
+  .m-xl-0 {
+    margin: 0 !important;
+  }
+
+  .mt-xl-0,
+  .my-xl-0 {
+    margin-top: 0 !important;
+  }
+
+  .mr-xl-0,
+  .mx-xl-0 {
+    margin-right: 0 !important;
+  }
+
+  .mb-xl-0,
+  .my-xl-0 {
+    margin-bottom: 0 !important;
+  }
+
+  .ml-xl-0,
+  .mx-xl-0 {
+    margin-left: 0 !important;
+  }
+
+  .m-xl-1 {
+    margin: 0.25rem !important;
+  }
+
+  .mt-xl-1,
+  .my-xl-1 {
+    margin-top: 0.25rem !important;
+  }
+
+  .mr-xl-1,
+  .mx-xl-1 {
+    margin-right: 0.25rem !important;
+  }
+
+  .mb-xl-1,
+  .my-xl-1 {
+    margin-bottom: 0.25rem !important;
+  }
+
+  .ml-xl-1,
+  .mx-xl-1 {
+    margin-left: 0.25rem !important;
+  }
+
+  .m-xl-2 {
+    margin: 0.5rem !important;
+  }
+
+  .mt-xl-2,
+  .my-xl-2 {
+    margin-top: 0.5rem !important;
+  }
+
+  .mr-xl-2,
+  .mx-xl-2 {
+    margin-right: 0.5rem !important;
+  }
+
+  .mb-xl-2,
+  .my-xl-2 {
+    margin-bottom: 0.5rem !important;
+  }
+
+  .ml-xl-2,
+  .mx-xl-2 {
+    margin-left: 0.5rem !important;
+  }
+
+  .m-xl-3 {
+    margin: 1rem !important;
+  }
+
+  .mt-xl-3,
+  .my-xl-3 {
+    margin-top: 1rem !important;
+  }
+
+  .mr-xl-3,
+  .mx-xl-3 {
+    margin-right: 1rem !important;
+  }
+
+  .mb-xl-3,
+  .my-xl-3 {
+    margin-bottom: 1rem !important;
+  }
+
+  .ml-xl-3,
+  .mx-xl-3 {
+    margin-left: 1rem !important;
+  }
+
+  .m-xl-4 {
+    margin: 1.5rem !important;
+  }
+
+  .mt-xl-4,
+  .my-xl-4 {
+    margin-top: 1.5rem !important;
+  }
+
+  .mr-xl-4,
+  .mx-xl-4 {
+    margin-right: 1.5rem !important;
+  }
+
+  .mb-xl-4,
+  .my-xl-4 {
+    margin-bottom: 1.5rem !important;
+  }
+
+  .ml-xl-4,
+  .mx-xl-4 {
+    margin-left: 1.5rem !important;
+  }
+
+  .m-xl-5 {
+    margin: 3rem !important;
+  }
+
+  .mt-xl-5,
+  .my-xl-5 {
+    margin-top: 3rem !important;
+  }
+
+  .mr-xl-5,
+  .mx-xl-5 {
+    margin-right: 3rem !important;
+  }
+
+  .mb-xl-5,
+  .my-xl-5 {
+    margin-bottom: 3rem !important;
+  }
+
+  .ml-xl-5,
+  .mx-xl-5 {
+    margin-left: 3rem !important;
+  }
+
+  .p-xl-0 {
+    padding: 0 !important;
+  }
+
+  .pt-xl-0,
+  .py-xl-0 {
+    padding-top: 0 !important;
+  }
+
+  .pr-xl-0,
+  .px-xl-0 {
+    padding-right: 0 !important;
+  }
+
+  .pb-xl-0,
+  .py-xl-0 {
+    padding-bottom: 0 !important;
+  }
+
+  .pl-xl-0,
+  .px-xl-0 {
+    padding-left: 0 !important;
+  }
+
+  .p-xl-1 {
+    padding: 0.25rem !important;
+  }
+
+  .pt-xl-1,
+  .py-xl-1 {
+    padding-top: 0.25rem !important;
+  }
+
+  .pr-xl-1,
+  .px-xl-1 {
+    padding-right: 0.25rem !important;
+  }
+
+  .pb-xl-1,
+  .py-xl-1 {
+    padding-bottom: 0.25rem !important;
+  }
+
+  .pl-xl-1,
+  .px-xl-1 {
+    padding-left: 0.25rem !important;
+  }
+
+  .p-xl-2 {
+    padding: 0.5rem !important;
+  }
+
+  .pt-xl-2,
+  .py-xl-2 {
+    padding-top: 0.5rem !important;
+  }
+
+  .pr-xl-2,
+  .px-xl-2 {
+    padding-right: 0.5rem !important;
+  }
+
+  .pb-xl-2,
+  .py-xl-2 {
+    padding-bottom: 0.5rem !important;
+  }
+
+  .pl-xl-2,
+  .px-xl-2 {
+    padding-left: 0.5rem !important;
+  }
+
+  .p-xl-3 {
+    padding: 1rem !important;
+  }
+
+  .pt-xl-3,
+  .py-xl-3 {
+    padding-top: 1rem !important;
+  }
+
+  .pr-xl-3,
+  .px-xl-3 {
+    padding-right: 1rem !important;
+  }
+
+  .pb-xl-3,
+  .py-xl-3 {
+    padding-bottom: 1rem !important;
+  }
+
+  .pl-xl-3,
+  .px-xl-3 {
+    padding-left: 1rem !important;
+  }
+
+  .p-xl-4 {
+    padding: 1.5rem !important;
+  }
+
+  .pt-xl-4,
+  .py-xl-4 {
+    padding-top: 1.5rem !important;
+  }
+
+  .pr-xl-4,
+  .px-xl-4 {
+    padding-right: 1.5rem !important;
+  }
+
+  .pb-xl-4,
+  .py-xl-4 {
+    padding-bottom: 1.5rem !important;
+  }
+
+  .pl-xl-4,
+  .px-xl-4 {
+    padding-left: 1.5rem !important;
+  }
+
+  .p-xl-5 {
+    padding: 3rem !important;
+  }
+
+  .pt-xl-5,
+  .py-xl-5 {
+    padding-top: 3rem !important;
+  }
+
+  .pr-xl-5,
+  .px-xl-5 {
+    padding-right: 3rem !important;
+  }
+
+  .pb-xl-5,
+  .py-xl-5 {
+    padding-bottom: 3rem !important;
+  }
+
+  .pl-xl-5,
+  .px-xl-5 {
+    padding-left: 3rem !important;
+  }
+
+  .m-xl-auto {
+    margin: auto !important;
+  }
+
+  .mt-xl-auto,
+  .my-xl-auto {
+    margin-top: auto !important;
+  }
+
+  .mr-xl-auto,
+  .mx-xl-auto {
+    margin-right: auto !important;
+  }
+
+  .mb-xl-auto,
+  .my-xl-auto {
+    margin-bottom: auto !important;
+  }
+
+  .ml-xl-auto,
+  .mx-xl-auto {
+    margin-left: auto !important;
+  }
+}
+.text-justify {
+  text-align: justify !important;
+}
+
+.text-nowrap {
+  white-space: nowrap !important;
+}
+
+.text-truncate {
+  overflow: hidden;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+}
+
+.text-left {
+  text-align: left !important;
+}
+
+.text-right {
+  text-align: right !important;
+}
+
+.text-center {
+  text-align: center !important;
+}
+
+@media (min-width: 576px) {
+  .text-sm-left {
+    text-align: left !important;
+  }
+
+  .text-sm-right {
+    text-align: right !important;
+  }
+
+  .text-sm-center {
+    text-align: center !important;
+  }
+}
+@media (min-width: 768px) {
+  .text-md-left {
+    text-align: left !important;
+  }
+
+  .text-md-right {
+    text-align: right !important;
+  }
+
+  .text-md-center {
+    text-align: center !important;
+  }
+}
+@media (min-width: 992px) {
+  .text-lg-left {
+    text-align: left !important;
+  }
+
+  .text-lg-right {
+    text-align: right !important;
+  }
+
+  .text-lg-center {
+    text-align: center !important;
+  }
+}
+@media (min-width: 1200px) {
+  .text-xl-left {
+    text-align: left !important;
+  }
+
+  .text-xl-right {
+    text-align: right !important;
+  }
+
+  .text-xl-center {
+    text-align: center !important;
+  }
+}
+.text-lowercase {
+  text-transform: lowercase !important;
+}
+
+.text-uppercase {
+  text-transform: uppercase !important;
+}
+
+.text-capitalize {
+  text-transform: capitalize !important;
+}
+
+.font-weight-light {
+  font-weight: 300 !important;
+}
+
+.font-weight-normal {
+  font-weight: 400 !important;
+}
+
+.font-weight-bold {
+  font-weight: 700 !important;
+}
+
+.font-italic {
+  font-style: italic !important;
+}
+
+.text-white {
+  color: #fff !important;
+}
+
+.text-primary {
+  color: #007bff !important;
+}
+
+a.text-primary:hover, a.text-primary:focus {
+  color: #0062cc !important;
+}
+
+.text-secondary {
+  color: #6c757d !important;
+}
+
+a.text-secondary:hover, a.text-secondary:focus {
+  color: #545b62 !important;
+}
+
+.text-success {
+  color: #28a745 !important;
+}
+
+a.text-success:hover, a.text-success:focus {
+  color: #1e7e34 !important;
+}
+
+.text-info {
+  color: #17a2b8 !important;
+}
+
+a.text-info:hover, a.text-info:focus {
+  color: #117a8b !important;
+}
+
+.text-warning {
+  color: #ffc107 !important;
+}
+
+a.text-warning:hover, a.text-warning:focus {
+  color: #d39e00 !important;
+}
+
+.text-danger {
+  color: #dc3545 !important;
+}
+
+a.text-danger:hover, a.text-danger:focus {
+  color: #bd2130 !important;
+}
+
+.text-light {
+  color: #f8f9fa !important;
+}
+
+a.text-light:hover, a.text-light:focus {
+  color: #dae0e5 !important;
+}
+
+.text-dark {
+  color: #343a40 !important;
+}
+
+a.text-dark:hover, a.text-dark:focus {
+  color: #1d2124 !important;
+}
+
+.text-muted {
+  color: #6c757d !important;
+}
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+.text-hide {
+  font: 0/0 a;
+  color: transparent;
+  text-shadow: none;
+  background-color: transparent;
+  border: 0;
+}
+
+.visible {
+  visibility: visible !important;
+}
+
+.invisible {
+  visibility: hidden !important;
+}
+
+@media print {
+  *,
+  *::before,
+  *::after {
+    text-shadow: none !important;
+    -webkit-box-shadow: none !important;
+            box-shadow: none !important;
+  }
+
+  a:not(.btn) {
+    text-decoration: underline;
+  }
+
+  abbr[title]::after {
+    content: " (" attr(title) ")";
+  }
+
+  pre {
+    white-space: pre-wrap !important;
+  }
+
+  pre,
+  blockquote {
+    border: 1px solid #999;
+    page-break-inside: avoid;
+  }
+
+  thead {
+    display: table-header-group;
+  }
+
+  tr,
+  img {
+    page-break-inside: avoid;
+  }
+
+  p,
+  h2,
+  h3 {
+    orphans: 3;
+    widows: 3;
+  }
+
+  h2,
+  h3 {
+    page-break-after: avoid;
+  }
+
+  @page {
+    size: a3;
+  }
+  body {
+    min-width: 992px !important;
+  }
+
+  .container {
+    min-width: 992px !important;
+  }
+
+  .navbar {
+    display: none;
+  }
+
+  .badge {
+    border: 1px solid #000;
+  }
+
+  .table {
+    border-collapse: collapse !important;
+  }
+  .table td,
+  .table th {
+    background-color: #fff !important;
+  }
+
+  .table-bordered th,
+  .table-bordered td {
+    border: 1px solid #ddd !important;
+  }
+}
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+  padding: 5px;
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+  font-style: italic;
+}
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+  background-color: #e3d2d2;
+}
+
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+  background-color: #ffdddd;
+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  font-style: italic;
+  src: url("../fonts/FreightSans/freight-sans-bold-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-bold-italic.woff") format("woff");
+}
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+  font-style: normal;
+  src: url("../fonts/FreightSans/freight-sans-medium.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-medium.woff") format("woff");
+}
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+  font-style: italic;
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+}
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+}
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+  font-style: italic;
+  src: url("../fonts/FreightSans/freight-sans-light-italic.woff2") format("woff2"), url("../fonts/FreightSans/freight-sans-light-italic.woff") format("woff");
+}
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+}
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+}
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+}
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+  font-weight: 300;
+  font-style: normal;
+  unicode-range: u+0020-007f;
+  src: local("IBMPlexMono-Light"), url("../fonts/IBMPlexMono/IBMPlexMono-Light.woff2") format("woff2"), url("../fonts/IBMPlexMono/IBMPlexMono-Light.woff") format("woff");
+}
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+}
+@media screen and (min-width: 768px) {
+  html {
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+  }
+}
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+  -webkit-box-sizing: border-box;
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+}
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+body {
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+}
+
+a:link,
+a:visited,
+a:hover {
+  text-decoration: none;
+  color: #e44c2c;
+}
+
+a.with-right-arrow, .btn.with-right-arrow {
+  padding-right: 1.375rem;
+  position: relative;
+  background-image: url("../images/chevron-right-orange.svg");
+  background-size: 6px 13px;
+  background-position: center right 5px;
+  background-repeat: no-repeat;
+}
+@media screen and (min-width: 768px) {
+  a.with-right-arrow, .btn.with-right-arrow {
+    background-size: 8px 14px;
+    background-position: center right 12px;
+    padding-right: 2rem;
+  }
+}
+
+::-webkit-input-placeholder {
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+}
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+}
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+}
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+}
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+  border: none;
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+}
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+  right: 0;
+  top: 10px;
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+  width: 15px;
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+  background-color: transparent;
+  background-repeat: no-repeat;
+  background-size: 15px 15px;
+  background-position: center center;
+  -webkit-appearance: none;
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+  appearance: none;
+  border: 0;
+}
+
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+  position: relative;
+}
+
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+  color: #6c6c6d !important;
+}
+@media screen and (min-width: 768px) {
+  .anchorjs-link:hover {
+    color: inherit;
+    text-decoration: none !important;
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  color: #4974D1;
+}
+
+p code,
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+h4 code,
+h5 code,
+h6 code {
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+}
+
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+  word-wrap: break-word;
+}
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+      -ms-flex-align: center;
+          align-items: center;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  left: 0;
+  margin-left: auto;
+  margin-right: auto;
+  position: fixed;
+  right: 0;
+  top: 0;
+  width: 100%;
+  z-index: 9999;
+  background-color: #ffffff;
+  border-bottom: 1px solid #e2e2e2;
+}
+@media screen and (min-width: 1100px) {
+  .header-holder {
+    height: 90px;
+  }
+}
+
+.header-container {
+  position: relative;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+.header-container:before, .header-container:after {
+  content: "";
+  display: table;
+}
+.header-container:after {
+  clear: both;
+}
+.header-container {
+  *zoom: 1;
+}
+@media screen and (min-width: 1100px) {
+  .header-container {
+    display: block;
+  }
+}
+
+.header-logo {
+  height: 23px;
+  width: 93px;
+  background-image: url("../images/logo.svg");
+  background-repeat: no-repeat;
+  background-size: 93px 23px;
+  display: block;
+  float: left;
+  z-index: 10;
+}
+@media screen and (min-width: 1100px) {
+  .header-logo {
+    background-size: 108px 27px;
+    position: absolute;
+    height: 27px;
+    width: 108px;
+    top: 4px;
+    float: none;
+  }
+}
+
+.main-menu-open-button {
+  background-image: url("../images/icon-menu-dots.svg");
+  background-position: center center;
+  background-size: 25px 7px;
+  background-repeat: no-repeat;
+  width: 25px;
+  height: 17px;
+  position: absolute;
+  right: 0;
+  top: 4px;
+}
+@media screen and (min-width: 1100px) {
+  .main-menu-open-button {
+    display: none;
+  }
+}
+
+.header-holder .main-menu {
+  display: none;
+}
+@media screen and (min-width: 1100px) {
+  .header-holder .main-menu {
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    -webkit-box-align: center;
+        -ms-flex-align: center;
+            align-items: center;
+    -webkit-box-pack: end;
+        -ms-flex-pack: end;
+            justify-content: flex-end;
+  }
+}
+.header-holder .main-menu ul {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  margin: 0;
+}
+.header-holder .main-menu ul li {
+  display: inline-block;
+  margin-right: 40px;
+  position: relative;
+}
+.header-holder .main-menu ul li.active:after {
+  content: "•";
+  bottom: -24px;
+  color: #e44c2c;
+  font-size: 1.375rem;
+  left: 0;
+  position: absolute;
+  right: 0;
+  text-align: center;
+}
+.header-holder .main-menu ul li.active a {
+  color: #e44c2c;
+}
+.header-holder .main-menu ul li.docs-active:after {
+  content: "•";
+  bottom: -24px;
+  color: #e44c2c;
+  font-size: 1.375rem;
+  left: -24px;
+  position: absolute;
+  right: 0;
+  text-align: center;
+}
+.header-holder .main-menu ul li:last-of-type {
+  margin-right: 0;
+}
+.header-holder .main-menu ul li a {
+  color: #ffffff;
+  font-size: 1.3rem;
+  letter-spacing: 0;
+  line-height: 2.125rem;
+  text-align: center;
+  text-decoration: none;
+}
+@media screen and (min-width: 1100px) {
+  .header-holder .main-menu ul li a:hover {
+    color: #e44c2c;
+  }
+}
+
+.mobile-main-menu {
+  display: none;
+}
+.mobile-main-menu.open {
+  background-color: #262626;
+  display: block;
+  height: 100%;
+  left: 0;
+  margin-left: auto;
+  margin-right: auto;
+  min-height: 100%;
+  position: fixed;
+  right: 0;
+  top: 0;
+  width: 100%;
+  z-index: 99999;
+}
+
+.mobile-main-menu .container-fluid {
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  height: 68px;
+  position: relative;
+}
+.mobile-main-menu .container-fluid:before, .mobile-main-menu .container-fluid:after {
+  content: "";
+  display: table;
+}
+.mobile-main-menu .container-fluid:after {
+  clear: both;
+}
+.mobile-main-menu .container-fluid {
+  *zoom: 1;
+}
+
+.mobile-main-menu.open ul {
+  list-style-type: none;
+  padding: 0;
+}
+.mobile-main-menu.open ul li a, .mobile-main-menu.open .resources-mobile-menu-title {
+  font-size: 2rem;
+  color: #ffffff;
+  letter-spacing: 0;
+  line-height: 4rem;
+  text-decoration: none;
+}
+.mobile-main-menu.open ul li.active a {
+  color: #e44c2c;
+}
+
+.main-menu-close-button {
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+  background-position: center center;
+  background-repeat: no-repeat;
+  background-size: 24px 24px;
+  height: 24px;
+  position: absolute;
+  right: 0;
+  width: 24px;
+  top: -4px;
+}
+
+.mobile-main-menu-header-container {
+  position: relative;
+}
+
+.mobile-main-menu-links-container {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  padding-left: 2.8125rem;
+  height: 90vh;
+  margin-top: -25px;
+  padding-top: 50%;
+  overflow-y: scroll;
+}
+.mobile-main-menu-links-container .main-menu {
+  height: 100vh;
+}
+
+.mobile-main-menu-links-container ul.resources-mobile-menu-items li {
+  padding-left: 15px;
+}
+
+.site-footer {
+  padding: 2.5rem 0;
+  width: 100%;
+  background: #000000;
+  background-size: 100%;
+  margin-left: 0;
+  margin-right: 0;
+  position: relative;
+  z-index: 201;
+}
+@media screen and (min-width: 768px) {
+  .site-footer {
+    padding: 5rem 0;
+  }
+}
+
+.site-footer p {
+  color: #ffffff;
+}
+.site-footer ul {
+  list-style-type: none;
+  padding-left: 0;
+  margin-bottom: 0;
+}
+
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+  font-size: 1.125rem;
+  line-height: 2rem;
+  color: #A0A0A1;
+  padding-bottom: 0.375rem;
+}
+.site-footer ul li.list-title {
+  padding-bottom: 0.75rem;
+  color: #ffffff;
+}
+.site-footer a:link,
+.site-footer a:visited {
+  color: inherit;
+}
+@media screen and (min-width: 768px) {
+  .site-footer a:hover {
+    color: #e44c2c;
+  }
+}
+
+.site-footer .privacy-policy {
+    background: #000000;
+    display: flex;
+    border-bottom: 1px solid white;
+    padding-bottom: 10px;
+}
+
+.site-footer .privacy-policy-links {
+    background: #000000;
+    display: flex; 
+    padding-top: 1rem;
+    padding-right: 1rem;
+    display: inline-flex;
+    color: white;
+}
+
+.site-footer .footer-links-wrapper {
+    display: flex;
+    flex-wrap: wrap;
+    border-bottom: 1px solid white;
+    padding-bottom: 1rem;
+}
+
+.site-footer .copyright {
+    padding-top: 1rem;
+    padding-right: 1rem;
+    display: inline-flex;
+    color: white;
+}
+
+.site-footer .copyright p {
+    color: white;
+}
+
+.site-footer .copyright a {
+    color: red;
+}
+
+.docs-tutorials-resources {
+  background-color: #262626;
+  color: #ffffff;
+  padding-top: 2.5rem;
+  padding-bottom: 2.5rem;
+  position: relative;
+  z-index: 201;
+}
+@media screen and (min-width: 768px) {
+  .docs-tutorials-resources {
+    padding-top: 5rem;
+    padding-bottom: 5rem;
+  }
+}
+.docs-tutorials-resources p {
+  color: #929292;
+  font-size: 1.125rem;
+}
+.docs-tutorials-resources h2 {
+  font-size: 1.5rem;
+  letter-spacing: -0.25px;
+  text-transform: none;
+  margin-bottom: 0.25rem;
+}
+@media screen and (min-width: 768px) {
+  .docs-tutorials-resources h2 {
+    margin-bottom: 1.25rem;
+  }
+}
+.docs-tutorials-resources .col-md-4 {
+  margin-bottom: 2rem;
+  text-align: center;
+}
+@media screen and (min-width: 768px) {
+  .docs-tutorials-resources .col-md-4 {
+    margin-bottom: 0;
+  }
+}
+.docs-tutorials-resources .with-right-arrow {
+  margin-left: 12px;
+}
+.docs-tutorials-resources .with-right-arrow:hover {
+  background-image: url("../images/chevron-right-white.svg");
+}
+.docs-tutorials-resources p {
+  font-size: 1rem;
+  line-height: 1.5rem;
+  letter-spacing: 0.22px;
+  color: #939393;
+  margin-bottom: 0;
+}
+@media screen and (min-width: 768px) {
+  .docs-tutorials-resources p {
+    margin-bottom: 1.25rem;
+  }
+}
+.docs-tutorials-resources a {
+  font-size: 1.125rem;
+  color: #e44c2c;
+}
+.docs-tutorials-resources a:hover {
+  color: #ffffff;
+}
+
+.footer-container {
+  position: relative;
+}
+
+@media screen and (min-width: 768px) {
+  .footer-logo-wrapper {
+    position: absolute;
+    top: 0;
+    left: 30px;
+  }
+}
+
+.footer-logo {
+  background-image: url("../images/logo-icon.svg");
+  background-position: center;
+  background-repeat: no-repeat;
+  background-size: 20px 24px;
+  display: block;
+  height: 24px;
+  margin-bottom: 2.8125rem;
+  width: 20px;
+}
+@media screen and (min-width: 768px) {
+  .footer-logo {
+    background-size: 29px 36px;
+    height: 36px;
+    margin-bottom: 0;
+    margin-bottom: 0;
+    width: 29px;
+  }
+}
+
+.footer-links-wrapper {
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -ms-flex-wrap: wrap;
+      flex-wrap: wrap;
+}
+@media screen and (min-width: 768px) {
+  .footer-links-wrapper {
+    -ms-flex-wrap: initial;
+        flex-wrap: initial;
+    -webkit-box-pack: end;
+        -ms-flex-pack: end;
+            justify-content: flex-end;
+  }
+}
+
+.footer-links-col {
+  margin-bottom: 3.75rem;
+  width: 50%;
+}
+@media screen and (min-width: 768px) {
+  .footer-links-col {
+    margin-bottom: 0;
+    width: 14%;
+    margin-right: 23px;
+  }
+  .footer-links-col.follow-us-col {
+    width: 18%;
+    margin-right: 0;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  .footer-links-col {
+    width: 18%;
+    margin-right: 30px;
+  }
+}
+
+.footer-social-icons {
+  margin: 8.5625rem 0 2.5rem 0;
+}
+.footer-social-icons a {
+  height: 32px;
+  width: 32px;
+  display: inline-block;
+  background-color: #CCCDD1;
+  border-radius: 50%;
+  margin-right: 5px;
+}
+.footer-social-icons a.facebook {
+  background-image: url("../images/logo-facebook-dark.svg");
+  background-position: center center;
+  background-size: 9px 18px;
+  background-repeat: no-repeat;
+}
+.footer-social-icons a.twitter {
+  background-image: url("../images/logo-twitter-dark.svg");
+  background-position: center center;
+  background-size: 17px 17px;
+  background-repeat: no-repeat;
+}
+.footer-social-icons a.youtube {
+  background-image: url("../images/logo-youtube-dark.svg");
+  background-position: center center;
+  background-repeat: no-repeat;
+}
+
+.site-footer .mc-field-group {
+  margin-top: -2px;
+}
+
+article.pytorch-article {
+  max-width: 920px;
+  margin: 0 auto;
+}
+article.pytorch-article h2,
+article.pytorch-article h3,
+article.pytorch-article h4,
+article.pytorch-article h5,
+article.pytorch-article h6 {
+  margin: 1.375rem 0;
+  color: #262626;
+}
+article.pytorch-article h2 {
+  font-size: 1.625rem;
+  letter-spacing: 1.33px;
+  line-height: 2rem;
+  text-transform: none;
+}
+article.pytorch-article h3 {
+  font-size: 1.5rem;
+  letter-spacing: -0.25px;
+  line-height: 1.875rem;
+  text-transform: none;
+}
+article.pytorch-article h4,
+article.pytorch-article h5,
+article.pytorch-article h6 {
+  font-size: 1.125rem;
+  letter-spacing: -0.19px;
+  line-height: 1.875rem;
+}
+article.pytorch-article p {
+  margin-bottom: 1.125rem;
+}
+article.pytorch-article p,
+article.pytorch-article ul li,
+article.pytorch-article ol li,
+article.pytorch-article dl dt,
+article.pytorch-article dl dd,
+article.pytorch-article blockquote {
+  font-size: 1rem;
+  line-height: 1.375rem;
+  color: #262626;
+  letter-spacing: 0.01px;
+  font-weight: 500;
+}
+article.pytorch-article table {
+  margin-bottom: 2.5rem;
+  width: 100%;
+}
+article.pytorch-article table thead {
+  border-bottom: 1px solid #cacaca;
+}
+article.pytorch-article table th {
+  padding: 0.625rem;
+  color: #262626;
+}
+article.pytorch-article table td {
+  padding: 0.3125rem;
+}
+article.pytorch-article table tr th:first-of-type,
+article.pytorch-article table tr td:first-of-type {
+  padding-left: 0;
+}
+article.pytorch-article table.docutils.field-list th.field-name {
+  padding: 0.3125rem;
+  padding-left: 0;
+}
+article.pytorch-article table.docutils.field-list td.field-body {
+  padding: 0.3125rem;
+}
+article.pytorch-article table.docutils.field-list td.field-body p:last-of-type {
+  margin-bottom: 0;
+}
+article.pytorch-article ul,
+article.pytorch-article ol {
+  margin: 1.5rem 0 3.125rem 0;
+}
+@media screen and (min-width: 768px) {
+  article.pytorch-article ul,
+  article.pytorch-article ol {
+    padding-left: 6.25rem;
+  }
+}
+article.pytorch-article ul li,
+article.pytorch-article ol li {
+  margin-bottom: 0.625rem;
+}
+article.pytorch-article dl {
+  margin-bottom: 1.5rem;
+}
+article.pytorch-article dl dt {
+  margin-bottom: 0.75rem;
+}
+article.pytorch-article pre {
+  margin-bottom: 2.5rem;
+}
+article.pytorch-article hr {
+  margin-top: 4.6875rem;
+  margin-bottom: 4.6875rem;
+}
+article.pytorch-article blockquote {
+  margin: 0 auto;
+  margin-bottom: 2.5rem;
+  width: 65%;
+}
+article.pytorch-article img {
+  width: 100%;
+}
+
+html {
+  height: 100%;
+}
+@media screen and (min-width: 768px) {
+  html {
+    font-size: 16px;
+  }
+}
+
+body {
+  background: #ffffff;
+  height: 100%;
+  margin: 0;
+}
+body.no-scroll {
+  height: 100%;
+  overflow: hidden;
+}
+
+p {
+  margin-top: 0;
+  margin-bottom: 1.125rem;
+}
+p a:link,
+p a:visited,
+p a:hover {
+  color: #e44c2c;
+  text-decoration: none;
+}
+@media screen and (min-width: 768px) {
+  p a:hover {
+    text-decoration: underline;
+  }
+}
+p a:link,
+p a:visited,
+p a:hover {
+  color: #ee4c2c;
+}
+
+.wy-breadcrumbs li a {
+  color: #ee4c2c;
+}
+
+ul.pytorch-breadcrumbs {
+  padding-left: 0;
+  list-style-type: none;
+}
+ul.pytorch-breadcrumbs li {
+  display: inline-block;
+  font-size: 0.875rem;
+}
+ul.pytorch-breadcrumbs a {
+  color: #ee4c2c;
+  text-decoration: none;
+}
+
+.table-of-contents-link-wrapper {
+  display: block;
+  margin-top: 0;
+  padding: 1.25rem 1.875rem;
+  background-color: #f3f4f7;
+  position: relative;
+  color: #262626;
+  font-size: 1.25rem;
+}
+.table-of-contents-link-wrapper.is-open .toggle-table-of-contents {
+  -webkit-transform: rotate(180deg);
+          transform: rotate(180deg);
+}
+@media screen and (min-width: 1100px) {
+  .table-of-contents-link-wrapper {
+    display: none;
+  }
+}
+
+.toggle-table-of-contents {
+  background-image: url("../images/chevron-down-grey.svg");
+  background-position: center center;
+  background-repeat: no-repeat;
+  background-size: 18px 18px;
+  height: 100%;
+  position: absolute;
+  right: 21px;
+  width: 30px;
+  top: 0;
+}
+
+.tutorials-header .header-logo {
+  background-image: url("../images/logo-dark.svg");
+}
+.tutorials-header .main-menu ul li a {
+  color: #262626;
+}
+.tutorials-header .main-menu-open-button {
+  background-image: url("../images/icon-menu-dots-dark.svg");
+}
+
+.rst-content footer .rating-hr.hr-top {
+  margin-bottom: -0.0625rem;
+}
+.rst-content footer .rating-hr.hr-bottom {
+  margin-top: -0.0625rem;
+}
+.rst-content footer .rating-container {
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+  font-size: 1.125rem;
+}
+.rst-content footer .rating-container .rating-prompt, .rst-content footer .rating-container .was-helpful-thank-you {
+  padding: 0.625rem 1.25rem 0.625rem 1.25rem;
+}
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+  display: none;
+}
+.rst-content footer .rating-container .rating-prompt.yes-link, .rst-content footer .rating-container .rating-prompt.no-link {
+  color: #e44c2c;
+  cursor: pointer;
+}
+.rst-content footer .rating-container .rating-prompt.yes-link:hover, .rst-content footer .rating-container .rating-prompt.no-link:hover {
+  background-color: #e44c2c;
+  color: #ffffff;
+}
+.rst-content footer .rating-container .stars-outer {
+  display: inline-block;
+  position: relative;
+  font-family: FontAwesome;
+  padding: 0.625rem 1.25rem 0.625rem 1.25rem;
+}
+.rst-content footer .rating-container .stars-outer i {
+  cursor: pointer;
+}
+.rst-content footer .rating-container .stars-outer .star-fill {
+  color: #ee4c2c;
+}
+.rst-content footer div[role="contentinfo"] {
+  padding-top: 2.5rem;
+}
+.rst-content footer div[role="contentinfo"] p {
+  margin-bottom: 0;
+}
+
+h1 {
+  font-size: 2rem;
+  letter-spacing: 1.78px;
+  line-height: 2.5rem;
+  margin: 1.375rem 0;
+}
+
+span.pre {
+  color: #6c6c6d;
+  background-color: #f3f4f7;
+  padding: 2px 0px;
+}
+
+pre {
+  padding: 1.375rem;
+}
+
+.highlight .c1 {
+  color: #6c6c6d;
+}
+
+.headerlink {
+  display: none !important;
+}
+
+a:link.has-code,
+a:hover.has-code,
+a:visited.has-code {
+  color: #4974D1;
+}
+a:link.has-code span,
+a:hover.has-code span,
+a:visited.has-code span {
+  color: #4974D1;
+}
+
+article.pytorch-article ul,
+article.pytorch-article ol {
+  padding-left: 1.875rem;
+  margin: 0;
+}
+article.pytorch-article ul li,
+article.pytorch-article ol li {
+  margin: 0;
+  line-height: 1.75rem;
+}
+article.pytorch-article ul p,
+article.pytorch-article ol p {
+  line-height: 1.75rem;
+  margin-bottom: 0;
+}
+article.pytorch-article ul ul,
+article.pytorch-article ul ol,
+article.pytorch-article ol ul,
+article.pytorch-article ol ol {
+  margin: 0;
+}
+article.pytorch-article h1 {
+  font-weight: 600;
+}
+article.pytorch-article h2,
+article.pytorch-article h3,
+article.pytorch-article h4,
+article.pytorch-article h5,
+article.pytorch-article h6 {
+  font-weight: normal;
+}
+article.pytorch-article h1 a,
+article.pytorch-article h2 a,
+article.pytorch-article h3 a,
+article.pytorch-article h4 a,
+article.pytorch-article h5 a,
+article.pytorch-article h6 a {
+  color: #262626;
+}
+article.pytorch-article p.caption {
+  margin-top: 1.25rem;
+}
+
+article.pytorch-article .section:first-of-type h1:first-of-type {
+  margin-top: 0;
+}
+
+article.pytorch-article .sphx-glr-thumbcontainer {
+  margin: 0;
+  border: 1px solid #d6d7d8;
+  border-radius: 0;
+  width: 45%;
+  text-align: center;
+  margin-bottom: 5%;
+}
+@media screen and (max-width: 1100px) {
+  article.pytorch-article .sphx-glr-thumbcontainer:nth-child(odd) {
+    margin-left: 0;
+    margin-right: 2.5%;
+  }
+  article.pytorch-article .sphx-glr-thumbcontainer:nth-child(even) {
+    margin-right: 0;
+    margin-left: 2.5%;
+  }
+  article.pytorch-article .sphx-glr-thumbcontainer .figure {
+    width: 40%;
+  }
+}
+@media screen and (min-width: 1101px) {
+  article.pytorch-article .sphx-glr-thumbcontainer {
+    margin-right: 3%;
+    margin-bottom: 3%;
+    width: 30%;
+  }
+}
+article.pytorch-article .sphx-glr-thumbcontainer .caption-text a {
+  font-size: 1rem;
+  color: #262626;
+  letter-spacing: 0;
+  line-height: 1.5rem;
+  text-decoration: none;
+}
+article.pytorch-article .sphx-glr-thumbcontainer:hover {
+  -webkit-box-shadow: none;
+          box-shadow: none;
+  border-bottom-color: #ffffff;
+}
+article.pytorch-article .sphx-glr-thumbcontainer:hover .figure:before {
+  bottom: 100%;
+}
+article.pytorch-article .sphx-glr-thumbcontainer .figure {
+  width: 80%;
+}
+article.pytorch-article .sphx-glr-thumbcontainer .figure:before {
+  content: "";
+  display: block;
+  position: absolute;
+  top: 0;
+  bottom: 35%;
+  left: 0;
+  right: 0;
+  background: #8A94B3;
+  opacity: 0.10;
+}
+article.pytorch-article .sphx-glr-thumbcontainer .figure a.reference.internal {
+  text-align: left;
+}
+@media screen and (min-width: 768px) {
+  article.pytorch-article .sphx-glr-thumbcontainer:after {
+    content: "";
+    display: block;
+    width: 0;
+    height: 1px;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    background-color: #e44c2c;
+    -webkit-transition: width .250s ease-in-out;
+    transition: width .250s ease-in-out;
+  }
+  article.pytorch-article .sphx-glr-thumbcontainer:hover:after {
+    width: 100%;
+  }
+}
+@media screen and (min-width: 768px) {
+  article.pytorch-article .sphx-glr-thumbcontainer:after {
+    background-color: #ee4c2c;
+  }
+}
+
+article.pytorch-article .section :not(dt) > code {
+  color: #262626;
+  border-top: solid 2px #ffffff;
+  background-color: #ffffff;
+  border-bottom: solid 2px #ffffff;
+  padding: 0px 3px;
+  -webkit-box-decoration-break: clone;
+  box-decoration-break: clone;
+}
+article.pytorch-article .section :not(dt) > code .pre {
+  outline: 0px;
+  padding: 0px;
+}
+article.pytorch-article .function dt, article.pytorch-article .attribute dt, article.pytorch-article .class .attribute dt, article.pytorch-article .class dt {
+  position: relative;
+  background: #f3f4f7;
+  padding: 0.5rem;
+  border-left: 3px solid #ee4c2c;
+  word-wrap: break-word;
+  padding-right: 100px;
+}
+
+article.pytorch-article .class dt.field-odd, article.pytorch-article .class dt.field-even,
+article.pytorch-article .function dt.field-odd, article.pytorch-article .function dt.field-even,
+article.pytorch-article .attribute dt.field-odd, article.pytorch-article .attribute dt.field-even,
+article.pytorch-article .class .attribute dt.field-odd,article.pytorch-article .class .attribute dt.field-even,
+article.pytorch-article .class .method dt.field-odd, article.pytorch-article .class .staticmethod dt.field-odd,
+article.pytorch-article .class .method dt.field-even, article.pytorch-article .class .staticmethod dt.field-even
+{
+  background: none;
+  padding-right: -20px;
+  border-top: none;
+  border-left: none;
+  padding-left: 0.0rem;
+  padding-top: 0.0rem;
+  padding-bottom: 0.0rem;
+  font-weight: 700;
+}
+
+article.pytorch-article .function dt.sig {
+  position: relative;
+  background: #f3f4f7;
+  padding: 0.8rem;
+  border-left: 3px solid #ee4c2c;
+  word-wrap: break-word;
+  padding-right: 100px;
+  font-weight: 500;
+}
+
+article.pytorch-article .function dt {
+  background: #ffffff;
+  padding-right: -20px;
+  border-left: none;
+  border-top: none;
+  padding-left: 0.0rem;
+  padding-top: 0.0rem;
+  padding-bottom: 0.0rem;
+  font-weight: 700;
+}
+
+article.pytorch-article .function dt em.property, article.pytorch-article .attribute dt em.property, article.pytorch-article .class dt em.property {
+  font-family: inherit;
+}
+article.pytorch-article .function dt em, article.pytorch-article .attribute dt em, article.pytorch-article .class .attribute dt em, article.pytorch-article .class dt em, article.pytorch-article .function dt .sig-paren, article.pytorch-article .attribute dt .sig-paren, article.pytorch-article .class dt .sig-paren {
+  font-family: IBMPlexMono,SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier New",monospace;
+  font-size: 87.5%;
+}
+article.pytorch-article .function dt a, article.pytorch-article .attribute dt a, article.pytorch-article .class .attribute dt a, article.pytorch-article .class dt a {
+  right: 30px;
+  padding-right: 0;
+  top: 50%;
+  -webkit-transform: perspective(1px) translateY(-50%);
+          transform: perspective(1px) translateY(-50%);
+}
+article.pytorch-article .function dt:hover .viewcode-link, article.pytorch-article .attribute dt:hover .viewcode-link, article.pytorch-article .class dt:hover .viewcode-link {
+  color: #ee4c2c;
+}
+article.pytorch-article .function .anchorjs-link, article.pytorch-article .attribute .anchorjs-link, article.pytorch-article .class .anchorjs-link {
+  display: inline;
+  position: absolute;
+  right: 8px;
+  font-size: 1.5625rem !important;
+  padding-left: 0;
+}
+article.pytorch-article .function dt > code, article.pytorch-article .attribute dt > code, article.pytorch-article .class .attribute dt > code, article.pytorch-article .class dt > code {
+  color: #262626;
+  border-top: solid 2px #f3f4f7;
+  background-color: #f3f4f7;
+  border-bottom: solid 2px #f3f4f7;
+  -webkit-box-decoration-break: clone;
+  box-decoration-break: clone;
+}
+article.pytorch-article .function .viewcode-link, article.pytorch-article .attribute .viewcode-link, article.pytorch-article .class .viewcode-link {
+  padding-left: 0.6rem;
+  position: absolute;
+  font-size: 0.875rem;
+  color: #979797;
+  letter-spacing: 0;
+  line-height: 1.5rem;
+  text-transform: uppercase;
+}
+article.pytorch-article .function dd, article.pytorch-article .attribute dd, article.pytorch-article .class .attribute dd, article.pytorch-article .class dd {
+  padding-left: 3.75rem;
+}
+article.pytorch-article .function dd p, article.pytorch-article .attribute dd p, article.pytorch-article .class .attribute dd p, article.pytorch-article .class dd p {
+  color: #262626;
+}
+article.pytorch-article .function table tbody tr th.field-name, article.pytorch-article .attribute table tbody tr th.field-name, article.pytorch-article .class table tbody tr th.field-name {
+  white-space: nowrap;
+  color: #262626;
+  width: 20%;
+}
+@media screen and (min-width: 768px) {
+  article.pytorch-article .function table tbody tr th.field-name, article.pytorch-article .attribute table tbody tr th.field-name, article.pytorch-article .class table tbody tr th.field-name {
+    width: 15%;
+  }
+}
+article.pytorch-article .function table tbody tr td.field-body, article.pytorch-article .attribute table tbody tr td.field-body, article.pytorch-article .class table tbody tr td.field-body {
+  padding: 0.625rem;
+  width: 80%;
+  color: #262626;
+}
+@media screen and (min-width: 768px) {
+  article.pytorch-article .function table tbody tr td.field-body, article.pytorch-article .attribute table tbody tr td.field-body, article.pytorch-article .class table tbody tr td.field-body {
+    width: 85%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  article.pytorch-article .function table tbody tr td.field-body, article.pytorch-article .attribute table tbody tr td.field-body, article.pytorch-article .class table tbody tr td.field-body {
+    padding-left: 1.25rem;
+  }
+}
+article.pytorch-article .function table tbody tr td.field-body p, article.pytorch-article .attribute table tbody tr td.field-body p, article.pytorch-article .class table tbody tr td.field-body p {
+  padding-left: 0px;
+}
+article.pytorch-article .function table tbody tr td.field-body p:last-of-type, article.pytorch-article .attribute table tbody tr td.field-body p:last-of-type, article.pytorch-article .class table tbody tr td.field-body p:last-of-type {
+  margin-bottom: 0;
+}
+article.pytorch-article .function table tbody tr td.field-body ol, article.pytorch-article .attribute table tbody tr td.field-body ol, article.pytorch-article .class table tbody tr td.field-body ol, article.pytorch-article .function table tbody tr td.field-body ul, article.pytorch-article .attribute table tbody tr td.field-body ul, article.pytorch-article .class table tbody tr td.field-body ul {
+  padding-left: 1rem;
+  padding-bottom: 0;
+}
+article.pytorch-article .function table.docutils.field-list, article.pytorch-article .attribute table.docutils.field-list, article.pytorch-article .class table.docutils.field-list {
+  margin-bottom: 0.75rem;
+}
+article.pytorch-article .attribute .has-code {
+  float: none;
+}
+article.pytorch-article .class dt {
+  border-left: none;
+  border-top: 3px solid #ee4c2c;
+  padding-left: 4em;
+}
+article.pytorch-article .class dt em.property {
+  position: absolute;
+  left: 0.5rem;
+}
+article.pytorch-article .class dd .docutils dt {
+  padding-left: 0.5rem;
+}
+article.pytorch-article .class em.property {
+  text-transform: uppercase;
+  font-style: normal;
+  color: #ee4c2c;
+  font-size: 1rem;
+  letter-spacing: 0;
+  padding-right: 0.75rem;
+}
+article.pytorch-article .class dl dt em.property {
+  position: static;
+  left: 0;
+  padding-right: 0;
+}
+
+article.pytorch-article .class .method dt,
+article.pytorch-article .class .staticmethod dt {
+  border-left: 3px solid #ee4c2c;
+  border-top: none;
+}
+
+article.pytorch-article .class .method dt,
+article.pytorch-article .class .staticmethod dt {
+  padding-left: 0.5rem;
+}
+article.pytorch-article .class .attribute dt {
+  border-top: none;
+}
+article.pytorch-article .class .attribute dt em.property {
+  position: relative;
+  left: 0;
+}
+article.pytorch-article table {
+  table-layout: fixed;
+}
+
+div.deprecated p {
+  display: inline;
+}
+div.deprecated,
+div.versionchanged {
+  margin-top: 0.5rem;
+  padding: 0.5rem;
+  margin-bottom: 0.5rem;
+  border: none;
+}
+div.versionadded {
+  margin: 1rem 0;
+}
+div.deprecated p:last-child,
+div.versionchanged p:last-child,
+div.versionadded p:last-child {
+  margin-bottom: 0
+}
+div.deprecated {
+  color: #b94a48;
+  background-color: #fdede9;
+}
+div.versionchanged {
+  background-color: #fffbe8;
+}
+
+article.pytorch-article .note,
+article.pytorch-article .warning,
+article.pytorch-article .tip,
+article.pytorch-article .seealso,
+article.pytorch-article .hint,
+article.pytorch-article .important,
+article.pytorch-article .caution,
+article.pytorch-article .danger,
+article.pytorch-article .attention,
+article.pytorch-article .error {
+  background: #f3f4f7;
+  margin-top: 1.875rem;
+  margin-bottom: 1.125rem;
+}
+article.pytorch-article .note .admonition-title,
+article.pytorch-article .warning .admonition-title,
+article.pytorch-article .tip .admonition-title,
+article.pytorch-article .seealso .admonition-title,
+article.pytorch-article .hint .admonition-title,
+article.pytorch-article .important .admonition-title,
+article.pytorch-article .caution .admonition-title,
+article.pytorch-article .danger .admonition-title,
+article.pytorch-article .attention .admonition-title,
+article.pytorch-article .error .admonition-title {
+  color: #ffffff;
+  letter-spacing: 1px;
+  text-transform: uppercase;
+  margin-bottom: 1.125rem;
+  padding: 3px 0 3px 1.375rem;
+  position: relative;
+  font-size: 0.875rem;
+}
+article.pytorch-article .note .admonition-title:before,
+article.pytorch-article .warning .admonition-title:before,
+article.pytorch-article .tip .admonition-title:before,
+article.pytorch-article .seealso .admonition-title:before,
+article.pytorch-article .hint .admonition-title:before,
+article.pytorch-article .important .admonition-title:before,
+article.pytorch-article .caution .admonition-title:before,
+article.pytorch-article .danger .admonition-title:before,
+article.pytorch-article .attention .admonition-title:before,
+article.pytorch-article .error .admonition-title:before {
+  content: "\2022";
+  position: absolute;
+  left: 9px;
+  color: #ffffff;
+  top: 2px;
+}
+article.pytorch-article .note p:nth-child(n + 2),
+article.pytorch-article .warning p:nth-child(n + 2),
+article.pytorch-article .tip p:nth-child(n + 2),
+article.pytorch-article .seealso p:nth-child(n + 2),
+article.pytorch-article .hint p:nth-child(n + 2),
+article.pytorch-article .important p:nth-child(n + 2),
+article.pytorch-article .caution p:nth-child(n + 2),
+article.pytorch-article .danger p:nth-child(n + 2),
+article.pytorch-article .attention p:nth-child(n + 2),
+article.pytorch-article .error p:nth-child(n + 2) {
+  padding: 0 1.375rem;
+}
+article.pytorch-article .note table,
+article.pytorch-article .warning table,
+article.pytorch-article .tip table,
+article.pytorch-article .seealso table,
+article.pytorch-article .hint table,
+article.pytorch-article .important table,
+article.pytorch-article .caution table,
+article.pytorch-article .danger table,
+article.pytorch-article .attention table,
+article.pytorch-article .error table {
+  margin: 0 2rem;
+  width: auto;
+}
+article.pytorch-article .note :not(dt) > code,
+article.pytorch-article .warning :not(dt) > code,
+article.pytorch-article .tip :not(dt) > code,
+article.pytorch-article .seealso :not(dt) > code,
+article.pytorch-article .hint :not(dt) > code,
+article.pytorch-article .important :not(dt) > code,
+article.pytorch-article .caution :not(dt) > code,
+article.pytorch-article .danger :not(dt) > code,
+article.pytorch-article .attention :not(dt) > code,
+article.pytorch-article .error :not(dt) > code {
+  border-top: solid 2px #ffffff;
+  background-color: #ffffff;
+  border-bottom: solid 2px #ffffff;
+  padding: 0px 3px;
+  -webkit-box-decoration-break: clone;
+  box-decoration-break: clone;
+  outline: 1px solid #e9e9e9;
+}
+article.pytorch-article .note :not(dt) > code .pre,
+article.pytorch-article .warning :not(dt) > code .pre,
+article.pytorch-article .tip :not(dt) > code .pre,
+article.pytorch-article .seealso :not(dt) > code .pre,
+article.pytorch-article .hint :not(dt) > code .pre,
+article.pytorch-article .important :not(dt) > code .pre,
+article.pytorch-article .caution :not(dt) > code .pre,
+article.pytorch-article .danger :not(dt) > code .pre,
+article.pytorch-article .attention :not(dt) > code .pre,
+article.pytorch-article .error :not(dt) > code .pre {
+  outline: 0px;
+  padding: 0px;
+}
+article.pytorch-article .note pre,
+article.pytorch-article .warning pre,
+article.pytorch-article .tip pre,
+article.pytorch-article .seealso pre,
+article.pytorch-article .hint pre,
+article.pytorch-article .important pre,
+article.pytorch-article .caution pre,
+article.pytorch-article .danger pre,
+article.pytorch-article .attention pre,
+article.pytorch-article .error pre {
+  margin-bottom: 0;
+}
+article.pytorch-article .note .highlight,
+article.pytorch-article .warning .highlight,
+article.pytorch-article .tip .highlight,
+article.pytorch-article .seealso .highlight,
+article.pytorch-article .hint .highlight,
+article.pytorch-article .important .highlight,
+article.pytorch-article .caution .highlight,
+article.pytorch-article .danger .highlight,
+article.pytorch-article .attention .highlight,
+article.pytorch-article .error .highlight {
+  margin: 0 2rem 1.125rem 2rem;
+}
+article.pytorch-article .note ul,
+article.pytorch-article .note ol,
+article.pytorch-article .warning ul,
+article.pytorch-article .warning ol,
+article.pytorch-article .tip ul,
+article.pytorch-article .tip ol,
+article.pytorch-article .seealso ul,
+article.pytorch-article .seealso ol,
+article.pytorch-article .hint ul,
+article.pytorch-article .hint ol,
+article.pytorch-article .important ul,
+article.pytorch-article .important ol,
+article.pytorch-article .caution ul,
+article.pytorch-article .caution ol,
+article.pytorch-article .danger ul,
+article.pytorch-article .danger ol,
+article.pytorch-article .attention ul,
+article.pytorch-article .attention ol,
+article.pytorch-article .error ul,
+article.pytorch-article .error ol {
+  padding-left: 3.25rem;
+}
+article.pytorch-article .note ul li,
+article.pytorch-article .note ol li,
+article.pytorch-article .warning ul li,
+article.pytorch-article .warning ol li,
+article.pytorch-article .tip ul li,
+article.pytorch-article .tip ol li,
+article.pytorch-article .seealso ul li,
+article.pytorch-article .seealso ol li,
+article.pytorch-article .hint ul li,
+article.pytorch-article .hint ol li,
+article.pytorch-article .important ul li,
+article.pytorch-article .important ol li,
+article.pytorch-article .caution ul li,
+article.pytorch-article .caution ol li,
+article.pytorch-article .danger ul li,
+article.pytorch-article .danger ol li,
+article.pytorch-article .attention ul li,
+article.pytorch-article .attention ol li,
+article.pytorch-article .error ul li,
+article.pytorch-article .error ol li {
+  color: #262626;
+}
+article.pytorch-article .note p,
+article.pytorch-article .warning p,
+article.pytorch-article .tip p,
+article.pytorch-article .seealso p,
+article.pytorch-article .hint p,
+article.pytorch-article .important p,
+article.pytorch-article .caution p,
+article.pytorch-article .danger p,
+article.pytorch-article .attention p,
+article.pytorch-article .error p {
+  margin-top: 1.125rem;
+}
+article.pytorch-article .note .admonition-title {
+  background: #54c7ec;
+}
+article.pytorch-article .warning .admonition-title {
+  background: #e94f3b;
+}
+article.pytorch-article .tip .admonition-title {
+  background: #6bcebb;
+}
+article.pytorch-article .seealso .admonition-title {
+  background: #6bcebb;
+}
+article.pytorch-article .hint .admonition-title {
+  background: #a2cdde;
+}
+article.pytorch-article .important .admonition-title {
+  background: #5890ff;
+}
+article.pytorch-article .caution .admonition-title {
+  background: #f7923a;
+}
+article.pytorch-article .danger .admonition-title {
+  background: #db2c49;
+}
+article.pytorch-article .attention .admonition-title {
+  background: #f5a623;
+}
+article.pytorch-article .error .admonition-title {
+  background: #cc2f90;
+}
+article.pytorch-article .sphx-glr-download-link-note.admonition.note,
+article.pytorch-article .reference.download.internal, article.pytorch-article .sphx-glr-signature {
+  display: none;
+}
+article.pytorch-article .admonition > p:last-of-type {
+  margin-bottom: 0;
+  padding-bottom: 1.125rem !important;
+}
+
+.pytorch-article div.sphx-glr-download a {
+  background-color: #f3f4f7;
+  background-image: url("../images/arrow-down-orange.svg");
+  background-repeat: no-repeat;
+  background-position: left 10px center;
+  background-size: 15px 15px;
+  border-radius: 0;
+  border: none;
+  display: block;
+  text-align: left;
+  padding: 0.9375rem 3.125rem;
+  position: relative;
+  margin: 1.25rem auto;
+}
+@media screen and (min-width: 768px) {
+  .pytorch-article div.sphx-glr-download a:after {
+    content: "";
+    display: block;
+    width: 0;
+    height: 1px;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    background-color: #e44c2c;
+    -webkit-transition: width .250s ease-in-out;
+    transition: width .250s ease-in-out;
+  }
+  .pytorch-article div.sphx-glr-download a:hover:after {
+    width: 100%;
+  }
+}
+@media screen and (min-width: 768px) {
+  .pytorch-article div.sphx-glr-download a:after {
+    background-color: #ee4c2c;
+  }
+}
+@media screen and (min-width: 768px) {
+  .pytorch-article div.sphx-glr-download a {
+    background-position: left 20px center;
+  }
+}
+.pytorch-article div.sphx-glr-download a:hover {
+  -webkit-box-shadow: none;
+          box-shadow: none;
+  text-decoration: none;
+  background-image: url("../images/arrow-down-orange.svg");
+  background-color: #f3f4f7;
+}
+.pytorch-article div.sphx-glr-download a span.pre {
+  background-color: transparent;
+  font-size: 1.125rem;
+  padding: 0;
+  color: #262626;
+}
+.pytorch-article div.sphx-glr-download a code, .pytorch-article div.sphx-glr-download a kbd, .pytorch-article div.sphx-glr-download a pre, .pytorch-article div.sphx-glr-download a samp, .pytorch-article div.sphx-glr-download a span.pre {
+  font-family: FreightSans, Helvetica Neue, Helvetica, Arial, sans-serif;
+}
+
+.pytorch-article p.sphx-glr-script-out {
+  margin-bottom: 1.125rem;
+}
+
+.pytorch-article div.sphx-glr-script-out {
+  margin-bottom: 2.5rem;
+}
+.pytorch-article div.sphx-glr-script-out .highlight {
+  margin-left: 0;
+  margin-top: 0;
+}
+.pytorch-article div.sphx-glr-script-out .highlight pre {
+  background-color: #fdede9;
+  padding: 1.5625rem;
+  color: #837b79;
+}
+.pytorch-article div.sphx-glr-script-out + p {
+  margin-top: unset;
+}
+
+article.pytorch-article .wy-table-responsive table {
+  border: none;
+  border-color: #ffffff !important;
+  table-layout: fixed;
+}
+article.pytorch-article .wy-table-responsive table thead tr {
+  border-bottom: 2px solid #6c6c6d;
+}
+article.pytorch-article .wy-table-responsive table thead th {
+  line-height: 1.75rem;
+  padding-left: 0.9375rem;
+  padding-right: 0.9375rem;
+}
+article.pytorch-article .wy-table-responsive table tbody .row-odd {
+  background-color: #f3f4f7;
+}
+article.pytorch-article .wy-table-responsive table tbody td {
+  color: #6c6c6d;
+  white-space: normal;
+  padding: 0.9375rem;
+  font-size: 1rem;
+  line-height: 1.375rem;
+}
+article.pytorch-article .wy-table-responsive table tbody td .pre {
+  background: #ffffff;
+  color: #ee4c2c;
+  font-size: 87.5%;
+}
+article.pytorch-article .wy-table-responsive table tbody td code {
+  font-size: 87.5%;
+}
+
+a[rel~="prev"], a[rel~="next"] {
+  padding: 0.375rem 0 0 0;
+}
+
+img.next-page,
+img.previous-page {
+  width: 8px;
+  height: 10px;
+  position: relative;
+  top: -1px;
+}
+
+img.previous-page {
+  -webkit-transform: scaleX(-1);
+          transform: scaleX(-1);
+}
+
+.rst-footer-buttons {
+  margin-top: 1.875rem;
+  margin-bottom: 1.875rem;
+}
+.rst-footer-buttons .btn:focus,
+.rst-footer-buttons .btn.focus {
+  -webkit-box-shadow: none;
+          box-shadow: none;
+}
+
+article.pytorch-article blockquote {
+  margin-left: 3.75rem;
+  color: #6c6c6d;
+}
+
+article.pytorch-article .caption {
+  color: #6c6c6d;
+  letter-spacing: 0.25px;
+  line-height: 2.125rem;
+}
+
+article.pytorch-article .math {
+  color: #262626;
+  width: auto;
+  text-align: center;
+}
+article.pytorch-article .math img {
+  width: auto;
+}
+
+.pytorch-breadcrumbs-wrapper {
+  width: 100%;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-breadcrumbs-wrapper {
+    float: left;
+    margin-left: 3%;
+    width: 75%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-breadcrumbs-wrapper {
+    width: 850px;
+    margin-left: 1.875rem;
+  }
+}
+.pytorch-breadcrumbs-wrapper .pytorch-breadcrumbs-aside {
+  float: right;
+}
+.pytorch-breadcrumbs-wrapper .pytorch-breadcrumbs-aside .fa.fa-github {
+  margin-top: 5px;
+  display: block;
+}
+
+.pytorch-article .container {
+  padding-left: 0;
+  padding-right: 0;
+  max-width: none;
+}
+
+a:link,
+a:visited,
+a:hover {
+  color: #ee4c2c;
+}
+
+::-webkit-input-placeholder {
+  color: #ee4c2c;
+}
+
+::-moz-placeholder {
+  color: #ee4c2c;
+}
+
+:-ms-input-placeholder {
+  color: #ee4c2c;
+}
+
+:-moz-placeholder {
+  color: #ee4c2c;
+}
+
+@media screen and (min-width: 768px) {
+  .site-footer a:hover {
+    color: #ee4c2c;
+  }
+}
+
+.docs-tutorials-resources a {
+  color: #ee4c2c;
+}
+
+.header-holder {
+  position: relative;
+  z-index: 201;
+}
+
+.header-holder .main-menu ul li.active:after {
+  color: #ee4c2c;
+}
+.header-holder .main-menu ul li.active a {
+  color: #ee4c2c;
+}
+@media screen and (min-width: 1100px) {
+  .header-holder .main-menu ul li a:hover {
+    color: #ee4c2c;
+  }
+}
+
+.mobile-main-menu.open ul li.active a {
+  color: #ee4c2c;
+}
+
+.version {
+  padding-bottom: 1rem;
+}
+
+.pytorch-call-to-action-links {
+  padding-top: 0;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+}
+@media screen and (min-width: 768px) {
+  .pytorch-call-to-action-links {
+    padding-top: 2.5rem;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  .pytorch-call-to-action-links {
+    padding-top: 0;
+  }
+}
+@media (min-width: 1100px) and (max-width: 1239px) {
+  .pytorch-call-to-action-links {
+    padding-top: 2.5rem;
+  }
+}
+.pytorch-call-to-action-links #tutorial-type {
+  display: none;
+}
+.pytorch-call-to-action-links .call-to-action-img, .pytorch-call-to-action-links .call-to-action-notebook-img {
+  height: 1.375rem;
+  width: 1.375rem;
+  margin-right: 10px;
+}
+.pytorch-call-to-action-links .call-to-action-notebook-img {
+  height: 1rem;
+}
+.pytorch-call-to-action-links a {
+  padding-right: 1.25rem;
+  color: #000000;
+  cursor: pointer;
+}
+.pytorch-call-to-action-links a:hover {
+  color: #e44c2c;
+}
+.pytorch-call-to-action-links a .call-to-action-desktop-view {
+  display: none;
+}
+@media screen and (min-width: 768px) {
+  .pytorch-call-to-action-links a .call-to-action-desktop-view {
+    display: block;
+  }
+}
+.pytorch-call-to-action-links a .call-to-action-mobile-view {
+  display: block;
+}
+@media screen and (min-width: 768px) {
+  .pytorch-call-to-action-links a .call-to-action-mobile-view {
+    display: none;
+  }
+}
+.pytorch-call-to-action-links a #google-colab-link, .pytorch-call-to-action-links a #download-notebook-link,
+.pytorch-call-to-action-links a #github-view-link {
+  padding-bottom: 0.625rem;
+  border-bottom: 1px solid #f3f4f7;
+  padding-right: 2.5rem;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+}
+.pytorch-call-to-action-links a #google-colab-link:hover, .pytorch-call-to-action-links a #download-notebook-link:hover,
+.pytorch-call-to-action-links a #github-view-link:hover {
+  border-bottom-color: #e44c2c;
+  color: #e44c2c;
+}
+
+#tutorial-cards-container #tutorial-cards {
+  width: 100%;
+}
+#tutorial-cards-container .tutorials-nav {
+  padding-left: 0;
+  padding-right: 0;
+  padding-bottom: 0;
+}
+#tutorial-cards-container .tutorials-hr {
+  margin-top: 1rem;
+  margin-bottom: 1rem;
+}
+#tutorial-cards-container .card.tutorials-card {
+  border-radius: 0;
+  border-color: #f3f4f7;
+  height: 98px;
+  margin-bottom: 1.25rem;
+  margin-bottom: 1.875rem;
+  overflow: scroll;
+  background-color: #f3f4f7;
+  cursor: pointer;
+}
+@media screen and (min-width: 1240px) {
+  #tutorial-cards-container .card.tutorials-card {
+    height: 200px;
+    overflow: inherit;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card {
+    height: 200px;
+    overflow: scroll;
+  }
+}
+#tutorial-cards-container .card.tutorials-card .tutorials-image {
+  position: absolute;
+  top: 0px;
+  right: 0px;
+  height: 96px;
+  width: 96px;
+  opacity: 0.5;
+}
+#tutorial-cards-container .card.tutorials-card .tutorials-image img {
+  height: 100%;
+  width: 100%;
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card .tutorials-image {
+    height: 100%;
+    width: 25%;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card .tutorials-image {
+    height: 100%;
+    width: 198px;
+  }
+}
+#tutorial-cards-container .card.tutorials-card .tutorials-image:before {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  bottom: 0;
+  right: 0;
+  z-index: 1;
+  background: #000000;
+  opacity: .075;
+}
+#tutorial-cards-container .card.tutorials-card .card-title-container {
+  width: 70%;
+  display: -webkit-inline-box;
+  display: -ms-inline-flexbox;
+  display: inline-flex;
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card .card-title-container {
+    width: 75%;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card .card-title-container {
+    width: 70%;
+  }
+}
+#tutorial-cards-container .card.tutorials-card .card-title-container h4 {
+  margin-bottom: 1.125rem;
+  margin-top: 0;
+  font-size: 1.5rem;
+}
+#tutorial-cards-container .card.tutorials-card p.card-summary, #tutorial-cards-container .card.tutorials-card p.tags {
+  font-size: 0.9375rem;
+  line-height: 1.5rem;
+  margin-bottom: 0;
+  color: #6c6c6d;
+  font-weight: 400;
+  width: 70%;
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card p.card-summary, #tutorial-cards-container .card.tutorials-card p.tags {
+    width: 75%;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card p.card-summary, #tutorial-cards-container .card.tutorials-card p.tags {
+    width: 70%;
+  }
+}
+#tutorial-cards-container .card.tutorials-card p.tags {
+  margin-top: 30px;
+  text-overflow: ellipsis;
+  white-space: nowrap;
+  overflow: hidden;
+}
+#tutorial-cards-container .card.tutorials-card h4 {
+  color: #262626;
+  margin-bottom: 1.125rem;
+}
+#tutorial-cards-container .card.tutorials-card a {
+  height: 100%;
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card a {
+    min-height: 190px;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  #tutorial-cards-container .card.tutorials-card a {
+    min-height: 234px;
+  }
+}
+@media screen and (min-width: 768px) {
+  #tutorial-cards-container .card.tutorials-card:after {
+    content: "";
+    display: block;
+    width: 0;
+    height: 1px;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    background-color: #e44c2c;
+    -webkit-transition: width .250s ease-in-out;
+    transition: width .250s ease-in-out;
+  }
+  #tutorial-cards-container .card.tutorials-card:hover:after {
+    width: 100%;
+  }
+}
+#tutorial-cards-container .card.tutorials-card:hover {
+  background-color: #ffffff;
+  border: 1px solid #e2e2e2;
+  border-bottom: none;
+}
+#tutorial-cards-container .card.tutorials-card:hover p.card-summary {
+  color: #262626;
+}
+#tutorial-cards-container .card.tutorials-card:hover .tutorials-image {
+  opacity: unset;
+}
+#tutorial-cards-container .tutorial-tags-container {
+  width: 75%;
+}
+#tutorial-cards-container .tutorial-tags-container.active {
+  width: 0;
+}
+#tutorial-cards-container .tutorial-filter-menu ul {
+  list-style-type: none;
+  padding-left: 1.25rem;
+}
+#tutorial-cards-container .tutorial-filter-menu ul li {
+  padding-right: 1.25rem;
+  word-break: break-all;
+}
+#tutorial-cards-container .tutorial-filter-menu ul li a {
+  color: #979797;
+}
+#tutorial-cards-container .tutorial-filter-menu ul li a:hover {
+  color: #e44c2c;
+}
+#tutorial-cards-container .tutorial-filter {
+  cursor: pointer;
+}
+#tutorial-cards-container .filter-btn {
+  color: #979797;
+  border: 1px solid #979797;
+  display: inline-block;
+  text-align: center;
+  white-space: nowrap;
+  vertical-align: middle;
+  padding: 0.375rem 0.75rem;
+  font-size: 1rem;
+  line-height: 1.5;
+  margin-bottom: 5px;
+}
+#tutorial-cards-container .filter-btn:hover {
+  border: 1px solid #e44c2c;
+  color: #e44c2c;
+}
+#tutorial-cards-container .filter-btn.selected {
+  background-color: #e44c2c;
+  border: 1px solid #e44c2c;
+  color: #ffffff;
+}
+#tutorial-cards-container .all-tag-selected {
+  background-color: #979797;
+  color: #ffffff;
+}
+#tutorial-cards-container .all-tag-selected:hover {
+  border-color: #979797;
+  color: #ffffff;
+}
+#tutorial-cards-container .pagination .page {
+  border: 1px solid #dee2e6;
+  padding: 0.5rem 0.75rem;
+}
+#tutorial-cards-container .pagination .active .page {
+  background-color: #dee2e6;
+}
+
+article.pytorch-article .tutorials-callout-container {
+  padding-bottom: 50px;
+}
+article.pytorch-article .tutorials-callout-container .col-md-6 {
+  padding-bottom: 10px;
+}
+article.pytorch-article .tutorials-callout-container .text-container {
+  padding: 10px 0px 30px 0px;
+  padding-bottom: 10px;
+}
+article.pytorch-article .tutorials-callout-container .text-container .body-paragraph {
+  color: #666666;
+  font-weight: 300;
+  font-size: 1.125rem;
+  line-height: 1.875rem;
+}
+article.pytorch-article .tutorials-callout-container .btn.callout-button {
+  font-size: 1.125rem;
+  border-radius: 0;
+  border: none;
+  background-color: #f3f4f7;
+  color: #6c6c6d;
+  font-weight: 400;
+  position: relative;
+  letter-spacing: 0.25px;
+}
+@media screen and (min-width: 768px) {
+  article.pytorch-article .tutorials-callout-container .btn.callout-button:after {
+    content: "";
+    display: block;
+    width: 0;
+    height: 1px;
+    position: absolute;
+    bottom: 0;
+    left: 0;
+    background-color: #e44c2c;
+    -webkit-transition: width .250s ease-in-out;
+    transition: width .250s ease-in-out;
+  }
+  article.pytorch-article .tutorials-callout-container .btn.callout-button:hover:after {
+    width: 100%;
+  }
+}
+article.pytorch-article .tutorials-callout-container .btn.callout-button a {
+  color: inherit;
+}
+
+.pytorch-container {
+  margin: 0 auto;
+  padding: 0 1.875rem;
+  width: auto;
+  position: relative;
+}
+@media screen and (min-width: 1100px) {
+  .pytorch-container {
+    padding: 0;
+  }
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-container {
+    margin-left: 25%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-container {
+    margin-left: 350px;
+  }
+}
+.pytorch-container:before, .pytorch-container:after {
+  content: "";
+  display: table;
+}
+.pytorch-container:after {
+  clear: both;
+}
+.pytorch-container {
+  *zoom: 1;
+}
+
+.pytorch-content-wrap {
+  background-color: #ffffff;
+  display: -webkit-box;
+  display: -ms-flexbox;
+  display: flex;
+  position: relative;
+  padding-top: 0;
+}
+.pytorch-content-wrap:before, .pytorch-content-wrap:after {
+  content: "";
+  display: table;
+}
+.pytorch-content-wrap:after {
+  clear: both;
+}
+.pytorch-content-wrap {
+  *zoom: 1;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-content-wrap {
+    padding-top: 45px;
+    float: left;
+    width: 100%;
+    display: block;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-content-wrap {
+    width: 100%;
+  }
+}
+
+.pytorch-content {
+  background: #ffffff;
+  width: 100%;
+  max-width: 700px;
+  position: relative;
+}
+
+.pytorch-content-left {
+  min-height: 100vh;
+  margin-top: 2.5rem;
+  width: 100%;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-content-left {
+    margin-top: 0;
+    margin-left: 3%;
+    width: 75%;
+    float: left;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-content-left {
+    width: 850px;
+    margin-left: 30px;
+  }
+}
+.pytorch-content-left .main-content {
+  padding-top: 0.9375rem;
+}
+.pytorch-content-left .main-content ul.simple {
+  padding-bottom: 1.25rem;
+}
+.pytorch-content-left .main-content .note:nth-child(1), .pytorch-content-left .main-content .warning:nth-child(1) {
+  margin-top: 0;
+}
+
+.pytorch-content-right {
+  display: none;
+  position: relative;
+  overflow-x: hidden;
+  overflow-y: hidden;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-content-right {
+    display: block;
+    margin-left: 0;
+    width: 19%;
+    float: left;
+    height: 100%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-content-right {
+    width: 280px;
+  }
+}
+
+@media screen and (min-width: 1101px) {
+  .pytorch-side-scroll {
+    position: relative;
+    overflow-x: hidden;
+    overflow-y: scroll;
+    height: 100%;
+  }
+}
+
+.pytorch-menu-vertical {
+  padding: 1.25rem 1.875rem 2.5rem 1.875rem;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-menu-vertical {
+    display: block;
+    padding-top: 0;
+    padding-right: 13.5%;
+    padding-bottom: 5.625rem;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-menu-vertical {
+    padding-left: 0;
+    padding-right: 1.5625rem;
+  }
+}
+
+.pytorch-left-menu {
+  display: none;
+  background-color: #f3f4f7;
+  color: #262626;
+  overflow: scroll;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-left-menu {
+    display: block;
+    overflow-x: hidden;
+    overflow-y: hidden;
+    padding-bottom: 110px;
+    padding: 0 1.875rem 0 0;
+    width: 25%;
+    z-index: 200;
+    float: left;
+  }
+  .pytorch-left-menu.make-fixed {
+    position: fixed;
+    top: 0;
+    bottom: 0;
+    left: 0;
+    float: none;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-left-menu {
+    padding: 0 0 0 1.875rem;
+    width: 350px;
+  }
+}
+
+.expand-menu, .hide-menu {
+  color: #6c6c6d;
+  padding-left: 10px;
+  cursor: pointer;
+}
+
+.collapse {
+  display: none;
+}
+
+.left-nav-top-caption {
+  padding-top: 1rem;
+}
+
+.pytorch-left-menu p.caption {
+  color: #262626;
+  display: block;
+  font-size: 1rem;
+  line-height: 1.375rem;
+  margin-bottom: 1rem;
+  text-transform: none;
+  white-space: normal;
+}
+
+.pytorch-left-menu-search {
+  margin-bottom: 2.5rem;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-left-menu-search {
+    margin: 1.25rem 0.625rem 1.875rem 0;
+  }
+}
+
+.pytorch-left-menu-search ::-webkit-input-placeholder {
+  color: #262626;
+}
+.pytorch-left-menu-search ::-moz-placeholder {
+  color: #262626;
+}
+.pytorch-left-menu-search :-ms-input-placeholder {
+  color: #262626;
+}
+.pytorch-left-menu-search ::-ms-input-placeholder {
+  color: #262626;
+}
+.pytorch-left-menu-search ::placeholder {
+  color: #262626;
+}
+
+.pytorch-left-menu-search :focus::-webkit-input-placeholder {
+  color: transparent;
+}
+.pytorch-left-menu-search :focus::-moz-placeholder {
+  color: transparent;
+}
+.pytorch-left-menu-search :focus:-ms-input-placeholder {
+  color: transparent;
+}
+.pytorch-left-menu-search :focus::-ms-input-placeholder {
+  color: transparent;
+}
+.pytorch-left-menu-search :focus::placeholder {
+  color: transparent;
+}
+
+.pytorch-left-menu-search input[type=text] {
+  border-radius: 0;
+  padding: 0.5rem 0.75rem;
+  border-color: #ffffff;
+  color: #262626;
+  border-style: solid;
+  font-size: 1rem;
+  width: 100%;
+  background-color: #f3f4f7;
+  background-image: url("../images/search-icon.svg");
+  background-repeat: no-repeat;
+  background-size: 18px 18px;
+  background-position: 12px 10px;
+  padding-left: 40px;
+  background-color: #ffffff;
+}
+.pytorch-left-menu-search input[type=text]:focus {
+  outline: 0;
+}
+
+@media screen and (min-width: 1101px) {
+  .pytorch-left-menu .pytorch-side-scroll {
+    width: 120%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-left-menu .pytorch-side-scroll {
+    width: 340px;
+  }
+}
+
+.pytorch-right-menu {
+  min-height: 100px;
+  overflow-x: hidden;
+  overflow-y: hidden;
+  left: 0;
+  z-index: 200;
+  padding-top: 0;
+  position: relative;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-right-menu {
+    width: 100%;
+  }
+  .pytorch-right-menu.scrolling-fixed {
+    position: fixed;
+    top: 45px;
+    left: 83.5%;
+    width: 14%;
+  }
+  .pytorch-right-menu.scrolling-absolute {
+    position: absolute;
+    left: 0;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-right-menu {
+    left: 0;
+    width: 380px;
+  }
+  .pytorch-right-menu.scrolling-fixed {
+    position: fixed;
+    top: 45px;
+    left: 1230px;
+  }
+  .pytorch-right-menu.scrolling-absolute {
+    position: absolute;
+    left: 0;
+  }
+}
+
+.pytorch-left-menu ul,
+.pytorch-right-menu ul {
+  list-style-type: none;
+  padding-left: 0;
+  margin-bottom: 2.5rem;
+}
+.pytorch-left-menu > ul,
+.pytorch-right-menu > ul {
+  margin-bottom: 2.5rem;
+}
+.pytorch-left-menu a:link,
+.pytorch-left-menu a:visited,
+.pytorch-left-menu a:hover,
+.pytorch-right-menu a:link,
+.pytorch-right-menu a:visited,
+.pytorch-right-menu a:hover {
+  color: #6c6c6d;
+  font-size: 0.875rem;
+  line-height: 1rem;
+  padding: 0;
+  text-decoration: none;
+}
+.pytorch-left-menu a:link.reference.internal,
+.pytorch-left-menu a:visited.reference.internal,
+.pytorch-left-menu a:hover.reference.internal,
+.pytorch-right-menu a:link.reference.internal,
+.pytorch-right-menu a:visited.reference.internal,
+.pytorch-right-menu a:hover.reference.internal {
+  margin-bottom: 0.3125rem;
+  position: relative;
+}
+.pytorch-left-menu li code,
+.pytorch-right-menu li code {
+  border: none;
+  background: inherit;
+  color: inherit;
+  padding-left: 0;
+  padding-right: 0;
+}
+.pytorch-left-menu li span.toctree-expand,
+.pytorch-right-menu li span.toctree-expand {
+  display: block;
+  float: left;
+  margin-left: -1.2em;
+  font-size: 0.8em;
+  line-height: 1.6em;
+}
+.pytorch-left-menu li.on a, .pytorch-left-menu li.current > a,
+.pytorch-right-menu li.on a,
+.pytorch-right-menu li.current > a {
+  position: relative;
+  border: none;
+}
+.pytorch-left-menu li.on a span.toctree-expand, .pytorch-left-menu li.current > a span.toctree-expand,
+.pytorch-right-menu li.on a span.toctree-expand,
+.pytorch-right-menu li.current > a span.toctree-expand {
+  display: block;
+  font-size: 0.8em;
+  line-height: 1.6em;
+}
+.pytorch-left-menu li.toctree-l1.current > a,
+.pytorch-right-menu li.toctree-l1.current > a {
+  color: #ee4c2c;
+}
+.pytorch-left-menu li.toctree-l1.current > a:before,
+.pytorch-right-menu li.toctree-l1.current > a:before {
+  content: "\2022";
+  display: inline-block;
+  position: absolute;
+  left: -15px;
+  top: -10%;
+  font-size: 1.375rem;
+  color: #ee4c2c;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-left-menu li.toctree-l1.current > a:before,
+  .pytorch-right-menu li.toctree-l1.current > a:before {
+    left: -20px;
+  }
+}
+.pytorch-left-menu li.toctree-l1.current li.toctree-l2 > ul, .pytorch-left-menu li.toctree-l2.current li.toctree-l3 > ul,
+.pytorch-right-menu li.toctree-l1.current li.toctree-l2 > ul,
+.pytorch-right-menu li.toctree-l2.current li.toctree-l3 > ul {
+  display: none;
+}
+.pytorch-left-menu li.toctree-l1.current li.toctree-l2.current > ul, .pytorch-left-menu li.toctree-l2.current li.toctree-l3.current > ul,
+.pytorch-right-menu li.toctree-l1.current li.toctree-l2.current > ul,
+.pytorch-right-menu li.toctree-l2.current li.toctree-l3.current > ul {
+  display: block;
+}
+.pytorch-left-menu li.toctree-l2.current li.toctree-l3 > a,
+.pytorch-right-menu li.toctree-l2.current li.toctree-l3 > a {
+  display: block;
+}
+.pytorch-left-menu li.toctree-l3,
+.pytorch-right-menu li.toctree-l3 {
+  font-size: 0.9em;
+}
+.pytorch-left-menu li.toctree-l3.current li.toctree-l4 > a,
+.pytorch-right-menu li.toctree-l3.current li.toctree-l4 > a {
+  display: block;
+}
+.pytorch-left-menu li.toctree-l4,
+.pytorch-right-menu li.toctree-l4 {
+  font-size: 0.9em;
+}
+.pytorch-left-menu li.current ul,
+.pytorch-right-menu li.current ul {
+  display: block;
+}
+.pytorch-left-menu li ul,
+.pytorch-right-menu li ul {
+  margin-bottom: 0;
+  display: none;
+}
+.pytorch-left-menu li ul li a,
+.pytorch-right-menu li ul li a {
+  margin-bottom: 0;
+}
+.pytorch-left-menu a,
+.pytorch-right-menu a {
+  display: inline-block;
+  position: relative;
+}
+.pytorch-left-menu a:hover,
+.pytorch-right-menu a:hover {
+  cursor: pointer;
+}
+.pytorch-left-menu a:active,
+.pytorch-right-menu a:active {
+  cursor: pointer;
+}
+
+.pytorch-left-menu ul {
+  padding-left: 0;
+}
+
+.pytorch-right-menu a:link,
+.pytorch-right-menu a:visited,
+.pytorch-right-menu a:hover {
+  color: #6c6c6d;
+}
+.pytorch-right-menu a:link span.pre,
+.pytorch-right-menu a:visited span.pre,
+.pytorch-right-menu a:hover span.pre {
+  color: #6c6c6d;
+}
+.pytorch-right-menu a.reference.internal.expanded:before {
+  content: "-";
+  font-family: monospace;
+  position: absolute;
+  left: -12px;
+}
+.pytorch-right-menu a.reference.internal.not-expanded:before {
+  content: "+";
+  font-family: monospace;
+  position: absolute;
+  left: -12px;
+}
+.pytorch-right-menu li.active > a {
+  color: #ee4c2c;
+}
+.pytorch-right-menu li.active > a span.pre, .pytorch-right-menu li.active > a:before {
+  color: #ee4c2c;
+}
+.pytorch-right-menu li.active > a:after {
+  content: "\2022";
+  color: #e44c2c;
+  display: inline-block;
+  font-size: 1.375rem;
+  left: -17px;
+  position: absolute;
+  top: 1px;
+}
+.pytorch-right-menu .pytorch-side-scroll > ul > li > ul > li {
+  margin-bottom: 0;
+}
+.pytorch-right-menu ul ul {
+  padding-left: 0;
+}
+.pytorch-right-menu ul ul li {
+  padding-left: 0px;
+}
+.pytorch-right-menu ul ul li a.reference.internal {
+  padding-left: 0;
+}
+.pytorch-right-menu ul ul li ul {
+  display: none;
+  padding-left: 10px;
+}
+.pytorch-right-menu ul ul li li a.reference.internal {
+  padding-left: 0;
+}
+.pytorch-right-menu li ul {
+  display: block;
+}
+
+.pytorch-right-menu .pytorch-side-scroll {
+  padding-top: 20px;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-right-menu .pytorch-side-scroll {
+    width: 120%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-right-menu .pytorch-side-scroll {
+    width: 400px;
+  }
+}
+.pytorch-right-menu .pytorch-side-scroll > ul {
+  padding-left: 10%;
+  padding-right: 10%;
+  margin-bottom: 0;
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-right-menu .pytorch-side-scroll > ul {
+    padding-left: 25px;
+  }
+}
+.pytorch-right-menu .pytorch-side-scroll > ul > li > a.reference.internal {
+  color: #262626;
+  font-weight: 500;
+}
+.pytorch-right-menu .pytorch-side-scroll ul li {
+  position: relative;
+}
+
+#pytorch-right-menu .side-scroll-highlight {
+  color: #ee4c2c;
+}
+
+.header-container {
+  max-width: none;
+  margin-top: 4px;
+}
+@media screen and (min-width: 1101px) {
+  .header-container {
+    margin-top: 0;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .header-container {
+    margin-top: 0;
+  }
+}
+
+.container-fluid.header-holder {
+  padding-right: 0;
+  padding-left: 0;
+}
+
+.header-holder .container {
+  max-width: none;
+  padding-right: 1.875rem;
+  padding-left: 1.875rem;
+}
+@media screen and (min-width: 1101px) {
+  .header-holder .container {
+    padding-right: 1.875rem;
+    padding-left: 1.875rem;
+  }
+}
+
+.header-holder .main-menu {
+  -webkit-box-pack: unset;
+      -ms-flex-pack: unset;
+          justify-content: unset;
+  position: relative;
+}
+@media screen and (min-width: 1101px) {
+  .header-holder .main-menu ul {
+    padding-left: 0;
+    margin-left: 26%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .header-holder .main-menu ul {
+    padding-left: 38px;
+    margin-left: 310px;
+  }
+}
+
+.pytorch-page-level-bar {
+  display: none;
+  -webkit-box-align: center;
+      -ms-flex-align: center;
+          align-items: center;
+  background-color: #ffffff;
+  border-bottom: 1px solid #e2e2e2;
+  width: 100%;
+  z-index: 201;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-page-level-bar {
+    left: 0;
+    display: -webkit-box;
+    display: -ms-flexbox;
+    display: flex;
+    height: 45px;
+    padding-left: 0;
+    width: 100%;
+    position: absolute;
+    z-index: 1;
+  }
+  .pytorch-page-level-bar.left-menu-is-fixed {
+    position: fixed;
+    top: 0;
+    left: 25%;
+    padding-left: 0;
+    right: 0;
+    width: 75%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-page-level-bar {
+    left: 0;
+    right: 0;
+    width: auto;
+    z-index: 1;
+  }
+  .pytorch-page-level-bar.left-menu-is-fixed {
+    left: 350px;
+    right: 0;
+    width: auto;
+  }
+}
+.pytorch-page-level-bar ul, .pytorch-page-level-bar li {
+  margin: 0;
+}
+
+.pytorch-shortcuts-wrapper {
+  display: none;
+}
+@media screen and (min-width: 1101px) {
+  .pytorch-shortcuts-wrapper {
+    font-size: 0.875rem;
+    float: left;
+    margin-left: 2%;
+  }
+}
+@media screen and (min-width: 1600px) {
+  .pytorch-shortcuts-wrapper {
+    margin-left: 1.875rem;
+  }
+}
+
+.cookie-banner-wrapper {
+  display: none;
+}
+.cookie-banner-wrapper .container {
+  padding-left: 1.875rem;
+  padding-right: 1.875rem;
+  max-width: 1240px;
+}
+.cookie-banner-wrapper.is-visible {
+  display: block;
+  position: fixed;
+  bottom: 0;
+  background-color: #f3f4f7;
+  min-height: 100px;
+  width: 100%;
+  z-index: 401;
+  border-top: 3px solid #ededee;
+}
+.cookie-banner-wrapper .gdpr-notice {
+  color: #6c6c6d;
+  margin-top: 1.5625rem;
+  text-align: left;
+  max-width: 1440px;
+}
+@media screen and (min-width: 768px) {
+  .cookie-banner-wrapper .gdpr-notice {
+    width: 77%;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  .cookie-banner-wrapper .gdpr-notice {
+    width: inherit;
+  }
+}
+.cookie-banner-wrapper .gdpr-notice .cookie-policy-link {
+  color: #343434;
+}
+.cookie-banner-wrapper .close-button {
+  -webkit-appearance: none;
+     -moz-appearance: none;
+          appearance: none;
+  background: transparent;
+  border: 1px solid #f3f4f7;
+  height: 1.3125rem;
+  position: absolute;
+  bottom: 42px;
+  right: 0;
+  top: 0;
+  cursor: pointer;
+  outline: none;
+}
+@media screen and (min-width: 768px) {
+  .cookie-banner-wrapper .close-button {
+    right: 20%;
+    top: inherit;
+  }
+}
+@media (min-width: 768px) and (max-width: 1239px) {
+  .cookie-banner-wrapper .close-button {
+    right: 0;
+    top: 0;
+  }
+}
+
+.main-menu ul li .resources-dropdown a {
+  cursor: pointer;
+}
+.main-menu ul li .dropdown-menu {
+  border-radius: 0;
+  padding: 0;
+}
+.main-menu ul li .dropdown-menu .dropdown-item {
+  color: #6c6c6d;
+  border-bottom: 1px solid #e2e2e2;
+}
+.main-menu ul li .dropdown-menu .dropdown-item:last-of-type {
+  border-bottom-color: transparent;
+}
+.main-menu ul li .dropdown-menu .dropdown-item:hover {
+  background-color: #e44c2c;
+}
+.main-menu ul li .dropdown-menu .dropdown-item p {
+  font-size: 1rem;
+  color: #979797;
+}
+.main-menu ul li .dropdown-menu a.dropdown-item:hover {
+  color: #ffffff;
+}
+.main-menu ul li .dropdown-menu a.dropdown-item:hover p {
+  color: #ffffff;
+}
+
+.resources-dropdown-menu {
+  left: -75px;
+  width: 226px;
+  display: none;
+  position: absolute;
+  z-index: 1000;
+  display: none;
+  float: left;
+  min-width: 10rem;
+  padding: 0.5rem 0;
+  font-size: 1rem;
+  color: #212529;
+  text-align: left;
+  list-style: none;
+  background-color: #ffffff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, 0.15);
+  border-radius: 0.25rem;
+}
+
+.resources-dropdown:hover .resources-dropdown-menu {
+  display: block;
+}
+
+.main-menu ul li .resources-dropdown-menu {
+  border-radius: 0;
+  padding: 0;
+}
+.main-menu ul li.active:hover .resources-dropdown-menu {
+  display: block;
+}
+
+.main-menu ul li .resources-dropdown-menu .dropdown-item {
+  color: #6c6c6d;
+  border-bottom: 1px solid #e2e2e2;
+}
+
+.resources-dropdown .with-down-orange-arrow {
+  padding-right: 2rem;
+  position: relative;
+  background: url("../images/chevron-down-orange.svg");
+  background-size: 14px 18px;
+  background-position: top 7px right 10px;
+  background-repeat: no-repeat;
+}
+
+.with-down-arrow {
+  padding-right: 2rem;
+  position: relative;
+  background-image: url("../images/chevron-down-black.svg");
+  background-size: 14px 18px;
+  background-position: top 7px right 10px;
+  background-repeat: no-repeat;
+}
+.with-down-arrow:hover {
+  background-image: url("../images/chevron-down-orange.svg");
+  background-repeat: no-repeat;
+}
+
+.header-holder .main-menu ul li .resources-dropdown .doc-dropdown-option {
+  padding-top: 1rem;
+}
+
+.header-holder .main-menu ul li a.nav-dropdown-item {
+  display: block;
+  font-size: 1rem;
+  line-height: 1.3125rem;
+  width: 100%;
+  padding: 0.25rem 1.5rem;
+  clear: both;
+  font-weight: 400;
+  color: #979797;
+  text-align: center;
+  background-color: transparent;
+  border-bottom: 1px solid #e2e2e2;
+}
+.header-holder .main-menu ul li a.nav-dropdown-item:last-of-type {
+  border-bottom-color: transparent;
+}
+.header-holder .main-menu ul li a.nav-dropdown-item:hover {
+  background-color: #e44c2c;
+  color: white;
+}
+.header-holder .main-menu ul li a.nav-dropdown-item .dropdown-title {
+  font-size: 1.125rem;
+  color: #6c6c6d;
+  letter-spacing: 0;
+  line-height: 34px;
+}
+
+.header-holder .main-menu ul li a.nav-dropdown-item:hover .dropdown-title {
+  background-color: #e44c2c;
+  color: white;
+}
+
+/*# sourceMappingURL=theme.css.map */
diff --git a/docs/v2.2.0/_static/doctools.js b/docs/v2.2.0/_static/doctools.js
new file mode 100644
index 0000000000..e1bfd708b7
--- /dev/null
+++ b/docs/v2.2.0/_static/doctools.js
@@ -0,0 +1,358 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for all documentation.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/**
+ * select a different prefix for underscore
+ */
+$u = _.noConflict();
+
+/**
+ * make the code below compatible with browsers without
+ * an installed firebug like debugger
+if (!window.console || !console.firebug) {
+  var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
+    "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
+    "profile", "profileEnd"];
+  window.console = {};
+  for (var i = 0; i < names.length; ++i)
+    window.console[names[i]] = function() {};
+}
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+  if (!x) {
+    return x
+  }
+  return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+  if (typeof s === 'undefined')
+    s = document.location.search;
+  var parts = s.substr(s.indexOf('?') + 1).split('&');
+  var result = {};
+  for (var i = 0; i < parts.length; i++) {
+    var tmp = parts[i].split('=', 2);
+    var key = jQuery.urldecode(tmp[0]);
+    var value = jQuery.urldecode(tmp[1]);
+    if (key in result)
+      result[key].push(value);
+    else
+      result[key] = [value];
+  }
+  return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+  function highlight(node, addItems) {
+    if (node.nodeType === 3) {
+      var val = node.nodeValue;
+      var pos = val.toLowerCase().indexOf(text);
+      if (pos >= 0 &&
+          !jQuery(node.parentNode).hasClass(className) &&
+          !jQuery(node.parentNode).hasClass("nohighlight")) {
+        var span;
+        var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+        if (isInSVG) {
+          span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+        } else {
+          span = document.createElement("span");
+          span.className = className;
+        }
+        span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+        node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling));
+        node.nodeValue = val.substr(0, pos);
+        if (isInSVG) {
+          var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+          var bbox = node.parentElement.getBBox();
+          rect.x.baseVal.value = bbox.x;
+          rect.y.baseVal.value = bbox.y;
+          rect.width.baseVal.value = bbox.width;
+          rect.height.baseVal.value = bbox.height;
+          rect.setAttribute('class', className);
+          addItems.push({
+              "parent": node.parentNode,
+              "target": rect});
+        }
+      }
+    }
+    else if (!jQuery(node).is("button, select, textarea")) {
+      jQuery.each(node.childNodes, function() {
+        highlight(this, addItems);
+      });
+    }
+  }
+  var addItems = [];
+  var result = this.each(function() {
+    highlight(this, addItems);
+  });
+  for (var i = 0; i < addItems.length; ++i) {
+    jQuery(addItems[i].parent).before(addItems[i].target);
+  }
+  return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+  jQuery.uaMatch = function(ua) {
+    ua = ua.toLowerCase();
+
+    var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+      /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+      /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+      /(msie) ([\w.]+)/.exec(ua) ||
+      ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+      [];
+
+    return {
+      browser: match[ 1 ] || "",
+      version: match[ 2 ] || "0"
+    };
+  };
+  jQuery.browser = {};
+  jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+var Documentation = {
+
+  init : function() {
+    this.fixFirefoxAnchorBug();
+    this.highlightSearchWords();
+    this.initIndexTable();
+    this.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS : {},
+  PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
+  LOCALE : 'unknown',
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext : function(string) {
+    var translated = Documentation.TRANSLATIONS[string];
+    if (typeof translated === 'undefined')
+      return string;
+    return (typeof translated === 'string') ? translated : translated[0];
+  },
+
+  ngettext : function(singular, plural, n) {
+    var translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated === 'undefined')
+      return (n == 1) ? singular : plural;
+    return translated[Documentation.PLURALEXPR(n)];
+  },
+
+  addTranslations : function(catalog) {
+    for (var key in catalog.messages)
+      this.TRANSLATIONS[key] = catalog.messages[key];
+    this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
+    this.LOCALE = catalog.locale;
+  },
+
+  /**
+   * add context elements like header anchor links
+   */
+  addContextElements : function() {
+    $('div[id] > :header:first').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this headline')).
+      appendTo(this);
+    });
+    $('dt[id]').each(function() {
+      $('<a class="headerlink">\u00B6</a>').
+      attr('href', '#' + this.id).
+      attr('title', _('Permalink to this definition')).
+      appendTo(this);
+    });
+  },
+
+  /**
+   * workaround a firefox stupidity
+   * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
+   */
+  fixFirefoxAnchorBug : function() {
+    if (document.location.hash && $.browser.mozilla)
+      window.setTimeout(function() {
+        document.location.href += '';
+      }, 10);
+  },
+
+  /**
+   * highlight the search words provided in the url in the text
+   */
+  highlightSearchWords : function() {
+    var params = $.getQueryParameters();
+    var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
+    if (terms.length) {
+      var body = $('div.body');
+      if (!body.length) {
+        body = $('body');
+      }
+      window.setTimeout(function() {
+        $.each(terms, function() {
+          body.highlightText(this.toLowerCase(), 'highlighted');
+        });
+      }, 10);
+      $('<p class="highlight-link"><a href="javascript:Documentation.' +
+        'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
+          .appendTo($('#searchbox'));
+    }
+  },
+
+  /**
+   * init the domain index toggle buttons
+   */
+  initIndexTable : function() {
+    var togglers = $('img.toggler').click(function() {
+      var src = $(this).attr('src');
+      var idnum = $(this).attr('id').substr(7);
+      $('tr.cg-' + idnum).toggle();
+      if (src.substr(-9) === 'minus.png')
+        $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
+      else
+        $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
+    }).css('display', '');
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
+        togglers.click();
+    }
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords : function() {
+    $('#searchbox .highlight-link').fadeOut(300);
+    $('span.highlighted').removeClass('highlighted');
+    var url = new URL(window.location);
+    url.searchParams.delete('highlight');
+    window.history.replaceState({}, '', url);
+  },
+
+   /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar : function() {
+    $('input[name=q]').first().focus();
+  },
+
+  /**
+   * make the url absolute
+   */
+  makeURL : function(relativeURL) {
+    return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
+  },
+
+  /**
+   * get the current relative url
+   */
+  getCurrentURL : function() {
+    var path = document.location.pathname;
+    var parts = path.split(/\//);
+    $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
+      if (this === '..')
+        parts.pop();
+    });
+    var url = parts.join('/');
+    return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
+  },
+
+  initOnKeyListeners: function() {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+        !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+        return;
+
+    $(document).keydown(function(event) {
+      var activeElementType = document.activeElement.tagName;
+      // don't navigate when in search box, textarea, dropdown or button
+      if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
+          && activeElementType !== 'BUTTON') {
+        if (event.altKey || event.ctrlKey || event.metaKey)
+          return;
+
+          if (!event.shiftKey) {
+            switch (event.key) {
+              case 'ArrowLeft':
+                if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS)
+                  break;
+                var prevHref = $('link[rel="prev"]').prop('href');
+                if (prevHref) {
+                  window.location.href = prevHref;
+                  return false;
+                }
+                break;
+              case 'ArrowRight':
+                if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS)
+                  break;
+                var nextHref = $('link[rel="next"]').prop('href');
+                if (nextHref) {
+                  window.location.href = nextHref;
+                  return false;
+                }
+                break;
+              case 'Escape':
+                if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+                  break;
+                Documentation.hideSearchWords();
+                return false;
+          }
+        }
+
+        // some keyboard layouts may need Shift to get /
+        switch (event.key) {
+          case '/':
+            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS)
+              break;
+            Documentation.focusSearchBar();
+            return false;
+        }
+      }
+    });
+  }
+};
+
+// quick alias for translations
+_ = Documentation.gettext;
+
+$(document).ready(function() {
+  Documentation.init();
+});
diff --git a/docs/v2.2.0/_static/documentation_options.js b/docs/v2.2.0/_static/documentation_options.js
new file mode 100644
index 0000000000..faaddfa25f
--- /dev/null
+++ b/docs/v2.2.0/_static/documentation_options.js
@@ -0,0 +1,14 @@
+var DOCUMENTATION_OPTIONS = {
+    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
+    VERSION: 'v2.2.0',
+    LANGUAGE: 'None',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
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new file mode 100644
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diff --git a/docs/v2.2.0/_static/fonts/FreightSans/freight-sans-light-italic.woff2 b/docs/v2.2.0/_static/fonts/FreightSans/freight-sans-light-italic.woff2
new file mode 100644
index 0000000000000000000000000000000000000000..e2fa0134b1a5bc4b6805e6ec3dbf5acb17c5b976
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new file mode 100644
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diff --git a/docs/v2.2.0/_static/jquery-3.5.1.js b/docs/v2.2.0/_static/jquery-3.5.1.js
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+/*!
+ * jQuery JavaScript Library v3.5.1
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2020-05-04T22:49Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+      // Support: Chrome <=57, Firefox <=52
+      // In some browsers, typeof returns "function" for HTML <object> elements
+      // (i.e., `typeof document.createElement( "object" ) === "function"`).
+      // We don't want to classify *any* DOM node as a function.
+      return typeof obj === "function" && typeof obj.nodeType !== "number";
+  };
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.5.1",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+					[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+function( _i, name ) {
+	class2type[ "[object " + name + "]" ] = name.toLowerCase();
+} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.5
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2020-03-14
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem.namespaceURI,
+		docElem = ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+  return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+};
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the master Deferred
+			master = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						master.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( master.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return master.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
+		}
+
+		return master.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+					value :
+					value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var
+	rkeyEvent = /^key/,
+	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
+	rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+							return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+							return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+						return result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+
+	which: function( event ) {
+		var button = event.button;
+
+		// Add which for key events
+		if ( event.which == null && rkeyEvent.test( event.type ) ) {
+			return event.charCode != null ? event.charCode : event.keyCode;
+		}
+
+		// Add which for click: 1 === left; 2 === middle; 3 === right
+		if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
+			if ( button & 1 ) {
+				return 1;
+			}
+
+			if ( button & 2 ) {
+				return 3;
+			}
+
+			if ( button & 4 ) {
+				return 2;
+			}
+
+			return 0;
+		}
+
+		return event.which;
+	}
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px";
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+						swap( elem, cssShow, function() {
+							return getWidthOrHeight( elem, dimension, extra );
+						} ) :
+						getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+				) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+					jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+			/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+						animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+			doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+						"" :
+						dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+					return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {
+		xml = undefined;
+	}
+
+	if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
+		jQuery.error( "Invalid XML: " + data );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} )
+		.filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} )
+		.map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+	originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+					jQuery( callbackContext ) :
+					jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
+		function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	} );
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/v2.2.0/_static/jquery.js b/docs/v2.2.0/_static/jquery.js
new file mode 100644
index 0000000000..b0614034ad
--- /dev/null
+++ b/docs/v2.2.0/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.5.1 | (c) JS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var 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--- /dev/null
+++ b/docs/v2.2.0/_static/js/modernizr.min.js
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new file mode 100644
index 0000000000..ceee8f062c
--- /dev/null
+++ b/docs/v2.2.0/_static/js/theme.js
@@ -0,0 +1,1128 @@
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+window.utilities = {
+  scrollTop: function() {
+    var supportPageOffset = window.pageXOffset !== undefined;
+    var isCSS1Compat = ((document.compatMode || "") === "CSS1Compat");
+    var scrollLeft = supportPageOffset ? window.pageXOffset : isCSS1Compat ? document.documentElement.scrollLeft : document.body.scrollLeft;
+    return supportPageOffset ? window.pageYOffset : isCSS1Compat ? document.documentElement.scrollTop : document.body.scrollTop;
+  },
+
+  // Modified from https://stackoverflow.com/a/27078401
+  throttle: function(func, wait, options) {
+    var context, args, result;
+    var timeout = null;
+    var previous = 0;
+    if (!options) options = {};
+    var later = function() {
+      previous = options.leading === false ? 0 : Date.now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+    return function() {
+      var now = Date.now();
+      if (!previous && options.leading === false) previous = now;
+      var remaining = wait - (now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+  },
+
+  closest: function (el, selector) {
+    var matchesFn;
+
+    // find vendor prefix
+    ['matches','webkitMatchesSelector','mozMatchesSelector','msMatchesSelector','oMatchesSelector'].some(function(fn) {
+      if (typeof document.body[fn] == 'function') {
+        matchesFn = fn;
+        return true;
+      }
+      return false;
+    });
+
+    var parent;
+
+    // traverse parents
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+      parent = el.parentElement;
+      if (parent && parent[matchesFn](selector)) {
+        return parent;
+      }
+      el = parent;
+    }
+
+    return null;
+  },
+
+  // Modified from https://stackoverflow.com/a/18953277
+  offset: function(elem) {
+    if (!elem) {
+      return;
+    }
+
+    rect = elem.getBoundingClientRect();
+
+    // Make sure element is not hidden (display: none) or disconnected
+    if (rect.width || rect.height || elem.getClientRects().length) {
+      var doc = elem.ownerDocument;
+      var docElem = doc.documentElement;
+
+      return {
+        top: rect.top + window.pageYOffset - docElem.clientTop,
+        left: rect.left + window.pageXOffset - docElem.clientLeft
+      };
+    }
+  },
+
+  headersHeight: function() {
+    if (document.getElementById("pytorch-left-menu").classList.contains("make-fixed")) {
+      return document.getElementById("pytorch-page-level-bar").offsetHeight;
+    } else {
+      return document.getElementById("header-holder").offsetHeight +
+             document.getElementById("pytorch-page-level-bar").offsetHeight;
+    }
+  },
+
+  windowHeight: function() {
+    return window.innerHeight ||
+           document.documentElement.clientHeight ||
+           document.body.clientHeight;
+  }
+}
+
+},{}],2:[function(require,module,exports){
+var cookieBanner = {
+  init: function() {
+    cookieBanner.bind();
+
+    var cookieExists = cookieBanner.cookieExists();
+
+    if (!cookieExists) {
+      cookieBanner.setCookie();
+      cookieBanner.showCookieNotice();
+    }
+  },
+
+  bind: function() {
+    $(".close-button").on("click", cookieBanner.hideCookieNotice);
+  },
+
+  cookieExists: function() {
+    var cookie = localStorage.getItem("returningPytorchUser");
+
+    if (cookie) {
+      return true;
+    } else {
+      return false;
+    }
+  },
+
+  setCookie: function() {
+    localStorage.setItem("returningPytorchUser", true);
+  },
+
+  showCookieNotice: function() {
+    $(".cookie-banner-wrapper").addClass("is-visible");
+  },
+
+  hideCookieNotice: function() {
+    $(".cookie-banner-wrapper").removeClass("is-visible");
+  }
+};
+
+$(function() {
+  cookieBanner.init();
+});
+
+},{}],3:[function(require,module,exports){
+window.filterTags = {
+  bind: function() {
+    var options = {
+      valueNames: [{ data: ["tags"] }],
+      page: "6",
+      pagination: true
+    };
+
+    var tutorialList = new List("tutorial-cards", options);
+
+    function filterSelectedTags(cardTags, selectedTags) {
+      return cardTags.some(function(tag) {
+        return selectedTags.some(function(selectedTag) {
+          return selectedTag == tag;
+        });
+      });
+    }
+
+    function updateList() {
+      var selectedTags = [];
+
+      $(".selected").each(function() {
+        selectedTags.push($(this).data("tag"));
+      });
+
+      tutorialList.filter(function(item) {
+        var cardTags;
+
+        if (item.values().tags == null) {
+          cardTags = [""];
+        } else {
+          cardTags = item.values().tags.split(",");
+        }
+
+        if (selectedTags.length == 0) {
+          return true;
+        } else {
+          return filterSelectedTags(cardTags, selectedTags);
+        }
+      });
+    }
+
+    $(".filter-btn").on("click", function() {
+      if ($(this).data("tag") == "all") {
+        $(this).addClass("all-tag-selected");
+        $(".filter").removeClass("selected");
+      } else {
+        $(this).toggleClass("selected");
+        $("[data-tag='all']").removeClass("all-tag-selected");
+      }
+
+      // If no tags are selected then highlight the 'All' tag
+
+      if (!$(".selected")[0]) {
+        $("[data-tag='all']").addClass("all-tag-selected");
+      }
+
+      updateList();
+    });
+  }
+};
+
+},{}],4:[function(require,module,exports){
+// Modified from https://stackoverflow.com/a/32396543
+window.highlightNavigation = {
+  navigationListItems: document.querySelectorAll("#pytorch-right-menu li"),
+  sections: document.querySelectorAll(".pytorch-article .section"),
+  sectionIdTonavigationLink: {},
+
+  bind: function() {
+    if (!sideMenus.displayRightMenu) {
+      return;
+    };
+
+    for (var i = 0; i < highlightNavigation.sections.length; i++) {
+      var id = highlightNavigation.sections[i].id;
+      highlightNavigation.sectionIdTonavigationLink[id] =
+        document.querySelectorAll('#pytorch-right-menu li a[href="#' + id + '"]')[0];
+    }
+
+    $(window).scroll(utilities.throttle(highlightNavigation.highlight, 100));
+  },
+
+  highlight: function() {
+    var rightMenu = document.getElementById("pytorch-right-menu");
+
+    // If right menu is not on the screen don't bother
+    if (rightMenu.offsetWidth === 0 && rightMenu.offsetHeight === 0) {
+      return;
+    }
+
+    var scrollPosition = utilities.scrollTop();
+    var OFFSET_TOP_PADDING = 25;
+    var offset = document.getElementById("header-holder").offsetHeight +
+                 document.getElementById("pytorch-page-level-bar").offsetHeight +
+                 OFFSET_TOP_PADDING;
+
+    var sections = highlightNavigation.sections;
+
+    for (var i = (sections.length - 1); i >= 0; i--) {
+      var currentSection = sections[i];
+      var sectionTop = utilities.offset(currentSection).top;
+
+      if (scrollPosition >= sectionTop - offset) {
+        var navigationLink = highlightNavigation.sectionIdTonavigationLink[currentSection.id];
+        var navigationListItem = utilities.closest(navigationLink, "li");
+
+        if (navigationListItem && !navigationListItem.classList.contains("active")) {
+          for (var i = 0; i < highlightNavigation.navigationListItems.length; i++) {
+            var el = highlightNavigation.navigationListItems[i];
+            if (el.classList.contains("active")) {
+              el.classList.remove("active");
+            }
+          }
+
+          navigationListItem.classList.add("active");
+
+          // Scroll to active item. Not a requested feature but we could revive it. Needs work.
+
+          // var menuTop = $("#pytorch-right-menu").position().top;
+          // var itemTop = navigationListItem.getBoundingClientRect().top;
+          // var TOP_PADDING = 20
+          // var newActiveTop = $("#pytorch-side-scroll-right").scrollTop() + itemTop - menuTop - TOP_PADDING;
+
+          // $("#pytorch-side-scroll-right").animate({
+          //   scrollTop: newActiveTop
+          // }, 100);
+        }
+
+        break;
+      }
+    }
+  }
+};
+
+},{}],5:[function(require,module,exports){
+window.mainMenuDropdown = {
+  bind: function() {
+    $("[data-toggle='ecosystem-dropdown']").on("click", function() {
+      toggleDropdown($(this).attr("data-toggle"));
+    });
+
+    $("[data-toggle='resources-dropdown']").on("click", function() {
+      toggleDropdown($(this).attr("data-toggle"));
+    });
+
+    function toggleDropdown(menuToggle) {
+      var showMenuClass = "show-menu";
+      var menuClass = "." + menuToggle + "-menu";
+
+      if ($(menuClass).hasClass(showMenuClass)) {
+        $(menuClass).removeClass(showMenuClass);
+      } else {
+        $("[data-toggle=" + menuToggle + "].show-menu").removeClass(
+          showMenuClass
+        );
+        $(menuClass).addClass(showMenuClass);
+      }
+    }
+  }
+};
+
+},{}],6:[function(require,module,exports){
+window.mobileMenu = {
+  bind: function() {
+    $("[data-behavior='open-mobile-menu']").on('click', function(e) {
+      e.preventDefault();
+      $(".mobile-main-menu").addClass("open");
+      $("body").addClass('no-scroll');
+
+      mobileMenu.listenForResize();
+    });
+
+    $("[data-behavior='close-mobile-menu']").on('click', function(e) {
+      e.preventDefault();
+      mobileMenu.close();
+    });
+  },
+
+  listenForResize: function() {
+    $(window).on('resize.ForMobileMenu', function() {
+      if ($(this).width() > 768) {
+        mobileMenu.close();
+      }
+    });
+  },
+
+  close: function() {
+    $(".mobile-main-menu").removeClass("open");
+    $("body").removeClass('no-scroll');
+    $(window).off('resize.ForMobileMenu');
+  }
+};
+
+},{}],7:[function(require,module,exports){
+window.mobileTOC = {
+  bind: function() {
+    $("[data-behavior='toggle-table-of-contents']").on("click", function(e) {
+      e.preventDefault();
+
+      var $parent = $(this).parent();
+
+      if ($parent.hasClass("is-open")) {
+        $parent.removeClass("is-open");
+        $(".pytorch-left-menu").slideUp(200, function() {
+          $(this).css({display: ""});
+        });
+      } else {
+        $parent.addClass("is-open");
+        $(".pytorch-left-menu").slideDown(200);
+      }
+    });
+  }
+}
+
+},{}],8:[function(require,module,exports){
+window.pytorchAnchors = {
+  bind: function() {
+    // Replace Sphinx-generated anchors with anchorjs ones
+    $(".headerlink").text("");
+
+    window.anchors.add(".pytorch-article .headerlink");
+
+    $(".anchorjs-link").each(function() {
+      var $headerLink = $(this).closest(".headerlink");
+      var href = $headerLink.attr("href");
+      var clone = this.outerHTML;
+
+      $clone = $(clone).attr("href", href);
+      $headerLink.before($clone);
+      $headerLink.remove();
+    });
+  }
+};
+
+},{}],9:[function(require,module,exports){
+// Modified from https://stackoverflow.com/a/13067009
+// Going for a JS solution to scrolling to an anchor so we can benefit from
+// less hacky css and smooth scrolling.
+
+window.scrollToAnchor = {
+  bind: function() {
+    var document = window.document;
+    var history = window.history;
+    var location = window.location
+    var HISTORY_SUPPORT = !!(history && history.pushState);
+
+    var anchorScrolls = {
+      ANCHOR_REGEX: /^#[^ ]+$/,
+      offsetHeightPx: function() {
+        var OFFSET_HEIGHT_PADDING = 20;
+        // TODO: this is a little janky. We should try to not rely on JS for this
+        return utilities.headersHeight() + OFFSET_HEIGHT_PADDING;
+      },
+
+      /**
+       * Establish events, and fix initial scroll position if a hash is provided.
+       */
+      init: function() {
+        this.scrollToCurrent();
+        // This interferes with clicks below it, causing a double fire
+        // $(window).on('hashchange', $.proxy(this, 'scrollToCurrent'));
+        $('body').on('click', 'a', $.proxy(this, 'delegateAnchors'));
+        $('body').on('click', '#pytorch-right-menu li span', $.proxy(this, 'delegateSpans'));
+      },
+
+      /**
+       * Return the offset amount to deduct from the normal scroll position.
+       * Modify as appropriate to allow for dynamic calculations
+       */
+      getFixedOffset: function() {
+        return this.offsetHeightPx();
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+
+      /**
+       * If the provided href is an anchor which resolves to an element on the
+       * page, scroll to it.
+       * @param  {String} href
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+      scrollIfAnchor: function(href, pushToHistory) {
+        var match, anchorOffset;
+
+        if(!this.ANCHOR_REGEX.test(href)) {
+          return false;
+        }
+
+        match = document.getElementById(href.slice(1));
+
+        if(match) {
+          var anchorOffset = $(match).offset().top - this.getFixedOffset();
+
+          $('html, body').scrollTop(anchorOffset);
+
+          // Add the state to history as-per normal anchor links
+          if(HISTORY_SUPPORT && pushToHistory) {
+            history.pushState({}, document.title, location.pathname + href);
+          }
+        }
+
+        return !!match;
+      },
+
+      /**
+       * Attempt to scroll to the current location's hash.
+       */
+      scrollToCurrent: function(e) {
+        if(this.scrollIfAnchor(window.location.hash) && e) {
+          e.preventDefault();
+        }
+      },
+
+      delegateSpans: function(e) {
+        var elem = utilities.closest(e.target, "a");
+
+        if(this.scrollIfAnchor(elem.getAttribute('href'), true)) {
+          e.preventDefault();
+        }
+      },
+
+      /**
+       * If the click event's target was an anchor, fix the scroll position.
+       */
+      delegateAnchors: function(e) {
+        var elem = e.target;
+
+        if(this.scrollIfAnchor(elem.getAttribute('href'), true)) {
+          e.preventDefault();
+        }
+      }
+    };
+
+    $(document).ready($.proxy(anchorScrolls, 'init'));
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+};
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+},{}],10:[function(require,module,exports){
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+  rightMenuIsOnScreen: function() {
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+  isFixedToBottom: false,
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+  bind: function() {
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+
+    var rightMenuLinks = document.querySelectorAll("#pytorch-right-menu li");
+    var rightMenuHasLinks = rightMenuLinks.length > 1;
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+    if (!rightMenuHasLinks) {
+      for (var i = 0; i < rightMenuLinks.length; i++) {
+        rightMenuLinks[i].style.display = "none";
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+    }
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+    if (rightMenuHasLinks) {
+      // Don't show the Shortcuts menu title text unless there are menu items
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+
+      // We are hiding the titles of the pages in the right side menu but there are a few
+      // pages that include other pages in the right side menu (see 'torch.nn' in the docs)
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+      var titleLinks = document.querySelectorAll(
+        "#pytorch-right-menu #pytorch-side-scroll-right \
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+
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+        link.classList.add("title-link");
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+      // Add + expansion signifiers to normal right menu links that have sub menus
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+          menuLinks[i].nextElementSibling.tagName === "UL"
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+      // If a hash is present on page load recursively expand menu items leading to selected item
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+
+      if (linkWithHash) {
+        // Expand immediate sibling list if present
+        if (
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+          linkWithHash.nextElementSibling.tagName === "UL" &&
+          linkWithHash.nextElementSibling.children.length > 0
+        ) {
+          linkWithHash.nextElementSibling.style.display = "block";
+          linkWithHash.classList.add("expanded");
+        }
+
+        // Expand ancestor lists if any
+        sideMenus.expandClosestUnexpandedParentList(linkWithHash);
+      }
+
+      // Bind click events on right menu links
+      $("#pytorch-right-menu a.reference.internal").on("click", function() {
+        if (this.classList.contains("expanded")) {
+          this.nextElementSibling.style.display = "none";
+          this.classList.remove("expanded");
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+          this.nextElementSibling.style.display = "block";
+          this.classList.remove("not-expanded");
+          this.classList.add("expanded");
+        }
+      });
+
+      sideMenus.handleRightMenu();
+    }
+
+    $(window).on('resize scroll', function(e) {
+      sideMenus.handleNavBar();
+
+      sideMenus.handleLeftMenu();
+
+      if (sideMenus.rightMenuIsOnScreen()) {
+        sideMenus.handleRightMenu();
+      }
+    });
+  },
+
+  leftMenuIsFixed: function() {
+    return document.getElementById("pytorch-left-menu").classList.contains("make-fixed");
+  },
+
+  handleNavBar: function() {
+    var mainHeaderHeight = document.getElementById('header-holder').offsetHeight;
+
+    // If we are scrolled past the main navigation header fix the sub menu bar to top of page
+    if (utilities.scrollTop() >= mainHeaderHeight) {
+      document.getElementById("pytorch-left-menu").classList.add("make-fixed");
+      document.getElementById("pytorch-page-level-bar").classList.add("left-menu-is-fixed");
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+      document.getElementById("pytorch-left-menu").classList.remove("make-fixed");
+      document.getElementById("pytorch-page-level-bar").classList.remove("left-menu-is-fixed");
+    }
+  },
+
+  expandClosestUnexpandedParentList: function (el) {
+    var closestParentList = utilities.closest(el, "ul");
+
+    if (closestParentList) {
+      var closestParentLink = closestParentList.previousElementSibling;
+      var closestParentLinkExists = closestParentLink &&
+                                    closestParentLink.tagName === "A" &&
+                                    closestParentLink.classList.contains("reference");
+
+      if (closestParentLinkExists) {
+        // Don't add expansion class to any title links
+         if (closestParentLink.classList.contains("title-link")) {
+           return;
+         }
+
+        closestParentList.style.display = "block";
+        closestParentLink.classList.remove("not-expanded");
+        closestParentLink.classList.add("expanded");
+        sideMenus.expandClosestUnexpandedParentList(closestParentLink);
+      }
+    }
+  },
+
+  handleLeftMenu: function () {
+    var windowHeight = utilities.windowHeight();
+    var topOfFooterRelativeToWindow = document.getElementById("docs-tutorials-resources").getBoundingClientRect().top;
+
+    if (topOfFooterRelativeToWindow >= windowHeight) {
+      document.getElementById("pytorch-left-menu").style.height = "100%";
+    } else {
+      var howManyPixelsOfTheFooterAreInTheWindow = windowHeight - topOfFooterRelativeToWindow;
+      var leftMenuDifference = howManyPixelsOfTheFooterAreInTheWindow;
+      document.getElementById("pytorch-left-menu").style.height = (windowHeight - leftMenuDifference) + "px";
+    }
+  },
+
+  handleRightMenu: function() {
+    var rightMenuWrapper = document.getElementById("pytorch-content-right");
+    var rightMenu = document.getElementById("pytorch-right-menu");
+    var rightMenuList = rightMenu.getElementsByTagName("ul")[0];
+    var article = document.getElementById("pytorch-article");
+    var articleHeight = article.offsetHeight;
+    var articleBottom = utilities.offset(article).top + articleHeight;
+    var mainHeaderHeight = document.getElementById('header-holder').offsetHeight;
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+    if (utilities.scrollTop() < mainHeaderHeight) {
+      rightMenuWrapper.style.height = "100%";
+      rightMenu.style.top = 0;
+      rightMenu.classList.remove("scrolling-fixed");
+      rightMenu.classList.remove("scrolling-absolute");
+    } else {
+      if (rightMenu.classList.contains("scrolling-fixed")) {
+        var rightMenuBottom =
+          utilities.offset(rightMenuList).top + rightMenuList.offsetHeight;
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+        if (rightMenuBottom >= articleBottom) {
+          rightMenuWrapper.style.height = articleHeight + mainHeaderHeight + "px";
+          rightMenu.style.top = utilities.scrollTop() - mainHeaderHeight + "px";
+          rightMenu.classList.add("scrolling-absolute");
+          rightMenu.classList.remove("scrolling-fixed");
+        }
+      } else {
+        rightMenuWrapper.style.height = articleHeight + mainHeaderHeight + "px";
+        rightMenu.style.top =
+          articleBottom - mainHeaderHeight - rightMenuList.offsetHeight + "px";
+        rightMenu.classList.add("scrolling-absolute");
+      }
+
+      if (utilities.scrollTop() < articleBottom - rightMenuList.offsetHeight) {
+        rightMenuWrapper.style.height = "100%";
+        rightMenu.style.top = "";
+        rightMenu.classList.remove("scrolling-absolute");
+        rightMenu.classList.add("scrolling-fixed");
+      }
+    }
+
+    var rightMenuSideScroll = document.getElementById("pytorch-side-scroll-right");
+    var sideScrollFromWindowTop = rightMenuSideScroll.getBoundingClientRect().top;
+
+    rightMenuSideScroll.style.height = utilities.windowHeight() - sideScrollFromWindowTop + "px";
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+},{}],"pytorch-sphinx-theme":[function(require,module,exports){
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+
+// Sphinx theme nav state
+function ThemeNav () {
+
+    var nav = {
+        navBar: null,
+        win: null,
+        winScroll: false,
+        winResize: false,
+        linkScroll: false,
+        winPosition: 0,
+        winHeight: null,
+        docHeight: null,
+        isRunning: false
+    };
+
+    nav.enable = function (withStickyNav) {
+        var self = this;
+
+        // TODO this can likely be removed once the theme javascript is broken
+        // out from the RTD assets. This just ensures old projects that are
+        // calling `enable()` get the sticky menu on by default. All other cals
+        // to `enable` should include an argument for enabling the sticky menu.
+        if (typeof(withStickyNav) == 'undefined') {
+            withStickyNav = true;
+        }
+
+        if (self.isRunning) {
+            // Only allow enabling nav logic once
+            return;
+        }
+
+        self.isRunning = true;
+        jQuery(function ($) {
+            self.init($);
+
+            self.reset();
+            self.win.on('hashchange', self.reset);
+
+            if (withStickyNav) {
+                // Set scroll monitor
+                self.win.on('scroll', function () {
+                    if (!self.linkScroll) {
+                        if (!self.winScroll) {
+                            self.winScroll = true;
+                            requestAnimationFrame(function() { self.onScroll(); });
+                        }
+                    }
+                });
+            }
+
+            // Set resize monitor
+            self.win.on('resize', function () {
+                if (!self.winResize) {
+                    self.winResize = true;
+                    requestAnimationFrame(function() { self.onResize(); });
+                }
+            });
+
+            self.onResize();
+        });
+
+    };
+
+    // TODO remove this with a split in theme and Read the Docs JS logic as
+    // well, it's only here to support 0.3.0 installs of our theme.
+    nav.enableSticky = function() {
+        this.enable(true);
+    };
+
+    nav.init = function ($) {
+        var doc = $(document),
+            self = this;
+
+        this.navBar = $('div.pytorch-side-scroll:first');
+        this.win = $(window);
+
+        // Set up javascript UX bits
+        $(document)
+            // Shift nav in mobile when clicking the menu.
+            .on('click', "[data-toggle='pytorch-left-menu-nav-top']", function() {
+                $("[data-toggle='wy-nav-shift']").toggleClass("shift");
+                $("[data-toggle='rst-versions']").toggleClass("shift");
+            })
+
+            // Nav menu link click operations
+            .on('click', ".pytorch-menu-vertical .current ul li a", function() {
+                var target = $(this);
+                // Close menu when you click a link.
+                $("[data-toggle='wy-nav-shift']").removeClass("shift");
+                $("[data-toggle='rst-versions']").toggleClass("shift");
+                // Handle dynamic display of l3 and l4 nav lists
+                self.toggleCurrent(target);
+                self.hashChange();
+            })
+            .on('click', "[data-toggle='rst-current-version']", function() {
+                $("[data-toggle='rst-versions']").toggleClass("shift-up");
+            })
+
+        // Make tables responsive
+        $("table.docutils:not(.field-list,.footnote,.citation)")
+            .wrap("<div class='wy-table-responsive'></div>");
+
+        // Add extra class to responsive tables that contain
+        // footnotes or citations so that we can target them for styling
+        $("table.docutils.footnote")
+            .wrap("<div class='wy-table-responsive footnote'></div>");
+        $("table.docutils.citation")
+            .wrap("<div class='wy-table-responsive citation'></div>");
+
+        // Add expand links to all parents of nested ul
+        $('.pytorch-menu-vertical ul').not('.simple').siblings('a').each(function () {
+            var link = $(this);
+                expand = $('<span class="toctree-expand"></span>');
+            expand.on('click', function (ev) {
+                self.toggleCurrent(link);
+                ev.stopPropagation();
+                return false;
+            });
+            link.prepend(expand);
+        });
+    };
+
+    nav.reset = function () {
+        // Get anchor from URL and open up nested nav
+        var anchor = encodeURI(window.location.hash) || '#';
+
+        try {
+            var vmenu = $('.pytorch-menu-vertical');
+            var link = vmenu.find('[href="' + anchor + '"]');
+            if (link.length === 0) {
+                // this link was not found in the sidebar.
+                // Find associated id element, then its closest section
+                // in the document and try with that one.
+                var id_elt = $('.document [id="' + anchor.substring(1) + '"]');
+                var closest_section = id_elt.closest('div.section');
+                link = vmenu.find('[href="#' + closest_section.attr("id") + '"]');
+                if (link.length === 0) {
+                    // still not found in the sidebar. fall back to main section
+                    link = vmenu.find('[href="#"]');
+                }
+            }
+            // If we found a matching link then reset current and re-apply
+            // otherwise retain the existing match
+            if (link.length > 0) {
+                $('.pytorch-menu-vertical .current').removeClass('current');
+                link.addClass('current');
+                link.closest('li.toctree-l1').addClass('current');
+                link.closest('li.toctree-l1').parent().addClass('current');
+                link.closest('li.toctree-l1').addClass('current');
+                link.closest('li.toctree-l2').addClass('current');
+                link.closest('li.toctree-l3').addClass('current');
+                link.closest('li.toctree-l4').addClass('current');
+            }
+        }
+        catch (err) {
+            console.log("Error expanding nav for anchor", err);
+        }
+
+    };
+
+    nav.onScroll = function () {
+        this.winScroll = false;
+        var newWinPosition = this.win.scrollTop(),
+            winBottom = newWinPosition + this.winHeight,
+            navPosition = this.navBar.scrollTop(),
+            newNavPosition = navPosition + (newWinPosition - this.winPosition);
+        if (newWinPosition < 0 || winBottom > this.docHeight) {
+            return;
+        }
+        this.navBar.scrollTop(newNavPosition);
+        this.winPosition = newWinPosition;
+    };
+
+    nav.onResize = function () {
+        this.winResize = false;
+        this.winHeight = this.win.height();
+        this.docHeight = $(document).height();
+    };
+
+    nav.hashChange = function () {
+        this.linkScroll = true;
+        this.win.one('hashchange', function () {
+            this.linkScroll = false;
+        });
+    };
+
+    nav.toggleCurrent = function (elem) {
+        var parent_li = elem.closest('li');
+        parent_li.siblings('li.current').removeClass('current');
+        parent_li.siblings().find('li.current').removeClass('current');
+        parent_li.find('> ul li.current').removeClass('current');
+        parent_li.toggleClass('current');
+    }
+
+    return nav;
+};
+
+module.exports.ThemeNav = ThemeNav();
+
+if (typeof(window) != 'undefined') {
+    window.SphinxRtdTheme = {
+        Navigation: module.exports.ThemeNav,
+        // TODO remove this once static assets are split up between the theme
+        // and Read the Docs. For now, this patches 0.3.0 to be backwards
+        // compatible with a pre-0.3.0 layout.html
+        StickyNav: module.exports.ThemeNav,
+    };
+}
+
+
+// requestAnimationFrame polyfill by Erik Möller. fixes from Paul Irish and Tino Zijdel
+// https://gist.github.com/paulirish/1579671
+// MIT license
+
+(function() {
+    var lastTime = 0;
+    var vendors = ['ms', 'moz', 'webkit', 'o'];
+    for(var x = 0; x < vendors.length && !window.requestAnimationFrame; ++x) {
+        window.requestAnimationFrame = window[vendors[x]+'RequestAnimationFrame'];
+        window.cancelAnimationFrame = window[vendors[x]+'CancelAnimationFrame']
+                                   || window[vendors[x]+'CancelRequestAnimationFrame'];
+    }
+
+    if (!window.requestAnimationFrame)
+        window.requestAnimationFrame = function(callback, element) {
+            var currTime = new Date().getTime();
+            var timeToCall = Math.max(0, 16 - (currTime - lastTime));
+            var id = window.setTimeout(function() { callback(currTime + timeToCall); },
+              timeToCall);
+            lastTime = currTime + timeToCall;
+            return id;
+        };
+
+    if (!window.cancelAnimationFrame)
+        window.cancelAnimationFrame = function(id) {
+            clearTimeout(id);
+        };
+}());
+
+$(".sphx-glr-thumbcontainer").removeAttr("tooltip");
+$("table").removeAttr("border");
+
+// This code replaces the default sphinx gallery download buttons
+// with the 3 download buttons at the top of the page
+
+var downloadNote = $(".sphx-glr-download-link-note.admonition.note");
+if (downloadNote.length >= 1) {
+    var tutorialUrlArray = $("#tutorial-type").text().split('/');
+        tutorialUrlArray[0] = tutorialUrlArray[0] + "_source"
+
+    var githubLink = "https://github.com/pytorch/tutorials/blob/master/" + tutorialUrlArray.join("/") + ".py",
+        notebookLink = $(".reference.download")[1].href,
+        notebookDownloadPath = notebookLink.split('_downloads')[1],
+        colabLink = "https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads" + notebookDownloadPath;
+
+    $("#google-colab-link").wrap("<a href=" + colabLink + " data-behavior='call-to-action-event' data-response='Run in Google Colab' target='_blank'/>");
+    $("#download-notebook-link").wrap("<a href=" + notebookLink + " data-behavior='call-to-action-event' data-response='Download Notebook'/>");
+    $("#github-view-link").wrap("<a href=" + githubLink + " data-behavior='call-to-action-event' data-response='View on Github' target='_blank'/>");
+} else {
+    $(".pytorch-call-to-action-links").hide();
+}
+
+//This code handles the Expand/Hide toggle for the Docs/Tutorials left nav items
+
+$(document).ready(function() {
+  var caption = "#pytorch-left-menu p.caption";
+  var collapseAdded = $(this).not("checked");
+  $(caption).each(function () {
+    var menuName = this.innerText.replace(/[^\w\s]/gi, "").trim();
+    $(this).find("span").addClass("checked");
+    if (collapsedSections.includes(menuName) == true && collapseAdded && sessionStorage.getItem(menuName) !== "expand" || sessionStorage.getItem(menuName) == "collapse") {
+      $(this.firstChild).after("<span class='expand-menu'>[ + ]</span>");
+      $(this.firstChild).after("<span class='hide-menu collapse'>[ - ]</span>");
+      $(this).next("ul").hide();
+    } else if (collapsedSections.includes(menuName) == false && collapseAdded || sessionStorage.getItem(menuName) == "expand") {
+      $(this.firstChild).after("<span class='expand-menu collapse'>[ + ]</span>");
+      $(this.firstChild).after("<span class='hide-menu'>[ - ]</span>");
+    }
+  });
+
+  $(".expand-menu").on("click", function () {
+    $(this).prev(".hide-menu").toggle();
+    $(this).parent().next("ul").toggle();
+    var menuName = $(this).parent().text().replace(/[^\w\s]/gi, "").trim();
+    if (sessionStorage.getItem(menuName) == "collapse") {
+      sessionStorage.removeItem(menuName);
+    }
+    sessionStorage.setItem(menuName, "expand");
+    toggleList(this);
+  });
+
+  $(".hide-menu").on("click", function () {
+    $(this).next(".expand-menu").toggle();
+    $(this).parent().next("ul").toggle();
+    var menuName = $(this).parent().text().replace(/[^\w\s]/gi, "").trim();
+    if (sessionStorage.getItem(menuName) == "expand") {
+      sessionStorage.removeItem(menuName);
+    }
+    sessionStorage.setItem(menuName, "collapse");
+    toggleList(this);
+  });
+
+  function toggleList(menuCommand) {
+    $(menuCommand).toggle();
+  }
+});
+
+// Build an array from each tag that's present
+
+var tagList = $(".tutorials-card-container").map(function() {
+    return $(this).data("tags").split(",").map(function(item) {
+        return item.trim();
+      });
+}).get();
+
+function unique(value, index, self) {
+      return self.indexOf(value) == index && value != ""
+    }
+
+// Only return unique tags
+
+var tags = tagList.sort().filter(unique);
+
+// Add filter buttons to the top of the page for each tag
+
+function createTagMenu() {
+    tags.forEach(function(item){
+    $(".tutorial-filter-menu").append(" <div class='tutorial-filter filter-btn filter' data-tag='" + item + "'>" + item + "</div>")
+  })
+};
+
+createTagMenu();
+
+// Remove hyphens if they are present in the filter buttons
+
+$(".tags").each(function(){
+    var tags = $(this).text().split(",");
+    tags.forEach(function(tag, i ) {
+       tags[i] = tags[i].replace(/-/, ' ')
+    })
+    $(this).html(tags.join(", "));
+});
+
+// Remove hyphens if they are present in the card body
+
+$(".tutorial-filter").each(function(){
+    var tag = $(this).text();
+    $(this).html(tag.replace(/-/, ' '))
+})
+
+// Remove any empty p tags that Sphinx adds
+
+$("#tutorial-cards p").each(function(index, item) {
+    if(!$(item).text().trim()) {
+        $(item).remove();
+    }
+});
+
+// Jump back to top on pagination click
+
+$(document).on("click", ".page", function() {
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+
+var link = $("a[href='intermediate/speech_command_recognition_with_torchaudio.html']");
+
+if (link.text() == "SyntaxError") {
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+    });
+
+    $(".stars-outer > i").each(function() {
+        $(this).unbind("mouseenter mouseleave").css({
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+
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+var lastId,
+  topMenu = $("#pytorch-side-scroll-right"),
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+  // All sidenav items
+  menuItems = topMenu.find("a"),
+  // Anchors for menu items
+  scrollItems = menuItems.map(function () {
+    var item = $(this).attr("href");
+    if (item.length) {
+      return item;
+    }
+  });
+
+$(window).scroll(function () {
+  var fromTop = $(this).scrollTop() + topMenuHeight;
+  var article = ".section";
+
+  $(article).each(function (i) {
+    var offsetScroll = $(this).offset().top - $(window).scrollTop();
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+  });
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+
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+},{"jquery":"jquery"}]},{},[1,2,3,4,5,6,7,8,9,10,"pytorch-sphinx-theme"]);
diff --git a/docs/v2.2.0/_static/js/vendor/anchor.min.js b/docs/v2.2.0/_static/js/vendor/anchor.min.js
new file mode 100644
index 0000000000..1216eeac26
--- /dev/null
+++ b/docs/v2.2.0/_static/js/vendor/anchor.min.js
@@ -0,0 +1,9 @@
+// @license magnet:?xt=urn:btih:d3d9a9a6595521f9666a5e94cc830dab83b65699&dn=expat.txt Expat
+//
+// AnchorJS - v4.3.0 - 2020-10-21
+// https://www.bryanbraun.com/anchorjs/
+// Copyright (c) 2020 Bryan Braun; Licensed MIT
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+// @license-end
\ No newline at end of file
diff --git a/docs/v2.2.0/_static/js/vendor/bootstrap.min.js b/docs/v2.2.0/_static/js/vendor/bootstrap.min.js
new file mode 100644
index 0000000000..c4c0d1f95c
--- /dev/null
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+/*!
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t={relatedTarget:this._element},e=g.Event(kt.SHOW,t),n=c._getParentFromElement(this._element);g(n).trigger(e),e.isDefaultPrevented()||(g(this._menu).toggleClass(Lt),g(n).toggleClass(Lt).trigger(g.Event(kt.SHOWN,t)))}},t.hide=function(){if(!this._element.disabled&&!g(this._element).hasClass(Pt)&&g(this._menu).hasClass(Lt)){var t={relatedTarget:this._element},e=g.Event(kt.HIDE,t),n=c._getParentFromElement(this._element);g(n).trigger(e),e.isDefaultPrevented()||(g(this._menu).toggleClass(Lt),g(n).toggleClass(Lt).trigger(g.Event(kt.HIDDEN,t)))}},t.dispose=function(){g.removeData(this._element,Dt),g(this._element).off(wt),this._element=null,(this._menu=null)!==this._popper&&(this._popper.destroy(),this._popper=null)},t.update=function(){this._inNavbar=this._detectNavbar(),null!==this._popper&&this._popper.scheduleUpdate()},t._addEventListeners=function(){var e=this;g(this._element).on(kt.CLICK,function(t){t.preventDefault(),t.stopPropagation(),e.toggle()})},t._getConfig=function(t){return 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t={placement:this._getPlacement(),modifiers:{offset:this._getOffset(),flip:{enabled:this._config.flip},preventOverflow:{boundariesElement:this._config.boundary}}};return"static"===this._config.display&&(t.modifiers.applyStyle={enabled:!1}),t},c._jQueryInterface=function(e){return this.each(function(){var t=g(this).data(Dt);if(t||(t=new c(this,"object"==typeof e?e:null),g(this).data(Dt,t)),"string"==typeof e){if("undefined"==typeof t[e])throw new TypeError('No method named "'+e+'"');t[e]()}})},c._clearMenus=function(t){if(!t||3!==t.which&&("keyup"!==t.type||9===t.which))for(var e=[].slice.call(document.querySelectorAll(Ut)),n=0,i=e.length;n<i;n++){var o=c._getParentFromElement(e[n]),r=g(e[n]).data(Dt),s={relatedTarget:e[n]};if(t&&"click"===t.type&&(s.clickEvent=t),r){var a=r._menu;if(g(o).hasClass(Lt)&&!(t&&("click"===t.type&&/input|textarea/i.test(t.target.tagName)||"keyup"===t.type&&9===t.which)&&g.contains(o,t.target))){var 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Zt="modal",te="bs.modal",ee="."+te,ne=g.fn[Zt],ie={backdrop:!0,keyboard:!0,focus:!0,show:!0},oe={backdrop:"(boolean|string)",keyboard:"boolean",focus:"boolean",show:"boolean"},re={HIDE:"hide"+ee,HIDDEN:"hidden"+ee,SHOW:"show"+ee,SHOWN:"shown"+ee,FOCUSIN:"focusin"+ee,RESIZE:"resize"+ee,CLICK_DISMISS:"click.dismiss"+ee,KEYDOWN_DISMISS:"keydown.dismiss"+ee,MOUSEUP_DISMISS:"mouseup.dismiss"+ee,MOUSEDOWN_DISMISS:"mousedown.dismiss"+ee,CLICK_DATA_API:"click"+ee+".data-api"},se="modal-dialog-scrollable",ae="modal-scrollbar-measure",le="modal-backdrop",ce="modal-open",he="fade",ue="show",fe=".modal-dialog",de=".modal-body",ge='[data-toggle="modal"]',_e='[data-dismiss="modal"]',me=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",pe=".sticky-top",ve=function(){function 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e._showElement(t)}))}},t.hide=function(t){var e=this;if(t&&t.preventDefault(),this._isShown&&!this._isTransitioning){var n=g.Event(re.HIDE);if(g(this._element).trigger(n),this._isShown&&!n.isDefaultPrevented()){this._isShown=!1;var i=g(this._element).hasClass(he);if(i&&(this._isTransitioning=!0),this._setEscapeEvent(),this._setResizeEvent(),g(document).off(re.FOCUSIN),g(this._element).removeClass(ue),g(this._element).off(re.CLICK_DISMISS),g(this._dialog).off(re.MOUSEDOWN_DISMISS),i){var o=_.getTransitionDurationFromElement(this._element);g(this._element).one(_.TRANSITION_END,function(t){return e._hideModal(t)}).emulateTransitionEnd(o)}else this._hideModal()}}},t.dispose=function(){[window,this._element,this._dialog].forEach(function(t){return 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diff --git a/docs/v2.2.0/_static/js/vendor/popper.min.js b/docs/v2.2.0/_static/js/vendor/popper.min.js
new file mode 100644
index 0000000000..bb1aaae3e8
--- /dev/null
+++ b/docs/v2.2.0/_static/js/vendor/popper.min.js
@@ -0,0 +1,5 @@
+/*
+ Copyright (C) Federico Zivolo 2020
+ Distributed under the MIT License (license terms are at http://opensource.org/licenses/MIT).
+ */(function(e,t){'object'==typeof exports&&'undefined'!=typeof module?module.exports=t():'function'==typeof define&&define.amd?define(t):e.Popper=t()})(this,function(){'use strict';function e(e){return e&&'[object Function]'==={}.toString.call(e)}function t(e,t){if(1!==e.nodeType)return[];var o=e.ownerDocument.defaultView,n=o.getComputedStyle(e,null);return t?n[t]:n}function o(e){return'HTML'===e.nodeName?e:e.parentNode||e.host}function n(e){if(!e)return document.body;switch(e.nodeName){case'HTML':case'BODY':return e.ownerDocument.body;case'#document':return e.body;}var i=t(e),r=i.overflow,p=i.overflowX,s=i.overflowY;return /(auto|scroll|overlay)/.test(r+s+p)?e:n(o(e))}function i(e){return e&&e.referenceNode?e.referenceNode:e}function r(e){return 11===e?re:10===e?pe:re||pe}function p(e){if(!e)return document.documentElement;for(var o=r(10)?document.body:null,n=e.offsetParent||null;n===o&&e.nextElementSibling;)n=(e=e.nextElementSibling).offsetParent;var i=n&&n.nodeName;return 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diff --git a/docs/v2.2.0/_static/jupyterlite_badge_logo.svg b/docs/v2.2.0/_static/jupyterlite_badge_logo.svg
new file mode 100644
index 0000000000..5de36d7fd5
--- /dev/null
+++ b/docs/v2.2.0/_static/jupyterlite_badge_logo.svg
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\ No newline at end of file
diff --git a/docs/v2.2.0/_static/language_data.js b/docs/v2.2.0/_static/language_data.js
new file mode 100644
index 0000000000..ebe2f03bf0
--- /dev/null
+++ b/docs/v2.2.0/_static/language_data.js
@@ -0,0 +1,297 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
+
+
+
+var splitChars = (function() {
+    var result = {};
+    var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
+         1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
+         2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
+         2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
+         3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
+         3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
+         4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
+         8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
+         11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
+         43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
+    var i, j, start, end;
+    for (i = 0; i < singles.length; i++) {
+        result[singles[i]] = true;
+    }
+    var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
+         [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
+         [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
+         [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
+         [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
+         [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
+         [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
+         [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
+         [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
+         [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
+         [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
+         [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
+         [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
+         [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
+         [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
+         [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
+         [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
+         [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
+         [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
+         [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
+         [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
+         [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
+         [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
+         [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
+         [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
+         [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
+         [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
+         [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
+         [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
+         [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
+         [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
+         [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
+         [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
+         [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
+         [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
+         [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
+         [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
+         [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
+         [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
+         [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
+         [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
+         [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
+         [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
+         [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
+         [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
+         [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
+         [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
+         [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
+         [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
+    for (i = 0; i < ranges.length; i++) {
+        start = ranges[i][0];
+        end = ranges[i][1];
+        for (j = start; j <= end; j++) {
+            result[j] = true;
+        }
+    }
+    return result;
+})();
+
+function splitQuery(query) {
+    var result = [];
+    var start = -1;
+    for (var i = 0; i < query.length; i++) {
+        if (splitChars[query.charCodeAt(i)]) {
+            if (start !== -1) {
+                result.push(query.slice(start, i));
+                start = -1;
+            }
+        } else if (start === -1) {
+            start = i;
+        }
+    }
+    if (start !== -1) {
+        result.push(query.slice(start));
+    }
+    return result;
+}
+
+
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new file mode 100644
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literal 0
HcmV?d00001

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diff --git a/docs/v2.2.0/_static/pygments.css b/docs/v2.2.0/_static/pygments.css
new file mode 100644
index 0000000000..0d49244eda
--- /dev/null
+++ b/docs/v2.2.0/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #eeffcc; }
+.highlight .c { color: #408090; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #007020; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #408090; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #408090; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #007020 } /* Comment.Preproc */
+.highlight .cpf { color: #408090; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #408090; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #FF0000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #00A000 } /* Generic.Inserted */
+.highlight .go { color: #333333 } /* Generic.Output */
+.highlight .gp { color: #c65d09; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #007020; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #007020; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #007020; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #007020 } /* Keyword.Pseudo */
+.highlight .kr { color: #007020; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #902000 } /* Keyword.Type */
+.highlight .m { color: #208050 } /* Literal.Number */
+.highlight .s { color: #4070a0 } /* Literal.String */
+.highlight .na { color: #4070a0 } /* Name.Attribute */
+.highlight .nb { color: #007020 } /* Name.Builtin */
+.highlight .nc { color: #0e84b5; font-weight: bold } /* Name.Class */
+.highlight .no { color: #60add5 } /* Name.Constant */
+.highlight .nd { color: #555555; font-weight: bold } /* Name.Decorator */
+.highlight .ni { color: #d55537; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #007020 } /* Name.Exception */
+.highlight .nf { color: #06287e } /* Name.Function */
+.highlight .nl { color: #002070; font-weight: bold } /* Name.Label */
+.highlight .nn { color: #0e84b5; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #062873; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #bb60d5 } /* Name.Variable */
+.highlight .ow { color: #007020; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #208050 } /* Literal.Number.Bin */
+.highlight .mf { color: #208050 } /* Literal.Number.Float */
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+.highlight .mi { color: #208050 } /* Literal.Number.Integer */
+.highlight .mo { color: #208050 } /* Literal.Number.Oct */
+.highlight .sa { color: #4070a0 } /* Literal.String.Affix */
+.highlight .sb { color: #4070a0 } /* Literal.String.Backtick */
+.highlight .sc { color: #4070a0 } /* Literal.String.Char */
+.highlight .dl { color: #4070a0 } /* Literal.String.Delimiter */
+.highlight .sd { color: #4070a0; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #4070a0 } /* Literal.String.Double */
+.highlight .se { color: #4070a0; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #4070a0 } /* Literal.String.Heredoc */
+.highlight .si { color: #70a0d0; font-style: italic } /* Literal.String.Interpol */
+.highlight .sx { color: #c65d09 } /* Literal.String.Other */
+.highlight .sr { color: #235388 } /* Literal.String.Regex */
+.highlight .s1 { color: #4070a0 } /* Literal.String.Single */
+.highlight .ss { color: #517918 } /* Literal.String.Symbol */
+.highlight .bp { color: #007020 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #06287e } /* Name.Function.Magic */
+.highlight .vc { color: #bb60d5 } /* Name.Variable.Class */
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diff --git a/docs/v2.2.0/_static/searchtools.js b/docs/v2.2.0/_static/searchtools.js
new file mode 100644
index 0000000000..0a44e8582f
--- /dev/null
+++ b/docs/v2.2.0/_static/searchtools.js
@@ -0,0 +1,525 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+if (!Scorer) {
+  /**
+   * Simple result scoring code.
+   */
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [filename, title, anchor, descr, score]
+    // and returns the new score.
+    /*
+    score: function(result) {
+      return result[4];
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {0:  15,   // used to be importantResults
+              1:  5,   // used to be objectResults
+              2: -5},  // used to be unimportantResults
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2
+  };
+}
+
+if (!splitQuery) {
+  function splitQuery(query) {
+    return query.split(/\s+/);
+  }
+}
+
+/**
+ * Search Module
+ */
+var Search = {
+
+  _index : null,
+  _queued_query : null,
+  _pulse_status : -1,
+
+  htmlToText : function(htmlString) {
+      var virtualDocument = document.implementation.createHTMLDocument('virtual');
+      var htmlElement = $(htmlString, virtualDocument);
+      htmlElement.find('.headerlink').remove();
+      docContent = htmlElement.find('[role=main]')[0];
+      if(docContent === undefined) {
+          console.warn("Content block not found. Sphinx search tries to obtain it " +
+                       "via '[role=main]'. Could you check your theme or template.");
+          return "";
+      }
+      return docContent.textContent || docContent.innerText;
+  },
+
+  init : function() {
+      var params = $.getQueryParameters();
+      if (params.q) {
+          var query = params.q[0];
+          $('input[name="q"]')[0].value = query;
+          this.performSearch(query);
+      }
+  },
+
+  loadIndex : function(url) {
+    $.ajax({type: "GET", url: url, data: null,
+            dataType: "script", cache: true,
+            complete: function(jqxhr, textstatus) {
+              if (textstatus != "success") {
+                document.getElementById("searchindexloader").src = url;
+              }
+            }});
+  },
+
+  setIndex : function(index) {
+    var q;
+    this._index = index;
+    if ((q = this._queued_query) !== null) {
+      this._queued_query = null;
+      Search.query(q);
+    }
+  },
+
+  hasIndex : function() {
+      return this._index !== null;
+  },
+
+  deferQuery : function(query) {
+      this._queued_query = query;
+  },
+
+  stopPulse : function() {
+      this._pulse_status = 0;
+  },
+
+  startPulse : function() {
+    if (this._pulse_status >= 0)
+        return;
+    function pulse() {
+      var i;
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      var dotString = '';
+      for (i = 0; i < Search._pulse_status; i++)
+        dotString += '.';
+      Search.dots.text(dotString);
+      if (Search._pulse_status > -1)
+        window.setTimeout(pulse, 500);
+    }
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch : function(query) {
+    // create the required interface elements
+    this.out = $('#search-results');
+    this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
+    this.dots = $('<span></span>').appendTo(this.title);
+    this.status = $('<p class="search-summary">&nbsp;</p>').appendTo(this.out);
+    this.output = $('<ul class="search"/>').appendTo(this.out);
+
+    $('#search-progress').text(_('Preparing search...'));
+    this.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (this.hasIndex())
+      this.query(query);
+    else
+      this.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query : function(query) {
+    var i;
+
+    // stem the searchterms and add them to the correct list
+    var stemmer = new Stemmer();
+    var searchterms = [];
+    var excluded = [];
+    var hlterms = [];
+    var tmp = splitQuery(query);
+    var objectterms = [];
+    for (i = 0; i < tmp.length; i++) {
+      if (tmp[i] !== "") {
+          objectterms.push(tmp[i].toLowerCase());
+      }
+
+      if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
+        // skip this "word"
+        continue;
+      }
+      // stem the word
+      var word = stemmer.stemWord(tmp[i].toLowerCase());
+      var toAppend;
+      // select the correct list
+      if (word[0] == '-') {
+        toAppend = excluded;
+        word = word.substr(1);
+      }
+      else {
+        toAppend = searchterms;
+        hlterms.push(tmp[i].toLowerCase());
+      }
+      // only add if not already in the list
+      if (!$u.contains(toAppend, word))
+        toAppend.push(word);
+    }
+    var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
+
+    // console.debug('SEARCH: searching for:');
+    // console.info('required: ', searchterms);
+    // console.info('excluded: ', excluded);
+
+    // prepare search
+    var terms = this._index.terms;
+    var titleterms = this._index.titleterms;
+
+    // array of [filename, title, anchor, descr, score]
+    var results = [];
+    $('#search-progress').empty();
+
+    // lookup as object
+    for (i = 0; i < objectterms.length; i++) {
+      var others = [].concat(objectterms.slice(0, i),
+                             objectterms.slice(i+1, objectterms.length));
+      results = results.concat(this.performObjectSearch(objectterms[i], others));
+    }
+
+    // lookup as search terms in fulltext
+    results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      for (i = 0; i < results.length; i++)
+        results[i][4] = Scorer.score(results[i]);
+    }
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort(function(a, b) {
+      var left = a[4];
+      var right = b[4];
+      if (left > right) {
+        return 1;
+      } else if (left < right) {
+        return -1;
+      } else {
+        // same score: sort alphabetically
+        left = a[1].toLowerCase();
+        right = b[1].toLowerCase();
+        return (left > right) ? -1 : ((left < right) ? 1 : 0);
+      }
+    });
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    //console.info('search results:', Search.lastresults);
+
+    // print the results
+    var resultCount = results.length;
+    function displayNextItem() {
+      // results left, load the summary and display it
+      if (results.length) {
+        var item = results.pop();
+        var listItem = $('<li></li>');
+        var requestUrl = "";
+        var linkUrl = "";
+        if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
+          // dirhtml builder
+          var dirname = item[0] + '/';
+          if (dirname.match(/\/index\/$/)) {
+            dirname = dirname.substring(0, dirname.length-6);
+          } else if (dirname == 'index/') {
+            dirname = '';
+          }
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
+          linkUrl = requestUrl;
+
+        } else {
+          // normal html builders
+          requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+          linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+        }
+        listItem.append($('<a/>').attr('href',
+            linkUrl +
+            highlightstring + item[2]).html(item[1]));
+        if (item[3]) {
+          listItem.append($('<span> (' + item[3] + ')</span>'));
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        } else if (DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY) {
+          $.ajax({url: requestUrl,
+                  dataType: "text",
+                  complete: function(jqxhr, textstatus) {
+                    var data = jqxhr.responseText;
+                    if (data !== '' && data !== undefined) {
+                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
+                      if (summary) {
+                        listItem.append(summary);
+                      }
+                    }
+                    Search.output.append(listItem);
+                    setTimeout(function() {
+                      displayNextItem();
+                    }, 5);
+                  }});
+        } else {
+          // just display title
+          Search.output.append(listItem);
+          setTimeout(function() {
+            displayNextItem();
+          }, 5);
+        }
+      }
+      // search finished, update title and status message
+      else {
+        Search.stopPulse();
+        Search.title.text(_('Search Results'));
+        if (!resultCount)
+          Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
+        else
+            Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
+        Search.status.fadeIn(500);
+      }
+    }
+    displayNextItem();
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch : function(object, otherterms) {
+    var filenames = this._index.filenames;
+    var docnames = this._index.docnames;
+    var objects = this._index.objects;
+    var objnames = this._index.objnames;
+    var titles = this._index.titles;
+
+    var i;
+    var results = [];
+
+    for (var prefix in objects) {
+      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
+        var match = objects[prefix][iMatch];
+        var name = match[4];
+        var fullname = (prefix ? prefix + '.' : '') + name;
+        var fullnameLower = fullname.toLowerCase()
+        if (fullnameLower.indexOf(object) > -1) {
+          var score = 0;
+          var parts = fullnameLower.split('.');
+          // check for different match types: exact matches of full name or
+          // "last name" (i.e. last dotted part)
+          if (fullnameLower == object || parts[parts.length - 1] == object) {
+            score += Scorer.objNameMatch;
+          // matches in last name
+          } else if (parts[parts.length - 1].indexOf(object) > -1) {
+            score += Scorer.objPartialMatch;
+          }
+          var objname = objnames[match[1]][2];
+          var title = titles[match[0]];
+          // If more than one term searched for, we require other words to be
+          // found in the name/title/description
+          if (otherterms.length > 0) {
+            var haystack = (prefix + ' ' + name + ' ' +
+                            objname + ' ' + title).toLowerCase();
+            var allfound = true;
+            for (i = 0; i < otherterms.length; i++) {
+              if (haystack.indexOf(otherterms[i]) == -1) {
+                allfound = false;
+                break;
+              }
+            }
+            if (!allfound) {
+              continue;
+            }
+          }
+          var descr = objname + _(', in ') + title;
+
+          var anchor = match[3];
+          if (anchor === '')
+            anchor = fullname;
+          else if (anchor == '-')
+            anchor = objnames[match[1]][1] + '-' + fullname;
+          // add custom score for some objects according to scorer
+          if (Scorer.objPrio.hasOwnProperty(match[2])) {
+            score += Scorer.objPrio[match[2]];
+          } else {
+            score += Scorer.objPrioDefault;
+          }
+          results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
+        }
+      }
+    }
+
+    return results;
+  },
+
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
+   */
+  escapeRegExp : function(string) {
+    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch : function(searchterms, excluded, terms, titleterms) {
+    var docnames = this._index.docnames;
+    var filenames = this._index.filenames;
+    var titles = this._index.titles;
+
+    var i, j, file;
+    var fileMap = {};
+    var scoreMap = {};
+    var results = [];
+
+    // perform the search on the required terms
+    for (i = 0; i < searchterms.length; i++) {
+      var word = searchterms[i];
+      var files = [];
+      var _o = [
+        {files: terms[word], score: Scorer.term},
+        {files: titleterms[word], score: Scorer.title}
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        var word_regex = this.escapeRegExp(word);
+        for (var w in terms) {
+          if (w.match(word_regex) && !terms[word]) {
+            _o.push({files: terms[w], score: Scorer.partialTerm})
+          }
+        }
+        for (var w in titleterms) {
+          if (w.match(word_regex) && !titleterms[word]) {
+              _o.push({files: titleterms[w], score: Scorer.partialTitle})
+          }
+        }
+      }
+
+      // no match but word was a required one
+      if ($u.every(_o, function(o){return o.files === undefined;})) {
+        break;
+      }
+      // found search word in contents
+      $u.each(_o, function(o) {
+        var _files = o.files;
+        if (_files === undefined)
+          return
+
+        if (_files.length === undefined)
+          _files = [_files];
+        files = files.concat(_files);
+
+        // set score for the word in each file to Scorer.term
+        for (j = 0; j < _files.length; j++) {
+          file = _files[j];
+          if (!(file in scoreMap))
+            scoreMap[file] = {};
+          scoreMap[file][word] = o.score;
+        }
+      });
+
+      // create the mapping
+      for (j = 0; j < files.length; j++) {
+        file = files[j];
+        if (file in fileMap && fileMap[file].indexOf(word) === -1)
+          fileMap[file].push(word);
+        else
+          fileMap[file] = [word];
+      }
+    }
+
+    // now check if the files don't contain excluded terms
+    for (file in fileMap) {
+      var valid = true;
+
+      // check if all requirements are matched
+      var filteredTermCount = // as search terms with length < 3 are discarded: ignore
+        searchterms.filter(function(term){return term.length > 2}).length
+      if (
+        fileMap[file].length != searchterms.length &&
+        fileMap[file].length != filteredTermCount
+      ) continue;
+
+      // ensure that none of the excluded terms is in the search result
+      for (i = 0; i < excluded.length; i++) {
+        if (terms[excluded[i]] == file ||
+            titleterms[excluded[i]] == file ||
+            $u.contains(terms[excluded[i]] || [], file) ||
+            $u.contains(titleterms[excluded[i]] || [], file)) {
+          valid = false;
+          break;
+        }
+      }
+
+      // if we have still a valid result we can add it to the result list
+      if (valid) {
+        // select one (max) score for the file.
+        // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
+        var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
+        results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
+      }
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words, hlwords is the list of normal, unstemmed
+   * words. the first one is used to find the occurrence, the
+   * latter for highlighting it.
+   */
+  makeSearchSummary : function(htmlText, keywords, hlwords) {
+    var text = Search.htmlToText(htmlText);
+    if (text == "") {
+      return null;
+    }
+    var textLower = text.toLowerCase();
+    var start = 0;
+    $.each(keywords, function() {
+      var i = textLower.indexOf(this.toLowerCase());
+      if (i > -1)
+        start = i;
+    });
+    start = Math.max(start - 120, 0);
+    var excerpt = ((start > 0) ? '...' : '') +
+      $.trim(text.substr(start, 240)) +
+      ((start + 240 - text.length) ? '...' : '');
+    var rv = $('<p class="context"></p>').text(excerpt);
+    $.each(hlwords, function() {
+      rv = rv.highlightText(this, 'highlighted');
+    });
+    return rv;
+  }
+};
+
+$(document).ready(function() {
+  Search.init();
+});
diff --git a/docs/v2.2.0/_static/sg_gallery-binder.css b/docs/v2.2.0/_static/sg_gallery-binder.css
new file mode 100644
index 0000000000..420005d227
--- /dev/null
+++ b/docs/v2.2.0/_static/sg_gallery-binder.css
@@ -0,0 +1,11 @@
+/* CSS for binder integration */
+
+div.binder-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.lite-badge {
+  margin: 1em auto;
+  vertical-align: middle;
+}
diff --git a/docs/v2.2.0/_static/sg_gallery-dataframe.css b/docs/v2.2.0/_static/sg_gallery-dataframe.css
new file mode 100644
index 0000000000..25be73092e
--- /dev/null
+++ b/docs/v2.2.0/_static/sg_gallery-dataframe.css
@@ -0,0 +1,46 @@
+/* Pandas dataframe css */
+/* Taken from: https://github.com/spatialaudio/nbsphinx/blob/fb3ba670fc1ba5f54d4c487573dbc1b4ecf7e9ff/src/nbsphinx.py#L587-L619 */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-tr-odd-color: #f5f5f5;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-tr-odd-color: #373737;
+  --sg-tr-hover-color: rgba(30, 81, 122, 0.2);
+}
+
+table.dataframe {
+  border: none !important;
+  border-collapse: collapse;
+  border-spacing: 0;
+  border-color: transparent;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+}
+table.dataframe thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+table.dataframe tr,
+table.dataframe th,
+table.dataframe td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+table.dataframe th {
+  font-weight: bold;
+}
+table.dataframe tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+table.dataframe tbody tr:hover {
+  background: var(--sg-tr-hover-color);
+}
diff --git a/docs/v2.2.0/_static/sg_gallery-rendered-html.css b/docs/v2.2.0/_static/sg_gallery-rendered-html.css
new file mode 100644
index 0000000000..93dc2ffb02
--- /dev/null
+++ b/docs/v2.2.0/_static/sg_gallery-rendered-html.css
@@ -0,0 +1,224 @@
+/* Adapted from notebook/static/style/style.min.css */
+html[data-theme="light"] {
+  --sg-text-color: #000;
+  --sg-background-color: #ffffff;
+  --sg-code-background-color: #eff0f1;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #f5f5f5;
+}
+html[data-theme="dark"] {
+  --sg-text-color: #fff;
+  --sg-background-color: #121212;
+  --sg-code-background-color: #2f2f30;
+  --sg-tr-hover-color: rgba(66, 165, 245, 0.2);
+  --sg-tr-odd-color: #1f1f1f;
+}
+
+.rendered_html {
+  color: var(--sg-text-color);
+  /* any extras will just be numbers: */
+}
+.rendered_html em {
+  font-style: italic;
+}
+.rendered_html strong {
+  font-weight: bold;
+}
+.rendered_html u {
+  text-decoration: underline;
+}
+.rendered_html :link {
+  text-decoration: underline;
+}
+.rendered_html :visited {
+  text-decoration: underline;
+}
+.rendered_html h1 {
+  font-size: 185.7%;
+  margin: 1.08em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h2 {
+  font-size: 157.1%;
+  margin: 1.27em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h3 {
+  font-size: 128.6%;
+  margin: 1.55em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h4 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+}
+.rendered_html h5 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h6 {
+  font-size: 100%;
+  margin: 2em 0 0 0;
+  font-weight: bold;
+  line-height: 1.0;
+  font-style: italic;
+}
+.rendered_html h1:first-child {
+  margin-top: 0.538em;
+}
+.rendered_html h2:first-child {
+  margin-top: 0.636em;
+}
+.rendered_html h3:first-child {
+  margin-top: 0.777em;
+}
+.rendered_html h4:first-child {
+  margin-top: 1em;
+}
+.rendered_html h5:first-child {
+  margin-top: 1em;
+}
+.rendered_html h6:first-child {
+  margin-top: 1em;
+}
+.rendered_html ul:not(.list-inline),
+.rendered_html ol:not(.list-inline) {
+  padding-left: 2em;
+}
+.rendered_html ul {
+  list-style: disc;
+}
+.rendered_html ul ul {
+  list-style: square;
+  margin-top: 0;
+}
+.rendered_html ul ul ul {
+  list-style: circle;
+}
+.rendered_html ol {
+  list-style: decimal;
+}
+.rendered_html ol ol {
+  list-style: upper-alpha;
+  margin-top: 0;
+}
+.rendered_html ol ol ol {
+  list-style: lower-alpha;
+}
+.rendered_html ol ol ol ol {
+  list-style: lower-roman;
+}
+.rendered_html ol ol ol ol ol {
+  list-style: decimal;
+}
+.rendered_html * + ul {
+  margin-top: 1em;
+}
+.rendered_html * + ol {
+  margin-top: 1em;
+}
+.rendered_html hr {
+  color: var(--sg-text-color);
+  background-color: var(--sg-text-color);
+}
+.rendered_html pre {
+  margin: 1em 2em;
+  padding: 0px;
+  background-color: var(--sg-background-color);
+}
+.rendered_html code {
+  background-color: var(--sg-code-background-color);
+}
+.rendered_html p code {
+  padding: 1px 5px;
+}
+.rendered_html pre code {
+  background-color: var(--sg-background-color);
+}
+.rendered_html pre,
+.rendered_html code {
+  border: 0;
+  color: var(--sg-text-color);
+  font-size: 100%;
+}
+.rendered_html blockquote {
+  margin: 1em 2em;
+}
+.rendered_html table {
+  margin-left: auto;
+  margin-right: auto;
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: var(--sg-text-color);
+  font-size: 12px;
+  table-layout: fixed;
+}
+.rendered_html thead {
+  border-bottom: 1px solid var(--sg-text-color);
+  vertical-align: bottom;
+}
+.rendered_html tr,
+.rendered_html th,
+.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.rendered_html th {
+  font-weight: bold;
+}
+.rendered_html tbody tr:nth-child(odd) {
+  background: var(--sg-tr-odd-color);
+}
+.rendered_html tbody tr:hover {
+  color: var(--sg-text-color);
+  background: var(--sg-tr-hover-color);
+}
+.rendered_html * + table {
+  margin-top: 1em;
+}
+.rendered_html p {
+  text-align: left;
+}
+.rendered_html * + p {
+  margin-top: 1em;
+}
+.rendered_html img {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+.rendered_html * + img {
+  margin-top: 1em;
+}
+.rendered_html img,
+.rendered_html svg {
+  max-width: 100%;
+  height: auto;
+}
+.rendered_html img.unconfined,
+.rendered_html svg.unconfined {
+  max-width: none;
+}
+.rendered_html .alert {
+  margin-bottom: initial;
+}
+.rendered_html * + .alert {
+  margin-top: 1em;
+}
+[dir="rtl"] .rendered_html p {
+  text-align: right;
+}
diff --git a/docs/v2.2.0/_static/sg_gallery.css b/docs/v2.2.0/_static/sg_gallery.css
new file mode 100644
index 0000000000..72227837d3
--- /dev/null
+++ b/docs/v2.2.0/_static/sg_gallery.css
@@ -0,0 +1,342 @@
+/*
+Sphinx-Gallery has compatible CSS to fix default sphinx themes
+Tested for Sphinx 1.3.1 for all themes: default, alabaster, sphinxdoc,
+scrolls, agogo, traditional, nature, haiku, pyramid
+Tested for Read the Docs theme 0.1.7 */
+
+/* Define light colors */
+:root, html[data-theme="light"], body[data-theme="light"]{
+  --sg-tooltip-foreground: black;
+  --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+  --sg-tooltip-border: #ccc transparent;
+  --sg-thumb-box-shadow-color: #6c757d40;
+  --sg-thumb-hover-border: #0069d9;
+  --sg-script-out: #888;
+  --sg-script-pre: #fafae2;
+  --sg-pytb-foreground: #000;
+  --sg-pytb-background: #ffe4e4;
+  --sg-pytb-border-color: #f66;
+  --sg-download-a-background-color: #ffc;
+  --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+  --sg-download-a-border-color: 1px solid #c2c22d;
+  --sg-download-a-color: #000;
+  --sg-download-a-hover-background-color: #d5d57e;
+  --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+}
+@media(prefers-color-scheme: light) {
+  :root[data-theme="auto"], html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: black;
+    --sg-tooltip-background: rgba(250, 250, 250, 0.9);
+    --sg-tooltip-border: #ccc transparent;
+    --sg-thumb-box-shadow-color: #6c757d40;
+    --sg-thumb-hover-border: #0069d9;
+    --sg-script-out: #888;
+    --sg-script-pre: #fafae2;
+    --sg-pytb-foreground: #000;
+    --sg-pytb-background: #ffe4e4;
+    --sg-pytb-border-color: #f66;
+    --sg-download-a-background-color: #ffc;
+    --sg-download-a-background-image: linear-gradient(to bottom, #ffc, #d5d57e);
+    --sg-download-a-border-color: 1px solid #c2c22d;
+    --sg-download-a-color: #000;
+    --sg-download-a-hover-background-color: #d5d57e;
+    --sg-download-a-hover-box-shadow-1: rgba(255, 255, 255, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(0, 0, 0, 0.25);
+  }
+}
+
+html[data-theme="dark"], body[data-theme="dark"] {
+  --sg-tooltip-foreground: white;
+  --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+  --sg-tooltip-border: #333 transparent;
+  --sg-thumb-box-shadow-color: #79848d40;
+  --sg-thumb-hover-border: #003975;
+  --sg-script-out: rgb(179, 179, 179);
+  --sg-script-pre: #2e2e22;
+  --sg-pytb-foreground: #fff;
+  --sg-pytb-background: #1b1717;
+  --sg-pytb-border-color: #622;
+  --sg-download-a-background-color: #443;
+  --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+  --sg-download-a-border-color: 1px solid #3a3a0d;
+  --sg-download-a-color: #fff;
+  --sg-download-a-hover-background-color: #616135;
+  --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+  --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+}
+@media(prefers-color-scheme: dark){
+  html[data-theme="auto"], body[data-theme="auto"] {
+    --sg-tooltip-foreground: white;
+    --sg-tooltip-background: rgba(10, 10, 10, 0.9);
+    --sg-tooltip-border: #333 transparent;
+    --sg-thumb-box-shadow-color: #79848d40;
+    --sg-thumb-hover-border: #003975;
+    --sg-script-out: rgb(179, 179, 179);
+    --sg-script-pre: #2e2e22;
+    --sg-pytb-foreground: #fff;
+    --sg-pytb-background: #1b1717;
+    --sg-pytb-border-color: #622;
+    --sg-download-a-background-color: #443;
+    --sg-download-a-background-image: linear-gradient(to bottom, #443, #221);
+    --sg-download-a-border-color: 1px solid #3a3a0d;
+    --sg-download-a-color: #fff;
+    --sg-download-a-hover-background-color: #616135;
+    --sg-download-a-hover-box-shadow-1: rgba(0, 0, 0, 0.1);
+    --sg-download-a-hover-box-shadow-2: rgba(255, 255, 255, 0.25);
+  }
+}
+
+.sphx-glr-thumbnails {
+  width: 100%;
+  margin: 0px 0px 20px 0px;
+
+  /* align thumbnails on a grid */
+  justify-content: space-between;
+  display: grid;
+  /* each grid column should be at least 160px (this will determine
+  the actual number of columns) and then take as much of the
+  remaining width as possible */
+  grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+  gap: 15px;
+}
+.sphx-glr-thumbnails .toctree-wrapper {
+  /* hide empty toctree divs added to the DOM
+  by sphinx even though the toctree is hidden
+  (they would fill grid places with empty divs) */
+  display: none;
+}
+.sphx-glr-thumbcontainer {
+  background: transparent;
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  box-shadow: 0 0 10px var(--sg-thumb-box-shadow-color);
+
+  /* useful to absolutely position link in div */
+  position: relative;
+
+  /* thumbnail width should include padding and borders
+  and take all available space */
+  box-sizing: border-box;
+  width: 100%;
+  padding: 10px;
+  border: 1px solid transparent;
+
+  /* align content in thumbnail */
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  gap: 7px;
+}
+.sphx-glr-thumbcontainer p {
+  position: absolute;
+  top: 0;
+  left: 0;
+}
+.sphx-glr-thumbcontainer p,
+.sphx-glr-thumbcontainer p a {
+  /* link should cover the whole thumbnail div */
+  width: 100%;
+  height: 100%;
+}
+.sphx-glr-thumbcontainer p a span {
+  /* text within link should be masked
+  (we are just interested in the href) */
+  display: none;
+}
+.sphx-glr-thumbcontainer:hover {
+  border: 1px solid;
+  border-color: var(--sg-thumb-hover-border);
+  cursor: pointer;
+}
+.sphx-glr-thumbcontainer a.internal {
+  bottom: 0;
+  display: block;
+  left: 0;
+  box-sizing: border-box;
+  padding: 150px 10px 0;
+  position: absolute;
+  right: 0;
+  top: 0;
+}
+/* Next one is to avoid Sphinx traditional theme to cover all the
+thumbnail with its default link Background color */
+.sphx-glr-thumbcontainer a.internal:hover {
+  background-color: transparent;
+}
+
+.sphx-glr-thumbcontainer p {
+  margin: 0 0 0.1em 0;
+}
+.sphx-glr-thumbcontainer .figure {
+  margin: 10px;
+  width: 160px;
+}
+.sphx-glr-thumbcontainer img {
+  display: inline;
+  max-height: 112px;
+  max-width: 160px;
+}
+.sphx-glr-thumbcontainer[tooltip]:hover:after {
+  background: var(--sg-tooltip-background);
+  -webkit-border-radius: 4px;
+  -moz-border-radius: 4px;
+  border-radius: 4px;
+  color: var(--sg-tooltip-foreground);
+  content: attr(tooltip);
+  padding: 10px;
+  z-index: 98;
+  width: 100%;
+  height: 100%;
+  position: absolute;
+  pointer-events: none;
+  top: 0;
+  box-sizing: border-box;
+  overflow: hidden;
+  backdrop-filter: blur(3px);
+}
+
+.sphx-glr-script-out {
+  color: var(--sg-script-out);
+  display: flex;
+  gap: 0.5em;
+}
+.sphx-glr-script-out::before {
+  content: "Out:";
+  /* These numbers come from the pre style in the pydata sphinx theme. This
+   * turns out to match perfectly on the rtd theme, but be a bit too low for
+   * the pydata sphinx theme. As I could not find a dimension to use that was
+   * scaled the same way, I just picked one option that worked pretty close for
+   * both. */
+  line-height: 1.4;
+  padding-top: 10px;
+}
+.sphx-glr-script-out .highlight {
+  background-color: transparent;
+  /* These options make the div expand... */
+  flex-grow: 1;
+  /* ... but also keep it from overflowing its flex container. */
+  overflow: auto;
+}
+.sphx-glr-script-out .highlight pre {
+  background-color: var(--sg-script-pre);
+  border: 0;
+  max-height: 30em;
+  overflow: auto;
+  padding-left: 1ex;
+  /* This margin is necessary in the pydata sphinx theme because pre has a box
+   * shadow which would be clipped by the overflow:auto in the parent div
+   * above. */
+  margin: 2px;
+  word-break: break-word;
+}
+.sphx-glr-script-out + p {
+  margin-top: 1.8em;
+}
+blockquote.sphx-glr-script-out {
+  margin-left: 0pt;
+}
+.sphx-glr-script-out.highlight-pytb .highlight pre {
+  color: var(--sg-pytb-foreground);
+  background-color: var(--sg-pytb-background);
+  border: 1px solid var(--sg-pytb-border-color);
+  margin-top: 10px;
+  padding: 7px;
+}
+
+div.sphx-glr-footer {
+  text-align: center;
+}
+
+div.sphx-glr-download {
+  margin: 1em auto;
+  vertical-align: middle;
+}
+
+div.sphx-glr-download a {
+  background-color: var(--sg-download-a-background-color);
+  background-image: var(--sg-download-a-background-image);
+  border-radius: 4px;
+  border: 1px solid var(--sg-download-a-border-color);
+  color: var(--sg-download-a-color);
+  display: inline-block;
+  font-weight: bold;
+  padding: 1ex;
+  text-align: center;
+}
+
+div.sphx-glr-download code.download {
+  display: inline-block;
+  white-space: normal;
+  word-break: normal;
+  overflow-wrap: break-word;
+  /* border and background are given by the enclosing 'a' */
+  border: none;
+  background: none;
+}
+
+div.sphx-glr-download a:hover {
+  box-shadow: inset 0 1px 0 var(--sg-download-a-hover-box-shadow-1), 0 1px 5px var(--sg-download-a-hover-box-shadow-2);
+  text-decoration: none;
+  background-image: none;
+  background-color: var(--sg-download-a-hover-background-color);
+}
+
+.sphx-glr-example-title:target::before {
+  display: block;
+  content: "";
+  margin-top: -50px;
+  height: 50px;
+  visibility: hidden;
+}
+
+ul.sphx-glr-horizontal {
+  list-style: none;
+  padding: 0;
+}
+ul.sphx-glr-horizontal li {
+  display: inline;
+}
+ul.sphx-glr-horizontal img {
+  height: auto !important;
+}
+
+.sphx-glr-single-img {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+
+.sphx-glr-multi-img {
+  max-width: 42%;
+  height: auto;
+}
+
+div.sphx-glr-animation {
+  margin: auto;
+  display: block;
+  max-width: 100%;
+}
+div.sphx-glr-animation .animation {
+  display: block;
+}
+
+p.sphx-glr-signature a.reference.external {
+  -moz-border-radius: 5px;
+  -webkit-border-radius: 5px;
+  border-radius: 5px;
+  padding: 3px;
+  font-size: 75%;
+  text-align: right;
+  margin-left: auto;
+  display: table;
+}
+
+.sphx-glr-clear {
+  clear: both;
+}
+
+a.sphx-glr-backref-instance {
+  text-decoration: none;
+}
diff --git a/docs/v2.2.0/_static/underscore-1.13.1.js b/docs/v2.2.0/_static/underscore-1.13.1.js
new file mode 100644
index 0000000000..ffd77af964
--- /dev/null
+++ b/docs/v2.2.0/_static/underscore-1.13.1.js
@@ -0,0 +1,2042 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.13.1
+  //     https://underscorejs.org
+  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.13.1';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has$1(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has$1(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key]; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has$1(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _$1(obj) {
+    if (obj instanceof _$1) return obj;
+    if (!(this instanceof _$1)) return new _$1(obj);
+    this._wrapped = obj;
+  }
+
+  _$1.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _$1.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
+
+  _$1.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _$1) a = a._wrapped;
+    if (b instanceof _$1) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath$1(path) {
+    return isArray(path) ? path : [path];
+  }
+  _$1.toPath = toPath$1;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath(path) {
+    return _$1.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has(obj, path) {
+    path = toPath(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has$1(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _$1.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _$1.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // In order to prevent third-party code injection through
+  // `_.templateSettings.variable`, we test it against the following regular
+  // expression. It is intentionally a bit more liberal than just matching valid
+  // identifiers, but still prevents possible loopholes through defaults or
+  // destructuring assignment.
+  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _$1.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    var argument = settings.variable;
+    if (argument) {
+      // Insure against third-party code injection. (CVE-2021-23358)
+      if (!bareIdentifier.test(argument)) throw new Error(
+        'variable is not a bare identifier: ' + argument
+      );
+    } else {
+      // If a variable is not specified, place data values in local scope.
+      source = 'with(obj||{}){\n' + source + '}\n';
+      argument = 'obj';
+    }
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(argument, '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _$1);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _$1(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _$1;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten$1(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten$1(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten$1(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _$1, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten$1(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten$1(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten(array, depth) {
+    return flatten$1(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten$1(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten$1(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _$1(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _$1[name] = obj[name];
+      _$1.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_$1, args));
+      };
+    });
+    return _$1;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath$1,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _$1
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _ = mixin(allExports);
+  // Legacy Node.js API.
+  _._ = _;
+
+  return _;
+
+})));
+//# sourceMappingURL=underscore-umd.js.map
diff --git a/docs/v2.2.0/_static/underscore.js b/docs/v2.2.0/_static/underscore.js
new file mode 100644
index 0000000000..cf177d4285
--- /dev/null
+++ b/docs/v2.2.0/_static/underscore.js
@@ -0,0 +1,6 @@
+!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
+//     Underscore.js 1.13.1
+//     https://underscorejs.org
+//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+//     Underscore may be freely distributed under the MIT license.
+var n="1.13.1",r="object"==typeof self&&self.self===self&&self||"object"==typeof global&&global.global===global&&global||Function("return this")()||{},t=Array.prototype,e=Object.prototype,u="undefined"!=typeof Symbol?Symbol.prototype:null,o=t.push,i=t.slice,a=e.toString,f=e.hasOwnProperty,c="undefined"!=typeof ArrayBuffer,l="undefined"!=typeof DataView,s=Array.isArray,p=Object.keys,v=Object.create,h=c&&ArrayBuffer.isView,y=isNaN,d=isFinite,g=!{toString:null}.propertyIsEnumerable("toString"),b=["valueOf","isPrototypeOf","toString","propertyIsEnumerable","hasOwnProperty","toLocaleString"],m=Math.pow(2,53)-1;function j(n,r){return r=null==r?n.length-1:+r,function(){for(var t=Math.max(arguments.length-r,0),e=Array(t),u=0;u<t;u++)e[u]=arguments[u+r];switch(r){case 0:return n.call(this,e);case 1:return n.call(this,arguments[0],e);case 2:return n.call(this,arguments[0],arguments[1],e)}var o=Array(r+1);for(u=0;u<r;u++)o[u]=arguments[u];return o[r]=e,n.apply(this,o)}}function _(n){var r=typeof 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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</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>
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+<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 Documenation</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+              
+  <section id="torchtrtc">
+<span id="id1"></span><h1>torchtrtc<a class="headerlink" href="#torchtrtc" title="Permalink to this headline">¶</a></h1>
+<p><code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> is a CLI application for using the Torch-TensorRT compiler. It serves as an easy way to compile a
+TorchScript Module with Torch-TensorRT from the command-line to quickly check support or as part of
+a deployment pipeline. All basic features of the compiler are supported including post training
+quantization (though you must already have a calibration cache file to use the PTQ feature). The compiler can
+output two formats, either a TorchScript program with the TensorRT engine embedded or
+the TensorRT engine itself as a PLAN file.</p>
+<p>All that is required to run the program after compilation is for C++ linking against <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code>
+or in Python importing the torch_tensorrt package. All other aspects of using compiled modules are identical
+to standard TorchScript. Load with <code class="docutils literal notranslate"><span class="pre">torch.jit.load()</span></code> and run like you would run any other module.</p>
+<div class="highlight-txt notranslate"><div class="highlight"><pre><span></span>torchtrtc [input_file_path] [output_file_path]
+  [input_specs...] {OPTIONS}
+
+  torchtrtc is a compiler for TorchScript, it will compile and optimize
+  TorchScript programs to run on NVIDIA GPUs using TensorRT
+
+OPTIONS:
+
+    -h, --help                        Display this help menu
+    Verbiosity of the compiler
+      -v, --verbose                     Dumps debugging information about the
+                                        compilation process onto the console
+      -w, --warnings                    Disables warnings generated during
+                                        compilation onto the console (warnings
+                                        are on by default)
+      --i, --info                       Dumps info messages generated during
+                                        compilation onto the console
+    --build-debuggable-engine         Creates a debuggable engine
+    --allow-gpu-fallback              (Only used when targeting DLA
+                                      (device-type)) Lets engine run layers on
+                                      GPU if they are not supported on DLA
+    --require-full-compilation        Require that the model should be fully
+                                      compiled to TensorRT or throw an error
+    --check-method-support=[method_name]
+                                      Check the support for end to end
+                                      compilation of a specified method in the
+                                      TorchScript module
+    --disable-tf32                    Prevent Float32 layers from using the
+                                      TF32 data format
+    --sparse-weights                  Enable sparsity for weights of conv and
+                                      FC layers
+    -p[precision...],
+    --enable-precision=[precision...] (Repeatable) Enabling an operating
+                                      precision for kernels to use when
+                                      building the engine (Int8 requires a
+                                      calibration-cache argument) [ float |
+                                      float32 | f32 | fp32 | half | float16 |
+                                      f16 | fp16 | int8 | i8 | char ]
+                                      (default: float)
+    -d[type], --device-type=[type]    The type of device the engine should be
+                                      built for [ gpu | dla ] (default: gpu)
+    --gpu-id=[gpu_id]                 GPU id if running on multi-GPU platform
+                                      (defaults to 0)
+    --dla-core=[dla_core]             DLACore id if running on available DLA
+                                      (defaults to 0)
+    --engine-capability=[capability]  The type of device the engine should be
+                                      built for [ standard | safety |
+                                      dla_standalone ]
+    --calibration-cache-file=[file_path]
+                                      Path to calibration cache file to use
+                                      for post training quantization
+    --teo=[op_name...],
+    --torch-executed-op=[op_name...]  (Repeatable) Operator in the graph that
+                                      should always be run in PyTorch for
+                                      execution (partial compilation must be
+                                      enabled)
+    --tem=[module_name...],
+    --torch-executed-mod=[module_name...]
+                                      (Repeatable) Module that should always
+                                      be run in Pytorch for execution (partial
+                                      compilation must be enabled)
+    --mbs=[num_ops],
+    --min-block-size=[num_ops]        Minimum number of contiguous TensorRT
+                                      supported ops to compile a subgraph to
+                                      TensorRT
+    --embed-engine                    Whether to treat input file as a
+                                      serialized TensorRT engine and embed it
+                                      into a TorchScript module (device spec
+                                      must be provided)
+    --num-avg-timing-iters=[num_iters]
+                                      Number of averaging timing iterations
+                                      used to select kernels
+    --workspace-size=[workspace_size] Maximum size of workspace given to
+                                      TensorRT
+    --dla-sram-size=[dla_sram_size]   Fast software managed RAM used by DLA
+                                      to communicate within a layer.
+    --dla-local-dram-size=[dla_local_dram_size]  Host RAM used by DLA to share
+                                      intermediate tensor data across operations.
+    --dla-global-dram-size=[dla_global_dram_size] Host RAM used by DLA to store
+                                      weights and metadata for execution
+    --atol=[atol]                     Absolute tolerance threshold for acceptable
+                                      numerical deviation from standard torchscript
+                                      output (default 1e-8)
+    --rtol=[rtol]                     Relative tolerance threshold for acceptable
+                                      numerical deviation from standard torchscript
+                                      output  (default 1e-5)
+    --no-threshold-check              Skip checking threshold compliance
+    --truncate-long-double,
+    --truncate, --truncate-64bit      Truncate weights that are provided in
+                                      64bit to 32bit (Long, Double to Int,
+                                      Float)
+    --save-engine                     Instead of compiling a full a
+                                      TorchScript program, save the created
+                                      engine to the path specified as the
+                                      output path
+    --custom-torch-ops                (repeatable) Shared object/DLL containing custom torch operators
+    --custom-converters               (repeatable) Shared object/DLL containing custom converters
+    input_file_path                   Path to input TorchScript file
+    output_file_path                  Path for compiled TorchScript (or
+                                      TensorRT engine) file
+    input_specs...                    Specs for inputs to engine, can either
+                                      be a single size or a range defined by
+                                      Min, Optimal, Max sizes, e.g.
+                                      &quot;(N,..,C,H,W)&quot;
+                                      &quot;[(MIN_N,..,MIN_C,MIN_H,MIN_W);(OPT_N,..,OPT_C,OPT_H,OPT_W);(MAX_N,..,MAX_C,MAX_H,MAX_W)]&quot;.
+                                      Data Type and format can be specified by
+                                      adding an &quot;@&quot; followed by dtype and &quot;%&quot;
+                                      followed by format to the end of the
+                                      shape spec. e.g. &quot;(3, 3, 32,
+                                      32)@f16%NHWC&quot;
+    &quot;--&quot; can be used to terminate flag options and force all following
+    arguments to be treated as positional options
+</pre></div>
+</div>
+<p>e.g.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc<span class="w"> </span>tests/modules/ssd_traced.jit.pt<span class="w"> </span>ssd_trt.ts<span class="w"> </span><span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@f16%contiguous&quot;</span><span class="w"> </span>-p<span class="w"> </span>f16
+</pre></div>
+</div>
+<ul class="simple">
+<li><p>To include a set of custom operators</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc<span class="w"> </span>tests/modules/ssd_traced.jit.pt<span class="w"> </span>ssd_trt.ts<span class="w"> </span>--custom-torch-ops<span class="o">=</span>&lt;path<span class="w"> </span>to<span class="w"> </span>custom<span class="w"> </span>library<span class="w"> </span>.so<span class="w"> </span>file&gt;<span class="w"> </span><span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous&quot;</span><span class="w"> </span>-p<span class="w"> </span>f16
+</pre></div>
+</div>
+<ul class="simple">
+<li><p>To include a set of custom converters</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc<span class="w"> </span>tests/modules/ssd_traced.jit.pt<span class="w"> </span>ssd_trt.ts<span class="w"> </span>--custom-converters<span class="o">=</span>&lt;path<span class="w"> </span>to<span class="w"> </span>custom<span class="w"> </span>library<span class="w"> </span>.so<span class="w"> </span>file&gt;<span class="w"> </span><span class="s2">&quot;[(1,3,300,300); (1,3,512,512); (1, 3, 1024, 1024)]@fp16%contiguous&quot;</span><span class="w"> </span>-p<span class="w"> </span>f16
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../dynamo/dynamo_export.html">Compiling Exported Programs with Torch-TensorRT</a></li>
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+<ul>
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+<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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<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>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="reference internal" href="system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo_converters.html">Writing Dynamo Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ts_converters.html">Writing TorchScript Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
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+              
+  <section id="conversion-phase">
+<span id="conversion"></span><h1>Conversion Phase<a class="headerlink" href="#conversion-phase" title="Permalink to this headline">¶</a></h1>
+<p>Once the graph has be simplified to a form thats easy to convert, we then set up a conversion context
+to manage the construction of a TensorRT <code class="docutils literal notranslate"><span class="pre">INetworkDefinition</span></code> from the blocks nodes. The conversion context
+records the set of converted nodes, block inputs and outputs and other information about the conversion
+of the graph. This data is then used to help converters link together layers and also hold build time
+information like weights required to construct the engine. After the context is created, the block
+converter starts iterating through the list of nodes, for each node, the converter will look at its
+inputs and assemble an array of resources to pass to the converter. Inputs can be in a couple of states:</p>
+<ul class="simple">
+<li><p>The input is a block parameter</p>
+<ul>
+<li><p>In this case the input should have already been stored in as an IValue in the
+conversion context <code class="docutils literal notranslate"><span class="pre">evaluated_value_map</span></code>. The conversion stage will add the IValue to the list of args for the
+converter</p></li>
+</ul>
+</li>
+<li><p>The input is an output of a node that has already been converted</p>
+<ul>
+<li><p>In this case the ITensor of the output has added to the <code class="docutils literal notranslate"><span class="pre">value_tensor_map</span></code>,
+The conversion stage will add the ITensor to the list of args for the converter</p></li>
+</ul>
+</li>
+<li><p>The input is from a node that produces a static value</p>
+<ul>
+<li><p>There are nodes that produce static values, typically used to store parameters for operators, we need to
+evaluate these nodes at conversion time to be able to convert a op. The conversion system will look for a node
+evaluator in the evaluator registry and run it on the node. The IValue produced will be entered in the
+conversion context <code class="docutils literal notranslate"><span class="pre">evaluated_value_map</span></code> and added to the list of args for the converter. If the node
+to be evaluated takes inputs, the conversion stage will recursively resolve dependencies until the final
+static value has been evaluated</p></li>
+</ul>
+</li>
+<li><p>The input is from a node that has not been converted</p>
+<ul>
+<li><p>Torch-TensorRT will error out here</p></li>
+</ul>
+</li>
+</ul>
+<section id="node-evaluation">
+<h2>Node Evaluation<a class="headerlink" href="#node-evaluation" title="Permalink to this headline">¶</a></h2>
+<p>There are some nodes that contain static data and are resources for operations. These can be evaluated at
+conversion time so that you can use those values when doing node conversion. In theory any node kind can have
+a conversion time evaluator as long as it produces a static IValue, This IValue will be stored in the conversion
+context so it can be consumed by any node that takes the evaluated node as an input. Common node types are
+<code class="docutils literal notranslate"><span class="pre">prim::Constant</span></code> which emits a constant and <code class="docutils literal notranslate"><span class="pre">prim::ListConstruct</span></code> which makes lists.</p>
+</section>
+<section id="node-converters">
+<h2>Node Converters<a class="headerlink" href="#node-converters" title="Permalink to this headline">¶</a></h2>
+<p>Node converters map JIT nodes to layers or subgraphs of layers. They then associate outputs from the JIT graph
+and the TRT graph together in the conversion context. This allows the conversion stage to assemble the inputs
+for the next node. There are some cases where a node produces an output that is not a Tensor but a static result
+from a calculation done on inputs which need to be converted first. In this case the converter may associate the outputs in
+the <code class="docutils literal notranslate"><span class="pre">evaluated_value_map</span></code> instead of the <code class="docutils literal notranslate"><span class="pre">value_tensor_map</span></code>. For more information take a look at: <span class="xref std std-ref">writing_converters</span></p>
+</section>
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diff --git a/docs/v2.2.0/contributors/dynamo_converters.html b/docs/v2.2.0/contributors/dynamo_converters.html
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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+<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>
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+<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>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
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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="system_overview.html">System Overview</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Writing Dynamo Converters</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="writing-dynamo-converters">
+<span id="dynamo-converters"></span><h1>Writing Dynamo Converters<a class="headerlink" href="#writing-dynamo-converters" title="Permalink to this headline">¶</a></h1>
+<p>The dynamo converter library in Torch-TensorRT is located in <code class="docutils literal notranslate"><span class="pre">TensorRT/py/torch_tensorrt/dynamo/conversion</span></code>.</p>
+<section id="converter-implementation">
+<h2>Converter implementation<a class="headerlink" href="#converter-implementation" title="Permalink to this headline">¶</a></h2>
+<section id="registration">
+<h3>Registration<a class="headerlink" href="#registration" title="Permalink to this headline">¶</a></h3>
+<p>A converter is a function decrorated with  <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.dynamo_tensorrt_converter</span></code> that follows the function signature:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">conversion</span><span class="o">.</span><span class="n">dynamo_tensorrt_converter</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">leaky_relu</span><span class="o">.</span><span class="n">default</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">leaky_relu_converter</span><span class="p">(</span>
+    <span class="n">ctx</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">conversion</span><span class="o">.</span><span class="n">ConversionCtx</span><span class="p">,</span>
+    <span class="n">target</span><span class="p">:</span> <span class="n">Target</span><span class="p">,</span>
+    <span class="n">args</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
+    <span class="n">kwargs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Argument</span><span class="p">],</span>
+    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">tensorrt</span><span class="o">.</span><span class="n">ITensor</span><span class="p">]]:</span>
+</pre></div>
+</div>
+<p>The decorator takes a number of arguments:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">key</span></code>: Node target for which the converter is implemented for (for example, torch.ops.aten.leaky_relu.default)</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">enabled</span></code>: Whether the converter should be enabled as a converter that can be used in the converter registry</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">capability_validator</span></code>: A lambda that can take a <code class="docutils literal notranslate"><span class="pre">torch.fx.Node</span></code> and determine if the converter can properly handle this Node. If the validator returns <code class="docutils literal notranslate"><span class="pre">False</span></code>, the subgraph partitioner will make sure this Node is run in PyTorch in the compiled graph.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">priority</span></code>: Allows developers to override existing converters in the converter registry</p></li>
+</ul>
+</div></blockquote>
+<p>All that is required for a converter is the key.</p>
+<p>The function body is responsible for taking the current state of the network and adding the next subgraph to perform the op specified in the decorator with TensorRT operations.
+The function is provided arguments as the native PyTorch op would be provided with the added case of numpy arrays for frozen Tensor attributes or TensorRT ITensors which are ouput Tensors of previous nodes, correspoding to edges/output Tensors of intermediate operations in the graph.
+To determine the types expected as well as the return type of the converter, look at the definition of the op being converted. In the case of <code class="docutils literal notranslate"><span class="pre">aten</span></code> operations, this file will be the source of truth: <a class="reference external" href="https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml">https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml</a>
+Since many converters a developer may write are a composition of lower level operators, instead of needing to implement the converter in raw TensorRT, the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.impl</span></code> subpackage contains many implementations of operations that can be chained to create a TensorRT subgraph.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">ctx</span></code> : The current state of the compiler. Converters primarily will manipulate ctx.net which is the <code class="docutils literal notranslate"><span class="pre">tensorrt.INetworkDefinition</span></code> being constructed. Additional metadata including user provided settings is available in this struct as well.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">target</span></code>: Target key in the <code class="docutils literal notranslate"><span class="pre">call_module</span></code> or <code class="docutils literal notranslate"><span class="pre">call_function</span></code> above. eg: <code class="docutils literal notranslate"><span class="pre">torch.ops.aten_.leaky_relu.default</span></code>. Note that <code class="docutils literal notranslate"><span class="pre">torch.ops.aten._leaky_relu</span></code> is the <code class="docutils literal notranslate"><span class="pre">OpOverloadPacket</span></code> while <code class="docutils literal notranslate"><span class="pre">torch.ops.aten_.leaky_relu.default</span></code> is <code class="docutils literal notranslate"><span class="pre">OpOverload</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">args</span></code>: The arguments being passed to a particular Node (as collected by the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.TRTInterpreter</span></code>). These arguments along with the kwargs are to be used to construct a specific TensorRT subgraph representing the current node in the INetworkDefinition.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">kwargs</span></code>: The arguments being passed to a particular Node (as collected by the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.TRTInterpreter</span></code>).</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">name</span></code>: String containing the name of the target</p></li>
+</ul>
+</div></blockquote>
+<p>The function is expected to return the <code class="docutils literal notranslate"><span class="pre">tensorrt.ITensor</span></code> or some collection of <code class="docutils literal notranslate"><span class="pre">tensorrt.ITensor</span></code> for use in the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.TRTInterpreter</span></code> matching the output signature of the operation being converted</p>
+</section>
+<section id="capability-validation">
+<h3>Capability Validation<a class="headerlink" href="#capability-validation" title="Permalink to this headline">¶</a></h3>
+<p>There are some converters which have special cases to be accounted for. In those cases, one should use <code class="docutils literal notranslate"><span class="pre">capability_validators</span></code> to register the converter using <code class="docutils literal notranslate"><span class="pre">&#64;dynamo_tensorrt_converter</span></code>
+We illustrate this through <code class="docutils literal notranslate"><span class="pre">torch.ops.aten.embedding.default</span></code>. It has parameters - <code class="docutils literal notranslate"><span class="pre">scale_grad_by_freq</span></code> and <code class="docutils literal notranslate"><span class="pre">sparse</span></code> which are not currently supported by the implementation.
+In such cases we can write validator <code class="docutils literal notranslate"><span class="pre">embedding_param_validator</span></code> which implements that given those paramters the converter is not supported and register the converter by</p>
+</section>
+<section id="type-contract">
+<h3>Type Contract<a class="headerlink" href="#type-contract" title="Permalink to this headline">¶</a></h3>
+<p>The function is expected to follow the type contract established by the signature. This includes accepting the union of valid PyTorch types + numpy arrays for constant tensors and TensorRT ITensors.
+In the case that only a subset of types is supported in the converter, you can also add the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.conversion.converter_utils.enforce_tensor_types</span></code>, which allows you to specify a dictionary mapping between input positions and types that those inputs can take. Where possible the decorator will convert inputs to match these types prefering the order provided.
+<code class="docutils literal notranslate"><span class="pre">int</span></code> keys in the dictionary will refer to positional arguments in <code class="docutils literal notranslate"><span class="pre">args</span></code>. <code class="docutils literal notranslate"><span class="pre">str</span></code> keys will refer to keyword arguments in <code class="docutils literal notranslate"><span class="pre">kwargs</span></code>.</p>
+</section>
+<section id="example-convolution">
+<h3>Example: <code class="docutils literal notranslate"><span class="pre">Convolution</span></code><a class="headerlink" href="#example-convolution" title="Permalink to this headline">¶</a></h3>
+<p>The default convolution converter both uses a capability validator and type enforcement to prevent being run in unsupported situations.
+The capability validator is run during partitioning to determine if a particular convolution node can be converted to TensorRT or needs to run in PyTorch. Here the validator ensures that the convolution is no greater than 3D.
+The type enforcer will autocast before the converter is called, inputs to the supported type in the converter, thereby limiting the number of cases an author must handle.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@dynamo_tensorrt_converter</span><span class="p">(</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">convolution</span><span class="o">.</span><span class="n">default</span><span class="p">,</span> <span class="n">capability_validator</span><span class="o">=</span><span class="k">lambda</span> <span class="n">conv_node</span><span class="p">:</span> <span class="n">conv_node</span><span class="o">.</span><span class="n">args</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span> <span class="ow">in</span> <span class="p">([</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
+<span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
+<span class="nd">@enforce_tensor_types</span><span class="p">(</span>
+    <span class="p">{</span>
+        <span class="mi">0</span><span class="p">:</span> <span class="p">(</span><span class="n">TRTTensor</span><span class="p">,),</span>
+        <span class="mi">1</span><span class="p">:</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">TRTTensor</span><span class="p">),</span>
+        <span class="mi">2</span><span class="p">:</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">TRTTensor</span><span class="p">),</span>
+    <span class="p">}</span>
+<span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
+<span class="k">def</span> <span class="nf">aten_ops_convolution</span><span class="p">(</span>
+    <span class="n">ctx</span><span class="p">:</span> <span class="n">ConversionContext</span><span class="p">,</span>
+    <span class="n">target</span><span class="p">:</span> <span class="n">Target</span><span class="p">,</span>
+    <span class="n">args</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">Argument</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
+    <span class="n">kwargs</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Argument</span><span class="p">],</span>
+    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">TRTTensor</span><span class="p">,</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">TRTTensor</span><span class="p">]]:</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="evaluators">
+<h2>Evaluators<a class="headerlink" href="#evaluators" title="Permalink to this headline">¶</a></h2>
+<p>Some operations do not produce TensorRT subgraphs as a side-effect. These are termed evaluators.</p>
+<blockquote>
+<div><p>Example: <code class="docutils literal notranslate"><span class="pre">operator.getitem</span></code></p>
+<p>Evaluators are categorized as so since they do not make any modification to the graph. This is implemented in <code class="docutils literal notranslate"><span class="pre">py/torch_tensorrt/dynamo/conversion/op_evaluators.py</span></code>, with the corresponding <code class="docutils literal notranslate"><span class="pre">capbility_validator</span></code>.
+The opcode is <code class="docutils literal notranslate"><span class="pre">operator.getitem</span></code>.</p>
+</div></blockquote>
+</section>
+<section id="operator-decomposition">
+<h2>Operator Decomposition<a class="headerlink" href="#operator-decomposition" title="Permalink to this headline">¶</a></h2>
+<p>There are some converters which can be decomposed into suboperations in PyTorch and need not have seperate converter registration.
+Such converters can be implemented via a decomposition</p>
+<section id="example-addmm">
+<h3>Example: <code class="docutils literal notranslate"><span class="pre">addmm</span></code><a class="headerlink" href="#example-addmm" title="Permalink to this headline">¶</a></h3>
+<p>The decompositions are registered via <code class="docutils literal notranslate"><span class="pre">register_torch_trt_decomposition</span></code> decorator
+We define <code class="docutils literal notranslate"><span class="pre">addmm_replacement</span></code> and replace it with the torch ops, which will have their corresponding converters called.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">lowering</span><span class="o">.</span><span class="n">register_torch_trt_decomposition</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">addmm</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">addmm_replacement</span><span class="p">(</span>
+    <span class="n">input_</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">mat1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">mat2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">*</span><span class="p">,</span> <span class="n">beta</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">add</span><span class="p">(</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">input_</span><span class="p">,</span> <span class="n">beta</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">mul</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">mat1</span><span class="p">,</span> <span class="n">mat2</span><span class="p">),</span> <span class="n">alpha</span><span class="p">)</span>
+    <span class="p">)</span>
+</pre></div>
+</div>
+<p>You can modify the decompositions run by editing <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering.torch_enabled_decompositions</span></code> and <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering.torch_disabled_decompositions</span></code></p>
+<blockquote>
+<div><p>Note: <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering.torch_enabled_decompositions</span></code> and <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering.torch_disabled_decompositions</span></code> must be disjoint sets and that the decompositions already defined in <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.lowering</span></code> will take precedence over torch lowering ops.</p>
+</div></blockquote>
+<p>Much of the time, this is significantly easier than implementing a converter. So where possible, this is what should be tried first.</p>
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+<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>
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+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="lowering-phase">
+<span id="lowering"></span><h1>Lowering Phase<a class="headerlink" href="#lowering-phase" title="Permalink to this headline">¶</a></h1>
+<p>The lowering phase is made up out of passes which are operations which map a graph from a high level representation
+to a lower level one. Each pass does something specific for instance inlining method calls. The idea is to
+significantly reduce what the conversion phase needs to be able to handle when actually mapping to TensorRT.
+We aim for closer to 1-&gt;1 op conversion vs looking for applicable subgraphs, limiting the number of converters and
+reduce the scope of each converter.</p>
+<p>You can see the effects of each pass by setting the log level to <code class="docutils literal notranslate"><span class="pre">Level::kGraph</span></code></p>
+<section id="passes-used">
+<h2>Passes Used<a class="headerlink" href="#passes-used" title="Permalink to this headline">¶</a></h2>
+<section id="eliminatecommonsubexpression">
+<h3>EliminateCommonSubexpression<a class="headerlink" href="#eliminatecommonsubexpression" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/common_subexpression_elimination.h">torch/csrc/jit/passes/common_subexpression_elimination.h</a></p>
+</div></blockquote>
+<p>Removes common subexpressions in the graph</p>
+</section>
+<section id="eliminate-dead-code">
+<h3>Eliminate Dead Code<a class="headerlink" href="#eliminate-dead-code" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/dead_code_elimination.h">torch/csrc/jit/passes/dead_code_elimination.h</a></p>
+</div></blockquote>
+<p>Dead code elimination will check if a node has side effects and not delete it if it does.</p>
+</section>
+<section id="eliminate-exeception-or-pass-pattern">
+<h3>Eliminate Exeception Or Pass Pattern<a class="headerlink" href="#eliminate-exeception-or-pass-pattern" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/exception_elimination.cpp">Torch-TensorRT/core/lowering/passes/exception_elimination.cpp</a></p>
+</div></blockquote>
+<p>A common pattern in scripted modules are dimension gaurds which will throw execptions if
+the input dimension is not what was expected.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%1013 : bool = aten::ne(%1012, %24) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:248:11
+    = prim::If(%1013) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:248:8
+    block0():
+        = prim::RaiseException(%23) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/batchnorm.py:249:12
+    -&gt; ()
+    block1():
+    -&gt; ()
+</pre></div>
+</div>
+<p>Since we are resolving all of this at compile time and there are no execptions in the TensorRT graph, we just remove it.</p>
+</section>
+<section id="eliminate-redundant-gaurds">
+<h3>Eliminate Redundant Gaurds<a class="headerlink" href="#eliminate-redundant-gaurds" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/guard_elimination.h">torch/csrc/jit/passes/guard_elimination.h</a></p>
+</div></blockquote>
+<p>Eliminate redundant guards for ops whose outputs are fully determined by their inputs i.e. if inputs to such ops are
+guarded we are allowed to remove a guard on ops’ outputs</p>
+</section>
+<section id="freeze-module">
+<h3>Freeze Module<a class="headerlink" href="#freeze-module" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/freeze_module.h">torch/csrc/jit/passes/freeze_module.h</a></p>
+</div></blockquote>
+<p>Freeze attributes and inline constants and modules. Propogates constants in the graph.</p>
+</section>
+<section id="fuse-addmm-branches">
+<h3>Fuse AddMM Branches<a class="headerlink" href="#fuse-addmm-branches" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_addmm_branches.cpp">Torch-TensorRT/core/lowering/passes/fuse_addmm_branches.cpp</a></p>
+</div></blockquote>
+<p>A common pattern in scripted modules is tensors of different dimensions use different constructions for implementing linear layers. We fuse these
+different varients into a single one that will get caught by the Unpack AddMM pass.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%ret : Tensor = prim::If(%622)
+block0():
+  %ret.1 : Tensor = aten::addmm(%self.fc.bias, %x9.1, %3677, %3, %3)
+  -&gt; (%ret.1)
+block1():
+  %output.1 : Tensor = aten::matmul(%x9.1, %3677)
+  %output0.1 : Tensor = aten::add_(%output.1, %self.fc.bias, %3)
+  -&gt; (%output0.1)
+</pre></div>
+</div>
+<p>We fuse this set of blocks into a graph like this:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%ret : Tensor = aten::addmm(%self.fc.bias, %x9.1, %3677, %3, %3)
+</pre></div>
+</div>
+</section>
+<section id="fuse-linear">
+<h3>Fuse Linear<a class="headerlink" href="#fuse-linear" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/fuse_linear.h">torch/csrc/jit/passes/fuse_linear.h</a></p>
+</div></blockquote>
+<p>Match the <code class="docutils literal notranslate"><span class="pre">aten::linear</span></code> pattern and fuse it into a single <code class="docutils literal notranslate"><span class="pre">aten::linear</span></code>
+This pass fuse the addmm or matmul + add generated by JIT back to linear</p>
+</section>
+<section id="fuse-flatten-linear">
+<h3>Fuse Flatten Linear<a class="headerlink" href="#fuse-flatten-linear" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/fuse_flatten_linear.cpp">Torch-TensorRT/core/lowering/passes/fuse_flatten_linear.cpp</a></p>
+</div></blockquote>
+<p>TensorRT implicity flattens input layers into fully connected layers when they are higher than 1D. So when there is a
+<code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code> -&gt; <code class="docutils literal notranslate"><span class="pre">aten::linear</span></code> pattern we remove the <code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code>.</p>
+</section>
+<section id="lower-graph">
+<h3>Lower Graph<a class="headerlink" href="#lower-graph" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/lower_graph.h">torch/csrc/jit/passes/lower_graph.h</a></p>
+</div></blockquote>
+<p>Given a graph with of a method which first argument is %self, lower it to a graph where
+all attributes accesses are replaced with explicit inputs of the graph
+(rather than results of prim::GetAttr executed on %self). Returns a tuple
+(graph, parameters) where the last module.parameters.size() inputs to the
+graph are the trainable parameters used in this method. The remaining inputs
+are the true inputs to the function.</p>
+</section>
+<section id="lower-tuples">
+<h3>Lower Tuples<a class="headerlink" href="#lower-tuples" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/lower_tuples.h">torch/csrc/jit/passes/lower_tuples.h</a></p>
+</div></blockquote>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">LowerSimpleTuples</span></code>:</p></li>
+</ul>
+<p>Removes tuples where TupleConstruct and TupleUnpack are matched but leaves tuples in place across if statements, loops, and as inputs/outputs</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">LowerAllTuples</span></code>:</p></li>
+</ul>
+<p>Removes _all_ tuples and raises an error if some cannot be removed, this is used by ONNX to ensure there are not tuples before conversion, but will not work on graphs whose inputs contain tuples.</p>
+</section>
+<section id="module-fallback">
+<h3>Module Fallback<a class="headerlink" href="#module-fallback" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/module_fallback.cpp">Torch-TensorRT/core/lowering/passes/module_fallback.cpp</a></p>
+</div></blockquote>
+<p>Module fallback consists of two lowering passes that must be run as a pair. The first pass is run before freezing to place delimiters in the graph around modules
+that should run in PyTorch. The second pass marks nodes between these delimiters after freezing to signify they should run in PyTorch.</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">NotateModuleForFallback</span></code></p></li>
+</ul>
+<p>Places delimiting nodes around module calls pre freezing to signify where in the graph nodes should run in PyTorch</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">MarkNodesForFallback</span></code></p></li>
+</ul>
+<p>Looks for delimiters then marks all nodes between the delimiters to tell partitioning to run them in PyTorch</p>
+</section>
+<section id="peephole-optimze">
+<h3>Peephole Optimze<a class="headerlink" href="#peephole-optimze" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/ppeephole_optimze.h">torch/csrc/jit/passes/peephole_optimze.h</a></p>
+</div></blockquote>
+<p>The intent for this optimization pass is to catch all of the small, easy to catch peephole optimizations you might be interested in doing.</p>
+<dl class="simple">
+<dt>Right now, it does:</dt><dd><ul class="simple">
+<li><p>Eliminate no-op ‘expand’ nodes</p></li>
+<li><p>Simply x.t().t() to x</p></li>
+</ul>
+</dd>
+</dl>
+</section>
+<section id="remove-contiguous">
+<h3>Remove Contiguous<a class="headerlink" href="#remove-contiguous" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_contiguous.cpp">Torch-TensorRT/core/lowering/passes/remove_contiguous.cpp</a></p>
+</div></blockquote>
+<p>Removes contiguous operators since we are doing TensorRT memory is already contiguous.</p>
+</section>
+<section id="remove-dropout">
+<h3>Remove Dropout<a class="headerlink" href="#remove-dropout" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_dropout.cpp">Torch-TensorRT/core/lowering/passes/remove_dropout.cpp</a></p>
+</div></blockquote>
+<p>Removes dropout operators since we are doing inference.</p>
+</section>
+<section id="remove-to">
+<h3>Remove To<a class="headerlink" href="#remove-to" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/remove_to.cpp">Torch-TensorRT/core/lowering/passes/remove_to.cpp</a></p>
+</div></blockquote>
+<p>Removes <code class="docutils literal notranslate"><span class="pre">aten::to</span></code> operators that do casting, since TensorRT mangages it itself. It is important that this is one of the last passes run so that
+other passes have a change to move required cast operators out of the main namespace.</p>
+</section>
+<section id="unpack-addmm">
+<h3>Unpack AddMM<a class="headerlink" href="#unpack-addmm" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/unpack_addmm.cpp">Torch-TensorRT/core/lowering/passes/unpack_addmm.cpp</a></p>
+</div></blockquote>
+<p>Unpacks <code class="docutils literal notranslate"><span class="pre">aten::addmm</span></code> into <code class="docutils literal notranslate"><span class="pre">aten::matmul</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::add_</span></code> (with an additional <code class="docutils literal notranslate"><span class="pre">trt::const</span></code>
+op to freeze the bias in the TensorRT graph). This lets us reuse the <code class="docutils literal notranslate"><span class="pre">aten::matmul</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::add_</span></code>
+converters instead of needing a dedicated converter.</p>
+</section>
+<section id="unpack-logsoftmax">
+<h3>Unpack LogSoftmax<a class="headerlink" href="#unpack-logsoftmax" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/unpack_log_softmax.cpp">Torch-TensorRT/core/lowering/passes/unpack_log_softmax.cpp</a></p>
+</div></blockquote>
+<p>Unpacks <code class="docutils literal notranslate"><span class="pre">aten::logsoftmax</span></code> into <code class="docutils literal notranslate"><span class="pre">aten::softmax</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::log</span></code>. This lets us reuse the
+<code class="docutils literal notranslate"><span class="pre">aten::softmax</span></code> and <code class="docutils literal notranslate"><span class="pre">aten::log</span></code> converters instead of needing a dedicated converter.</p>
+</section>
+<section id="unroll-loops">
+<h3>Unroll Loops<a class="headerlink" href="#unroll-loops" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/pytorch/blob/master/torch/csrc/jit/passes/loop_unrolling.h">torch/csrc/jit/passes/loop_unrolling.h</a></p>
+</div></blockquote>
+<p>Unrolls the operations of compatable loops (e.g. sufficently short) so that you only have to go through the loop once.</p>
+</section>
+<section id="replace-tile-with-repeat">
+<h3>Replace Tile with Repeat<a class="headerlink" href="#replace-tile-with-repeat" title="Permalink to this headline">¶</a></h3>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/lowering/passes/tile_to_repeat.cpp">Torch-TensorRT/core/lowering/passes/tile_to_repeat.cpp</a></p>
+</div></blockquote>
+<p>Removes dropout operators since we are doing inference.</p>
+</section>
+</section>
+</section>
+
+
+             </article>
+             
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+            <footer>
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+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">Lowering Phase</a><ul>
+<li><a class="reference internal" href="#passes-used">Passes Used</a><ul>
+<li><a class="reference internal" href="#eliminatecommonsubexpression">EliminateCommonSubexpression</a></li>
+<li><a class="reference internal" href="#eliminate-dead-code">Eliminate Dead Code</a></li>
+<li><a class="reference internal" href="#eliminate-exeception-or-pass-pattern">Eliminate Exeception Or Pass Pattern</a></li>
+<li><a class="reference internal" href="#eliminate-redundant-gaurds">Eliminate Redundant Gaurds</a></li>
+<li><a class="reference internal" href="#freeze-module">Freeze Module</a></li>
+<li><a class="reference internal" href="#fuse-addmm-branches">Fuse AddMM Branches</a></li>
+<li><a class="reference internal" href="#fuse-linear">Fuse Linear</a></li>
+<li><a class="reference internal" href="#fuse-flatten-linear">Fuse Flatten Linear</a></li>
+<li><a class="reference internal" href="#lower-graph">Lower Graph</a></li>
+<li><a class="reference internal" href="#lower-tuples">Lower Tuples</a></li>
+<li><a class="reference internal" href="#module-fallback">Module Fallback</a></li>
+<li><a class="reference internal" href="#peephole-optimze">Peephole Optimze</a></li>
+<li><a class="reference internal" href="#remove-contiguous">Remove Contiguous</a></li>
+<li><a class="reference internal" href="#remove-dropout">Remove Dropout</a></li>
+<li><a class="reference internal" href="#remove-to">Remove To</a></li>
+<li><a class="reference internal" href="#unpack-addmm">Unpack AddMM</a></li>
+<li><a class="reference internal" href="#unpack-logsoftmax">Unpack LogSoftmax</a></li>
+<li><a class="reference internal" href="#unroll-loops">Unroll Loops</a></li>
+<li><a class="reference internal" href="#replace-tile-with-repeat">Replace Tile with Repeat</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="partitioning-phase">
+<span id="partitioning"></span><h1>Partitioning Phase<a class="headerlink" href="#partitioning-phase" title="Permalink to this headline">¶</a></h1>
+<p>The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.</p>
+<p>On a high level, Torch-TensorRT partitioning phase does the following:</p>
+<ul class="simple">
+<li><p>Segmentation. Go through the set of operators in order and verify if there is converter for each operator. Then, roughly separate the graph into parts that Torch-TensorRT can support and parts Torch-TensorRT cannot.</p></li>
+<li><p>Dependency Analysis. For every to be compiled operator there is a “complete dependency graph”, which means that every input can to traced back to an input as Tensor or TensorList. Go through all segments after segmentation then do dependency analysis to ensure that there are only Tensor/TensorList inputs and outputs for TensorRT segments.</p></li>
+<li><p>Shape Analysis. For each segments, figure out the input and outputs shapes starting from the provided input shape from the user. Shapes can be calculated by running the graphs with JIT.</p></li>
+<li><p>Conversion. Every TensorRT segments will be converted to TensorRT engine. This part is done in compiler.cpp, but it’s still a phase in our partitioning process.</p></li>
+<li><p>Stitching. Stitch all TensorRT engines with PyTorch nodes altogether.</p></li>
+</ul>
+<p>Here are the brief description of these functions of each file:</p>
+<section id="partitoninfo-h-cpp">
+<h2>PartitonInfo.h/.cpp<a class="headerlink" href="#partitoninfo-h-cpp" title="Permalink to this headline">¶</a></h2>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/partitioning/PartitionInfo.h">core/partitioning/PartitionInfo.h</a></p>
+</div></blockquote>
+<p>The automatic fallback APIs that is used for partitioning.</p>
+</section>
+<section id="segmentedblock-h-cpp">
+<h2>SegmentedBlock.h/.cpp<a class="headerlink" href="#segmentedblock-h-cpp" title="Permalink to this headline">¶</a></h2>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/partitioning/SegmentedBlock.h">core/partitioning/SegmentedBlock.h</a></p>
+</div></blockquote>
+<p>The main data structures that is used to maintain information for each segments after segmentation.</p>
+</section>
+<section id="shape-analysis-h-cpp">
+<h2>shape_analysis.h/.cpp<a class="headerlink" href="#shape-analysis-h-cpp" title="Permalink to this headline">¶</a></h2>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/partitioning/shape_analysis.h">core/partitioning/shape_analysis.h</a></p>
+</div></blockquote>
+<p>Code implementation to get the shapes for each segments by running them in JIT.</p>
+</section>
+<section id="partitioning-h-cpp">
+<h2>partitioning.h/.cpp<a class="headerlink" href="#partitioning-h-cpp" title="Permalink to this headline">¶</a></h2>
+<blockquote>
+<div><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/core/partitioning/partitioning.h">core/partitioning/partitioning.h</a></p>
+</div></blockquote>
+<p>APIs and main code implementation for partitioning phase.</p>
+</section>
+</section>
+<section id="automatic-fallback">
+<h1>Automatic Fallback<a class="headerlink" href="#automatic-fallback" title="Permalink to this headline">¶</a></h1>
+<p>To enable automatic fallback feature, you can set following attributes in Python:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>import torch
+import torch_tensorrt as torchtrt
+
+...
+model = MyModel()
+ts_model = torch.jit.script(model)
+trt_model = torchtrt.ts.compile(model, **{
+  ...
+  &quot;min_block_size&quot; : 3,
+  &quot;torch_executed_ops&quot;: [&quot;aten::add&quot;],
+  &quot;torch_executed_modules&quot;: [],
+})
+</pre></div>
+</div>
+<ul class="simple">
+<li><p>enabled: By default automatic fallback will be off. It is enabled by setting it to True.</p></li>
+<li><p>min_block_size: The minimum number of consecutive operations that must satisfy to be converted to TensorRT. For example, if it’s set to 3, then there must be 3 consecutive supported operators then this segments will be converted.</p></li>
+<li><p>forced_fallback_ops: A list of strings that will be the names of operations that the user explicitly want to be in PyTorch nodes.</p></li>
+</ul>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>#include &quot;torch/script.h&quot;
+#include &quot;torch_tensorrt/torch_tensorrt.h&quot;
+
+...
+auto in = torch::randn({1, 3, 224, 224}, {torch::kCUDA});
+
+auto mod = torch::jit::load(&quot;trt_ts_module.ts&quot;);
+auto input_sizes =  std::vector&lt;torchtrt::InputRange&gt;{{in.sizes()}};
+torchtrt::ts::CompileSpec cfg(input_sizes);
+cfg.min_block_size = 2;
+cfg.torch_executed_ops.push_back(&quot;aten::relu&quot;);
+auto trt_mod = torchtrt::ts::compile(mod, cfg);
+auto out = trt_mod.forward({in});
+</pre></div>
+</div>
+</section>
+<section id="dependency-aware-partitioning">
+<h1>Dependency Aware Partitioning<a class="headerlink" href="#dependency-aware-partitioning" title="Permalink to this headline">¶</a></h1>
+<p>During segmentation, Torch-TensorRT uses a dependency graph of the input TorchScript nodes to reduce the number of segments created. Consider this example from test Partitioning.SegmentModelWithDependencyAwareness in <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/tests/core/partitioning/test_segmentation.cpp">tests/core/partitioning/test_segmentation.cpp</a></p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%x : Tensor, %y : Tensor):
+    %3 : int = prim::Constant[value=0]()
+    %20 : int = prim::Constant[value=1]()
+    %add : Tensor = aten::add(%x, %y, %20)
+    %x_lgamma : Tensor = aten::lgamma(%x)
+    %mul : Tensor = aten::mul(%x, %y)
+    %y_lgamma : Tensor = aten::lgamma(%y)
+    %div : Tensor = aten::div(%x, %y)
+    %div_lgamma : Tensor = aten::lgamma(%div)
+    %27 : Tensor[] = prim::ListConstruct(%x_lgamma, %y_lgamma, %div_lgamma, %add, %mul)
+    %12 : Tensor = aten::cat(%27, %3)
+    return (%12)
+</pre></div>
+</div>
+<p>In this graph <cite>aten::lgamma</cite> is not supported by conversion and must be partitioned in a Torch fallback segment. If Torch-TensorRT uses a greedy segmentation strategy that traverses nodes in the input graph in order and gathers ops with the same target (TensorRT or Torch) into a segment until it encounters an op with a different target, the resulting partition includes 7 segments, many with just a single op.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Segment Block @0:
+    Target: TensorRT
+
+    Graph: graph(%x : Tensor,
+        %y : Tensor):
+    %3 : int = prim::Constant[value=1]()
+    %0 : Tensor = aten::add(%x, %y, %3)
+    return ()
+
+Segment Block @1:
+    Target: Torch
+
+    Graph: graph(%x : Tensor):
+    %0 : Tensor = aten::lgamma(%x)
+    return ()
+
+Segment Block @2:
+    Target: TensorRT
+
+    Graph: graph(%x : Tensor,
+        %y : Tensor):
+    %0 : Tensor = aten::mul(%x, %y)
+    return ()
+
+Segment Block @3:
+    Target: Torch
+
+    Graph: graph(%y : Tensor):
+    %0 : Tensor = aten::lgamma(%y)
+    return ()
+
+Segment Block @4:
+    Target: TensorRT
+
+    Graph: graph(%x : Tensor,
+        %y : Tensor):
+    %0 : Tensor = aten::div(%x, %y)
+    return ()
+
+Segment Block @5:
+    Target: Torch
+
+    Graph: graph(%1 : Tensor):
+    %0 : Tensor = aten::lgamma(%1)
+    return ()
+
+Segment Block @6:
+    Target: TensorRT
+
+    Graph: graph(%1 : Tensor,
+        %2 : Tensor,
+        %3 : Tensor,
+        %4 : Tensor,
+        %5 : Tensor):
+    %7 : int = prim::Constant[value=0]()
+    %0 : Tensor[] = prim::ListConstruct(%1, %2, %3, %4, %5)
+    %6 : Tensor = aten::cat(%0, %7)
+    return ()
+</pre></div>
+</div>
+<p>This partition is valid, but the segmentation is suboptimal. These arithmetic ops and <cite>aten::lgamma</cite> ops are each split into their own segment as we alternate between Torch and TensorRT targets in the linear traversal of the graph.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%add : Tensor = aten::add(%x, %y, %20)
+%x_lgamma : Tensor = aten::lgamma(%x)
+%mul : Tensor = aten::mul(%x, %y)
+%y_lgamma : Tensor = aten::lgamma(%y)
+%div : Tensor = aten::div(%x, %y)
+%div_lgamma : Tensor = aten::lgamma(%div)
+</pre></div>
+</div>
+<p>Each of the arithmetic ops in this segment is only dependent on constants and the inputs <cite>%x</cite> and <cite>%y</cite>. The <cite>aten::lgamma</cite> ops are dependent on the inputs <cite>%x</cite>, <cite>%y</cite> and the output of the <cite>aten::div</cite>. This means that we could rewrite this portion of the input graph as below without changing the behavior of the graph. This reordered series of ops could be cleanly partitioned into just 2 segments using the greedy segmentation approach described above.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>%add : Tensor = aten::add(%x, %y, %20)
+%mul : Tensor = aten::mul(%x, %y)
+%div : Tensor = aten::div(%x, %y)
+%x_lgamma : Tensor = aten::lgamma(%x)
+%y_lgamma : Tensor = aten::lgamma(%y)
+%div_lgamma : Tensor = aten::lgamma(%div)
+</pre></div>
+</div>
+<p>By adding awareness of the dependencies between ops to the basic greedy segmentation approach we can achieve the same partition without rewriting the graph. Now we will maintain both Torch and TensorRT targeted segments at the same time as we traverse the graph. We will only finalize a segment once we hit an op that is both dependent on an op in the segment and has a different target. This will allow the partition to create larger segments by reordering nodes across the segment boundary while guaranteeing that we will not modify the behavior of the graph by reordering nodes relative to their dependencies.
+In this example we will collect the arithmetic ops in a TensorRT segment and the <cite>aten::lgamma</cite> ops in a Torch segment. When we encounter the <cite>%div_lgamma : Tensor = aten::lgamma(%div)</cite> op we can see it is dependent on <cite>%div : Tensor = aten::div(%x, %y)</cite> in the current TensorRT segment. This triggers finalization of the TensorRT segment containing the <cite>aten::div</cite> op to guarantee it will appear before its dependency in the final partition. The Torch segment containing the <cite>aten::lgamma</cite> op is finalized when we encounter the <cite>prim::ListConstruct</cite> op which targets TensorRT and is dependent on the results of the <cite>aten::lgamma</cite> ops.</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>Segment Block @0:
+    Target: TensorRT
+
+    Graph: graph(%x : Tensor,
+        %y : Tensor):
+    %3 : int = prim::Constant[value=1]()
+    %0 : Tensor = aten::add(%x, %y, %3)
+    %4 : Tensor = aten::mul(%x, %y)
+    %5 : Tensor = aten::div(%x, %y)
+    return ()
+
+Segment Block @1:
+    Target: Torch
+
+    Graph: graph(%x : Tensor,
+        %y : Tensor,
+        %5 : Tensor):
+    %0 : Tensor = aten::lgamma(%x)
+    %2 : Tensor = aten::lgamma(%y)
+    %4 : Tensor = aten::lgamma(%5)
+    return ()
+
+Segment Block @2:
+    Target: TensorRT
+
+    Graph: graph(%1 : Tensor,
+        %2 : Tensor,
+        %3 : Tensor,
+        %4 : Tensor,
+        %5 : Tensor):
+    %7 : int = prim::Constant[value=0]()
+    %0 : Tensor[] = prim::ListConstruct(%1, %2, %3, %4, %5)
+    %6 : Tensor = aten::cat(%0, %7)
+    return ()
+</pre></div>
+</div>
+<p>In some cases this approach may create adjacent segments in the partition which have the same target. As a clean-up step we can consolidate these adjacent segments to further reduce the number of segments in the final partition.
+The merge segments step identifies a list of segments that are adjacent in the graph, have the same target, and are not marked as <cite>do_not_merge</cite>. The nodes from these segments will be combined into a single new segment that will replace the merged segments in the partition.
+The <cite>do_not_merge</cite> marking is used to prevent merging of segments created for conditional nodes and loops that are handled as special cases in graph stitching and should not be merged with adjacent segments of the same type.</p>
+</section>
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
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+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo_converters.html">Writing Dynamo Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ts_converters.html">Writing TorchScript Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
+
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="compiler-phases">
+<h1>Compiler Phases<a class="headerlink" href="#compiler-phases" title="Permalink to this headline">¶</a></h1>
+<div class="toctree-wrapper compound">
+</div>
+<section id="lowering">
+<h2>Lowering<a class="headerlink" href="#lowering" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference internal" href="lowering.html#lowering"><span class="std std-ref">Lowering Phase</span></a></p>
+<p>The lowering is made up of a set of passes (some from PyTorch and some specific to Torch-TensorRT)
+run over the graph IR to map the large PyTorch opset to a reduced opset that is easier to convert to
+TensorRT.</p>
+</section>
+<section id="partitioning">
+<h2>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference internal" href="partitioning.html#partitioning"><span class="std std-ref">Partitioning Phase</span></a></p>
+<p>The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.</p>
+</section>
+<section id="conversion">
+<h2>Conversion<a class="headerlink" href="#conversion" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference internal" href="conversion.html#conversion"><span class="std std-ref">Conversion Phase</span></a></p>
+<p>In the conversion phase we traverse the lowered graph and construct an equivalent TensorRT graph.
+The conversion phase is made up of three main components, a context to manage compile time data,
+a evaluator library which will execute operations that can be resolved at compile time and a converter
+library which maps an op from JIT to TensorRT.</p>
+</section>
+<section id="compilation-and-runtime">
+<h2>Compilation and Runtime<a class="headerlink" href="#compilation-and-runtime" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference internal" href="../user_guide/runtime.html#runtime"><span class="std std-ref">Deploying Torch-TensorRT Programs</span></a></p>
+<p>The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
+takes a serialized engine and instantiates it within a engine manager, then the compiler will
+build out a JIT graph that references this engine and wraps it in a module to return to the user.
+When the user executes the module, the JIT program run in the JIT runtime extended by Torch-TensorRT with the data providied from the user.</p>
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+<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
+<ul>
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+<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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+              
+  <section id="runtime-phase">
+<span id="execution"></span><h1>Runtime Phase<a class="headerlink" href="#runtime-phase" title="Permalink to this headline">¶</a></h1>
+<p>The Runtime phase is responsible for constructing self standing TorchScript graphs with embedded TensorRT engines and serving as the runtime
+when these engines are called. The main interface accepts a serialized TensorRT engine. The execution phase
+will deserialize and wrap this engine in a class which maintains a execution context for each engine
+and some metadata about its inputs and outputs and is compatable with the TorchScript interpreter so that
+it can be moved around and used like other TorchScript IValues. The engine is run by providing it and inputs
+to the <code class="docutils literal notranslate"><span class="pre">tensorrt::execute_engine</span></code> operator which will take the engine and its inputs and return the results of engine exeuction.</p>
+<section id="background">
+<h2>Background<a class="headerlink" href="#background" title="Permalink to this headline">¶</a></h2>
+<p>PyTorch JIT’s runtime is based around a stack machine, all operators pop off arguments from the stack, pass them to
+some implementation of the operator then push results back onto the stack. The actual elements of the stack
+are <code class="docutils literal notranslate"><span class="pre">torch::jit::IValues</span></code>, the same type we evaluate in the conversion phase (the realization of the abstract
+torch::jit::Value type).</p>
+</section>
+<section id="tensorrt-engine-executor-op">
+<h2>TensorRT Engine Executor Op<a class="headerlink" href="#tensorrt-engine-executor-op" title="Permalink to this headline">¶</a></h2>
+<p>When the Torch-TensorRT is loaded, it registers an operator in the PyTorch JIT operator library called
+<code class="docutils literal notranslate"><span class="pre">trt::execute_engine(Tensor[]</span> <span class="pre">inputs,</span> <span class="pre">__torch__.torch.classes.tensorrt.Engine</span> <span class="pre">engine)</span> <span class="pre">-&gt;</span> <span class="pre">Tensor[]</span></code> which takes an
+instantiated engine and list of inputs. Compiled graphs store this engine in an attribute so that it is portable and serializable.
+When the op is called, an instnantiated engine and input tensors are popped off the runtime stack. These inputs are passed into a generic engine execution function which
+will run the tensors through the TensorRT engine and return new tensors as results. These tensors are pushed on to the
+stack so that the next op whatever it is can use it.</p>
+</section>
+<section id="constructing-the-resulting-graph">
+<h2>Constructing the Resulting Graph<a class="headerlink" href="#constructing-the-resulting-graph" title="Permalink to this headline">¶</a></h2>
+<p>Once the engine is deserialized and instantiated, the compiler will construct a graph that will execute the engine when the module is called.
+Here is an example:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="p">(</span><span class="o">%</span><span class="n">self_1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">__torch__</span><span class="p">.</span><span class="n">torchvision</span><span class="p">.</span><span class="n">models</span><span class="p">.</span><span class="n">resnet</span><span class="p">.</span><span class="n">___torch_mangle_4847</span><span class="p">.</span><span class="n">ResNet_trt</span><span class="p">,</span>
+<span class="w">  </span><span class="o">%</span><span class="n">input_0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">)</span><span class="o">:</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">__torch__</span><span class="p">.</span><span class="n">torch</span><span class="p">.</span><span class="n">classes</span><span class="p">.</span><span class="n">tensorrt</span><span class="p">.</span><span class="n">Engine</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">GetAttr</span><span class="p">[</span><span class="n">name</span><span class="o">=</span><span class="s">&quot;__torch___torchvision_models_resnet____torch_mangle_4847_ResNet_trt_engine&quot;</span><span class="p">](</span><span class="o">%</span><span class="n">self_1</span><span class="p">)</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">3</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">ListConstruct</span><span class="p">(</span><span class="o">%</span><span class="n">input_0</span><span class="p">)</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">4</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt</span><span class="o">::</span><span class="n">execute_engine</span><span class="p">(</span><span class="o">%</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="o">%</span><span class="mi">1</span><span class="p">)</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">5</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">ListUnpack</span><span class="p">(</span><span class="o">%</span><span class="mi">4</span><span class="p">)</span>
+<span class="k">return</span><span class="w"> </span><span class="p">(</span><span class="o">%</span><span class="mi">5</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>You can see the engine attribute in the graph and the <code class="docutils literal notranslate"><span class="pre">trt::execute_engine</span></code> op taking a list of input tensors and an engine in
+and produces a list of output tensors which is returned. When <code class="docutils literal notranslate"><span class="pre">forward</span></code> is called on the module this graph is executed, thereby
+running the TensorRT engine.</p>
+<p>In the case of multiple outputs, the compiled graph may repack output tensors into a Tuple to return back to the user.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">graph</span><span class="p">(</span><span class="o">%</span><span class="n">self_1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">__torch__</span><span class="p">.</span><span class="n">PyTorch</span><span class="p">.</span><span class="n">Detection</span><span class="p">.</span><span class="n">SSD</span><span class="p">.</span><span class="n">src</span><span class="p">.</span><span class="n">model</span><span class="p">.</span><span class="n">SSD300_trt</span><span class="p">,</span>
+<span class="w">  </span><span class="o">%</span><span class="n">input_0</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">)</span><span class="o">:</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">1</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">__torch__</span><span class="p">.</span><span class="n">torch</span><span class="p">.</span><span class="n">classes</span><span class="p">.</span><span class="n">tensorrt</span><span class="p">.</span><span class="n">Engine</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">GetAttr</span><span class="p">[</span><span class="n">name</span><span class="o">=</span><span class="s">&quot;__torch___PyTorch_Detection_SSD_src_model_SSD300_trt_engine&quot;</span><span class="p">](</span><span class="o">%</span><span class="n">self_1</span><span class="p">)</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">3</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">ListConstruct</span><span class="p">(</span><span class="o">%</span><span class="n">input_0</span><span class="p">)</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">4</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">[]</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt</span><span class="o">::</span><span class="n">execute_engine</span><span class="p">(</span><span class="o">%</span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="o">%</span><span class="mi">1</span><span class="p">)</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">5</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="p">,</span><span class="w"> </span><span class="o">%</span><span class="mi">6</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="n">Tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">ListUnpack</span><span class="p">(</span><span class="o">%</span><span class="mi">4</span><span class="p">)</span>
+<span class="w">    </span><span class="o">%</span><span class="mi">7</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="p">(</span><span class="n">Tensor</span><span class="p">,</span><span class="w"> </span><span class="n">Tensor</span><span class="p">)</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">prim</span><span class="o">::</span><span class="n">TupleConstruct</span><span class="p">(</span><span class="o">%</span><span class="mi">5</span><span class="p">,</span><span class="w"> </span><span class="o">%</span><span class="mi">6</span><span class="p">)</span>
+<span class="k">return</span><span class="w"> </span><span class="p">(</span><span class="o">%</span><span class="mi">7</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="serialization-and-deserialization">
+<h2>Serialization and Deserialization<a class="headerlink" href="#serialization-and-deserialization" title="Permalink to this headline">¶</a></h2>
+<p>Serialization and deserialization of TensorRT engines embedded in TorchScript graphs are handled by the holder class for the engine and TorchBind.
+When a TorchScript module is saved, the pickler will run serilization on the cuda engine and store the serialized engine in the zip file created.
+When deserializing, the depickler will call a constructor for the engine holder class with the serialized engine so that it can be set up again for
+execution.</p>
+</section>
+</section>
+<section id="abi-versioning-and-serialization-format">
+<h1>ABI Versioning and Serialization Format<a class="headerlink" href="#abi-versioning-and-serialization-format" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT programs are standard TorchScript with TensorRT engines as objects embedded in the graph. Therefore there is a serialization format
+for the TensorRT engines. The format for Torch-TensorRT serialized programs are versioned with an “ABI” version which tells the runtime about runtime compatibility.</p>
+<p>&gt; Current ABI version is 3</p>
+<p>The format is a vector of serialized strings. They encode the following information</p>
+<ul class="simple">
+<li><p>ABI Version for the program</p></li>
+<li><p>Name of the TRT engine</p></li>
+<li><p>Device information: Includes the target device the engine was built on, SM capability and other device information. This information is used at deserialization time to select the correct device to run the engine</p></li>
+<li><p>Serialized TensorRT engine</p></li>
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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/logging.html">torch_tensorrt.logging</a></li>
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+<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/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo_converters.html">Writing Dynamo Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ts_converters.html">Writing TorchScript Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+              
+  <section id="system-overview">
+<span id="id1"></span><h1>System Overview<a class="headerlink" href="#system-overview" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT is primarily a C++ Library with a Python API planned. We use Bazel as our build system and target Linux x86_64 and
+Linux aarch64 (only natively) right now. The compiler we use is GCC 7.5.0 and the library is untested with compilers before that
+version so there may be compilation errors if you try to use an older compiler.</p>
+<p>The repository is structured into:</p>
+<ul class="simple">
+<li><p>core: Main compiler source code</p></li>
+<li><p>cpp: C++ API</p></li>
+<li><p>tests: tests of the C++ API, the core and converters</p></li>
+<li><p>py: Python API</p></li>
+<li><p>notebooks: Example applications built with Torch-TensorRT</p></li>
+<li><p>docs: Documentation</p></li>
+<li><p>docsrc: Documentation Source</p></li>
+<li><p>third_party: BUILD files for dependency libraries</p></li>
+<li><p>toolchains: Toolchains for different platforms</p></li>
+</ul>
+<p>The C++ API is unstable and subject to change until the library matures, though most work is done under the hood in the core.</p>
+<p>The core has a couple major parts: The top level compiler interface which coordinates ingesting a module, lowering,
+converting and generating a new module and returning it back to the user. There are the three main phases of the
+compiler, the lowering phase, the conversion phase, and the execution phase.</p>
+<section id="compiler-phases">
+<h2>Compiler Phases<a class="headerlink" href="#compiler-phases" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+</div>
+<section id="lowering">
+<h3>Lowering<a class="headerlink" href="#lowering" title="Permalink to this headline">¶</a></h3>
+<p><a class="reference internal" href="lowering.html#lowering"><span class="std std-ref">Lowering Phase</span></a></p>
+<p>The lowering is made up of a set of passes (some from PyTorch and some specific to Torch-TensorRT)
+run over the graph IR to map the large PyTorch opset to a reduced opset that is easier to convert to
+TensorRT.</p>
+</section>
+<section id="partitioning">
+<h3>Partitioning<a class="headerlink" href="#partitioning" title="Permalink to this headline">¶</a></h3>
+<p><a class="reference internal" href="partitioning.html#partitioning"><span class="std std-ref">Partitioning Phase</span></a></p>
+<p>The phase is optional and enabled by the user. It instructs the compiler to separate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
+Criteria for separation include: Lack of a converter, operator is explicitly set to run in PyTorch by the user or the node has a flag which tells partitioning to
+run in PyTorch by the module fallback passes.</p>
+</section>
+<section id="conversion">
+<h3>Conversion<a class="headerlink" href="#conversion" title="Permalink to this headline">¶</a></h3>
+<p><a class="reference internal" href="conversion.html#conversion"><span class="std std-ref">Conversion Phase</span></a></p>
+<p>In the conversion phase we traverse the lowered graph and construct an equivalent TensorRT graph.
+The conversion phase is made up of three main components, a context to manage compile time data,
+a evaluator library which will execute operations that can be resolved at compile time and a converter
+library which maps an op from JIT to TensorRT.</p>
+</section>
+<section id="compilation-and-runtime">
+<h3>Compilation and Runtime<a class="headerlink" href="#compilation-and-runtime" title="Permalink to this headline">¶</a></h3>
+<p><a class="reference internal" href="../user_guide/runtime.html#runtime"><span class="std std-ref">Deploying Torch-TensorRT Programs</span></a></p>
+<p>The final compilation phase constructs a TorchScript program to run the converted TensorRT engine. It
+takes a serialized engine and instantiates it within a engine manager, then the compiler will
+build out a JIT graph that references this engine and wraps it in a module to return to the user.
+When the user executes the module, the JIT program run in the JIT runtime extended by Torch-TensorRT with the data providied from the user.</p>
+</section>
+</section>
+</section>
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+<li><a class="reference internal" href="#">System Overview</a><ul>
+<li><a class="reference internal" href="#compiler-phases">Compiler Phases</a><ul>
+<li><a class="reference internal" href="#lowering">Lowering</a></li>
+<li><a class="reference internal" href="#partitioning">Partitioning</a></li>
+<li><a class="reference internal" href="#conversion">Conversion</a></li>
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+            
+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="system_overview.html">System Overview</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo_converters.html">Writing Dynamo Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Writing TorchScript Converters</a></li>
+<li class="toctree-l1"><a class="reference internal" href="useful_links.html">Useful Links for Torch-TensorRT Development</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+              
+  <section id="writing-torchscript-converters">
+<span id="ts-converters"></span><h1>Writing TorchScript Converters<a class="headerlink" href="#writing-torchscript-converters" title="Permalink to this headline">¶</a></h1>
+<section id="background">
+<h2>Background<a class="headerlink" href="#background" title="Permalink to this headline">¶</a></h2>
+<p>In the JIT IR, operations are represented as nodes in a graph. A node has inputs and outputs, represented by <code class="docutils literal notranslate"><span class="pre">torch::jit::Values</span></code>
+which are typed abstract representation of data flowing into and out of a node. TensorRT represents its graph though the
+use of <code class="docutils literal notranslate"><span class="pre">nvinfer1::ILayers</span></code> and <code class="docutils literal notranslate"><span class="pre">nvinfer1::ITensors</span></code> which are its analogues to nodes and values. The goal of
+converters create new ILayers and subgraphs that do operation specified by the node and associate produced ITensors
+and Values together.</p>
+</section>
+<section id="converters">
+<h2>Converters<a class="headerlink" href="#converters" title="Permalink to this headline">¶</a></h2>
+<p>Converters should be functions which will use a list of inputs (either <code class="docutils literal notranslate"><span class="pre">nvinfer1::ITensors</span></code> or <code class="docutils literal notranslate"><span class="pre">torch::jit::IValues</span></code>) to
+construct an equivalent layer to the LibTorch op.</p>
+<p>Converters can be registered using the <code class="docutils literal notranslate"><span class="pre">RegisterNodeConversionPatterns</span></code> helper class where you instantiate a
+RegisterNodeConversionPatterns object and call the pattern function on it (like below) which takes a string
+which describes the function schema of the op that will cause the converter to be run and a lambda or function
+which will do the actual conversion:</p>
+<blockquote>
+<div><p>Note the pattern function can be chained</p>
+</div></blockquote>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">acthardtanh</span><span class="w"> </span><span class="n">TORCHTRT_UNUSED</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">RegisterNodeConversionPatterns</span><span class="p">()</span>
+<span class="w">    </span><span class="p">.</span><span class="n">pattern</span><span class="p">({</span>
+<span class="w">        </span><span class="s">&quot;aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -&gt; (Tensor)&quot;</span><span class="p">,</span>
+<span class="w">        </span><span class="p">[](</span><span class="n">ConversionCtx</span><span class="o">*</span><span class="w"> </span><span class="n">ctx</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Node</span><span class="o">*</span><span class="w"> </span><span class="n">n</span><span class="p">,</span><span class="w"> </span><span class="n">args</span><span class="o">&amp;</span><span class="w"> </span><span class="n">args</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="p">{</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">ITensor</span><span class="p">();</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">min</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">unwrapToDouble</span><span class="p">();</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">max</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">unwrapToDouble</span><span class="p">();</span>
+
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">new_layer</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">net</span><span class="o">-&gt;</span><span class="n">addActivation</span><span class="p">(</span><span class="o">*</span><span class="n">in</span><span class="p">,</span><span class="w"> </span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">ActivationType</span><span class="o">::</span><span class="n">kCLIP</span><span class="p">);</span>
+<span class="w">            </span><span class="n">TORCHTRT_CHECK</span><span class="p">(</span><span class="n">new_layer</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;Unable to create layer for aten::hardtanh&quot;</span><span class="p">);</span>
+
+<span class="w">            </span><span class="n">new_layer</span><span class="o">-&gt;</span><span class="n">setAlpha</span><span class="p">(</span><span class="n">min</span><span class="p">);</span>
+<span class="w">            </span><span class="n">new_layer</span><span class="o">-&gt;</span><span class="n">setBeta</span><span class="p">(</span><span class="n">max</span><span class="p">);</span>
+
+<span class="w">            </span><span class="n">new_layer</span><span class="o">-&gt;</span><span class="n">setName</span><span class="p">(</span><span class="n">util</span><span class="o">::</span><span class="n">node_info</span><span class="p">(</span><span class="n">n</span><span class="p">).</span><span class="n">c_str</span><span class="p">());</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">out_tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">AssociateValueAndTensor</span><span class="p">(</span><span class="n">n</span><span class="o">-&gt;</span><span class="n">outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">new_layer</span><span class="o">-&gt;</span><span class="n">getOutput</span><span class="p">(</span><span class="mi">0</span><span class="p">));</span>
+
+<span class="w">            </span><span class="n">LOG_DEBUG</span><span class="p">(</span><span class="s">&quot;Output shape: &quot;</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">out_tensor</span><span class="o">-&gt;</span><span class="n">getDimensions</span><span class="p">());</span>
+<span class="w">            </span><span class="k">return</span><span class="w"> </span><span class="nb">true</span><span class="p">;</span>
+<span class="w">        </span><span class="p">}</span>
+<span class="w">    </span><span class="p">});</span>
+</pre></div>
+</div>
+</section>
+<section id="converter-contract">
+<h2>Converter Contract<a class="headerlink" href="#converter-contract" title="Permalink to this headline">¶</a></h2>
+<section id="what-is-guaranteed-to-converters">
+<h3>What is guaranteed to converters<a class="headerlink" href="#what-is-guaranteed-to-converters" title="Permalink to this headline">¶</a></h3>
+<ol class="arabic simple">
+<li><p>In the args there will be an entry for each node input value, either a ITensor or IValue</p></li>
+<li><p>Inputs will be provided in order according to the function schema</p></li>
+</ol>
+</section>
+<section id="responsibilities-of-a-converter">
+<h3>Responsibilities of a converter<a class="headerlink" href="#responsibilities-of-a-converter" title="Permalink to this headline">¶</a></h3>
+<ol class="arabic simple">
+<li><p>Args must be guaranteed to be a type to unwrap the Arg union without checking, typically input tensor arguments can be expected to be ITensors</p></li>
+<li><p>Any weights or static values must guaranteed to be valid until the end of conversion time</p>
+<ol class="loweralpha simple">
+<li><p>A helpful tool is the Weights helper class described below</p></li>
+</ol>
+</li>
+<li><p>Converters are expected to produce an IValue or ITensor for each output of a node. The compiler will check this and produce warnings if there are Values that don’t have associated ITensors or IValues.</p></li>
+<li><p>Outputs must be annotated</p>
+<ol class="loweralpha simple">
+<li><p>There must be an association between a JIT nodes output values and the new TRT layers output tensors in the <code class="docutils literal notranslate"><span class="pre">value_tensor_map</span></code> in the conversion context</p></li>
+</ol>
+</li>
+<li><p>Name your layers</p>
+<ol class="loweralpha simple">
+<li><p>Its much easier to debug when we can track which layers and nodes correspond with each other. The system we are currently using is to use the “node info” of the node as the name of the layer</p></li>
+</ol>
+</li>
+<li><p>Name your tensors</p>
+<ol class="loweralpha simple">
+<li><p>Use the output value debug name as the name for the new ITensor (again for debugging)</p></li>
+</ol>
+</li>
+</ol>
+</section>
+</section>
+<section id="conversion-context">
+<h2>Conversion Context<a class="headerlink" href="#conversion-context" title="Permalink to this headline">¶</a></h2>
+<p>The conversion context maintains the state of conversion, it manages the Network Definition, two maps
+one that stores associations between Values and IValues (the evaluated_value_map) and one that stores
+associations between Values and ITensors, and any sort of memory that needs to live until the end of
+conversion. The main apis that you will interface with in converters is directly accessing the network
+definition to add layers <code class="docutils literal notranslate"><span class="pre">ctx-&gt;net</span></code> and data association functions <code class="docutils literal notranslate"><span class="pre">ctx-&gt;AssociateValueAndTensor()</span></code>
+and <code class="docutils literal notranslate"><span class="pre">ctx-&gt;AssociateValueAndIValue()</span></code>, which you will use to add layers to the TRT layers and log
+pairs of node outputs and static values or TensorRT layer outputs.</p>
+</section>
+<section id="args">
+<h2>Args<a class="headerlink" href="#args" title="Permalink to this headline">¶</a></h2>
+<p>Arguments provided to the converter are inspectable unions of <code class="docutils literal notranslate"><span class="pre">nvinfer1::ITensors</span></code> and <code class="docutils literal notranslate"><span class="pre">torch::jit::IValues</span></code> (i.e.
+abstract dataflow in the TensorRT graph and static values). You are guaranteed that you will have some
+argument for each input value for the node. They are provided in the order of the function schema.
+It can be expected that inputs (meaning the parameters that would be passed into the forward
+function of a module in PyTorch) will be ITensors but the Arg class also has mechanisms to inspect arguments safely
+before unwrapping if you are unsure. Args also have deep unwrap methods that let you get straight to the
+underlying data in an IValue if you know it’s safe. You can also pass in a fallback value if there is a
+chance the IValue is None. IValues have been extended to be able to hold a wrapper around ITensors only in the case of TensorLists.
+You can get an ITensor from an IValue by a pattern similar to this: <code class="docutils literal notranslate"><span class="pre">ivalue.toCustomClass&lt;TensorContainer&gt;()-&gt;tensor()</span></code>.
+You can tell if an IValue contains a Tensor or an ITensor by using <code class="docutils literal notranslate"><span class="pre">ivalue.isTensor()</span></code> or <code class="docutils literal notranslate"><span class="pre">ivalue.isCustomClass()</span></code>.</p>
+</section>
+<section id="weights">
+<h2>Weights<a class="headerlink" href="#weights" title="Permalink to this headline">¶</a></h2>
+<p>Weights are used during build time, so any weights need to be guaranteed to live until the end of the conversion phase.
+TensorRT also uses its own weights structure to hold the weights. There is a wrapper around this class available
+to converts which abstracts a lot of this.</p>
+<p>The weights wrapper class can accept either <code class="docutils literal notranslate"><span class="pre">at::Tensors</span></code> or singular values (right now). You also need to pass the
+conversion context when constructing these weights because internally the weights class will allocate memory managed
+by the conversion context to store a copy of the tensor data. This data gets freed when the conversion context
+destructor gets destroyed so converters don’t really need to think about it.</p>
+<p>There is metadata generated from the shape of the input data which becomes useful in interfacing with TensorRT, such
+as number of input maps, number of output maps and kernel shape.</p>
+</section>
+<section id="other-advice">
+<h2>Other advice<a class="headerlink" href="#other-advice" title="Permalink to this headline">¶</a></h2>
+<p>You have the benefit of the full aten library when dealing with weights and other static values. This means that you
+can do quite a bit of work during conversion time to produce efficient conversion. A good example is batch_norm
+converter where the converter does fusion of operations with PyTorch before creating the TensorRT layer.</p>
+</section>
+</section>
+
+
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diff --git a/docs/v2.2.0/contributors/useful_links.html b/docs/v2.2.0/contributors/useful_links.html
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diff --git a/docs/v2.2.0/contributors/writing_dynamo_aten_lowering_passes.html b/docs/v2.2.0/contributors/writing_dynamo_aten_lowering_passes.html
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+  <section id="writing-dynamo-aten-lowering-passes">
+<span id="id1"></span><h1>Writing Dynamo ATen Lowering Passes<a class="headerlink" href="#writing-dynamo-aten-lowering-passes" title="Permalink to this headline">¶</a></h1>
+<section id="basics-of-a-lowering-pass">
+<h2>Basics of a Lowering Pass<a class="headerlink" href="#basics-of-a-lowering-pass" title="Permalink to this headline">¶</a></h2>
+<p>ATen lowering passes are Python functions which take as input a graph of ATen operators, apply some desired modification such as operator coalescing/fusion, operator replacement, subgraph rewriting, custom operator insertion, or other operation on a <cite>torch.fx.GraphModule</cite>, then return the modified graph to the caller. These lowering passes generally modify the graph in-place and return the same input object.</p>
+</section>
+<section id="lowering-pass-requirements">
+<h2>Lowering Pass Requirements<a class="headerlink" href="#lowering-pass-requirements" title="Permalink to this headline">¶</a></h2>
+<p>An ATen lowering pass function in Torch-TRT must satisfy two requirements:
+- The function must take as input a <cite>torch.fx.GraphModule</cite> and a sequence of torch Tensors, <cite>Sequence[torch.Tensor]</cite>, and return the lowered <cite>torch.fx.GraphModule</cite>
+- The function must leave the graph in a valid and invoke-able state, including performing any necessary linting and recompilation</p>
+<p>See this link for information on <a class="reference external" href="https://pytorch.org/docs/stable/fx.html#graph-manipulation">Graph Manipulations</a> in FX. See below for an example of a lowering pass which repairs graphs that have inputs which are also outputs, a disallowed configuration for TRT Engines.</p>
+</section>
+<section id="example-lowering-pass">
+<h2>Example Lowering Pass<a class="headerlink" href="#example-lowering-pass" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">repair_input_as_output</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">sample_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Repair scenarios where inputs are also outputs of the graph</span>
+
+<span class="sd">    TRT does not allow such cases, so we insert a clone (identity) layer</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">modified_graph</span> <span class="o">=</span> <span class="kc">False</span>
+
+    <span class="c1"># Extract graph placeholder Tensors</span>
+    <span class="n">placeholders</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">node</span>
+        <span class="k">for</span> <span class="n">node</span> <span class="ow">in</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">nodes</span>
+        <span class="k">if</span> <span class="p">(</span>
+            <span class="n">node</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;placeholder&quot;</span>
+            <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="nb">type</span><span class="p">)</span>
+            <span class="ow">and</span> <span class="nb">issubclass</span><span class="p">(</span><span class="n">node</span><span class="o">.</span><span class="n">type</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span>
+        <span class="p">)</span>
+    <span class="p">]</span>
+
+    <span class="k">for</span> <span class="n">placeholder</span> <span class="ow">in</span> <span class="n">placeholders</span><span class="p">:</span>
+        <span class="c1"># If any placeholder has any users which are direct graph outputs</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">placeholder</span><span class="o">.</span><span class="n">users</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="mi">1</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span>
+            <span class="n">user</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;output&quot;</span> <span class="k">for</span> <span class="n">user</span> <span class="ow">in</span> <span class="n">placeholder</span><span class="o">.</span><span class="n">users</span>
+        <span class="p">):</span>
+            <span class="n">modified_graph</span> <span class="o">=</span> <span class="kc">True</span>
+
+            <span class="c1"># Get direct graph outputs which are direct uses of placeholders</span>
+            <span class="n">direct_outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">user</span> <span class="k">for</span> <span class="n">user</span> <span class="ow">in</span> <span class="n">placeholder</span><span class="o">.</span><span class="n">users</span> <span class="k">if</span> <span class="n">user</span><span class="o">.</span><span class="n">op</span> <span class="o">==</span> <span class="s2">&quot;output&quot;</span><span class="p">]</span>
+
+            <span class="c1"># Insert clone node for placeholder to ensure</span>
+            <span class="c1"># placeholder is not a direct output</span>
+            <span class="k">with</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">inserting_after</span><span class="p">(</span><span class="n">placeholder</span><span class="p">):</span>
+                <span class="n">cloned_placeholder</span> <span class="o">=</span> <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">call_function</span><span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">clone</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+                    <span class="n">args</span><span class="o">=</span><span class="p">(</span><span class="n">placeholder</span><span class="p">,),</span>
+                <span class="p">)</span>
+
+            <span class="c1"># Replace placeholder as output with cloned version</span>
+            <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">direct_outputs</span><span class="p">:</span>
+                <span class="n">output</span><span class="o">.</span><span class="n">replace_input_with</span><span class="p">(</span><span class="n">placeholder</span><span class="p">,</span> <span class="n">cloned_placeholder</span><span class="p">)</span>
+
+    <span class="c1"># If the graph was modified, clean up the graph and ensure it is up-to-date</span>
+    <span class="k">if</span> <span class="n">modified_graph</span><span class="p">:</span>
+        <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">eliminate_dead_code</span><span class="p">()</span>
+        <span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">lint</span><span class="p">()</span>
+        <span class="n">gm</span><span class="o">.</span><span class="n">recompile</span><span class="p">()</span>
+        <span class="n">logger</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Graph after repair_input_as_output:</span><span class="se">\n</span><span class="si">{</span><span class="n">gm</span><span class="o">.</span><span class="n">graph</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">gm</span>
+</pre></div>
+</div>
+</section>
+<section id="registering-lowering-passes">
+<h2>Registering Lowering Passes<a class="headerlink" href="#registering-lowering-passes" title="Permalink to this headline">¶</a></h2>
+<p>Lowering passes are currently registered in <cite>py/torch_tensorrt/dynamo/lowering/passes/__init__.py</cite>, using the <cite>torch.fx.passes.pass_manager.PassManager</cite> utility to assemble the list of passes in a desired order. New passes added directly to that list will be applied to graphs in the Torch-TensorRT <cite>torch.compile</cite> backend. Currently, we offer an ATen lowering pass registration decorator for convenience, which can be invoked either directly, or with the optional <cite>index</cite> keyword argument which controls where in the pass list the lowering pass will be inserted.</p>
+<p>For instance, to insert the pass at the default location (end of the list), the following code can be used:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@_aten_lowering_pass</span>
+<span class="k">def</span> <span class="nf">my_custom_pass</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">sample_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+    <span class="o">...</span>
+</pre></div>
+</div>
+<p>Alternatively, to insert the pass at a custom index (such as the front of the list) in the passlist, the following code can be used:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@_aten_lowering_pass</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="k">def</span> <span class="nf">my_custom_pass</span><span class="p">(</span><span class="n">gm</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">,</span> <span class="n">sample_inputs</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">fx</span><span class="o">.</span><span class="n">GraphModule</span><span class="p">:</span>
+    <span class="o">...</span>
+</pre></div>
+</div>
+<p>There are also provided utilities in <cite>torch_tensorrt.dynamo.lowering.passes</cite> for displaying the currently-available lowering pass list, applying those passes to an arbitrary <cite>torch.fx.GraphModule</cite>, and removing the lowering pass at a specific index.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Print all lowering passes in the list</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">dump_lowering_passes</span><span class="p">())</span>
+
+<span class="c1"># Apply lowering passes to a GraphModule</span>
+<span class="n">apply_lowering_passes</span><span class="p">(</span><span class="n">graph_module</span><span class="p">,</span> <span class="n">sample_inputs</span><span class="p">)</span>
+
+<span class="c1"># Remove the lowering pass at index 1</span>
+<span class="n">_remove_lowering_pass</span><span class="p">(</span><span class="n">index</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+<p><strong>Note:</strong> The above APIs are subject to change, as the lowering pass system evolves.</p>
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+<li><a class="reference internal" href="#basics-of-a-lowering-pass">Basics of a Lowering Pass</a></li>
+<li><a class="reference internal" href="#lowering-pass-requirements">Lowering Pass Requirements</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="torch_compile.html">TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">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>
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+<li class="toctree-l1"><a class="reference internal" href="../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+  <section id="compiling-exported-programs-with-torch-tensorrt">
+<span id="dynamo-export"></span><h1>Compiling Exported Programs with Torch-TensorRT<a class="headerlink" href="#compiling-exported-programs-with-torch-tensorrt" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.dynamo"></span><p>Pytorch 2.1 introduced <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> APIs which
+can export graphs from Pytorch programs into <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> objects. Torch-TensorRT dynamo
+frontend compiles these <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> objects and optimizes them using TensorRT. Here’s a simple
+usage of the dynamo frontend</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()]</span>
+<span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">export</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">inputs</span><span class="p">))</span>
+<span class="n">trt_gm</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">exp_program</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> <span class="c1"># Output is a torch.fx.GraphModule</span>
+<span class="n">trt_gm</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code> is the main API for users to interact with Torch-TensorRT dynamo frontend. The input type of the model should be <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> (ideally the output of <code class="docutils literal notranslate"><span class="pre">torch.export.export</span></code> or <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.trace</span></code> (discussed in the section below)) and output type is a <code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code> object.</p>
+</div>
+<section id="customizeable-settings">
+<h2>Customizeable Settings<a class="headerlink" href="#customizeable-settings" title="Permalink to this headline">¶</a></h2>
+<p>There are lot of options for users to customize their settings for optimizing with TensorRT.
+Some of the frequently used options are as follows:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">inputs</span></code> - For static shapes, this can be a list of torch tensors or <cite>torch_tensorrt.Input</cite> objects. For dynamic shapes, this should be a list of <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input</span></code> objects.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">enabled_precisions</span></code> - Set of precisions that TensorRT builder can use during optimization.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">truncate_long_and_double</span></code> - Truncates long and double values to int and floats respectively.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">torch_executed_ops</span></code> - Operators which are forced to be executed by Torch.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">min_block_size</span></code> - Minimum number of consecutive operators required to be executed as a TensorRT segment.</p></li>
+</ul>
+<p>The complete list of options can be found <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/123a486d6644a5bbeeec33e2f32257349acc0b8f/py/torch_tensorrt/dynamo/compile.py#L51-L77">here</a></p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>We do not support INT precision currently in Dynamo. Support for this currently exists in our Torchscript IR. We plan to implement similar support for dynamo in our next release.</p>
+</div>
+</section>
+<section id="under-the-hood">
+<h2>Under the hood<a class="headerlink" href="#under-the-hood" title="Permalink to this headline">¶</a></h2>
+<p>Under the hood, <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code> performs the following on the graph.</p>
+<ul class="simple">
+<li><p>Lowering - Applies lowering passes to add/remove operators for optimal conversion.</p></li>
+<li><p>Partitioning - Partitions the graph into Pytorch and TensorRT segments based on the <code class="docutils literal notranslate"><span class="pre">min_block_size</span></code> and <code class="docutils literal notranslate"><span class="pre">torch_executed_ops</span></code> field.</p></li>
+<li><p>Conversion - Pytorch ops get converted into TensorRT ops in this phase.</p></li>
+<li><p>Optimization - Post conversion, we build the TensorRT engine and embed this inside the pytorch graph.</p></li>
+</ul>
+</section>
+<section id="tracing">
+<h2>Tracing<a class="headerlink" href="#tracing" title="Permalink to this headline">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.trace</span></code> can be used to trace a Pytorch graphs and produce <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code>.
+This internally performs some decompositions of operators for downstream optimization.
+The <code class="docutils literal notranslate"><span class="pre">ExportedProgram</span></code> can then be used with <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code> API.
+If you have dynamic input shapes in your model, you can use this <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.trace</span></code> to export
+the model with dynamic shapes. Alternatively, you can use <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> <a class="reference external" href="https://pytorch.org/docs/stable/export.html#expressing-dynamism">with constraints</a> directly as well.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">min_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
+                              <span class="n">opt_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
+                              <span class="n">max_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
+                              <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)]</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="n">exp_program</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend</a></li>
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+<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/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+              
+  <section id="tensorrt-backend-for-torch-compile">
+<span id="torch-compile"></span><h1>TensorRT Backend for <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code><a class="headerlink" href="#tensorrt-backend-for-torch-compile" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.dynamo"></span><p>This guide presents the Torch-TensorRT <cite>torch.compile</cite> backend: a deep learning compiler which uses TensorRT to accelerate JIT-style workflows across a wide variety of models.</p>
+<section id="key-features">
+<h2>Key Features<a class="headerlink" href="#key-features" title="Permalink to this headline">¶</a></h2>
+<p>The primary goal of the Torch-TensorRT <cite>torch.compile</cite> backend is to enable Just-In-Time compilation workflows by combining the simplicity of <cite>torch.compile</cite> API with the performance of TensorRT. Invoking the <cite>torch.compile</cite> backend is as simple as importing the <cite>torch_tensorrt</cite> package and specifying the backend:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="o">...</span>
+<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Many additional customization options are available to the user. These will be discussed in further depth in this guide.</p>
+</div>
+<p>The backend can handle a variety of challenging model structures and offers a simple-to-use interface for effective acceleration of models. Additionally, it has many customization options to ensure the compilation process is fitting to the specific use case.</p>
+</section>
+<section id="customizeable-settings">
+<h2>Customizeable Settings<a class="headerlink" href="#customizeable-settings" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.dynamo.CompilationSettings">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">CompilationSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">precision:</span> <span class="pre">~torch.dtype</span> <span class="pre">=</span> <span class="pre">torch.float32,</span> <span class="pre">debug:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0,</span> <span class="pre">min_block_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">5,</span> <span class="pre">torch_executed_ops:</span> <span class="pre">~typing.Collection[~typing.Union[~typing.Callable[[...],</span> <span class="pre">~typing.Any],</span> <span class="pre">str]]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">pass_through_build_failures:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">max_aux_streams:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">version_compatible:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">optimization_level:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">use_python_runtime:</span> <span class="pre">~typing.Optional[bool]</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">truncate_long_and_double:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_fast_partitioner:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">enable_experimental_decompositions:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">device:</span> <span class="pre">~torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">require_full_compilation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">disable_tf32:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">sparse_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">refit:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">engine_capability:</span> <span class="pre">~tensorrt_bindings.tensorrt.EngineCapability</span> <span class="pre">=</span> <span class="pre">&lt;EngineCapability.DEFAULT:</span> <span class="pre">0&gt;,</span> <span class="pre">num_avg_timing_iters:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">dla_sram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1048576,</span> <span class="pre">dla_local_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1073741824,</span> <span class="pre">dla_global_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">536870912,</span> <span class="pre">output_format:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'exported_program'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_settings.html#CompilationSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.CompilationSettings" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compilation settings for Torch-TensorRT Dynamo Paths</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>precision</strong> (<em>torch.dpython:type</em>) – Model Layer precision</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Whether to print out verbose debugging information</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Workspace TRT is allowed to use for the module (0 is default)</p></li>
+<li><p><strong>min_block_size</strong> (<em>python:int</em>) – Minimum number of operators per TRT-Engine Block</p></li>
+<li><p><strong>torch_executed_ops</strong> (<em>Collection</em><em>[</em><em>Target</em><em>]</em>) – Collection of operations to run in Torch, regardless of converter coverage</p></li>
+<li><p><strong>pass_through_build_failures</strong> (<em>bool</em>) – Whether to fail on TRT engine build errors (True) or not (False)</p></li>
+<li><p><strong>max_aux_streams</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Maximum number of allowed auxiliary TRT streams for each engine</p></li>
+<li><p><strong>version_compatible</strong> (<em>bool</em>) – Provide version forward-compatibility for engine plan files</p></li>
+<li><p><strong>optimization_level</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Builder optimization 0-5, higher levels imply longer build time,
+searching for more optimization options. TRT defaults to 3</p></li>
+<li><p><strong>use_python_runtime</strong> (<em>Optional</em><em>[</em><em>bool</em><em>]</em>) – Whether to strictly use Python runtime or C++ runtime. To auto-select a runtime
+based on C++ dependency presence (preferentially choosing C++ runtime if available), leave the
+argument as None</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Whether to truncate int64/float64 TRT engine inputs or weights to int32/float32</p></li>
+<li><p><strong>use_fast_partitioner</strong> (<em>bool</em>) – Whether to use the fast or global graph partitioning system</p></li>
+<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Whether to enable all core aten decompositions
+or only a selected subset of them</p></li>
+<li><p><strong>device</strong> (<a class="reference internal" href="../py_api/torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a>) – GPU to compile the model on</p></li>
+<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Whether to require the graph is fully compiled in TensorRT.
+Only applicable for <cite>ir=”dynamo”</cite>; has no effect for <cite>torch.compile</cite> path</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Whether to disable TF32 computation for TRT layers</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Whether to allow the builder to use sparse weights</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Whether to build a refittable engine</p></li>
+<li><p><strong>engine_capability</strong> (<em>trt.EngineCapability</em>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
+<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
+<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
+<li><p><strong>output_format</strong> (<em>str</em>) – Output format of the result of TRT compilation. Options include “exported_program” (or) “ep” | “torchscript” (or) “ts” | “graph_module” (or) “fx”. Default is “exported_program”</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<section id="custom-setting-usage">
+<h3>Custom Setting Usage<a class="headerlink" href="#custom-setting-usage" title="Permalink to this headline">¶</a></h3>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="o">...</span>
+<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+                                         <span class="s2">&quot;precision&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span>
+                                         <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+                                         <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+                                         <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;torch.ops.aten.sub.Tensor&quot;</span><span class="p">},</span>
+                                         <span class="s2">&quot;optimization_level&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
+                                         <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,})</span>
+</pre></div>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Quantization/INT8 support is slated for a future release; currently, we support FP16 and FP32 precision layers.</p>
+</div>
+</section>
+</section>
+<section id="compilation">
+<h2>Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline">¶</a></h2>
+<p>Compilation is triggered by passing inputs to the model, as so:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="o">...</span>
+<span class="c1"># Causes model compilation to occur</span>
+<span class="n">first_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+
+<span class="c1"># Subsequent inference runs with the same, or similar inputs will not cause recompilation</span>
+<span class="c1"># For a full discussion of this, see &quot;Recompilation Conditions&quot; below</span>
+<span class="n">second_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="after-compilation">
+<h2>After Compilation<a class="headerlink" href="#after-compilation" title="Permalink to this headline">¶</a></h2>
+<p>The compilation object can be used for inference within the Python session, and will recompile according to the recompilation conditions detailed below. In addition to general inference, the compilation process can be a helpful tool in determining model performance, current operator coverage, and feasibility of serialization. Each of these points will be covered in detail below.</p>
+<section id="model-performance">
+<h3>Model Performance<a class="headerlink" href="#model-performance" title="Permalink to this headline">¶</a></h3>
+<p>The optimized model returned from <cite>torch.compile</cite> is useful for model benchmarking since it can automatically handle changes in the compilation context, or differing inputs that could require recompilation. When benchmarking inputs of varying distributions, batch sizes, or other criteria, this can save time.</p>
+</section>
+<section id="operator-coverage">
+<h3>Operator Coverage<a class="headerlink" href="#operator-coverage" title="Permalink to this headline">¶</a></h3>
+<p>Compilation is also a useful tool in determining operator coverage for a particular model. For instance, the following compilation command will display the operator coverage for each graph, but will not compile the model - effectively providing a “dryrun” mechanism:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="o">...</span>
+<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+                                         <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">),})</span>
+</pre></div>
+</div>
+<p>If key operators for your model are unsupported, see <span class="xref std std-ref">dynamo_conversion</span> to contribute your own converters, or file an issue here: <a class="reference external" href="https://github.com/pytorch/TensorRT/issues">https://github.com/pytorch/TensorRT/issues</a>.</p>
+</section>
+<section id="feasibility-of-serialization">
+<h3>Feasibility of Serialization<a class="headerlink" href="#feasibility-of-serialization" title="Permalink to this headline">¶</a></h3>
+<p>Compilation can also be helpful in demonstrating graph breaks and the feasibility of serialization of a particular model. For instance, if a model has no graph breaks and compiles successfully with the Torch-TensorRT backend, then that model should be compileable and serializeable via the <cite>torch_tensorrt</cite> Dynamo IR, as discussed in <a class="reference internal" href="../user_guide/dynamic_shapes.html#dynamic-shapes"><span class="std std-ref">Dynamic shapes with Torch-TensorRT</span></a>. To determine the number of graph breaks in a model, the <cite>torch._dynamo.explain</cite> function is very useful:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="o">...</span>
+<span class="n">explanation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">explain</span><span class="p">(</span><span class="n">model</span><span class="p">)(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Graph breaks: </span><span class="si">{</span><span class="n">explanation</span><span class="o">.</span><span class="n">graph_break_count</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">options</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">})</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="dynamic-shape-support">
+<h2>Dynamic Shape Support<a class="headerlink" href="#dynamic-shape-support" title="Permalink to this headline">¶</a></h2>
+<p>The Torch-TensorRT <cite>torch.compile</cite> backend will currently require recompilation for each new batch size encountered, and it is preferred to use the <cite>dynamic=False</cite> argument when compiling with this backend. Full dynamic shape support is planned for a future release.</p>
+</section>
+<section id="recompilation-conditions">
+<h2>Recompilation Conditions<a class="headerlink" href="#recompilation-conditions" title="Permalink to this headline">¶</a></h2>
+<p>Once the model has been compiled, subsequent inference inputs with the same shape and data type, which traverse the graph in the same way will not require recompilation. Furthermore, each new recompilation will be cached for the duration of the Python session. For instance, if inputs of batch size 4 and 8 are provided to the model, causing two recompilations, no further recompilation would be necessary for future inputs with those batch sizes during inference within the same session. Support for engine cache serialization is planned for a future release.</p>
+<p>Recompilation is generally triggered by one of two events: encountering inputs of different sizes or inputs which traverse the model code differently. The latter scenario can occur when the model code includes conditional logic, complex loops, or data-dependent-shapes. <cite>torch.compile</cite> handles guarding in both of these scenario and determines when recompilation is necessary.</p>
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diff --git a/docs/v2.2.0/fx/getting_started_with_fx_path.html b/docs/v2.2.0/fx/getting_started_with_fx_path.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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+<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>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Torch-TensorRT (FX Frontend) User Guide</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<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>
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+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</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>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
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+<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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+              
+  <section id="torch-tensorrt-fx-frontend-user-guide">
+<span id="getting-started-with-fx"></span><h1>Torch-TensorRT (FX Frontend) User Guide<a class="headerlink" href="#torch-tensorrt-fx-frontend-user-guide" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT (FX Frontend) is a tool that can convert a PyTorch model through <code class="docutils literal notranslate"><span class="pre">torch.fx</span></code> to an
+TensorRT engine optimized targeting running on Nvidia GPUs. TensorRT is the inference engine
+developed by NVIDIA which composed of various kinds of optimization including kernel fusion,
+graph optimization, low precision, etc.. This tool is developed in Python environment which allows this
+workflow to be very accessible to researchers and engineers. There are a few stages that a
+user want to use this tool and we will introduce them here.</p>
+<p>&gt; Torch-TensorRT (FX Frontend) is in <code class="docutils literal notranslate"><span class="pre">Beta</span></code> and currently it is recommended to work with PyTorch nightly.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># Test an example by</span>
+$<span class="w"> </span>python<span class="w"> </span>py/torch_tensorrt/fx/example/lower_example.py
+</pre></div>
+</div>
+<section id="converting-a-pytorch-model-to-tensorrt-engine">
+<h2>Converting a PyTorch Model to TensorRT Engine<a class="headerlink" href="#converting-a-pytorch-model-to-tensorrt-engine" title="Permalink to this headline">¶</a></h2>
+<p>In general, users are welcome to use the <code class="docutils literal notranslate"><span class="pre">compile()</span></code> to finish the conversion from a model to tensorRT engine. It is a
+wrapper API that consists of the major steps needed to finish this converison. Please refer to an example usage in <code class="docutils literal notranslate"><span class="pre">lower_example.py</span></code> file under <code class="docutils literal notranslate"><span class="pre">examples/fx</span></code>.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>def<span class="w"> </span>compile<span class="o">(</span>
+<span class="w">    </span>module:<span class="w"> </span>nn.Module,
+<span class="w">    </span>input,
+<span class="w">    </span><span class="nv">max_batch_size</span><span class="o">=</span><span class="m">2048</span>,
+<span class="w">    </span><span class="nv">max_workspace_size</span><span class="o">=</span><span class="m">33554432</span>,
+<span class="w">    </span><span class="nv">explicit_batch_dimension</span><span class="o">=</span>False,
+<span class="w">    </span><span class="nv">lower_precision</span><span class="o">=</span>LowerPrecision.FP16,
+<span class="w">    </span><span class="nv">verbose_log</span><span class="o">=</span>False,
+<span class="w">    </span><span class="nv">timing_cache_prefix</span><span class="o">=</span><span class="s2">&quot;&quot;</span>,
+<span class="w">    </span><span class="nv">save_timing_cache</span><span class="o">=</span>False,
+<span class="w">    </span><span class="nv">cuda_graph_batch_size</span><span class="o">=</span>-1,
+<span class="w">    </span><span class="nv">dynamic_batch</span><span class="o">=</span>True,
+<span class="o">)</span><span class="w"> </span>-&gt;<span class="w"> </span>nn.Module:
+
+<span class="w">    </span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    Takes in original module, input and lowering setting, run lowering workflow to turn module</span>
+<span class="s2">    into lowered module, or so called TRTModule.</span>
+
+<span class="s2">    Args:</span>
+<span class="s2">        module: Original module for lowering.</span>
+<span class="s2">        input: Input for module.</span>
+<span class="s2">        max_batch_size: Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</span>
+<span class="s2">        max_workspace_size: Maximum size of workspace given to TensorRT.</span>
+<span class="s2">        explicit_batch_dimension: Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</span>
+<span class="s2">        lower_precision: lower_precision config given to TRTModule.</span>
+<span class="s2">        verbose_log: Enable verbose log for TensorRT if set True.</span>
+<span class="s2">        timing_cache_prefix: Timing cache file name for timing cache used by fx2trt.</span>
+<span class="s2">        save_timing_cache: Update timing cache with current timing cache data if set to True.</span>
+<span class="s2">        cuda_graph_batch_size: Cuda graph batch size, default to be -1.</span>
+<span class="s2">        dynamic_batch: batch dimension (dim=0) is dynamic.</span>
+<span class="s2">    Returns:</span>
+<span class="s2">        A torch.nn.Module lowered by TensorRT.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+</pre></div>
+</div>
+<p>In this section, we will go through an example to illustrate the major steps that fx path uses.
+Users can refer to <code class="docutils literal notranslate"><span class="pre">fx2trt_example.py</span></code> file in <code class="docutils literal notranslate"><span class="pre">examples/fx</span></code>.</p>
+<ul class="simple">
+<li><p><strong>Step 1: Trace the model with acc_tracer</strong></p></li>
+</ul>
+<p>Acc_tracer is a tracer inheritated from FX tracer. It comes with args normalizer to convert all args to kwargs and pass to TRT converters.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>import<span class="w"> </span>torch_tensorrt.fx.tracer.acc_tracer.acc_tracer<span class="w"> </span>as<span class="w"> </span>acc_tracer
+
+<span class="c1"># Build the model which needs to be a PyTorch nn.Module.</span>
+<span class="nv">my_pytorch_model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>build_model<span class="o">()</span>
+
+<span class="c1"># Prepare inputs to the model. Inputs have to be a List of Tensors</span>
+<span class="nv">inputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">[</span>Tensor,<span class="w"> </span>Tensor,<span class="w"> </span>...<span class="o">]</span>
+
+<span class="c1"># Trace the model with acc_tracer.</span>
+<span class="nv">acc_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>acc_tracer.trace<span class="o">(</span>my_pytorch_model,<span class="w"> </span>inputs<span class="o">)</span>
+</pre></div>
+</div>
+<p><em>Common Errors:</em></p>
+<p>symbolically traced variables cannot be used as inputs to control flow
+This means the model contains dynamic control flow. Please refer to section “Dynamic Control Flow” in <a class="reference external" href="https://pytorch.org/docs/stable/fx.html#dynamic-control-flow">FX guide</a>.</p>
+<ul class="simple">
+<li><p><strong>Step 2: Build TensorRT engine</strong></p></li>
+</ul>
+<p>There are <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/developer-guide/index.html#explicit-implicit-batch">two different modes</a> for how TensorRT handles batch dimension, explicit batch dimension and implicit batch dimension. This mode was used by early versions of TensorRT, and is now deprecated but continues to be supported for backwards compatibility. In explicit batch mode, all dimensions are explicit and can be dynamic, that is their length can change at execution time. Many new features, such as dynamic shapes and loops, are available only in this mode. User can still choose to use implicit batch mode when they set <code class="docutils literal notranslate"><span class="pre">explicit_batch_dimension=False</span></code> in <code class="docutils literal notranslate"><span class="pre">compile()</span></code>. We do not recommend to use it since it will lack of support in future TensorRT versions.</p>
+<p>Explicit batch is the default mode and it must be set for dynamic shape. For most of vision task, user can choose to enable <code class="docutils literal notranslate"><span class="pre">dynamic_batch</span></code> in <code class="docutils literal notranslate"><span class="pre">compile()</span></code> if they want to get the similar effects as implicit mode where only batch dimension changes. It has some requirements:
+1. Shapes of inputs, outputs and activations are fixed except batch dimension.
+2. Inputs, outputs and activations have batch dimension as the major dimension.
+3. All the operators in the model do not modify batch dimension (permute, transpose, split, etc.) or compute over batch dimension (sum, softmax, etc.).</p>
+<p>For examples of the last path, if we have a 3D tensor t shaped as (batch, sequence, dimension), operations such as torch.transpose(0, 2). If any of these three are not satisfied, we’ll need to specify InputTensorSpec as inputs with dynamic range.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>import<span class="w"> </span>deeplearning.trt.fx2trt.converter.converters
+from<span class="w"> </span>torch.fx.experimental.fx2trt.fx2trt<span class="w"> </span>import<span class="w"> </span>InputTensorSpec,<span class="w"> </span>TRTInterpreter
+
+<span class="c1"># InputTensorSpec is a dataclass we use to store input information.</span>
+<span class="c1"># There&#39;re two ways we can build input_specs.</span>
+<span class="c1"># Option 1, build it manually.</span>
+<span class="nv">input_specs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">[</span>
+<span class="w">  </span>InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>,<span class="w"> </span><span class="m">2</span>,<span class="w"> </span><span class="m">3</span><span class="o">)</span>,<span class="w"> </span><span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+<span class="w">  </span>InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>,<span class="w"> </span><span class="m">4</span>,<span class="w"> </span><span class="m">5</span><span class="o">)</span>,<span class="w"> </span><span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+<span class="o">]</span>
+<span class="c1"># Option 2, build it using sample_inputs where user provide a sample</span>
+<span class="nv">inputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">[</span>
+torch.rand<span class="o">((</span><span class="m">1</span>,2,3<span class="o">)</span>,<span class="w"> </span><span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+torch.rand<span class="o">((</span><span class="m">1</span>,4,5<span class="o">)</span>,<span class="w"> </span><span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+<span class="o">]</span>
+<span class="nv">input_specs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>InputTensorSpec.from_tensors<span class="o">(</span>inputs<span class="o">)</span>
+
+<span class="c1"># IMPORTANT: If dynamic shape is needed, we need to build it slightly differently.</span>
+<span class="nv">input_specs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">[</span>
+<span class="w">    </span>InputTensorSpec<span class="o">(</span>
+<span class="w">        </span><span class="nv">shape</span><span class="o">=(</span>-1,<span class="w"> </span><span class="m">2</span>,<span class="w"> </span><span class="m">3</span><span class="o">)</span>,
+<span class="w">        </span><span class="nv">dtype</span><span class="o">=</span>torch.float32,
+<span class="w">        </span><span class="c1"># Currently we only support one set of dynamic range. User may set other dimensions but it is not promised to work for any models</span>
+<span class="w">        </span><span class="c1"># (min_shape, optimize_target_shape, max_shape)</span>
+<span class="w">        </span><span class="c1"># For more information refer to fx/input_tensor_spec.py</span>
+<span class="w">        </span><span class="nv">shape_ranges</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">[</span>
+<span class="w">            </span><span class="o">((</span><span class="m">1</span>,<span class="w"> </span><span class="m">2</span>,<span class="w"> </span><span class="m">3</span><span class="o">)</span>,<span class="w"> </span><span class="o">(</span><span class="m">4</span>,<span class="w"> </span><span class="m">2</span>,<span class="w"> </span><span class="m">3</span><span class="o">)</span>,<span class="w"> </span><span class="o">(</span><span class="m">100</span>,<span class="w"> </span><span class="m">2</span>,<span class="w"> </span><span class="m">3</span><span class="o">))</span>,
+<span class="w">        </span><span class="o">]</span>,
+<span class="w">    </span><span class="o">)</span>,
+<span class="w">    </span>InputTensorSpec<span class="o">(</span><span class="nv">shape</span><span class="o">=(</span><span class="m">1</span>,<span class="w"> </span><span class="m">4</span>,<span class="w"> </span><span class="m">5</span><span class="o">)</span>,<span class="w"> </span><span class="nv">dtype</span><span class="o">=</span>torch.float32<span class="o">)</span>,
+<span class="o">]</span>
+
+<span class="c1"># Build a TRT interpreter. Set explicit_batch_dimension accordingly.</span>
+<span class="nv">interpreter</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>TRTInterpreter<span class="o">(</span>
+<span class="w">    </span>acc_mod,<span class="w"> </span>input_specs,<span class="w"> </span><span class="nv">explicit_batch_dimension</span><span class="o">=</span>True/False
+<span class="o">)</span>
+
+<span class="c1"># The output of TRTInterpreter run() is wrapped as TRTInterpreterResult.</span>
+<span class="c1"># The TRTInterpreterResult contains required parameter to build TRTModule,</span>
+<span class="c1"># and other informational output from TRTInterpreter run.</span>
+class<span class="w"> </span>TRTInterpreterResult<span class="o">(</span>NamedTuple<span class="o">)</span>:
+<span class="w">    </span>engine:<span class="w"> </span>Any
+<span class="w">    </span>input_names:<span class="w"> </span>Sequence<span class="o">[</span>str<span class="o">]</span>
+<span class="w">    </span>output_names:<span class="w"> </span>Sequence<span class="o">[</span>str<span class="o">]</span>
+<span class="w">    </span>serialized_cache:<span class="w"> </span>bytearray
+
+<span class="c1">#max_batch_size: set accordingly for maximum batch size you will use.</span>
+<span class="c1">#max_workspace_size: set to the maximum size we can afford for temporary buffer</span>
+<span class="c1">#lower_precision: the precision model layers are running on (TensorRT will choose the best perforamnce precision).</span>
+<span class="c1">#sparse_weights: allow the builder to examine weights and use optimized functions when weights have suitable sparsity</span>
+<span class="c1">#force_fp32_output: force output to be fp32</span>
+<span class="c1">#strict_type_constraints: Usually we should set it to False unless we want to control the precision of certain layer for numeric #reasons.</span>
+<span class="c1">#algorithm_selector: set up algorithm selection for certain layer</span>
+<span class="c1">#timing_cache: enable timing cache for TensorRT</span>
+<span class="c1">#profiling_verbosity: TensorRT logging level</span>
+<span class="nv">trt_interpreter_result</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>interpreter.run<span class="o">(</span>
+<span class="w">    </span><span class="nv">max_batch_size</span><span class="o">=</span><span class="m">64</span>,
+<span class="w">    </span><span class="nv">max_workspace_size</span><span class="o">=</span><span class="m">1</span><span class="w"> </span><span class="s">&lt;&lt; 25,</span>
+<span class="s">    sparse_weights=False,</span>
+<span class="s">    force_fp32</span>_output<span class="o">=</span>False,
+<span class="w">    </span><span class="nv">strict_type_constraints</span><span class="o">=</span>False,
+<span class="w">    </span><span class="nv">algorithm_selector</span><span class="o">=</span>None,
+<span class="w">    </span><span class="nv">timing_cache</span><span class="o">=</span>None,
+<span class="w">    </span><span class="nv">profiling_verbosity</span><span class="o">=</span>None,
+<span class="o">)</span>
+</pre></div>
+</div>
+<p><em>Common Errors:</em></p>
+<p>RuntimeError: Conversion of function xxx not currently supported!
+- This means we don’t have the support for this xxx operator. Please refer to section “How to add a missing op” below for further instructions.</p>
+<ul class="simple">
+<li><p><strong>Step 3: Run the model</strong></p></li>
+</ul>
+<p>One way is using TRTModule, which is basically a PyTorch nn.Module.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>from<span class="w"> </span>torch_tensorrt.fx<span class="w"> </span>import<span class="w"> </span>TRTModule
+<span class="nv">mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>TRTModule<span class="o">(</span>
+<span class="w">    </span>trt_interpreter_result.engine,
+<span class="w">    </span>trt_interpreter_result.input_names,
+<span class="w">    </span>trt_interpreter_result.output_names<span class="o">)</span>
+<span class="c1"># Just like all other PyTorch modules</span>
+<span class="nv">outputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>mod<span class="o">(</span>*inputs<span class="o">)</span>
+torch.save<span class="o">(</span>mod,<span class="w"> </span><span class="s2">&quot;trt.pt&quot;</span><span class="o">)</span>
+<span class="nv">reload_trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>torch.load<span class="o">(</span><span class="s2">&quot;trt.pt&quot;</span><span class="o">)</span>
+<span class="nv">reload_model_output</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>reload_trt_mod<span class="o">(</span>*inputs<span class="o">)</span>
+</pre></div>
+</div>
+<p>So far, we give a detailed explanation of major steps in convterting a PyTorch model into TensorRT engine. Users are welcome to refer to the source code for some parameters explanations. In the converting scheme, there are two important actions in it. One is acc tracer which helps us to convert a PyTorch model to acc graph. The other is FX path converter which helps to convert the acc graph’s operation to corresponding TensorRT operation and build up the TensoRT engine for it.</p>
+</section>
+<section id="acc-tracer">
+<h2>Acc Tracer<a class="headerlink" href="#acc-tracer" title="Permalink to this headline">¶</a></h2>
+<p>Acc tracer is a custom FX symbolic tracer. It does a couple more things compare to the vanilla FX symbolic tracer. We mainly depend on it to convert PyTorch ops or builtin ops to acc ops. There are two main purposes for fx2trt to use acc ops:</p>
+<ol class="arabic simple">
+<li><p>there’re many ops that do similar things in PyTorch ops and builtin ops such like torch.add, builtin.add and torch.Tensor.add. Using acc tracer, we normalize these three ops to a single acc_ops.add. This helps reduce the number of converters we need to write.</p></li>
+<li><p>acc ops only have kwargs which makes writing converter easier as we don’t need to add additional logic to find arguments in args and kwargs.</p></li>
+</ol>
+</section>
+<section id="fx2trt">
+<h2>FX2TRT<a class="headerlink" href="#fx2trt" title="Permalink to this headline">¶</a></h2>
+<p>After symbolic tracing, we have the graph representation of a PyTorch model. fx2trt leverages the power of fx.Interpreter. fx.Interpreter goes through the whole graph node by node and calls the function that node represents. fx2trt overrides the original behavior of calling the function with invoking corresponding converts for each node. Each converter function adds corresponding TensorRT layer(s).</p>
+<p>Below is an example of a converter function. The decorator is used to register this converter function with the corresponding node. In this example, we register this converter to a fx node whose target is acc_ops.sigmoid.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@tensorrt_converter<span class="o">(</span>acc_ops.sigmoid<span class="o">)</span>
+def<span class="w"> </span>acc_ops_sigmoid<span class="o">(</span>network,<span class="w"> </span>target,<span class="w"> </span>args,<span class="w"> </span>kwargs,<span class="w"> </span>name<span class="o">)</span>:
+<span class="w">    </span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
+
+<span class="s2">    The rest arguments are attributes of fx node.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+<span class="w">    </span><span class="nv">input_val</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>kwargs<span class="o">[</span><span class="s1">&#39;input&#39;</span><span class="o">]</span>
+
+<span class="w">    </span><span class="k">if</span><span class="w"> </span>not<span class="w"> </span>isinstance<span class="o">(</span>input_val,<span class="w"> </span>trt.tensorrt.ITensor<span class="o">)</span>:
+<span class="w">        </span>raise<span class="w"> </span>RuntimeError<span class="o">(</span>f<span class="s1">&#39;Sigmoid received input {input_val} that is not part &#39;</span>
+<span class="w">                        </span><span class="s1">&#39;of the TensorRT region!&#39;</span><span class="o">)</span>
+
+<span class="w">    </span><span class="nv">layer</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>network.add_activation<span class="o">(</span><span class="nv">input</span><span class="o">=</span>input_val,<span class="w"> </span><span class="nv">type</span><span class="o">=</span>trt.ActivationType.SIGMOID<span class="o">)</span>
+<span class="w">    </span>layer.name<span class="w"> </span><span class="o">=</span><span class="w"> </span>name
+<span class="w">    </span><span class="k">return</span><span class="w"> </span>layer.get_output<span class="o">(</span><span class="m">0</span><span class="o">)</span>
+</pre></div>
+</div>
+<section id="how-to-add-a-missing-op">
+<h3>How to Add a Missing Op<a class="headerlink" href="#how-to-add-a-missing-op" title="Permalink to this headline">¶</a></h3>
+<p>You can actually add it wherever you want just need to remember import the file so that all acc ops and mapper will be registered before tracing with acc_tracer.</p>
+<ul class="simple">
+<li><p><strong>Step 1. Add a new acc op</strong></p></li>
+</ul>
+<p>TODO: Need to explain more on the logistic of acc op like when we want to break down an op and when we want to reuse other ops.</p>
+<p>In <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_tracer.py">acc tracer</a>, we convert nodes in the graph to acc ops if there’s a mapping registered for the node to an acc op.</p>
+<p>In order to make the conversion to acc ops to happen, there’re two things required. One is that there should be an acc op function defined and the other is there should be a mapping registered.</p>
+<p>Defining an acc op is simple, we first just need a function and register the function as an acc op via this decorator <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py">acc_normalizer.py</a>. e.g. the following code adds an acc op named foo() which adds two given inputs.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="c1"># NOTE: all acc ops should only take kwargs as inputs, therefore we need the &quot;*&quot;</span>
+<span class="c1"># at the beginning.</span>
+@register_acc_op
+def<span class="w"> </span>foo<span class="o">(</span>*,<span class="w"> </span>input,<span class="w"> </span>other,<span class="w"> </span>alpha<span class="o">)</span>:
+<span class="w">    </span><span class="k">return</span><span class="w"> </span>input<span class="w"> </span>+<span class="w"> </span>alpha<span class="w"> </span>*<span class="w"> </span>other
+</pre></div>
+</div>
+<p>There’re two ways to register a mapping. One is <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L164">register_acc_op_mapping()</a>. Let’s register a mapping from torch.add to foo() we just created above. We need to add decorator register_acc_op_mapping to it.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nv">this_arg_is_optional</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>True
+
+@register_acc_op_mapping<span class="o">(</span>
+<span class="w">    </span><span class="nv">op_and_target</span><span class="o">=(</span><span class="s2">&quot;call_function&quot;</span>,<span class="w"> </span>torch.add<span class="o">)</span>,
+<span class="w">    </span><span class="nv">arg_replacement_tuples</span><span class="o">=[</span>
+<span class="w">        </span><span class="o">(</span><span class="s2">&quot;input&quot;</span>,<span class="w"> </span><span class="s2">&quot;input&quot;</span><span class="o">)</span>,
+<span class="w">        </span><span class="o">(</span><span class="s2">&quot;other&quot;</span>,<span class="w"> </span><span class="s2">&quot;other&quot;</span><span class="o">)</span>,
+<span class="w">        </span><span class="o">(</span><span class="s2">&quot;alpha&quot;</span>,<span class="w"> </span><span class="s2">&quot;alpha&quot;</span>,<span class="w"> </span>this_arg_is_optional<span class="o">)</span>,
+<span class="w">    </span><span class="o">]</span>,
+<span class="o">)</span>
+@register_acc_op
+def<span class="w"> </span>foo<span class="o">(</span>*,<span class="w"> </span>input,<span class="w"> </span>other,<span class="w"> </span><span class="nv">alpha</span><span class="o">=</span><span class="m">1</span>.0<span class="o">)</span>:
+<span class="w">    </span><span class="k">return</span><span class="w"> </span>input<span class="w"> </span>+<span class="w"> </span>alpha<span class="w"> </span>*<span class="w"> </span>other
+</pre></div>
+</div>
+<p><code class="docutils literal notranslate"><span class="pre">op_and_target</span></code> determines which node will trigger this mapping. op and target are the attributes of FX node. In acc_normalization when we see a node with the same op and target as set in the <code class="docutils literal notranslate"><span class="pre">op_and_target</span></code>, we will trigger the mapping. Since we want to map from <code class="docutils literal notranslate"><span class="pre">torch.add</span></code>, then op would be call_function and target would be <code class="docutils literal notranslate"><span class="pre">torch.add</span></code>. <code class="docutils literal notranslate"><span class="pre">arg_replacement_tuples</span></code> determines how we construct kwargs for new acc op node using args and kwargs from original node. Each tuple in <code class="docutils literal notranslate"><span class="pre">arg_replacement_tuples</span></code> represents one argument mapping rule. It contains two or three elements. The third element is a boolean variable that determines whether this kwarg is optional in <em>original node</em>. We only need to specify the third element if it’s True. The first element is the argument name in original node which will be used as the acc op node’s argument whose name is the second element in the tuple. The sequence of the tuples does matter because the position of the tuple determines where the argument is in original node’s args. We use this information to map args from original node to kwargs in acc op node.
+We don’t have to specify arg_replacement_tuples if none of the followings are true.</p>
+<ol class="arabic simple">
+<li><p>kwargs of original nodes and acc op nodes have different name.</p></li>
+<li><p>there’re optional arguments.</p></li>
+</ol>
+<p>The other way to register a mapping is through <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L206">register_custom_acc_mapper_fn()</a>. This one is designed to reduce the redundant op registration as it allows you to use a function to map to one or more existing acc ops throught some combinations. In the function, you can do basically whatever you want. Let’s use an example to explain how it works.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@register_acc_op
+def<span class="w"> </span>foo<span class="o">(</span>*,<span class="w"> </span>input,<span class="w"> </span>other,<span class="w"> </span><span class="nv">alpha</span><span class="o">=</span><span class="m">1</span>.0<span class="o">)</span>:
+<span class="w">    </span><span class="k">return</span><span class="w"> </span>input<span class="w"> </span>+<span class="w"> </span>alpha<span class="w"> </span>*<span class="w"> </span>other
+
+@register_custom_acc_mapper_fn<span class="o">(</span>
+<span class="w">    </span><span class="nv">op_and_target</span><span class="o">=(</span><span class="s2">&quot;call_function&quot;</span>,<span class="w"> </span>torch.add<span class="o">)</span>,
+<span class="w">    </span><span class="nv">arg_replacement_tuples</span><span class="o">=[</span>
+<span class="w">        </span><span class="o">(</span><span class="s2">&quot;input&quot;</span>,<span class="w"> </span><span class="s2">&quot;input&quot;</span><span class="o">)</span>,
+<span class="w">        </span><span class="o">(</span><span class="s2">&quot;other&quot;</span>,<span class="w"> </span><span class="s2">&quot;other&quot;</span><span class="o">)</span>,
+<span class="w">        </span><span class="o">(</span><span class="s2">&quot;alpha&quot;</span>,<span class="w"> </span><span class="s2">&quot;alpha&quot;</span>,<span class="w"> </span>this_arg_is_optional<span class="o">)</span>,
+<span class="w">    </span><span class="o">]</span>,
+<span class="o">)</span>
+def<span class="w"> </span>custom_mapper<span class="o">(</span>node:<span class="w"> </span>torch.fx.Node,<span class="w"> </span>_:<span class="w"> </span>nn.Module<span class="o">)</span><span class="w"> </span>-&gt;<span class="w"> </span>torch.fx.Node:
+<span class="w">    </span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    `node` is original node, which is a call_function node with target</span>
+<span class="s2">    being torch.add.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+<span class="w">    </span><span class="nv">alpha</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="m">1</span>
+<span class="w">    </span><span class="k">if</span><span class="w"> </span><span class="s2">&quot;alpha&quot;</span><span class="w"> </span><span class="k">in</span><span class="w"> </span>node.kwargs:
+<span class="w">        </span><span class="nv">alpha</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>node.kwargs<span class="o">[</span><span class="s2">&quot;alpha&quot;</span><span class="o">]</span>
+<span class="w">    </span><span class="nv">foo_kwargs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="o">{</span><span class="s2">&quot;input&quot;</span>:<span class="w"> </span>node<span class="o">[</span><span class="s2">&quot;input&quot;</span><span class="o">]</span>,<span class="w"> </span><span class="s2">&quot;other&quot;</span>:<span class="w"> </span>node<span class="o">[</span><span class="s2">&quot;other&quot;</span><span class="o">]</span>,<span class="w"> </span><span class="s2">&quot;alpha&quot;</span>:<span class="w"> </span>alpha<span class="o">}</span>
+<span class="w">    </span>with<span class="w"> </span>node.graph.inserting_before<span class="o">(</span>node<span class="o">)</span>:
+<span class="w">        </span><span class="nv">foo_node</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>node.graph.call_function<span class="o">(</span>foo,<span class="w"> </span><span class="nv">kwargs</span><span class="o">=</span>foo_kwargs<span class="o">)</span>
+<span class="w">        </span>foo_node.meta<span class="w"> </span><span class="o">=</span><span class="w"> </span>node.meta.copy<span class="o">()</span>
+<span class="w">        </span><span class="k">return</span><span class="w"> </span>foo_node
+</pre></div>
+</div>
+<p>In the custom mapper function, we construct an acc op node and return it. The node we returns here would take over all the children nodes of original nodes <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/1a22204fecec690bc3c2a318dab4f57b98c57f05/py/torch_tensorrt/fx/tracer/acc_tracer/acc_normalizer.py#L379">acc_normalizer.py</a>.</p>
+<p>The last step would be <em>adding unit test</em> for the new acc op or mapper function we added. The place to add the unit test is here <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/test/tracer/test_acc_tracer.py">test_acc_tracer.py</a>.</p>
+<ul class="simple">
+<li><p><strong>Step 2. Add a new converter</strong></p></li>
+</ul>
+<p>All the developed converters for acc ops are all in <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/py/torch_tensorrt/fx/converters/acc_ops_converters.py">acc_op_converter.py</a>. It could give you a good example of how the converter is added.</p>
+<p>Essentially, the converter is the mapping mechanism that maps the acc ops to a TensorRT layer. If we are able to find all the TensorRT layers we need we can get start to add a converter for the node using <a class="reference external" href="https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/infer/Graph/Network.html">TensorRT APIs</a>.</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>@tensorrt_converter<span class="o">(</span>acc_ops.sigmoid<span class="o">)</span>
+def<span class="w"> </span>acc_ops_sigmoid<span class="o">(</span>network,<span class="w"> </span>target,<span class="w"> </span>args,<span class="w"> </span>kwargs,<span class="w"> </span>name<span class="o">)</span>:
+<span class="w">    </span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    network: TensorRT network. We&#39;ll be adding layers to it.</span>
+
+<span class="s2">    The rest arguments are attributes of fx node.</span>
+<span class="s2">    &quot;&quot;&quot;</span>
+<span class="w">    </span><span class="nv">input_val</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>kwargs<span class="o">[</span><span class="s1">&#39;input&#39;</span><span class="o">]</span>
+
+<span class="w">    </span><span class="k">if</span><span class="w"> </span>not<span class="w"> </span>isinstance<span class="o">(</span>input_val,<span class="w"> </span>trt.tensorrt.ITensor<span class="o">)</span>:
+<span class="w">        </span>raise<span class="w"> </span>RuntimeError<span class="o">(</span>f<span class="s1">&#39;Sigmoid received input {input_val} that is not part &#39;</span>
+<span class="w">                        </span><span class="s1">&#39;of the TensorRT region!&#39;</span><span class="o">)</span>
+
+<span class="w">    </span><span class="nv">layer</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>network.add_activation<span class="o">(</span><span class="nv">input</span><span class="o">=</span>input_val,<span class="w"> </span><span class="nv">type</span><span class="o">=</span>trt.ActivationType.SIGMOID<span class="o">)</span>
+<span class="w">    </span>layer.name<span class="w"> </span><span class="o">=</span><span class="w"> </span>name
+<span class="w">    </span><span class="k">return</span><span class="w"> </span>layer.get_output<span class="o">(</span><span class="m">0</span><span class="o">)</span>
+</pre></div>
+</div>
+<p>We need to use <code class="docutils literal notranslate"><span class="pre">tensorrt_converter</span></code> decorator to register the converter. The argument for the decorator is the target of the fx node that we need to convert. In the converter, we can find the inputs to the fx node in kwargs. As in the example, the original node is <cite>acc_ops.sigmoid</cite> which only has one argument “input” in acc_ops.py. We get the input and check if it’s a TensorRT tensor. After that, we add a sigmoid layer to TensorRT network and return the output of the layer. The output we returned will be passed to the children nodes of acc_ops.sigmoid by fx.Interpreter.</p>
+<p><strong>What if we can not find corresponding layers in TensorRT that do the same thing as the node.</strong></p>
+<p>In this case, we would need to do a bit more work. TensorRT provides plugins which serves as custom layers. <em>We have not implement this feature yet. We will update once it is enabled</em>.</p>
+<p>Last step would be adding the unit test for the new converter we added. User could add corresponding unit test in this <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/master/py/torch_tensorrt/fx/test/converters/acc_op">folder</a>.</p>
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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+<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/fx.html">torch_tensorrt.fx</a></li>
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+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</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>
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+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="indices/supported_ops.html">Operators Supported</a></li>
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+ <a href="#_"><strong>_</strong></a>
+ | <a href="#C"><strong>C</strong></a>
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+ | <a href="#F"><strong>F</strong></a>
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+</div>
+<h2 id="_">_</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.__init__">__init__() (torch_tensorrt.Device method)</a>
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.__init__">(torch_tensorrt.Input method)</a>
+</li>
+        <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator.__init__">(torch_tensorrt.ptq.CacheCalibrator method)</a>
+</li>
+        <li><a href="py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator.__init__">(torch_tensorrt.ptq.DataLoaderCalibrator method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#id1">CacheCalibrator (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.CacheCalibrator">[1]</a>
+</li>
+      <li><a href="py_api/ptq.html#id2">CalibrationAlgo (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo">[1]</a>
+</li>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.check_method_op_support">check_method_op_support() (in module torch_tensorrt.ts)</a>
+</li>
+      <li><a href="dynamo/torch_compile.html#torch_tensorrt.dynamo.CompilationSettings">CompilationSettings (class in torch_tensorrt.dynamo)</a>, <a href="py_api/dynamo.html#torch_tensorrt.dynamo.CompilationSettings">[1]</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.compile">compile() (in module torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/dynamo.html#torch_tensorrt.dynamo.compile">(in module torch_tensorrt.dynamo)</a>
+</li>
+        <li><a href="py_api/fx.html#torch_tensorrt.fx.compile">(in module torch_tensorrt.fx)</a>
+</li>
+        <li><a href="py_api/ts.html#torch_tensorrt.ts.compile">(in module torch_tensorrt.ts)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.convert_method_to_trt_engine">convert_method_to_trt_engine() (in module torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/ts.html#torch_tensorrt.ts.convert_method_to_trt_engine">(in module torch_tensorrt.ts)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#id0">DataLoaderCalibrator (class in torch_tensorrt.ptq)</a>, <a href="py_api/ptq.html#torch_tensorrt.ptq.DataLoaderCalibrator">[1]</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.debug">debug (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Debug">Debug (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device">Device (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.DeviceType">DeviceType (class in torch_tensorrt)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.dla_core">dla_core (torch_tensorrt.Device attribute)</a>
+</li>
+      <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Documentation"><strong>Documentation</strong></a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.dtype">dtype (class in torch_tensorrt)</a>
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.dtype">(torch_tensorrt.Input attribute)</a>
+</li>
+      </ul></li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.dump_build_info">dump_build_info() (in module torch_tensorrt)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.embed_engine_in_new_module">embed_engine_in_new_module() (in module torch_tensorrt.ts)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.EngineCapability">EngineCapability (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION">ENTROPY_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2">ENTROPY_CALIBRATION_2 (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Error">Error (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.errors">errors (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.example_tensor">example_tensor() (torch_tensorrt.Input method)</a>
+</li>
+      <li><a href="py_api/dynamo.html#torch_tensorrt.dynamo.export">export() (in module torch_tensorrt.dynamo)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.format">format (torch_tensorrt.Input attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.from_tensor">from_tensor() (torch_tensorrt.Input class method)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input.from_tensors">from_tensors() (torch_tensorrt.Input class method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_batch">get_batch() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_batch_size">get_batch_size() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.get_build_info">get_build_info() (in module torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.get_cache_mode_batch">get_cache_mode_batch() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_is_colored_output_on">get_is_colored_output_on() (in module torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_logging_prefix">get_logging_prefix() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.get_reportable_log_level">get_reportable_log_level() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Device.gpu_id">gpu_id (torch_tensorrt.Device attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Graph">Graph (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.graphs">graphs (class in torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.info">info (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.Info">Info (torch_tensorrt.logging.Level attribute)</a>
+</li>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.Input">Input (class in torch_tensorrt)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/fx.html#torch_tensorrt.fx.InputTensorSpec">InputTensorSpec (class in torch_tensorrt.fx)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.internal_errors">internal_errors (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level.InternalError">InternalError (torch_tensorrt.logging.Level attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION">LEGACY_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.Level">Level (class in torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.log">log() (in module torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION">MINMAX_CALIBRATION (torch_tensorrt.ptq.CalibrationAlgo attribute)</a>
+</li>
+      <li>
+    module
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#module-torch_tensorrt">torch_tensorrt</a>
+</li>
+        <li><a href="dynamo/dynamo_export.html#module-torch_tensorrt.dynamo">torch_tensorrt.dynamo</a>, <a href="dynamo/torch_compile.html#module-torch_tensorrt.dynamo">[1]</a>, <a href="py_api/dynamo.html#module-torch_tensorrt.dynamo">[2]</a>, <a href="user_guide/saving_models.html#module-torch_tensorrt.dynamo">[3]</a>
+</li>
+        <li><a href="py_api/fx.html#module-torch_tensorrt.fx">torch_tensorrt.fx</a>
+</li>
+        <li><a href="py_api/logging.html#module-torch_tensorrt.logging">torch_tensorrt.logging</a>
+</li>
+        <li><a href="py_api/ptq.html#module-torch_tensorrt.ptq">torch_tensorrt.ptq</a>
+</li>
+        <li><a href="py_api/ts.html#module-torch_tensorrt.ts">torch_tensorrt.ts</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    Python Enhancement Proposals
+
+      <ul>
+        <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#index-0">PEP 287</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/ptq.html#torch_tensorrt.ptq.read_calibration_cache">read_calibration_cache() (in module torch_tensorrt.ptq)</a>
+</li>
+      <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#term-Reading"><strong>Reading</strong></a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    RFC
+
+      <ul>
+        <li><a href="src/pytorch-sphinx-theme/docs/demo/demo.html#index-1">RFC 2822</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.set_device">set_device() (in module torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_is_colored_output_on">set_is_colored_output_on() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_logging_prefix">set_logging_prefix() (in module torch_tensorrt.logging)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/logging.html#torch_tensorrt.logging.set_reportable_log_level">set_reportable_log_level() (in module torch_tensorrt.logging)</a>
+</li>
+      <li><a href="py_api/dynamo.html#torch_tensorrt.dynamo.SourceIR">SourceIR (class in torch_tensorrt.dynamo)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1a18d295a837ac71add5578860b55e5502.html#c.STR">STR (C macro)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="py_api/torch_tensorrt.html#torch_tensorrt.TensorFormat">TensorFormat (class in torch_tensorrt)</a>
+</li>
+      <li><a href="py_api/ts.html#torch_tensorrt.ts.TensorRTCompileSpec">TensorRTCompileSpec() (in module torch_tensorrt.ts)</a>
+</li>
+      <li>
+    torch_tensorrt
+
+      <ul>
+        <li><a href="py_api/torch_tensorrt.html#module-torch_tensorrt">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.dynamo
+
+      <ul>
+        <li><a href="dynamo/dynamo_export.html#module-torch_tensorrt.dynamo">module</a>, <a href="dynamo/torch_compile.html#module-torch_tensorrt.dynamo">[1]</a>, <a href="py_api/dynamo.html#module-torch_tensorrt.dynamo">[2]</a>, <a href="user_guide/saving_models.html#module-torch_tensorrt.dynamo">[3]</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.fx
+
+      <ul>
+        <li><a href="py_api/fx.html#module-torch_tensorrt.fx">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.logging
+
+      <ul>
+        <li><a href="py_api/logging.html#module-torch_tensorrt.logging">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.ptq
+
+      <ul>
+        <li><a href="py_api/ptq.html#module-torch_tensorrt.ptq">module</a>
+</li>
+      </ul></li>
+      <li>
+    torch_tensorrt.ts
+
+      <ul>
+        <li><a href="py_api/ts.html#module-torch_tensorrt.ts">module</a>
+</li>
+      </ul></li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypeE">torch_tensorrt::DataType (C++ class)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeE5Value">torch_tensorrt::DataType::DataType (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeEN3c1010ScalarTypeE">[1]</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType8DataTypeEv">[2]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataTypecvbEv">torch_tensorrt::DataType::operator bool (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypecv5ValueEv">torch_tensorrt::DataType::operator Value (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypeneE8DataType">torch_tensorrt::DataType::operator!= (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypeneEN8DataType5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypeeqE8DataType">torch_tensorrt::DataType::operator== (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4NK14torch_tensorrt8DataTypeeqEN8DataType5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5ValueE">torch_tensorrt::DataType::Value (C++ enum)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value5kBoolE">torch_tensorrt::DataType::Value::kBool (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value5kCharE">torch_tensorrt::DataType::Value::kChar (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value7kDoubleE">torch_tensorrt::DataType::Value::kDouble (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value6kFloatE">torch_tensorrt::DataType::Value::kFloat (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value5kHalfE">torch_tensorrt::DataType::Value::kHalf (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value4kIntE">torch_tensorrt::DataType::Value::kInt (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value5kLongE">torch_tensorrt::DataType::Value::kLong (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1DataType.html#_CPPv4N14torch_tensorrt8DataType5Value8kUnknownE">torch_tensorrt::DataType::Value::kUnknown (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6DeviceE">torch_tensorrt::Device (C++ struct)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device18allow_gpu_fallbackE">torch_tensorrt::Device::allow_gpu_fallback (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device6DeviceEv">torch_tensorrt::Device::Device (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device11device_typeE">torch_tensorrt::Device::device_type (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE">torch_tensorrt::Device::DeviceType (C++ class)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceTypeE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value">torch_tensorrt::Device::DeviceType::DeviceType (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE">[1]</a>, <a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv">[2]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeE5Value">[3]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEN3c1010DeviceTypeE">[4]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType10DeviceTypeEv">[5]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv">torch_tensorrt::Device::DeviceType::operator bool (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceTypecvbEv">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv">torch_tensorrt::Device::DeviceType::operator Value (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypecv5ValueEv">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType">torch_tensorrt::Device::DeviceType::operator!= (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypeneE10DeviceType">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType">torch_tensorrt::Device::DeviceType::operator== (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4NK14torch_tensorrt6Device10DeviceTypeeqE10DeviceType">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE">torch_tensorrt::Device::DeviceType::Value (C++ enum)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE">torch_tensorrt::Device::DeviceType::Value::kDLA (C++ enumerator)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kDLAE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1Device_1_1DeviceType.html#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE">torch_tensorrt::Device::DeviceType::Value::kGPU (C++ enumerator)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device10DeviceType5Value4kGPUE">[1]</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device8dla_coreE">torch_tensorrt::Device::dla_core (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Device.html#_CPPv4N14torch_tensorrt6Device6gpu_idE">torch_tensorrt::Device::gpu_id (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1ad6a4ee8ca6c8f6e5519eb1128ec7f4a1.html#_CPPv4N14torch_tensorrt15dump_build_infoEv">torch_tensorrt::dump_build_info (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapabilityE">torch_tensorrt::EngineCapability (C++ enum)</a>
+</li>
+      <li><a href="_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapability15kDLA_STANDALONEE">torch_tensorrt::EngineCapability::kDLA_STANDALONE (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapability7kSAFETYE">torch_tensorrt::EngineCapability::kSAFETY (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_torch__tensorrt_8h_1a3fbe5d72e4fc624dbd038853079620eb.html#_CPPv4N14torch_tensorrt16EngineCapability9kSTANDARDE">torch_tensorrt::EngineCapability::kSTANDARD (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1ac4ab8313ae72c2c899ea31548b528528.html#_CPPv4N14torch_tensorrt14get_build_infoEv">torch_tensorrt::get_build_info (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1GraphInputs.html#_CPPv4N14torch_tensorrt11GraphInputsE">torch_tensorrt::GraphInputs (C++ struct)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1GraphInputs.html#_CPPv4N14torch_tensorrt11GraphInputs15input_signatureE">torch_tensorrt::GraphInputs::input_signature (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1GraphInputs.html#_CPPv4N14torch_tensorrt11GraphInputs6inputsE">torch_tensorrt::GraphInputs::inputs (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5InputE">torch_tensorrt::Input (C++ struct)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5dtypeE">torch_tensorrt::Input::dtype (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input6formatE">torch_tensorrt::Input::format (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN2at6TensorE">torch_tensorrt::Input::Input (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE12TensorFormat">[1]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">[2]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat">[3]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE12TensorFormat">[4]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataType12TensorFormat">[5]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat">[6]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEEN3c108ArrayRefI7int64_tEENSt6vectorIdEE12TensorFormat">[7]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEN3c108ArrayRefI7int64_tEENSt6vectorIdEE12TensorFormat">[8]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE12TensorFormat">[9]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataType12TensorFormat">[10]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat">[11]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE12TensorFormat">[12]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataType12TensorFormat">[13]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEE8DataTypeNSt6vectorIdEE12TensorFormat">[14]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorI7int64_tEENSt6vectorIdEE12TensorFormat">[15]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputENSt6vectorI7int64_tEENSt6vectorIdEE12TensorFormat">[16]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5InputEv">[17]</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input9max_shapeE">torch_tensorrt::Input::max_shape (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input9min_shapeE">torch_tensorrt::Input::min_shape (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input9opt_shapeE">torch_tensorrt::Input::opt_shape (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input5shapeE">torch_tensorrt::Input::shape (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1Input.html#_CPPv4N14torch_tensorrt5Input13tensor_domainE">torch_tensorrt::Input::tensor_domain (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1a56e110feaaba2c3fd44bd201fd21a76a.html#_CPPv4N14torch_tensorrt7logging24get_is_colored_output_onEv">torch_tensorrt::logging::get_is_colored_output_on (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1a0593f776f469c20469e2f729fc7861a3.html#_CPPv4N14torch_tensorrt7logging18get_logging_prefixEv">torch_tensorrt::logging::get_logging_prefix (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1a0c012cb374addd90eb1f42eaec570650.html#_CPPv4N14torch_tensorrt7logging24get_reportable_log_levelEv">torch_tensorrt::logging::get_reportable_log_level (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5LevelE">torch_tensorrt::logging::Level (C++ enum)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level6kDEBUGE">torch_tensorrt::logging::Level::kDEBUG (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level6kERRORE">torch_tensorrt::logging::Level::kERROR (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level6kGRAPHE">torch_tensorrt::logging::Level::kGRAPH (C++ enumerator)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level5kINFOE">torch_tensorrt::logging::Level::kINFO (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level15kINTERNAL_ERRORE">torch_tensorrt::logging::Level::kINTERNAL_ERROR (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/enum_logging_8h_1a130f65408ad8cbaee060f05e8db69558.html#_CPPv4N14torch_tensorrt7logging5Level8kWARNINGE">torch_tensorrt::logging::Level::kWARNING (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1ac46ac0901cb97e3ae6e93b45f24e90b8.html#_CPPv4N14torch_tensorrt7logging3logE5LevelNSt6stringE">torch_tensorrt::logging::log (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1ad2efd47b6c3689e58ccc595680579ae5.html#_CPPv4N14torch_tensorrt7logging24set_is_colored_output_onEb">torch_tensorrt::logging::set_is_colored_output_on (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1af8f3443813315af7901903d25dd495cc.html#_CPPv4N14torch_tensorrt7logging18set_logging_prefixENSt6stringE">torch_tensorrt::logging::set_logging_prefix (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_logging_8h_1a7cb50492421ea9de4e3db895819df6f2.html#_CPPv4N14torch_tensorrt7logging24set_reportable_log_levelE5Level">torch_tensorrt::logging::set_reportable_log_level (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4I0EN14torch_tensorrt3ptq19Int8CacheCalibratorE">torch_tensorrt::ptq::Int8CacheCalibrator (C++ class)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator8getBatchEA_PvA_PKci">torch_tensorrt::ptq::Int8CacheCalibrator::getBatch (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4NK14torch_tensorrt3ptq19Int8CacheCalibrator12getBatchSizeEv">torch_tensorrt::ptq::Int8CacheCalibrator::getBatchSize (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator19Int8CacheCalibratorERKNSt6stringE">torch_tensorrt::ptq::Int8CacheCalibrator::Int8CacheCalibrator (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibratorcvPN8nvinfer115IInt8CalibratorEEv">torch_tensorrt::ptq::Int8CacheCalibrator::operator nvinfer1::IInt8Calibrator* (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator20readCalibrationCacheER6size_t">torch_tensorrt::ptq::Int8CacheCalibrator::readCalibrationCache (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8CacheCalibrator.html#_CPPv4N14torch_tensorrt3ptq19Int8CacheCalibrator21writeCalibrationCacheEPKv6size_t">torch_tensorrt::ptq::Int8CacheCalibrator::writeCalibrationCache (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4I00EN14torch_tensorrt3ptq14Int8CalibratorE">torch_tensorrt::ptq::Int8Calibrator (C++ class)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator8getBatchEA_PvA_PKci">torch_tensorrt::ptq::Int8Calibrator::getBatch (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4NK14torch_tensorrt3ptq14Int8Calibrator12getBatchSizeEv">torch_tensorrt::ptq::Int8Calibrator::getBatchSize (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator14Int8CalibratorE19DataLoaderUniquePtrRKNSt6stringEb">torch_tensorrt::ptq::Int8Calibrator::Int8Calibrator (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8CalibratorcvPN8nvinfer115IInt8CalibratorEEv">torch_tensorrt::ptq::Int8Calibrator::operator nvinfer1::IInt8Calibrator* (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator20readCalibrationCacheER6size_t">torch_tensorrt::ptq::Int8Calibrator::readCalibrationCache (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1ptq_1_1Int8Calibrator.html#_CPPv4N14torch_tensorrt3ptq14Int8Calibrator21writeCalibrationCacheEPKv6size_t">torch_tensorrt::ptq::Int8Calibrator::writeCalibrationCache (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_ptq_8h_1a226e3c83379d1012cde8578c1c86b16c.html#_CPPv4I0EN14torch_tensorrt3ptq26make_int8_cache_calibratorE19Int8CacheCalibratorI9AlgorithmERKNSt6stringE">torch_tensorrt::ptq::make_int8_cache_calibrator (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_ptq_8h_1a6186e305f47c1d94b6130ef6c7f7e178.html#_CPPv4I00EN14torch_tensorrt3ptq20make_int8_calibratorE14Int8CalibratorI9Algorithm10DataLoaderE10DataLoaderRKNSt6stringEb">torch_tensorrt::ptq::make_int8_calibrator (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1ad1acd06eaeaffbbcf6e7ebf426891384.html#_CPPv4N14torch_tensorrt10set_deviceEKi">torch_tensorrt::set_device (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatE">torch_tensorrt::TensorFormat (C++ class)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormatcvbEv">torch_tensorrt::TensorFormat::operator bool (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormatcv5ValueEv">torch_tensorrt::TensorFormat::operator Value (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormatneE12TensorFormat">torch_tensorrt::TensorFormat::operator!= (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormatneEN12TensorFormat5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormateqE12TensorFormat">torch_tensorrt::TensorFormat::operator== (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4NK14torch_tensorrt12TensorFormateqEN12TensorFormat5ValueE">[1]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatE5Value">torch_tensorrt::TensorFormat::TensorFormat (C++ function)</a>, <a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEN2at12MemoryFormatE">[1]</a>, <a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat12TensorFormatEv">[2]</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5ValueE">torch_tensorrt::TensorFormat::Value (C++ enum)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value13kChannelsLastE">torch_tensorrt::TensorFormat::Value::kChannelsLast (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value11kContiguousE">torch_tensorrt::TensorFormat::Value::kContiguous (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/classtorch__tensorrt_1_1TensorFormat.html#_CPPv4N14torch_tensorrt12TensorFormat5Value8kUnknownE">torch_tensorrt::TensorFormat::Value::kUnknown (C++ enumerator)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1a5b405fd3bf3c8fc2e2a54cbbab979797.html#_CPPv4N14torch_tensorrt11torchscript29check_method_operator_supportERKN5torch3jit6ModuleENSt6stringE">torch_tensorrt::torchscript::check_method_operator_support (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1a6e19490a08fb1553c9dd347a5ae79db9.html#_CPPv4N14torch_tensorrt11torchscript7compileERKN5torch3jit6ModuleE11CompileSpec">torch_tensorrt::torchscript::compile (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpecE">torch_tensorrt::torchscript::CompileSpec (C++ struct)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec19allow_shape_tensorsE">torch_tensorrt::torchscript::CompileSpec::allow_shape_tensors (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec10capabilityE">torch_tensorrt::torchscript::CompileSpec::capability (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecEN5torch3jit6IValueE">torch_tensorrt::torchscript::CompileSpec::CompileSpec (C++ function)</a>, <a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorI5InputEE">[1]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorIN3c108ArrayRefI7int64_tEEEE">[2]</a>, <a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec11CompileSpecENSt6vectorINSt6vectorI7int64_tEEEE">[3]</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec5debugE">torch_tensorrt::torchscript::CompileSpec::debug (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec6deviceE">torch_tensorrt::torchscript::CompileSpec::device (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec12disable_tf32E">torch_tensorrt::torchscript::CompileSpec::disable_tf32 (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec20dla_global_dram_sizeE">torch_tensorrt::torchscript::CompileSpec::dla_global_dram_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec19dla_local_dram_sizeE">torch_tensorrt::torchscript::CompileSpec::dla_local_dram_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec13dla_sram_sizeE">torch_tensorrt::torchscript::CompileSpec::dla_sram_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18enabled_precisionsE">torch_tensorrt::torchscript::CompileSpec::enabled_precisions (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec12graph_inputsE">torch_tensorrt::torchscript::CompileSpec::graph_inputs (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14min_block_sizeE">torch_tensorrt::torchscript::CompileSpec::min_block_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec20num_avg_timing_itersE">torch_tensorrt::torchscript::CompileSpec::num_avg_timing_iters (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14ptq_calibratorE">torch_tensorrt::torchscript::CompileSpec::ptq_calibrator (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec5refitE">torch_tensorrt::torchscript::CompileSpec::refit (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec24require_full_compilationE">torch_tensorrt::torchscript::CompileSpec::require_full_compilation (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14sparse_weightsE">torch_tensorrt::torchscript::CompileSpec::sparse_weights (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec22torch_executed_modulesE">torch_tensorrt::torchscript::CompileSpec::torch_executed_modules (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec18torch_executed_opsE">torch_tensorrt::torchscript::CompileSpec::torch_executed_ops (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec24truncate_long_and_doubleE">torch_tensorrt::torchscript::CompileSpec::truncate_long_and_double (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/structtorch__tensorrt_1_1torchscript_1_1CompileSpec.html#_CPPv4N14torch_tensorrt11torchscript11CompileSpec14workspace_sizeE">torch_tensorrt::torchscript::CompileSpec::workspace_size (C++ member)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1ae8d56472106eeef37fbe51ff7f40c9b2.html#_CPPv4N14torch_tensorrt11torchscript28convert_method_to_trt_engineERKN5torch3jit6ModuleENSt6stringE11CompileSpec">torch_tensorrt::torchscript::convert_method_to_trt_engine (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/function_torch__tensorrt_8h_1a81f9783517335dda877d8cfcf38987c9.html#_CPPv4N14torch_tensorrt11torchscript26embed_engine_in_new_moduleERKNSt6stringE6DeviceRKNSt6vectorINSt6stringEEERKNSt6vectorINSt6stringEEE">torch_tensorrt::torchscript::embed_engine_in_new_module (C++ function)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1a31398a6d4d27e28817afb0f0139e909e.html#c.TORCH_TENSORRT_MAJOR_VERSION">TORCH_TENSORRT_MAJOR_VERSION (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1a35703561b26b1a9d2738ad7d58b27827.html#c.TORCH_TENSORRT_MINOR_VERSION">TORCH_TENSORRT_MINOR_VERSION (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1a282fd3c0b1c3a215148ae372070e1268.html#c.TORCH_TENSORRT_PATCH_VERSION">TORCH_TENSORRT_PATCH_VERSION (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1adad592a7b1b7eed529cdf6acd584c883.html#c.TORCH_TENSORRT_VERSION">TORCH_TENSORRT_VERSION (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1abd1465eb38256d3f22cc1426b23d516b.html#c.TORCHTRT_API">TORCHTRT_API (C macro)</a>
+</li>
+      <li><a href="_cpp_api/define_macros_8h_1ad19939408f7be171a74a89928b36eb59.html#c.TORCHTRT_HIDDEN">TORCHTRT_HIDDEN (C macro)</a>
+</li>
+      <li><a href="py_api/dynamo.html#torch_tensorrt.dynamo.trace">trace() (in module torch_tensorrt.dynamo)</a>
+</li>
+      <li><a href="py_api/fx.html#torch_tensorrt.fx.TRTInterpreter">TRTInterpreter (class in torch_tensorrt.fx)</a>
+</li>
+      <li><a href="py_api/fx.html#torch_tensorrt.fx.TRTInterpreterResult">TRTInterpreterResult (class in torch_tensorrt.fx)</a>
+</li>
+      <li><a href="py_api/fx.html#torch_tensorrt.fx.TRTModule">TRTModule (class in torch_tensorrt.fx)</a>
+</li>
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diff --git a/docs/v2.2.0/getting_started/getting_started_with_windows.html b/docs/v2.2.0/getting_started/getting_started_with_windows.html
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@@ -0,0 +1,917 @@
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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/logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</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/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+              
+  <section id="building-torch-tensorrt-on-windows">
+<span id="getting-started-windows"></span><h1>Building Torch-TensorRT on Windows<a class="headerlink" href="#building-torch-tensorrt-on-windows" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT has community support for Windows platform using CMake</p>
+<p>Prerequisite:</p>
+<ul class="simple">
+<li><p>Microsoft Visual Studio</p></li>
+<li><p>LibTorch</p></li>
+<li><p>TensorRT</p></li>
+<li><p>CUDA</p></li>
+<li><p>cuDNN</p></li>
+</ul>
+<section id="build-configuration">
+<h2>Build configuration<a class="headerlink" href="#build-configuration" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>Open Microsoft Visual Studio</p></li>
+<li><p>Open Torch-TensorRT source code folder</p></li>
+<li><p>Open Manage configurations -&gt; Edit JSON to open CMakeSettings.json file.</p></li>
+<li><p>Configure the CMake build configurations. Following is an example configuration:</p></li>
+</ul>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>{
+  &quot;configurations&quot;: [
+    {
+      &quot;name&quot;: &quot;x64-Debug&quot;,
+      &quot;generator&quot;: &quot;Ninja&quot;,
+      &quot;configurationType&quot;: &quot;Debug&quot;,
+      &quot;inheritEnvironments&quot;: [ &quot;msvc_x64_x64&quot; ],
+      &quot;buildRoot&quot;: &quot;${projectDir}\\out\\build\\${name}&quot;,
+      &quot;installRoot&quot;: &quot;${projectDir}\\out\\install\\${name}&quot;,
+      &quot;cmakeCommandArgs&quot;: &quot;-S . -B out&quot;,
+      &quot;buildCommandArgs&quot;: &quot;cmake --build out&quot;,
+      &quot;ctestCommandArgs&quot;: &quot;&quot;,
+      &quot;variables&quot;: [
+        {
+          &quot;name&quot;: &quot;CMAKE_MODULE_PATH&quot;,
+          &quot;value&quot;: &quot;$PWD\cmake\Modules&quot;,
+          &quot;type&quot;: &quot;FILEPATH&quot;
+        },
+        {
+          &quot;name&quot;: &quot;Torch_DIR&quot;,
+          &quot;value&quot;: &quot;&lt;Path to libtorch&gt;\share\cmake\Torch&quot;,
+          &quot;type&quot;: &quot;FILEPATH&quot;
+        },
+        {
+          &quot;name&quot;: &quot;TensorRT_ROOT&quot;,
+          &quot;value&quot;: &quot;&lt;Path to TensorRT directory&gt;&quot;,
+          &quot;type&quot;: &quot;FILEPATH&quot;
+        },
+        {
+          &quot;name&quot;: &quot;CMAKE_BUILD_TYPE&quot;,
+          &quot;value&quot;: &quot;Release&quot;,
+          &quot;type&quot;: &quot; STRING&quot;
+        }
+      ]
+    }
+  ]
+}
+</pre></div>
+</div>
+</section>
+<section id="compilation">
+<h2>Compilation<a class="headerlink" href="#compilation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>Click Build -&gt; Build All or directly press Ctrl + Shift + B</p></li>
+</ul>
+<p>Note: After successful compilation, the build artifacts will be present at buildRoot path configured.</p>
+</section>
+<section id="installation">
+<h2>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>Build -&gt; Install Torch-TensorRT</p></li>
+</ul>
+<p>Note: After successful installation, the artifacts will be present at installRoot.</p>
+</section>
+</section>
+<section id="building-with-visual-studio-code">
+<h1>Building With Visual Studio Code<a class="headerlink" href="#building-with-visual-studio-code" title="Permalink to this headline">¶</a></h1>
+<ol class="arabic">
+<li><p>Install Visual Studio Code</p></li>
+<li><p>Install Build Tools for Visual Studio 2022</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Select “Desktop Development with C++”
+&gt; Currently, this installs MSVC v143 - 2022. There are also options to install previous 2019/2017/2015 editions of MSVC
+&gt; License term “1b Build Tools additional use right” allows using Build Tools to compile Open Source Dependencies
+&gt; Also allows using Build Tools to develop and test Open Source Dependencies, to the minor extend of ensuring compatibility with Build Tools</p></li>
+</ul>
+</div></blockquote>
+</li>
+<li><p>Install CUDA (e.g. 11.7.1)</p></li>
+<li><p>Install cuDNN (e.g. 8.5.0.96)</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Set <code class="docutils literal notranslate"><span class="pre">cuDNN_ROOT_DIR</span></code></p></li>
+</ul>
+</div></blockquote>
+</li>
+<li><p>Install <cite>TensorRT</cite> (e.g 8.5.1.7)</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Set <code class="docutils literal notranslate"><span class="pre">TensorRT_ROOT</span></code></p></li>
+<li><p>Add <code class="docutils literal notranslate"><span class="pre">TensorRT_ROOT\lib</span></code> to <code class="docutils literal notranslate"><span class="pre">PATH</span></code></p></li>
+</ul>
+</div></blockquote>
+</li>
+<li><p>Install “libtorch-win-shared-with-deps-latest.zip”</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Select build targeting the appropriate CUDA version</p></li>
+<li><p>Set <code class="docutils literal notranslate"><span class="pre">Torch_DIR</span></code></p></li>
+<li><p>Add <code class="docutils literal notranslate"><span class="pre">Torch_DIR\lib</span></code> to <code class="docutils literal notranslate"><span class="pre">PATH</span></code></p></li>
+</ul>
+</div></blockquote>
+</li>
+<li><p>Clone TensorRT repo</p></li>
+<li><p>Install C++ and CMake Tools extensions from MS</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Change build to <code class="docutils literal notranslate"><span class="pre">RelWithDebInfo</span></code></p></li>
+</ul>
+</div></blockquote>
+</li>
+<li><p>Update <code class="docutils literal notranslate"><span class="pre">.vscode\settings.json</span></code></p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Clean, configure, build</p></li>
+</ul>
+</div></blockquote>
+</li>
+</ol>
+<p>e.g. /.vscode/settings.json</p>
+<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
+<span class="w">    </span><span class="nt">&quot;cmake.generator&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Ninja&quot;</span><span class="p">,</span>
+<span class="w">    </span><span class="nt">&quot;cmake.configureSettings&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
+<span class="w">        </span><span class="nt">&quot;CMAKE_MODULE_PATH&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
+<span class="w">            </span><span class="nt">&quot;type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;FILEPATH&quot;</span><span class="p">,</span>
+<span class="w">            </span><span class="nt">&quot;value&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;$PWD\\cmake\\Modules&quot;</span>
+<span class="w">        </span><span class="p">},</span>
+<span class="w">        </span><span class="nt">&quot;CMAKE_CXX_FLAGS&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
+<span class="w">            </span><span class="nt">&quot;type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;STRING&quot;</span><span class="p">,</span>
+<span class="w">            </span><span class="nt">&quot;value&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;-D_SILENCE_EXPERIMENTAL_FILESYSTEM_DEPRECATION_WARNING&quot;</span>
+<span class="w">        </span><span class="p">},</span>
+<span class="w">        </span><span class="nt">&quot;Torch_DIR&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
+<span class="w">            </span><span class="nt">&quot;type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;FILEPATH&quot;</span><span class="p">,</span>
+<span class="w">            </span><span class="nt">&quot;value&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;X:\\libtorch\\share\\cmake\\Torch&quot;</span>
+<span class="w">        </span><span class="p">},</span>
+<span class="w">        </span><span class="nt">&quot;TensorRT_ROOT&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
+<span class="w">            </span><span class="nt">&quot;type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;FILEPATH&quot;</span><span class="p">,</span>
+<span class="w">            </span><span class="nt">&quot;value&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;X:\\path\\to\\tensorrt&quot;</span>
+<span class="w">        </span><span class="p">},</span>
+<span class="w">        </span><span class="nt">&quot;cuDNN_ROOT_DIR&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
+<span class="w">            </span><span class="nt">&quot;type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;FILEPATH&quot;</span><span class="p">,</span>
+<span class="w">            </span><span class="nt">&quot;value&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;X:\\path\\to\\cudnn&quot;</span>
+<span class="w">        </span><span class="p">},</span>
+<span class="w">        </span><span class="nt">&quot;CMAKE_CUDA_FLAGS&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;-allow-unsupported-compiler&quot;</span>
+<span class="w">    </span><span class="p">},</span>
+<span class="w">    </span><span class="nt">&quot;cmake.buildDirectory&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;${workspaceFolder}/torch_tensorrt_build&quot;</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</section>
+
+
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diff --git a/docs/v2.2.0/getting_started/installation.html b/docs/v2.2.0/getting_started/installation.html
new file mode 100644
index 0000000000..67cdd1b86d
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+
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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/logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</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/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt__ptq.html">Namespace torch_tensorrt::ptq</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+              
+  <section id="installation">
+<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
+<section id="precompiled-binaries">
+<h2>Precompiled Binaries<a class="headerlink" href="#precompiled-binaries" title="Permalink to this headline">¶</a></h2>
+<p>Torch-TensorRT 2.x is centered primarily around Python. As such, precompiled releases can be found on pypi.org</p>
+<section id="dependencies">
+<h3>Dependencies<a class="headerlink" href="#dependencies" title="Permalink to this headline">¶</a></h3>
+<p>You need to have CUDA, PyTorch, and TensorRT (python package is sufficient) installed to use Torch-TensorRT</p>
+<blockquote>
+<div><ul class="simple">
+<li><p><a class="reference external" href="https://developer.nvidia.com/cuda">https://developer.nvidia.com/cuda</a></p></li>
+<li><p><a class="reference external" href="https://pytorch.org">https://pytorch.org</a></p></li>
+</ul>
+</div></blockquote>
+</section>
+<section id="installing-torch-tensorrt">
+<h3>Installing Torch-TensorRT<a class="headerlink" href="#installing-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>You can install the python package using</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>torch<span class="w"> </span>torch-tensorrt<span class="w"> </span>tensorrt
+</pre></div>
+</div>
+</section>
+<section id="installing-torch-tensorrt-for-a-specific-cuda-version">
+<h3>Installing Torch-TensorRT for a specific CUDA version<a class="headerlink" href="#installing-torch-tensorrt-for-a-specific-cuda-version" title="Permalink to this headline">¶</a></h3>
+<p>Similar to PyTorch, Torch-TensorRT has builds compiled for different versions of CUDA. These are distributed on PyTorch’s package index</p>
+<p>For example CUDA 11.8</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>torch<span class="w"> </span>torch-tensorrt<span class="w"> </span>tensorrt<span class="w"> </span>--extra-index-url<span class="w"> </span>https://download.pytorch.org/whl/cu118
+</pre></div>
+</div>
+</section>
+<section id="installing-nightly-builds">
+<h3>Installing Nightly Builds<a class="headerlink" href="#installing-nightly-builds" title="Permalink to this headline">¶</a></h3>
+<p>Torch-TensorRT distributed nightlies targeting the PyTorch nightly. These can be installed from the PyTorch nightly package index (separated by CUDA version)</p>
+<div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--pre<span class="w"> </span>torch<span class="w"> </span>torch-tensorrt<span class="w"> </span>tensorrt<span class="w"> </span>--extra-index-url<span class="w"> </span>https://download.pytorch.org/whl/nightly/cu121
+</pre></div>
+</div>
+</section>
+<section id="c-precompiled-binaries-torchscript-only">
+<span id="bin-dist"></span><h3>C++ Precompiled Binaries (TorchScript Only)<a class="headerlink" href="#c-precompiled-binaries-torchscript-only" title="Permalink to this headline">¶</a></h3>
+<p>Precompiled tarballs for releases are provided here: <a class="reference external" href="https://github.com/pytorch/TensorRT/releases">https://github.com/pytorch/TensorRT/releases</a></p>
+</section>
+</section>
+<section id="compiling-from-source">
+<span id="compile-from-source"></span><h2>Compiling From Source<a class="headerlink" href="#compiling-from-source" title="Permalink to this headline">¶</a></h2>
+<section id="dependencies-for-compilation">
+<span id="installing-deps"></span><h3>Dependencies for Compilation<a class="headerlink" href="#dependencies-for-compilation" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p>Torch-TensorRT is built with <strong>Bazel</strong>, so begin by installing it.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>The easiest way is to install bazelisk using the method of your choosing <a class="reference external" href="https://github.com/bazelbuild/bazelisk">https://github.com/bazelbuild/bazelisk</a></p></li>
+<li><p>Otherwise you can use the following instructions to install binaries <a class="reference external" href="https://docs.bazel.build/versions/master/install.html">https://docs.bazel.build/versions/master/install.html</a></p></li>
+<li><p>Finally if you need to compile from source (e.g. aarch64 until bazel distributes binaries for the architecture) you can use these instructions</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">BAZEL_VERSION</span><span class="o">=</span><span class="k">$(</span>cat<span class="w"> </span>&lt;PATH_TO_TORCHTRT_ROOT&gt;/.bazelversion<span class="k">)</span>
+mkdir<span class="w"> </span>bazel
+<span class="nb">cd</span><span class="w"> </span>bazel
+curl<span class="w"> </span>-fSsL<span class="w"> </span>-O<span class="w"> </span>https://github.com/bazelbuild/bazel/releases/download/<span class="nv">$BAZEL_VERSION</span>/bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
+unzip<span class="w"> </span>bazel-<span class="nv">$BAZEL_VERSION</span>-dist.zip
+bash<span class="w"> </span>./compile.sh
+cp<span class="w"> </span>output/bazel<span class="w"> </span>/usr/local/bin/
+</pre></div>
+</div>
+</div></blockquote>
+</li>
+<li><p>You will also need to have <strong>CUDA</strong> installed on the system (or if running in a container, the system must have the CUDA driver installed and the container must have CUDA)</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Specify your CUDA version here if not the version used in the branch being built: <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L46">https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L46</a></p></li>
+</ul>
+</div></blockquote>
+</li>
+<li><p>The correct <strong>LibTorch</strong> version will be pulled down for you by bazel.</p>
+<blockquote>
+<div><p>NOTE: By default bazel will pull the latest nightly from pytorch.org. For building main, this is usually sufficient however if there is a specific PyTorch you are targeting,
+edit these locations with updated URLs/paths:</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L53C1-L53C1">https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L53C1-L53C1</a></p></li>
+</ul>
+</div></blockquote>
+</li>
+<li><p><strong>cuDNN and TensorRT</strong> are not required to be installed on the system to build Torch-TensorRT, in fact this is preferable to ensure reproducable builds. Download the tarballs for cuDNN and TensorRT from <a class="reference external" href="https://developer.nvidia.com">https://developer.nvidia.com</a> and update the paths in the WORKSPACE file here <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L71">https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L71</a></p>
+<blockquote>
+<div><p>For example:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">http_archive</span><span class="p">(</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;cudnn&quot;</span><span class="p">,</span>
+    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">&quot;@//third_party/cudnn/archive:BUILD&quot;</span><span class="p">,</span>
+    <span class="n">sha256</span> <span class="o">=</span> <span class="s2">&quot;79d77a769c7e7175abc7b5c2ed5c494148c0618a864138722c887f95c623777c&quot;</span><span class="p">,</span>
+    <span class="n">strip_prefix</span> <span class="o">=</span> <span class="s2">&quot;cudnn-linux-x86_64-8.8.1.3_cuda12-archive&quot;</span><span class="p">,</span>
+    <span class="n">urls</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="c1">#&quot;https://developer.nvidia.com/downloads/compute/cudnn/secure/8.8.1/local_installers/12.0/cudnn-linux-x86_64-8.8.1.3_cuda12-archive.tar.xz&quot;,</span>
+        <span class="s2">&quot;file:///&lt;ABSOLUTE PATH TO FILE&gt;/cudnn-linux-x86_64-8.8.1.3_cuda12-archive.tar.xz&quot;</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+
+<span class="n">http_archive</span><span class="p">(</span>
+    <span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;tensorrt&quot;</span><span class="p">,</span>
+    <span class="n">build_file</span> <span class="o">=</span> <span class="s2">&quot;@//third_party/tensorrt/archive:BUILD&quot;</span><span class="p">,</span>
+    <span class="n">sha256</span> <span class="o">=</span> <span class="s2">&quot;0f8157a5fc5329943b338b893591373350afa90ca81239cdadd7580cd1eba254&quot;</span><span class="p">,</span>
+    <span class="n">strip_prefix</span> <span class="o">=</span> <span class="s2">&quot;TensorRT-8.6.1.6&quot;</span><span class="p">,</span>
+    <span class="n">urls</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="c1">#&quot;https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/tars/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz&quot;,</span>
+        <span class="s2">&quot;file:///&lt;ABSOLUTE PATH TO FILE&gt;/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-12.0.tar.gz&quot;</span>
+    <span class="p">],</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div></blockquote>
+</li>
+</ul>
+<p>If you have a local version of cuDNN and TensorRT installed, this can be used as well by commenting out the above lines and uncommenting the following lines <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L114C1-L124C3">https://github.com/pytorch/TensorRT/blob/4e5b0f6e860910eb510fa70a76ee3eb9825e7a4d/WORKSPACE#L114C1-L124C3</a></p>
+</section>
+<section id="building-the-package">
+<h3>Building the Package<a class="headerlink" href="#building-the-package" title="Permalink to this headline">¶</a></h3>
+<p>Once the WORKSPACE has been configured properly, all that is required to build torch-tensorrt is the following command</p>
+<blockquote>
+<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>--pre<span class="w"> </span>.<span class="w"> </span>--extra-index-url<span class="w"> </span>https://download.pytorch.org/whl/nightly/cu121
+</pre></div>
+</div>
+</div></blockquote>
+<p>To build the wheel file</p>
+<blockquote>
+<div><div class="highlight-sh notranslate"><div class="highlight"><pre><span></span>python<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>wheel<span class="w"> </span>--no-deps<span class="w"> </span>--pre<span class="w"> </span>.<span class="w"> </span>--extra-index-url<span class="w"> </span>https://download.pytorch.org/whl/nightly/cu121<span class="w"> </span>-w<span class="w"> </span>dist
+</pre></div>
+</div>
+</div></blockquote>
+</section>
+<section id="building-the-c-library-torchscript-only">
+<h3>Building the C++ Library (TorchScript Only)<a class="headerlink" href="#building-the-c-library-torchscript-only" title="Permalink to this headline">¶</a></h3>
+<section id="release-build">
+<h4>Release Build<a class="headerlink" href="#release-build" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel<span class="w"> </span>build<span class="w"> </span>//:libtorchtrt<span class="w"> </span>-c<span class="w"> </span>opt
+</pre></div>
+</div>
+<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
+</section>
+<section id="debug-build">
+<span id="build-from-archive-debug"></span><h4>Debug Build<a class="headerlink" href="#debug-build" title="Permalink to this headline">¶</a></h4>
+<p>To build with debug symbols use the following command</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel<span class="w"> </span>build<span class="w"> </span>//:libtorchtrt<span class="w"> </span>-c<span class="w"> </span>dbg
+</pre></div>
+</div>
+<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
+</section>
+<section id="pre-cxx11-abi-build">
+<h4>Pre CXX11 ABI Build<a class="headerlink" href="#pre-cxx11-abi-build" title="Permalink to this headline">¶</a></h4>
+<p>To build using the pre-CXX11 ABI use the <code class="docutils literal notranslate"><span class="pre">pre_cxx11_abi</span></code> config</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel<span class="w"> </span>build<span class="w"> </span>//:libtorchtrt<span class="w"> </span>--config<span class="w"> </span>pre_cxx11_abi<span class="w"> </span>-c<span class="w"> </span><span class="o">[</span>dbg/opt<span class="o">]</span>
+</pre></div>
+</div>
+<p>A tarball with the include files and library can then be found in <code class="docutils literal notranslate"><span class="pre">bazel-bin</span></code></p>
+</section>
+<section id="choosing-the-right-abi">
+<span id="abis"></span><h4>Choosing the Right ABI<a class="headerlink" href="#choosing-the-right-abi" title="Permalink to this headline">¶</a></h4>
+<p>Likely the most complicated thing about compiling Torch-TensorRT is selecting the correct ABI. There are two options
+which are incompatible with each other, pre-cxx11-abi and the cxx11-abi. The complexity comes from the fact that while
+the most popular distribution of PyTorch (wheels downloaded from pytorch.org/pypi directly) use the pre-cxx11-abi, most
+other distributions you might encounter (e.g. ones from NVIDIA - NGC containers, and builds for Jetson as well as certain
+libtorch builds and likely if you build PyTorch from source) use the cxx11-abi. It is important you compile Torch-TensorRT
+using the correct ABI to function properly. Below is a table with general pairings of PyTorch distribution sources and the
+recommended commands:</p>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 33%" />
+<col style="width: 31%" />
+<col style="width: 36%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>PyTorch Source</p></th>
+<th class="head"><p>Recommended Python Compilation Command</p></th>
+<th class="head"><p>Recommended C++ Compilation Command</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>PyTorch whl file from PyTorch.org</p></td>
+<td><p>python -m pip install .</p></td>
+<td><p>bazel build //:libtorchtrt -c opt –config pre_cxx11_abi</p></td>
+</tr>
+<tr class="row-odd"><td><p>libtorch-shared-with-deps-<a href="#id2"><span class="problematic" id="id3">*</span></a>.zip from PyTorch.org</p></td>
+<td><p>python -m pip install .</p></td>
+<td><p>bazel build //:libtorchtrt -c opt –config pre_cxx11_abi</p></td>
+</tr>
+<tr class="row-even"><td><p>libtorch-cxx11-abi-shared-with-deps-<a href="#id4"><span class="problematic" id="id5">*</span></a>.zip from PyTorch.org</p></td>
+<td><p>python setup.py bdist_wheel –use-cxx11-abi</p></td>
+<td><p>bazel build //:libtorchtrt -c opt</p></td>
+</tr>
+<tr class="row-odd"><td><p>PyTorch preinstalled in an NGC container</p></td>
+<td><p>python setup.py bdist_wheel –use-cxx11-abi</p></td>
+<td><p>bazel build //:libtorchtrt -c opt</p></td>
+</tr>
+<tr class="row-even"><td><p>PyTorch from the NVIDIA Forums for Jetson</p></td>
+<td><p>python setup.py bdist_wheel –use-cxx11-abi</p></td>
+<td><p>bazel build //:libtorchtrt -c opt</p></td>
+</tr>
+<tr class="row-odd"><td><p>PyTorch built from Source</p></td>
+<td><p>python setup.py bdist_wheel –use-cxx11-abi</p></td>
+<td><p>bazel build //:libtorchtrt -c opt</p></td>
+</tr>
+</tbody>
+</table>
+<blockquote>
+<div><p>NOTE: For all of the above cases you must correctly declare the source of PyTorch you intend to use in your WORKSPACE file for both Python and C++ builds. See below for more information</p>
+</div></blockquote>
+</section>
+</section>
+<section id="building-with-cmake-torchscript-only">
+<h3><strong>Building with CMake</strong> (TorchScript Only)<a class="headerlink" href="#building-with-cmake-torchscript-only" title="Permalink to this headline">¶</a></h3>
+<p>It is possible to build the API libraries (in cpp/) and the torchtrtc executable using CMake instead of Bazel.
+Currently, the python API and the tests cannot be built with CMake.
+Begin by installing CMake.</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>Latest releases of CMake and instructions on how to install are available for different platforms
+[on their website](<a class="reference external" href="https://cmake.org/download/">https://cmake.org/download/</a>).</p></li>
+</ul>
+</div></blockquote>
+<p>A few useful CMake options include:</p>
+<blockquote>
+<div><ul class="simple">
+<li><p>CMake finders for TensorRT and cuDNN are provided in <cite>cmake/Modules</cite>. In order for CMake to use them, pass
+<cite>-DCMAKE_MODULE_PATH=cmake/Modules</cite> when configuring the project with CMake.</p></li>
+<li><p>Libtorch provides its own CMake finder. In case CMake doesn’t find it, pass the path to your install of
+libtorch with <cite>-DTorch_DIR=&lt;path to libtorch&gt;/share/cmake/Torch</cite></p></li>
+<li><p>If TensorRT is not found with the provided cmake finder, specify <cite>-DTensorRT_ROOT=&lt;path to TensorRT&gt;</cite></p></li>
+<li><p>Finally, configure and build the project in a build directory of your choice with the following command
+from the root of Torch-TensorRT project:</p></li>
+</ul>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>cmake<span class="w"> </span>-S.<span class="w"> </span>-B&lt;build<span class="w"> </span>directory&gt;<span class="w"> </span><span class="se">\</span>
+<span class="w">    </span><span class="o">[</span>-DCMAKE_MODULE_PATH<span class="o">=</span>cmake/Module<span class="o">]</span><span class="w"> </span><span class="se">\</span>
+<span class="w">    </span><span class="o">[</span>-DTorch_DIR<span class="o">=</span>&lt;path<span class="w"> </span>to<span class="w"> </span>libtorch&gt;/share/cmake/Torch<span class="o">]</span><span class="w"> </span><span class="se">\</span>
+<span class="w">    </span><span class="o">[</span>-DTensorRT_ROOT<span class="o">=</span>&lt;path<span class="w"> </span>to<span class="w"> </span>TensorRT&gt;<span class="o">]</span><span class="w"> </span><span class="se">\</span>
+<span class="w">    </span><span class="o">[</span>-DCMAKE_BUILD_TYPE<span class="o">=</span>Debug<span class="p">|</span>Release<span class="o">]</span>
+cmake<span class="w"> </span>--build<span class="w"> </span>&lt;build<span class="w"> </span>directory&gt;
+</pre></div>
+</div>
+</div></blockquote>
+</section>
+<section id="building-natively-on-aarch64-jetson">
+<h3><strong>Building Natively on aarch64 (Jetson)</strong><a class="headerlink" href="#building-natively-on-aarch64-jetson" title="Permalink to this headline">¶</a></h3>
+<section id="prerequisites">
+<h4>Prerequisites<a class="headerlink" href="#prerequisites" title="Permalink to this headline">¶</a></h4>
+<p>Install or compile a build of PyTorch/LibTorch for aarch64</p>
+<p>NVIDIA hosts builds the latest release branch for Jetson here:</p>
+<blockquote>
+<div><p><a class="reference external" href="https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048">https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-now-available/72048</a></p>
+</div></blockquote>
+</section>
+<section id="enviorment-setup">
+<h4>Enviorment Setup<a class="headerlink" href="#enviorment-setup" title="Permalink to this headline">¶</a></h4>
+<p>To build natively on aarch64-linux-gnu platform, configure the <code class="docutils literal notranslate"><span class="pre">WORKSPACE</span></code> with local available dependencies.</p>
+<ol class="arabic">
+<li><p>Replace <code class="docutils literal notranslate"><span class="pre">WORKSPACE</span></code> with the corresponding WORKSPACE file in <code class="docutils literal notranslate"><span class="pre">//toolchains/jp_workspaces</span></code></p></li>
+<li><p>Configure the correct paths to directory roots containing local dependencies in the <code class="docutils literal notranslate"><span class="pre">new_local_repository</span></code> rules:</p>
+<blockquote>
+<div><p>NOTE: If you installed PyTorch using a pip package, the correct path is the path to the root of the python torch package.
+In the case that you installed with <code class="docutils literal notranslate"><span class="pre">sudo</span> <span class="pre">pip</span> <span class="pre">install</span></code> this will be <code class="docutils literal notranslate"><span class="pre">/usr/local/lib/python3.8/dist-packages/torch</span></code>.
+In the case you installed with <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">--user</span></code> this will be <code class="docutils literal notranslate"><span class="pre">$HOME/.local/lib/python3.8/site-packages/torch</span></code>.</p>
+</div></blockquote>
+</li>
+</ol>
+<p>In the case you are using NVIDIA compiled pip packages, set the path for both libtorch sources to the same path. This is because unlike
+PyTorch on x86_64, NVIDIA aarch64 PyTorch uses the CXX11-ABI. If you compiled for source using the pre_cxx11_abi and only would like to
+use that library, set the paths to the same path but when you compile make sure to add the flag <code class="docutils literal notranslate"><span class="pre">--config=pre_cxx11_abi</span></code></p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>new_local_repository<span class="o">(</span>
+<span class="w">    </span><span class="nv">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;libtorch&quot;</span>,
+<span class="w">    </span><span class="nv">path</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;/usr/local/lib/python3.8/dist-packages/torch&quot;</span>,
+<span class="w">    </span><span class="nv">build_file</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;third_party/libtorch/BUILD&quot;</span>
+<span class="o">)</span>
+
+new_local_repository<span class="o">(</span>
+<span class="w">    </span><span class="nv">name</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;libtorch_pre_cxx11_abi&quot;</span>,
+<span class="w">    </span><span class="nv">path</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;/usr/local/lib/python3.8/dist-packages/torch&quot;</span>,
+<span class="w">    </span><span class="nv">build_file</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="s2">&quot;third_party/libtorch/BUILD&quot;</span>
+<span class="o">)</span>
+</pre></div>
+</div>
+</section>
+<section id="compile-c-library-and-compiler-cli">
+<h4>Compile C++ Library and Compiler CLI<a class="headerlink" href="#compile-c-library-and-compiler-cli" title="Permalink to this headline">¶</a></h4>
+<blockquote>
+<div><p>NOTE: Due to shifting dependency locations between Jetpack 4.5 and 4.6 there is a now a flag to inform bazel of the Jetpack version</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>--platforms<span class="w"> </span>//toolchains:jetpack_x.x
+</pre></div>
+</div>
+</div></blockquote>
+<p>Compile Torch-TensorRT library using bazel command:</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>bazel<span class="w"> </span>build<span class="w"> </span>//:libtorchtrt<span class="w"> </span>--platforms<span class="w"> </span>//toolchains:jetpack_5.0
+</pre></div>
+</div>
+</section>
+<section id="compile-python-api">
+<h4>Compile Python API<a class="headerlink" href="#compile-python-api" title="Permalink to this headline">¶</a></h4>
+<blockquote>
+<div><p>NOTE: Due to shifting dependencies locations between Jetpack 4.5 and newer Jetpack verisons there is now a flag for <code class="docutils literal notranslate"><span class="pre">setup.py</span></code> which sets the jetpack version (default: 5.0)</p>
+</div></blockquote>
+<p>Compile the Python API using the following command from the <code class="docutils literal notranslate"><span class="pre">//py</span></code> directory:</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>setup.py<span class="w"> </span>install<span class="w"> </span>--use-cxx11-abi
+</pre></div>
+</div>
+<p>If you have a build of PyTorch that uses Pre-CXX11 ABI drop the <code class="docutils literal notranslate"><span class="pre">--use-cxx11-abi</span></code> flag</p>
+<p>If you are building for Jetpack 4.5 add the <code class="docutils literal notranslate"><span class="pre">--jetpack-version</span> <span class="pre">5.0</span></code> flag</p>
+</section>
+</section>
+</section>
+</section>
+
+
+             </article>
+             
+            </div>
+            <footer>
+  
+    <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
+      
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+        &copy; Copyright 2022, NVIDIA Corporation.
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+          <div class="pytorch-left-menu-search">
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+                  v2.2.0
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+          
+            
+            
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+            
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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/logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="py_api/ptq.html">torch_tensorrt.ptq</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/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="indices/supported_ops.html">Operators Supported</a></li>
+</ul>
+
+            
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+          
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="torch-tensorrt">
+<h1>Torch-TensorRT<a class="headerlink" href="#torch-tensorrt" title="Permalink to this headline">¶</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 headline">¶</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 headline">¶</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>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="dynamo-frontend">
+<h2>Dynamo Frontend<a class="headerlink" href="#dynamo-frontend" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="tutorials/_rendered_examples/dynamo/index.html#torch-compile"><span class="std std-ref">Dynamo / 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>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="torchscript-frontend">
+<h2>TorchScript Frontend<a class="headerlink" href="#torchscript-frontend" title="Permalink to this headline">¶</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>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="fx-frontend">
+<h2>FX Frontend<a class="headerlink" href="#fx-frontend" title="Permalink to this headline">¶</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>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="user-guide">
+<h2>User Guide<a class="headerlink" href="#user-guide" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<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="user_guide/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>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="tutorials">
+<h2>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="tutorials/_rendered_examples/index.html#torch-tensorrt-tutorials"><span class="std std-ref">Torch-TensorRT Tutorials</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/notebooks.html#notebooks"><span class="std std-ref">Example notebooks</span></a></p></li>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="python-api-documenation">
+<h2>Python API Documenation<a class="headerlink" href="#python-api-documenation" title="Permalink to this headline">¶</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/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/ptq.html#torch-tensorrt-ptq-py"><span class="std std-ref">torch_tensorrt.ptq</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/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/fx.html#torch-tensorrt-fx-py"><span class="std std-ref">torch_tensorrt.fx</span></a></p></li>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="c-api-documenation">
+<h2>C++ API Documenation<a class="headerlink" href="#c-api-documenation" title="Permalink to this headline">¶</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>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="cli-documentation">
+<h2>CLI Documentation<a class="headerlink" href="#cli-documentation" title="Permalink to this headline">¶</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>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="contributor-documentation">
+<h2>Contributor Documentation<a class="headerlink" href="#contributor-documentation" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="contributors/system_overview.html#system-overview"><span class="std std-ref">System Overview</span></a></p></li>
+<li><p><a class="reference internal" href="contributors/dynamo_converters.html#dynamo-converters"><span class="std std-ref">Writing Dynamo Converters</span></a></p></li>
+<li><p><a class="reference internal" href="contributors/writing_dynamo_aten_lowering_passes.html#writing-dynamo-aten-lowering-passes"><span class="std std-ref">Writing Dynamo ATen Lowering Passes</span></a></p></li>
+<li><p><a class="reference internal" href="contributors/ts_converters.html#ts-converters"><span class="std std-ref">Writing TorchScript Converters</span></a></p></li>
+<li><p><a class="reference internal" href="contributors/useful_links.html#useful-links"><span class="std std-ref">Useful Links for Torch-TensorRT Development</span></a></p></li>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="indices">
+<h2>Indices<a class="headerlink" href="#indices" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference internal" href="indices/supported_ops.html#supported-ops"><span class="std std-ref">Operators Supported</span></a></p></li>
+<li><p><a class="reference internal" href="genindex.html"><span class="std std-ref">Index</span></a></p></li>
+<li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
+</ul>
+<div class="toctree-wrapper compound">
+</div>
+</section>
+<section id="legacy-further-information-torchscript">
+<h2>Legacy Further Information (TorchScript)<a class="headerlink" href="#legacy-further-information-torchscript" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p><a class="reference external" href="https://developer.nvidia.com/blog/accelerating-inference-up-to-6x-faster-in-pytorch-with-torch-tensorrt/">Introductory Blog Post</a></p></li>
+<li><p><a class="reference external" href="https://www.nvidia.com/en-us/on-demand/session/gtcsj20-s21671/">GTC 2020 Talk</a></p></li>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</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>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Operators Supported</a></li>
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+              
+  <section id="operators-supported">
+<span id="supported-ops"></span><h1>Operators Supported<a class="headerlink" href="#operators-supported" title="Permalink to this headline">¶</a></h1>
+<section id="operators-currently-supported-through-converters">
+<h2>Operators Currently Supported Through Converters<a class="headerlink" href="#operators-currently-supported-through-converters" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>aten::_convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled, bool allow_tf32) -&gt; (Tensor)</p></li>
+<li><p>aten::_convolution.deprecated(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups, bool benchmark, bool deterministic, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::abs(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::acos(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::acosh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::adaptive_avg_pool1d(Tensor self, int[1] output_size) -&gt; (Tensor)</p></li>
+<li><p>aten::adaptive_avg_pool2d(Tensor self, int[2] output_size) -&gt; (Tensor)</p></li>
+<li><p>aten::adaptive_avg_pool3d(Tensor self, int[3] output_size) -&gt; (Tensor)</p></li>
+<li><p>aten::adaptive_max_pool1d(Tensor self, int[2] output_size) -&gt; (Tensor, Tensor)</p></li>
+<li><p>aten::adaptive_max_pool2d(Tensor self, int[2] output_size) -&gt; (Tensor, Tensor)</p></li>
+<li><p>aten::adaptive_max_pool3d(Tensor self, int[3] output_size) -&gt; (Tensor, Tensor)</p></li>
+<li><p>aten::add.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::add.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id41"><span class="problematic" id="id42">add_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id1"><span class="problematic" id="id2">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::argmax(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor)</p></li>
+<li><p>aten::argmin(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor)</p></li>
+<li><p>aten::asin(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::asinh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::atan(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::atanh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::avg_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[0], bool ceil_mode=False, bool count_include_pad=True) -&gt; (Tensor)</p></li>
+<li><p>aten::avg_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -&gt; (Tensor)</p></li>
+<li><p>aten::avg_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], bool ceil_mode=False, bool count_include_pad=True, int? divisor_override=None) -&gt; (Tensor)</p></li>
+<li><p>aten::batch_norm(Tensor input, Tensor? gamma, Tensor? beta, Tensor? mean, Tensor? var, bool training, float momentum, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::bitwise_not(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::bmm(Tensor self, Tensor mat2) -&gt; (Tensor)</p></li>
+<li><p>aten::cat(Tensor[] tensors, int dim=0) -&gt; (Tensor)</p></li>
+<li><p>aten::ceil(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::clamp(Tensor self, Scalar? min=None, Scalar? max=None) -&gt; (Tensor)</p></li>
+<li><p>aten::clamp_max(Tensor self, Scalar max) -&gt; (Tensor)</p></li>
+<li><p>aten::clamp_min(Tensor self, Scalar min) -&gt; (Tensor)</p></li>
+<li><p>aten::constant_pad_nd(Tensor self, int[] pad, Scalar value=0) -&gt; (Tensor)</p></li>
+<li><p>aten::cos(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::cosh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::cumsum(Tensor self, int dim, <a href="#id3"><span class="problematic" id="id4">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::div.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::div.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::div.Tensor_mode(Tensor self, Tensor other, <a href="#id5"><span class="problematic" id="id6">*</span></a>, str? rounding_mode) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id43"><span class="problematic" id="id44">div_</span></a>.Scalar(Tensor(a!) self, Scalar other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::<a href="#id45"><span class="problematic" id="id46">div_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::elu(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1) -&gt; (Tensor)</p></li>
+<li><p>aten::embedding(Tensor weight, Tensor indices, int padding_idx=-1, bool scale_grad_by_freq=False, bool sparse=False) -&gt; (Tensor)</p></li>
+<li><p>aten::eq.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::eq.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::erf(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::exp(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::expand(Tensor(a) self, int[] size, <a href="#id7"><span class="problematic" id="id8">*</span></a>, bool implicit=False) -&gt; (Tensor(a))</p></li>
+<li><p>aten::expand_as(Tensor(a) self, Tensor other) -&gt; (Tensor(a))</p></li>
+<li><p>aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -&gt; (Tensor)</p></li>
+<li><p>aten::fake_quantize_per_tensor_affine(Tensor self, float scale, int zero_point, int quant_min, int quant_max) -&gt; (Tensor)</p></li>
+<li><p>aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)</p></li>
+<li><p>aten::floor(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::floor_divide(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::floor_divide.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::ge.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::ge.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -&gt; (Tensor)</p></li>
+<li><p>aten::gt.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::gt.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::hardtanh(Tensor self, Scalar min_val=-1, Scalar max_val=1) -&gt; (Tensor)</p></li>
+<li><p>aten::hardtanh_(Tensor(a!) self, Scalar min_val=-1, Scalar max_val=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::index.Tensor(Tensor self, Tensor?[] indices) -&gt; (Tensor)</p></li>
+<li><p>aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? gamma, Tensor? beta, float eps, bool cudnn_enabled) -&gt; (Tensor)</p></li>
+<li><p>aten::le.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::le.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -&gt; (Tensor)</p></li>
+<li><p>aten::leaky_relu_(Tensor(a!) self, Scalar negative_slope=0.01) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -&gt; (Tensor)</p></li>
+<li><p>aten::log(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -&gt; (Tensor, Tensor)</p></li>
+<li><p>aten::lt.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::lt.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::masked_fill.Scalar(Tensor self, Tensor mask, Scalar value) -&gt; (Tensor)</p></li>
+<li><p>aten::matmul(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::max(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::max.dim(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor values, Tensor indices)</p></li>
+<li><p>aten::max.other(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::max_pool1d(Tensor self, int[1] kernel_size, int[1] stride=[], int[1] padding=[], int[1] dilation=[], bool ceil_mode=False) -&gt; (Tensor)</p></li>
+<li><p>aten::max_pool2d(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=[0, 0], int[2] dilation=[1, 1], bool ceil_mode=False) -&gt; (Tensor)</p></li>
+<li><p>aten::max_pool3d(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=[], int[3] dilation=[], bool ceil_mode=False) -&gt; (Tensor)</p></li>
+<li><p>aten::mean(Tensor self, <a href="#id9"><span class="problematic" id="id10">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::mean.dim(Tensor self, int[] dim, bool keepdim=False, <a href="#id11"><span class="problematic" id="id12">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::min(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::min.dim(Tensor self, int dim, bool keepdim=False) -&gt; (Tensor values, Tensor indices)</p></li>
+<li><p>aten::min.other(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::mul.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::mul.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id47"><span class="problematic" id="id48">mul_</span></a>.Tensor(Tensor(a!) self, Tensor other) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::narrow(Tensor(a) self, int dim, int start, int length) -&gt; (Tensor(a))</p></li>
+<li><p>aten::narrow.Tensor(Tensor(a) self, int dim, Tensor start, int length) -&gt; (Tensor(a))</p></li>
+<li><p>aten::ne.Scalar(Tensor self, Scalar other) -&gt; (Tensor)</p></li>
+<li><p>aten::ne.Tensor(Tensor self, Tensor other) -&gt; (Tensor)</p></li>
+<li><p>aten::neg(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::norm.ScalarOpt_dim(Tensor self, Scalar? p, int[1] dim, bool keepdim=False) -&gt; (Tensor)</p></li>
+<li><p>aten::permute(Tensor(a) self, int[] dims) -&gt; (Tensor(a))</p></li>
+<li><p>aten::pixel_shuffle(Tensor self, int upscale_factor) -&gt; (Tensor)</p></li>
+<li><p>aten::pow.Tensor_Scalar(Tensor self, Scalar exponent) -&gt; (Tensor)</p></li>
+<li><p>aten::pow.Tensor_Tensor(Tensor self, Tensor exponent) -&gt; (Tensor)</p></li>
+<li><p>aten::prelu(Tensor self, Tensor weight) -&gt; (Tensor)</p></li>
+<li><p>aten::prod(Tensor self, <a href="#id13"><span class="problematic" id="id14">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::prod.dim_int(Tensor self, int dim, bool keepdim=False, <a href="#id15"><span class="problematic" id="id16">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::reciprocal(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::reflection_pad1d(Tensor self, int[2] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::reflection_pad2d(Tensor self, int[4] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::relu(Tensor input) -&gt; (Tensor)</p></li>
+<li><p>aten::relu_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::repeat(Tensor self, int[] repeats) -&gt; (Tensor)</p></li>
+<li><p>aten::repeat_interleave.self_int(Tensor self, int repeats, int? dim=None, <a href="#id17"><span class="problematic" id="id18">*</span></a>, int? output_size=None) -&gt; (Tensor)</p></li>
+<li><p>aten::replication_pad1d(Tensor self, int[2] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::replication_pad2d(Tensor self, int[4] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::replication_pad3d(Tensor self, int[6] padding) -&gt; (Tensor)</p></li>
+<li><p>aten::reshape(Tensor self, int[] shape) -&gt; (Tensor)</p></li>
+<li><p>aten::roll(Tensor self, int[1] shifts, int[1] dims=[]) -&gt; (Tensor)</p></li>
+<li><p>aten::rsub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::rsub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::scatter.src(Tensor self, int dim, Tensor index, Tensor src) -&gt; (Tensor)</p></li>
+<li><p>aten::scatter.value(Tensor self, int dim, Tensor index, Scalar value) -&gt; (Tensor)</p></li>
+<li><p>aten::select.int(Tensor(a) self, int dim, int index) -&gt; (Tensor(a))</p></li>
+<li><p>aten::sigmoid(Tensor input) -&gt; (Tensor)</p></li>
+<li><p>aten::sigmoid_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::sin(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::sinh(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::slice.Tensor(Tensor(a) self, int dim=0, int? start=None, int? end=None, int step=1) -&gt; (Tensor(a))</p></li>
+<li><p>aten::softmax.int(Tensor self, int dim, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::split(Tensor self, int[] split_sizes, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::split.Tensor(Tensor(a) self, int split_size, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::split.sizes(Tensor(a -&gt; <a href="#id19"><span class="problematic" id="id20">*</span></a>) self, int[] split_size, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::split_with_sizes(Tensor(a) self, int[] split_sizes, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::sqrt(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::square(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::squeeze.dim(Tensor(a) self, int dim) -&gt; (Tensor(a))</p></li>
+<li><p>aten::stack(Tensor[] tensors, int dim=0) -&gt; (Tensor)</p></li>
+<li><p>aten::sub.Scalar(Tensor self, Scalar other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::sub.Tensor(Tensor self, Tensor other, Scalar alpha=1) -&gt; (Tensor)</p></li>
+<li><p>aten::<a href="#id49"><span class="problematic" id="id50">sub_</span></a>.Tensor(Tensor(a!) self, Tensor other, <a href="#id21"><span class="problematic" id="id22">*</span></a>, Scalar alpha=1) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::sum(Tensor self, <a href="#id23"><span class="problematic" id="id24">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, <a href="#id25"><span class="problematic" id="id26">*</span></a>, int? dtype=None) -&gt; (Tensor)</p></li>
+<li><p>aten::t(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::tan(Tensor self) -&gt; (Tensor)</p></li>
+<li><p>aten::tanh(Tensor input) -&gt; (Tensor)</p></li>
+<li><p>aten::tanh_(Tensor(a!) self) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::to.device(Tensor(a) self, Device device, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor(a))</p></li>
+<li><p>aten::to.dtype(Tensor self, int dtype, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor)</p></li>
+<li><p>aten::to.other(Tensor self, Tensor other, bool non_blocking=False, bool copy=False, int? memory_format=None) -&gt; (Tensor)</p></li>
+<li><p>aten::to.prim_Device(Tensor(a) self, Device? device, int? dtype=None, bool non_blocking=False, bool copy=False) -&gt; (Tensor(a|b))</p></li>
+<li><p>aten::topk(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True) -&gt; (Tensor values, Tensor indices)</p></li>
+<li><p>aten::transpose.int(Tensor(a) self, int dim0, int dim1) -&gt; (Tensor(a))</p></li>
+<li><p>aten::unbind.int(Tensor(a -&gt; <a href="#id27"><span class="problematic" id="id28">*</span></a>) self, int dim=0) -&gt; (Tensor[])</p></li>
+<li><p>aten::unsqueeze(Tensor(a) self, int dim) -&gt; (Tensor(a))</p></li>
+<li><p>aten::upsample_bilinear2d(Tensor self, int[2] output_size, bool align_corners, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_bilinear2d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_linear1d(Tensor self, int[1] output_size, bool align_corners, float? scales=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_linear1d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest1d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest2d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_nearest3d.vec(Tensor input, int[]? output_size, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_trilinear3d(Tensor self, int[3] output_size, bool align_corners, float? scales_d=None, float? scales_h=None, float? scales_w=None) -&gt; (Tensor)</p></li>
+<li><p>aten::upsample_trilinear3d.vec(Tensor input, int[]? output_size, bool align_corners, float[]? scale_factors) -&gt; (Tensor)</p></li>
+<li><p>aten::view(Tensor(a) self, int[] size) -&gt; (Tensor(a))</p></li>
+<li><p>trt::const(Tensor self) -&gt; (Tensor)</p></li>
+</ul>
+</section>
+<section id="operators-currently-supported-through-evaluators">
+<h2>Operators Currently Supported Through Evaluators<a class="headerlink" href="#operators-currently-supported-through-evaluators" title="Permalink to this headline">¶</a></h2>
+<ul class="simple">
+<li><p>aten::Bool.float(float b) -&gt; (bool)</p></li>
+<li><p>aten::Bool.int(int a) -&gt; (bool)</p></li>
+<li><p>aten::Float.Scalar(Scalar a) -&gt; float</p></li>
+<li><p>aten::Float.bool(bool a) -&gt; float</p></li>
+<li><p>aten::Float.int(int a) -&gt; float</p></li>
+<li><p>aten::Int.Scalar(Scalar a) -&gt; int</p></li>
+<li><p>aten::Int.bool(bool a) -&gt; int</p></li>
+<li><p>aten::Int.float(float a) -&gt; int</p></li>
+<li><p>aten::Int.int(int a) -&gt; int</p></li>
+<li><p>aten::__and__(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::__and__.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::__derive_index(int idx, int start, int step) -&gt; int</p></li>
+<li><p>aten::__getitem__.t(t[](a) list, int idx) -&gt; (t(*))</p></li>
+<li><p>aten::__is__(t1 self, t2 obj) -&gt; bool</p></li>
+<li><p>aten::__isnot__(t1 self, t2 obj) -&gt; bool</p></li>
+<li><p>aten::__not__(bool self) -&gt; bool</p></li>
+<li><p>aten::__or__(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::__range_length(int lo, int hi, int step) -&gt; int</p></li>
+<li><p>aten::__round_to_zero_floordiv(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::__xor__(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::add.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::add.int(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::add.str(str a, str b) -&gt; (str)</p></li>
+<li><p>aten::<a href="#id51"><span class="problematic" id="id52">add_</span></a>.t(t[](a!) self, t[] b) -&gt; (t[])</p></li>
+<li><p>aten::append.t(t[](a!) self, t(c -&gt; <a href="#id29"><span class="problematic" id="id30">*</span></a>) el) -&gt; (t[](a!))</p></li>
+<li><dl class="simple">
+<dt>aten::arange(Scalar end, <a href="#id31"><span class="problematic" id="id32">*</span></a>, int? dtype=None, int? layout=None,</dt><dd><p>Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>aten::arange.start(Scalar start, Scalar end, <a href="#id33"><span class="problematic" id="id34">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+</dd>
+</dl>
+</li>
+<li><dl class="simple">
+<dt>aten::arange.start_step(Scalar start, Scalar end, Scalar step, <a href="#id35"><span class="problematic" id="id36">*</span></a>, ScalarType? dtype=None,</dt><dd><p>Layout? layout=None, Device? device=None, bool? pin_memory=None) -&gt; (Tensor)</p>
+</dd>
+</dl>
+</li>
+<li><p>aten::clone(Tensor self, <a href="#id37"><span class="problematic" id="id38">*</span></a>, int? memory_format=None) -&gt; (Tensor)</p></li>
+<li><p>aten::copy_(Tensor(a!) self, Tensor src, bool non_blocking=False) -&gt; (Tensor(a!))</p></li>
+<li><p>aten::dim(Tensor self) -&gt; int</p></li>
+<li><p>aten::div.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::div.int(int a, int b) -&gt; (float)</p></li>
+<li><p>aten::eq.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::eq.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::eq.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::eq.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::eq.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::eq.str(str a, str b) -&gt; (bool)</p></li>
+<li><p>aten::extend.t(t[](a!) self, t[] other) -&gt; ()</p></li>
+<li><p>aten::floor.float(float a) -&gt; (int)</p></li>
+<li><p>aten::floor.int(int a) -&gt; (int)</p></li>
+<li><p>aten::floordiv.float(float a, float b) -&gt; (int)</p></li>
+<li><p>aten::floordiv.int(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::format(str self, …) -&gt; (str)</p></li>
+<li><p>aten::ge.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::ge.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::ge.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::ge.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::ge.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::gt.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::gt.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::gt.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::gt.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::gt.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::is_floating_point(Tensor self) -&gt; (bool)</p></li>
+<li><p>aten::le.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::le.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::le.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::le.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::le.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::len.t(t[] a) -&gt; (int)</p></li>
+<li><p>aten::lt.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::lt.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::lt.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::lt.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::lt.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::mul.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::mul.int(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::ne.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>aten::ne.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>aten::ne.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>aten::ne.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>aten::ne.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>aten::neg.int(int a) -&gt; (int)</p></li>
+<li><p>aten::numel(Tensor self) -&gt; int</p></li>
+<li><p>aten::pow.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::pow.float_int(float a, int b) -&gt; (float)</p></li>
+<li><p>aten::pow.int(int a, int b) -&gt; (float)</p></li>
+<li><p>aten::pow.int_float(int a, float b) -&gt; (float)</p></li>
+<li><p>aten::size(Tensor self) -&gt; (int[])</p></li>
+<li><p>aten::size.int(Tensor self, int dim) -&gt; (int)</p></li>
+<li><p>aten::slice.t(t[] l, int start, int end=9223372036854775807, int step=1) -&gt; (t[])</p></li>
+<li><p>aten::sqrt.float(float a) -&gt; (float)</p></li>
+<li><p>aten::sqrt.int(int a) -&gt; (float)</p></li>
+<li><p>aten::sub.float(float a, float b) -&gt; (float)</p></li>
+<li><p>aten::sub.int(int a, int b) -&gt; (int)</p></li>
+<li><p>aten::tensor(t[] data, <a href="#id39"><span class="problematic" id="id40">*</span></a>, int? dtype=None, Device? device=None, bool requires_grad=False) -&gt; (Tensor)</p></li>
+<li><p>prim::TupleIndex(Any tup, int i) -&gt; (Any)</p></li>
+<li><p>prim::dtype(Tensor a) -&gt; (int)</p></li>
+<li><p>prim::max.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>prim::max.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>prim::max.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>prim::max.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>prim::max.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>prim::max.self_int(int[] self) -&gt; (int)</p></li>
+<li><p>prim::min.bool(bool a, bool b) -&gt; (bool)</p></li>
+<li><p>prim::min.float(float a, float b) -&gt; (bool)</p></li>
+<li><p>prim::min.float_int(float a, int b) -&gt; (bool)</p></li>
+<li><p>prim::min.int(int a, int b) -&gt; (bool)</p></li>
+<li><p>prim::min.int_float(int a, float b) -&gt; (bool)</p></li>
+<li><p>prim::min.self_int(int[] self) -&gt; (int)</p></li>
+<li><p>prim::shape(Tensor a) -&gt; (int[])</p></li>
+</ul>
+</section>
+</section>
+
+
+             </article>
+             
+            </div>
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+<li class="toctree-l1"><a class="reference internal" href="getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
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+<ul>
+<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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+  <section id="torch-tensorrt-dynamo">
+<span id="torch-tensorrt-dynamo-py"></span><h1>torch_tensorrt.dynamo<a class="headerlink" href="#torch-tensorrt-dynamo" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.dynamo"></span><section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.dynamo.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">exported_program:</span> <span class="pre">~torch.export.exported_program.ExportedProgram,</span> <span class="pre">inputs:</span> <span class="pre">~typing.Tuple[~typing.Any,</span> <span class="pre">...],</span> <span class="pre">*,</span> <span class="pre">device:</span> <span class="pre">~typing.Optional[~typing.Union[~torch_tensorrt._Device.Device,</span> <span class="pre">~torch.device,</span> <span class="pre">str]]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">disable_tf32:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">sparse_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">enabled_precisions:</span> <span class="pre">~typing.Union[~typing.Set[~torch.dtype],</span> <span class="pre">~typing.Tuple[~torch.dtype]]</span> <span class="pre">=</span> <span class="pre">(torch.float32,),</span> <span class="pre">engine_capability:</span> <span class="pre">~torch_tensorrt._C.EngineCapability</span> <span class="pre">=</span> <span class="pre">&lt;EngineCapability.DEFAULT:</span> <span class="pre">0&gt;,</span> <span class="pre">refit:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">debug:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">num_avg_timing_iters:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0,</span> <span class="pre">dla_sram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1048576,</span> <span class="pre">dla_local_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1073741824,</span> <span class="pre">dla_global_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">536870912,</span> <span class="pre">truncate_long_and_double:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">require_full_compilation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">min_block_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">5,</span> <span class="pre">torch_executed_ops:</span> <span class="pre">~typing.Optional[~typing.Collection[~typing.Union[~typing.Callable[[...],</span> <span class="pre">~typing.Any],</span> <span class="pre">str]]]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">torch_executed_modules:</span> <span class="pre">~typing.Optional[~typing.List[str]]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">pass_through_build_failures:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">max_aux_streams:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">version_compatible:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">optimization_level:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">use_python_runtime:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_fast_partitioner:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">enable_experimental_decompositions:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">output_format:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'exported_program',</span> <span class="pre">**kwargs:</span> <span class="pre">~typing.Any</span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">GraphModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT</p>
+<p>Takes a existing TorchScript module and a set of settings to configure the compiler
+and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
+<p>Converts specifically the forward method of a TorchScript Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>exported_program</strong> (<em>torch.export.ExportedProgram</em>) – Source module, running torch.export on a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
+<li><p><strong>inputs</strong> (<em>Tuple</em><em>[</em><em>Any</em><em>, </em><em>...</em><em>]</em>) – <p>List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
+<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
+<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</p></li>
+<li><p><strong>min_block_size</strong> (<em>python:int</em>) – The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</p></li>
+<li><p><strong>torch_executed_ops</strong> (<em>Collection</em><em>[</em><em>Target</em><em>]</em>) – Set of aten operators that must be run in PyTorch. An error will be thrown if this set is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
+<li><p><strong>torch_executed_modules</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
+<li><p><strong>pass_through_build_failures</strong> (<em>bool</em>) – Error out if there are issues during compilation (only applicable to torch.compile workflows)</p></li>
+<li><p><strong>max_aux_stream</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Maximum streams in the engine</p></li>
+<li><p><strong>version_compatible</strong> (<em>bool</em>) – Build the TensorRT engines compatible with future versions of TensorRT (Restrict to lean runtime operators to provide version forward compatibility for the engines)</p></li>
+<li><p><strong>optimization_level</strong> – (Optional[int]): Setting a higher optimization level allows TensorRT to spend longer engine building time searching for more optimization options. The resulting engine may have better performance compared to an engine built with a lower optimization level. The default optimization level is 3. Valid values include integers from 0 to the maximum optimization level, which is currently 5. Setting it to be greater than the maximum level results in identical behavior to the maximum level.</p></li>
+<li><p><strong>use_python_runtime</strong> – (bool): Return a graph using a pure Python runtime, reduces options for serialization</p></li>
+<li><p><strong>use_fast_partitioner</strong> – (bool): Use the adjacency based partitioning scheme instead of the global partitioner. Adjacency partitioning is faster but may not be optiminal. Use the global paritioner (<code class="docutils literal notranslate"><span class="pre">False</span></code>) if looking for best performance</p></li>
+<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Use the full set of operator decompositions. These decompositions may not be tested but serve to make the grap easier to covert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</p></li>
+<li><p><strong>output_format</strong> (<em>str</em>) – Output format of the result of TRT compilation. Options include “exported_program” (or) “ep” | “torchscript” (or) “ts” | “graph_module” (or) “fx”. Default is “exported_program”</p></li>
+<li><p><strong>**kwargs</strong> – Any,</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Compiled FX Module, when run it will execute via TensorRT</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.fx.GraphModule</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.dynamo.trace">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">trace</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mod</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">torch.nn.modules.module.Module</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">torch.fx.graph_module.GraphModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ExportedProgram</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_tracer.html#trace"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.trace" title="Permalink to this definition">¶</a></dt>
+<dd><p>Exports a <code class="docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code> from a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code> specifically targeting being compiled with Torch-TensorRT</p>
+<p>Exports a <code class="docutils literal notranslate"><span class="pre">torch.export.ExportedProgram</span></code> from either a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> or torch.fx.GraphModule``. Runs specific operator decompositions geared towards
+compilation by Torch-TensorRT’s dynamo frontend.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mod</strong> (<em>torch.nn.Module</em><em> | </em><em>torch.fx.GraphModule</em>) – Source module to later be compiled by Torch-TensorRT’s dynamo fronted</p></li>
+<li><p><strong>inputs</strong> (<em>Tuple</em><em>[</em><em>Any</em><em>, </em><em>...</em><em>]</em>) – <p>List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+</ul>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="p">.</span><span class="n">device</span><span class="p">(</span><span class="s">&quot;cuda:0&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Use the full set of operator decompositions. These decompositions may not be tested but serve to make the grap easier to covert to TensorRT, potentially increasing the amount of graphs run in TensorRT.</p></li>
+<li><p><strong>**kwargs</strong> – Any,</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Compiled FX Module, when run it will execute via TensorRT</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.fx.GraphModule</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.dynamo.export">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">export</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gm</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">GraphModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_format</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ExportedProgram</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_exporter.html#export"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.export" title="Permalink to this definition">¶</a></dt>
+<dd><p>Export the result of TensorRT compilation into the desired output format.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>gm</strong> (<em>torch.fx.GraphModule</em>) – Compiled Torch-TensorRT module, generated by <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.dynamo.compile</span></code></p></li>
+<li><p><strong>inputs</strong> (<em>torch.Tensor</em>) – Torch input tensors</p></li>
+<li><p><strong>output_format</strong> (<em>str</em>) – Output format of the result of TRT compilation. Options include “exported_program” (or) “ep” | “torchscript” (or) “ts” | “graph_module” (or) “fx”. Default is “exported_program”</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.dynamo.CompilationSettings">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">CompilationSettings</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">precision:</span> <span class="pre">~torch.dtype</span> <span class="pre">=</span> <span class="pre">torch.float32,</span> <span class="pre">debug:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0,</span> <span class="pre">min_block_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">5,</span> <span class="pre">torch_executed_ops:</span> <span class="pre">~typing.Collection[~typing.Union[~typing.Callable[[...],</span> <span class="pre">~typing.Any],</span> <span class="pre">str]]</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">pass_through_build_failures:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">max_aux_streams:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">version_compatible:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">optimization_level:</span> <span class="pre">~typing.Optional[int]</span> <span class="pre">=</span> <span class="pre">None,</span> <span class="pre">use_python_runtime:</span> <span class="pre">~typing.Optional[bool]</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">truncate_long_and_double:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">use_fast_partitioner:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">True,</span> <span class="pre">enable_experimental_decompositions:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">device:</span> <span class="pre">~torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;,</span> <span class="pre">require_full_compilation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">disable_tf32:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">sparse_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">refit:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False,</span> <span class="pre">engine_capability:</span> <span class="pre">~tensorrt_bindings.tensorrt.EngineCapability</span> <span class="pre">=</span> <span class="pre">&lt;EngineCapability.DEFAULT:</span> <span class="pre">0&gt;,</span> <span class="pre">num_avg_timing_iters:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1,</span> <span class="pre">dla_sram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1048576,</span> <span class="pre">dla_local_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1073741824,</span> <span class="pre">dla_global_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">536870912,</span> <span class="pre">output_format:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'exported_program'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_settings.html#CompilationSettings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.CompilationSettings" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compilation settings for Torch-TensorRT Dynamo Paths</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>precision</strong> (<em>torch.dpython:type</em>) – Model Layer precision</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Whether to print out verbose debugging information</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Workspace TRT is allowed to use for the module (0 is default)</p></li>
+<li><p><strong>min_block_size</strong> (<em>python:int</em>) – Minimum number of operators per TRT-Engine Block</p></li>
+<li><p><strong>torch_executed_ops</strong> (<em>Collection</em><em>[</em><em>Target</em><em>]</em>) – Collection of operations to run in Torch, regardless of converter coverage</p></li>
+<li><p><strong>pass_through_build_failures</strong> (<em>bool</em>) – Whether to fail on TRT engine build errors (True) or not (False)</p></li>
+<li><p><strong>max_aux_streams</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Maximum number of allowed auxiliary TRT streams for each engine</p></li>
+<li><p><strong>version_compatible</strong> (<em>bool</em>) – Provide version forward-compatibility for engine plan files</p></li>
+<li><p><strong>optimization_level</strong> (<em>Optional</em><em>[</em><em>python:int</em><em>]</em>) – Builder optimization 0-5, higher levels imply longer build time,
+searching for more optimization options. TRT defaults to 3</p></li>
+<li><p><strong>use_python_runtime</strong> (<em>Optional</em><em>[</em><em>bool</em><em>]</em>) – Whether to strictly use Python runtime or C++ runtime. To auto-select a runtime
+based on C++ dependency presence (preferentially choosing C++ runtime if available), leave the
+argument as None</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Whether to truncate int64/float64 TRT engine inputs or weights to int32/float32</p></li>
+<li><p><strong>use_fast_partitioner</strong> (<em>bool</em>) – Whether to use the fast or global graph partitioning system</p></li>
+<li><p><strong>enable_experimental_decompositions</strong> (<em>bool</em>) – Whether to enable all core aten decompositions
+or only a selected subset of them</p></li>
+<li><p><strong>device</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a>) – GPU to compile the model on</p></li>
+<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Whether to require the graph is fully compiled in TensorRT.
+Only applicable for <cite>ir=”dynamo”</cite>; has no effect for <cite>torch.compile</cite> path</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Whether to disable TF32 computation for TRT layers</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Whether to allow the builder to use sparse weights</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Whether to build a refittable engine</p></li>
+<li><p><strong>engine_capability</strong> (<em>trt.EngineCapability</em>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
+<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
+<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
+<li><p><strong>output_format</strong> (<em>str</em>) – Output format of the result of TRT compilation. Options include “exported_program” (or) “ep” | “torchscript” (or) “ts” | “graph_module” (or) “fx”. Default is “exported_program”</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.dynamo.SourceIR">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.dynamo.</span></span><span class="sig-name descname"><span class="pre">SourceIR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/dynamo/_SourceIR.html#SourceIR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dynamo.SourceIR" title="Permalink to this definition">¶</a></dt>
+<dd><p>An enumeration.</p>
+</dd></dl>
+
+</section>
+</section>
+
+
+             </article>
+             
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+
+
+
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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+<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>
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+<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>
+</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>
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+<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>
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+  <section id="torch-tensorrt-fx">
+<span id="torch-tensorrt-fx-py"></span><h1>torch_tensorrt.fx<a class="headerlink" href="#torch-tensorrt-fx" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.fx"></span><section id="functions">
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+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Module</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_acc_module_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_batch_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">2048</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_workspace_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">33554432</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explicit_batch_dimension</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lower_precision</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">LowerPrecision.FP16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose_log</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">timing_cache_prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_timing_cache</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda_graph_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dynamic_batch</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_aten</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_experimental_fx_rt</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">correctness_atol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">correctness_rtol</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.1</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Module</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/fx/lower.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Takes in original module, input and lowering setting, run lowering workflow to turn module
+into lowered module, or so called TRTModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> – Original module for lowering.</p></li>
+<li><p><strong>input</strong> – Input for module.</p></li>
+<li><p><strong>max_batch_size</strong> – Maximum batch size (must be &gt;= 1 to be set, 0 means not set)</p></li>
+<li><p><strong>min_acc_module_size</strong> – Minimal number of nodes for an accelerated submodule</p></li>
+<li><p><strong>max_workspace_size</strong> – Maximum size of workspace given to TensorRT.</p></li>
+<li><p><strong>explicit_batch_dimension</strong> – Use explicit batch dimension in TensorRT if set True, otherwise use implicit batch dimension.</p></li>
+<li><p><strong>lower_precision</strong> – lower_precision config given to TRTModule.</p></li>
+<li><p><strong>verbose_log</strong> – Enable verbose log for TensorRT if set True.</p></li>
+<li><p><strong>timing_cache_prefix</strong> – Timing cache file name for timing cache used by fx2trt.</p></li>
+<li><p><strong>save_timing_cache</strong> – Update timing cache with current timing cache data if set to True.</p></li>
+<li><p><strong>cuda_graph_batch_size</strong> – Cuda graph batch size, default to be -1.</p></li>
+<li><p><strong>dynamic_batch</strong> – batch dimension (dim=0) is dynamic.</p></li>
+<li><p><strong>use_experimental_fx_rt</strong> – Uses the next generation TRTModule which supports both Python and TorchScript based execution (including in C++).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A torch.nn.Module lowered by TensorRT.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.TRTModule">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">TRTModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cuda_graph_batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/fx/trt_module.html#TRTModule"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.TRTModule" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.InputTensorSpec">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">InputTensorSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">shape</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">device</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">device(type='cpu')</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shape_ranges</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">Tuple</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">has_batch_dim</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/fx/input_tensor_spec.html#InputTensorSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.InputTensorSpec" title="Permalink to this definition">¶</a></dt>
+<dd><p>This class contains the information of a input tensor.</p>
+<p>shape: shape of the tensor.</p>
+<p>dtype: dtyep of the tensor.</p>
+<dl class="simple">
+<dt>device: device of the tensor. This is only used to generate inputs to the given model</dt><dd><p>in order to run shape prop. For TensorRT engine, inputs have to be on cuda device.</p>
+</dd>
+<dt>shape_ranges: If dynamic shape is needed (shape has dimensions of -1), then this field</dt><dd><p>has to be provided (default is empty list). Every shape_range is a tuple of three
+tuples ((min_input_shape), (optimized_input_shape), (max_input_shape)). Each shape_range
+is used to populate a TensorRT optimization profile.
+e.g. If the input shape varies from (1, 224) to (100, 224) and we want to optimize
+for (25, 224) because it’s the most common input shape, then we set shape_ranges to
+((1, 224), (25, 225), (100, 224)).</p>
+</dd>
+<dt>has_batch_dim: Whether the shape includes batch dimension. Batch dimension has to be provided</dt><dd><p>if the engine want to run with dynamic shape.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.TRTInterpreter">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">TRTInterpreter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">GraphModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_specs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch_tensorrt.fx.InputTensorSpec" title="torch_tensorrt.fx.input_tensor_spec.InputTensorSpec"><span class="pre">InputTensorSpec</span></a><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explicit_batch_dimension</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">explicit_precision</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_level</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/fx/fx2trt.html#TRTInterpreter"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.TRTInterpreter" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.fx.TRTInterpreterResult">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.fx.</span></span><span class="sig-name descname"><span class="pre">TRTInterpreterResult</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">engine</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_names</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">serialized_cache</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/fx/fx2trt.html#TRTInterpreterResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.fx.TRTInterpreterResult" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</section>
+</section>
+
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
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+<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>
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+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="reference internal" href="torch_tensorrt.html">torch_tensorrt</a></li>
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+<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>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<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-logging">
+<span id="torch-tensorrt-logging-py"></span><h1>torch_tensorrt.logging<a class="headerlink" href="#torch-tensorrt-logging" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.logging"></span><dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">Level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#Level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.Level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Enum</span></code></p>
+<p>Enum to set the minimum required logging level to print a message to stdout</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Debug">
+<span class="sig-name descname"><span class="pre">Debug</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.DEBUG:</span> <span class="pre">4&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Debug" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Error">
+<span class="sig-name descname"><span class="pre">Error</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.ERROR:</span> <span class="pre">1&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Error" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Graph">
+<span class="sig-name descname"><span class="pre">Graph</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.GRAPH:</span> <span class="pre">5&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Graph" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Info">
+<span class="sig-name descname"><span class="pre">Info</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.INFO:</span> <span class="pre">3&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Info" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.InternalError">
+<span class="sig-name descname"><span class="pre">InternalError</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.INTERNAL_ERROR:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.InternalError" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.Level.Warning">
+<span class="sig-name descname"><span class="pre">Warning</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;LogLevel.WARNING:</span> <span class="pre">2&gt;</span></em><a class="headerlink" href="#torch_tensorrt.logging.Level.Warning" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.debug">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">debug</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#debug"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.debug" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display full debug information through the logger</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.debug():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id1"><span class="problematic" id="id2">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.errors">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">errors</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#errors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.errors" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just errors and above</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.errors():</dt><dd><p>outputs = model_torchtrt(inputs)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_is_colored_output_on">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_is_colored_output_on</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_is_colored_output_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_is_colored_output_on" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get if colored output is enabled for logging</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>If colored output is one</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><em>bool</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_logging_prefix">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_logging_prefix</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_logging_prefix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_logging_prefix" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the prefix set for logging messages</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Prefix used for logger</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><em>str</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.get_reportable_log_level">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">get_reportable_log_level</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">Level</span></a></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#get_reportable_log_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.get_reportable_log_level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get the level required for a message to be printed in the log</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>The enum representing the level required to print</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>Level</em></a></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.graphs">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">graphs</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#graphs"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.graphs" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display the results of intermediate lowering passes
+as well as full debug information through the logger</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.graphs():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id3"><span class="problematic" id="id4">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.info">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">info</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to display all info and greater severity messages</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.info():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id5"><span class="problematic" id="id6">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.internal_errors">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">internal_errors</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#internal_errors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.internal_errors" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just internal errors</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.internal_errors():</dt><dd><p>outputs = model_torchtrt(inputs)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.log">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">log</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">Level</span></a></span></em>, <em class="sig-param"><span class="n"><span class="pre">msg</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#log"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.log" title="Permalink to this definition">¶</a></dt>
+<dd><p>Add a new message to the log</p>
+<p>Adds a new message to the log at a specified level. The message
+will only get printed out if Level &gt; reportable_log_level</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>level</strong> (<a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>Level</em></a>) – Severity of the message</p></li>
+<li><p><strong>msg</strong> (<em>str</em>) – Actual message text</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_is_colored_output_on">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_is_colored_output_on</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">colored_output_on</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_is_colored_output_on"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_is_colored_output_on" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enable or disable color in the log output</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>colored_output_on</strong> (<em>bool</em>) – If colored output should be enabled or not</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_logging_prefix">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_logging_prefix</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_logging_prefix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_logging_prefix" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the prefix used when logging messages</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>prefix</strong> (<em>str</em>) – Prefix to use for logging messages</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.set_reportable_log_level">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">set_reportable_log_level</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">level</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><span class="pre">Level</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#set_reportable_log_level"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.set_reportable_log_level" title="Permalink to this definition">¶</a></dt>
+<dd><p>Set the level required for a message to be printed to the log</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>level</strong> (<a class="reference internal" href="#torch_tensorrt.logging.Level" title="torch_tensorrt.logging.Level"><em>Level</em></a>) – The enum representing the level required to print</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.logging.warnings">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.logging.</span></span><span class="sig-name descname"><span class="pre">warnings</span></span><a class="reference internal" href="../_modules/torch_tensorrt/logging.html#warnings"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.logging.warnings" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Context-manager to limit displayed log messages to just warnings and above</p>
+<p>Example:</p>
+<dl class="simple">
+<dt>with torch_tensorrt.logging.warnings():</dt><dd><p>model_trt = torch_tensorrt.compile(model, <a href="#id7"><span class="problematic" id="id8">**</span></a>spec)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+
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+  <section id="torch-tensorrt-ptq">
+<span id="torch-tensorrt-ptq-py"></span><h1>torch_tensorrt.ptq<a class="headerlink" href="#torch-tensorrt-ptq" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.ptq"></span><dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CacheCalibrator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CacheCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CacheCalibrator" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
+:param cache_file: path to cache file.
+:param algo_type: choice of calibration algorithm.</p>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CalibrationAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CalibrationAlgo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Enum</span></code></p>
+<p>An enumeration.</p>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION">
+<span class="sig-name descname"><span class="pre">ENTROPY_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.ENTROPY_CALIBRATION:</span> <span class="pre">1&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2">
+<span class="sig-name descname"><span class="pre">ENTROPY_CALIBRATION_2</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.ENTROPY_CALIBRATION_2:</span> <span class="pre">2&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.ENTROPY_CALIBRATION_2" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION">
+<span class="sig-name descname"><span class="pre">LEGACY_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.LEGACY_CALIBRATION:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.LEGACY_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION">
+<span class="sig-name descname"><span class="pre">MINMAX_CALIBRATION</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;CalibrationAlgo.MINMAX_CALIBRATION:</span> <span class="pre">3&gt;</span></em><a class="headerlink" href="#torch_tensorrt.ptq.CalibrationAlgo.MINMAX_CALIBRATION" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.DataLoaderCalibrator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">DataLoaderCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.DataLoaderCalibrator" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<p>Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
+data which is passed during calibration.
+:param dataloader: an instance of pytorch dataloader which iterates through a given dataset.
+:param algo_type: choice of calibration algorithm.
+:param cache_file: path to cache file.
+:param use_cache: flag which enables usage of pre-existing cache.
+:param device: device on which calibration data is copied to.</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_batch">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">_</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_batch" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_batch_size">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_batch_size</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">int</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_batch_size"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_batch_size" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.get_cache_mode_batch">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">get_cache_mode_batch</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#get_cache_mode_batch"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.get_cache_mode_batch" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.read_calibration_cache">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">read_calibration_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bytes</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#read_calibration_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.read_calibration_cache" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.write_calibration_cache">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">write_calibration_cache</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">object</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cache</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bytes</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#write_calibration_cache"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.write_calibration_cache" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="id0">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">DataLoaderCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id0" title="Permalink to this definition">¶</a></dt>
+<dd><p>Constructs a calibrator class in TensorRT and uses pytorch dataloader to load/preproces
+data which is passed during calibration.
+:param dataloader: an instance of pytorch dataloader which iterates through a given dataset.
+:param algo_type: choice of calibration algorithm.
+:param cache_file: path to cache file.
+:param use_cache: flag which enables usage of pre-existing cache.
+:param device: device on which calibration data is copied to.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.DataLoaderCalibrator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#DataLoaderCalibrator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.DataLoaderCalibrator.__init__" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="id1">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CacheCalibrator</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id1" title="Permalink to this definition">¶</a></dt>
+<dd><p>Constructs a calibrator class in TensorRT which directly uses pre-existing cache file for calibration.
+:param cache_file: path to cache file.
+:param algo_type: choice of calibration algorithm.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.ptq.CacheCalibrator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CacheCalibrator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ptq.CacheCalibrator.__init__" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="id2">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ptq.</span></span><span class="sig-name descname"><span class="pre">CalibrationAlgo</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/ptq.html#CalibrationAlgo"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#id2" title="Permalink to this definition">¶</a></dt>
+<dd><p>An enumeration.</p>
+</dd></dl>
+
+</section>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling a Transformer using torch.compile and TensorRT</a></li>
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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>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<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">
+<span id="torch-tensorrt-py"></span><h1>torch_tensorrt<a class="headerlink" href="#torch-tensorrt" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt"></span><section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.set_device">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">set_device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">gpu_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_utils.html#set_device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.set_device" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt._Input.Input"><span class="pre">Input</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="fx.html#torch_tensorrt.fx.InputTensorSpec" title="torch_tensorrt.fx.input_tensor_spec.InputTensorSpec"><span class="pre">InputTensorSpec</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Set</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.dtype</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#torch_tensorrt.dtype" title="torch_tensorrt._C.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Module</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">ScriptModule</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">GraphModule</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Callable</span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">Any</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compile a PyTorch module for NVIDIA GPUs using TensorRT</p>
+<p>Takes a existing PyTorch module and a set of settings to configure the compiler
+and using the path specified in <code class="docutils literal notranslate"><span class="pre">ir</span></code> lower and compile the module to TensorRT
+returning a PyTorch Module back</p>
+<p>Converts specifically the forward method of a Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
+<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Compiled Module, when run it will execute via TensorRT</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.nn.Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.convert_method_to_trt_engine">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'forward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt._Input.Input"><span class="pre">Input</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">torch.Tensor</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ir</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'default'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">Set</span><span class="p"><span class="pre">[</span></span><span class="pre">torch.dtype</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference internal" href="#torch_tensorrt.dtype" title="torch_tensorrt._C.dtype"><span class="pre">dtype</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bytes</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_compile.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
+<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
+<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>Union</em><em>(</em><em>torch.nn.Module</em><em>,</em><em>torch.jit.ScriptModule</em>) – Source module</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>ir</strong> (<em>str</em>) – The requested strategy to compile. (Options: default - Let Torch-TensorRT decide, ts - TorchScript with scripting path)</p></li>
+<li><p><strong>**kwargs</strong> – Additional settings for the specific requested strategy (See submodules for more info)</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><em>bytes</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.get_build_info">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">get_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">str</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_utils.html#get_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.get_build_info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Returns a string containing the build information of torch_tensorrt distribution</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>String containing the build information for torch_tensorrt distribution</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><em>str</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.dump_build_info">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dump_build_info</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_utils.html#dump_build_info"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.dump_build_info" title="Permalink to this definition">¶</a></dt>
+<dd><p>Prints build information about the torch_tensorrt distribution to stdout</p>
+</dd></dl>
+
+</section>
+<section id="classes">
+<h2>Classes<a class="headerlink" href="#classes" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.Input">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Input</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input" title="Permalink to this definition">¶</a></dt>
+<dd><p>Defines an input to a module in terms of expected shape, data type and tensor format.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Variables</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>shape_mode</strong> (<em>torch_tensorrt.Input._ShapeMode</em>) – Is input statically or dynamically shaped</p></li>
+<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>Dict</em>) – <p>Either a single Tuple or a dict of tuples defining the input shape.
+Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form
+<a href="#id1"><span class="problematic" id="id2">``</span></a>{</p>
+<blockquote>
+<div><p>”min_shape”: Tuple,
+“opt_shape”: Tuple,
+“max_shape”: Tuple</p>
+</div></blockquote>
+<p>}``</p>
+</p></li>
+<li><p><strong>dtype</strong> (<em>torch_tensorrt.dpython:type</em>) – The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)</p></li>
+<li><p><strong>format</strong> (<a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">None</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.__init__" title="Permalink to this definition">¶</a></dt>
+<dd><p>__init__ Method for torch_tensorrt.Input</p>
+<p>Input accepts one of a few construction patterns</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Static shape of input tensor</p></li>
+<li><p><strong>min_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Min size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>opt_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Opt size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>max_shape</strong> (<em>Tuple</em><em> or </em><em>List</em><em>, </em><em>optional</em>) – Max size of input tensor’s shape range
+Note: All three of min_shape, opt_shape, max_shape must be provided, there must be no positional arguments, shape must not be defined and implictly this sets Input’s shape_mode to DYNAMIC</p></li>
+<li><p><strong>dtype</strong> (<em>torch.dpython:type</em><em> or </em><em>torch_tensorrt.dpython:type</em>) – Expected data type for input tensor (default: torch_tensorrt.dtype.float32)</p></li>
+<li><p><strong>format</strong> (<em>torch.memory_format</em><em> or </em><a class="reference internal" href="#torch_tensorrt.TensorFormat" title="torch_tensorrt.TensorFormat"><em>TensorFormat</em></a>) – The expected format of the input tensor (default: torch_tensorrt.TensorFormat.NCHW)</p></li>
+<li><p><strong>tensor_domain</strong> (<em>Tuple</em><em>(</em><em>python:float</em><em>, </em><em>python:float</em><em>)</em><em>, </em><em>optional</em>) – The domain of allowed values for the tensor, as interval notation: [tensor_domain[0], tensor_domain[1]).
+Note: Entering “None” (or not specifying) will set the bound to [0, 2)</p></li>
+<li><p><strong>torch_tensor</strong> (<em>torch.Tensor</em>) – Holds a corresponding torch tensor with this Input.</p></li>
+<li><p><strong>name</strong> (<em>str</em><em>, </em><em>optional</em>) – Name of this input in the input nn.Module’s forward function. Used to specify dynamic shapes for the corresponding input in dynamo tracer.</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<ul class="simple">
+<li><p>Input([1,3,32,32], dtype=torch.float32, format=torch.channel_last)</p></li>
+<li><p>Input(shape=(1,3,32,32), dtype=torch_tensorrt.dtype.int32, format=torch_tensorrt.TensorFormat.NCHW)</p></li>
+<li><p>Input(min_shape=(1,3,32,32), opt_shape=[2,3,32,32], max_shape=(3,3,32,32)) #Implicitly dtype=torch_tensorrt.dtype.float32, format=torch_tensorrt.TensorFormat.NCHW</p></li>
+</ul>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.dtype">
+<span class="sig-name descname"><span class="pre">dtype</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">_enums.dtype</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;dtype.unknown:</span> <span class="pre">7&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.dtype" title="Permalink to this definition">¶</a></dt>
+<dd><p>torch_tensorrt.dtype.float32)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>The expected data type of the input tensor (default</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.example_tensor">
+<span class="sig-name descname"><span class="pre">example_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">optimization_profile_field</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">Tensor</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.example_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.example_tensor" title="Permalink to this definition">¶</a></dt>
+<dd><p>Get an example tensor of the shape specified by the Input object</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>optimization_profile_field</strong> (<em>Optional</em><em>(</em><em>str</em><em>)</em>) – Name of the field to use for shape in the case the Input is dynamically shaped</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A PyTorch Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.format">
+<span class="sig-name descname"><span class="pre">format</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">_enums.TensorFormat</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">&lt;TensorFormat.contiguous:</span> <span class="pre">0&gt;</span></em><a class="headerlink" href="#torch_tensorrt.Input.format" title="Permalink to this definition">¶</a></dt>
+<dd><p>torch_tensorrt.TensorFormat.NCHW)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Type</dt>
+<dd class="field-odd"><p>The expected format of the input tensor (default</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.from_tensor">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">t</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_memory_format_check</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt._Input.Input"><span class="pre">Input</span></a></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.from_tensor"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.from_tensor" title="Permalink to this definition">¶</a></dt>
+<dd><p>Produce a Input which contains the information of the given PyTorch tensor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tensor</strong> (<em>torch.Tensor</em>) – A PyTorch tensor.</p></li>
+<li><p><strong>disable_memory_format_check</strong> (<em>bool</em>) – Whether to validate the memory formats of input tensors</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A Input object.</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Input.from_tensors">
+<em class="property"><span class="pre">classmethod</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">from_tensors</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Sequence</span><span class="p"><span class="pre">[</span></span><span class="pre">Tensor</span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_memory_format_check</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">List</span><span class="p"><span class="pre">[</span></span><a class="reference internal" href="#torch_tensorrt.Input" title="torch_tensorrt._Input.Input"><span class="pre">Input</span></a><span class="p"><span class="pre">]</span></span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/_Input.html#Input.from_tensors"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Input.from_tensors" title="Permalink to this definition">¶</a></dt>
+<dd><p>Produce a list of Inputs which contain
+the information of all the given PyTorch tensors.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tensors</strong> (<em>Iterable</em><em>[</em><em>torch.Tensor</em><em>]</em>) – A list of PyTorch tensors.</p></li>
+<li><p><strong>disable_memory_format_check</strong> (<em>bool</em>) – Whether to validate the memory formats of input tensors</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>A list of Inputs.</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.Device">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">Device</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device" title="Permalink to this definition">¶</a></dt>
+<dd><p>Defines a device that can be used to specify target devices for engines</p>
+<dl class="field-list simple">
+<dt class="field-odd">Variables</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>device_type</strong> (<a class="reference internal" href="#torch_tensorrt.DeviceType" title="torch_tensorrt.DeviceType"><em>DeviceType</em></a>) – Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.</p></li>
+<li><p><strong>gpu_id</strong> (<em>python:int</em>) – Device ID for target GPU</p></li>
+<li><p><strong>dla_core</strong> (<em>python:int</em>) – Core ID for target DLA core</p></li>
+<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Whether falling back to GPU if DLA cannot support an op should be allowed</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Any</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch_tensorrt/_Device.html#Device.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.Device.__init__" title="Permalink to this definition">¶</a></dt>
+<dd><p>__init__ Method for torch_tensorrt.Device</p>
+<p>Device accepts one of a few construction patterns</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>spec</strong> (<em>str</em>) – String with device spec e.g. “dla:0” for dla, core_id 0</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>gpu_id</strong> (<em>python:int</em>) – ID of target GPU (will get overrided if dla_core is specified to the GPU managing DLA). If specified, no positional arguments should be provided</p></li>
+<li><p><strong>dla_core</strong> (<em>python:int</em>) – ID of target DLA core. If specified, no positional arguments should be provided.</p></li>
+<li><p><strong>allow_gpu_fallback</strong> (<em>bool</em>) – Allow TensorRT to schedule operations on GPU if they are not supported on DLA (ignored if device type is not DLA)</p></li>
+</ul>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<ul class="simple">
+<li><p>Device(“gpu:1”)</p></li>
+<li><p>Device(“cuda:1”)</p></li>
+<li><p>Device(“dla:0”, allow_gpu_fallback=True)</p></li>
+<li><p>Device(gpu_id=0, dla_core=0, allow_gpu_fallback=True)</p></li>
+<li><p>Device(dla_core=0, allow_gpu_fallback=True)</p></li>
+<li><p>Device(gpu_id=1)</p></li>
+</ul>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.dla_core">
+<span class="sig-name descname"><span class="pre">dla_core</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.dla_core" title="Permalink to this definition">¶</a></dt>
+<dd><p>Core ID for target DLA core</p>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="torch_tensorrt.Device.gpu_id">
+<span class="sig-name descname"><span class="pre">gpu_id</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">-1</span></em><a class="headerlink" href="#torch_tensorrt.Device.gpu_id" title="Permalink to this definition">¶</a></dt>
+<dd><p>Device ID for target GPU</p>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="enums">
+<h2>Enums<a class="headerlink" href="#enums" title="Permalink to this headline">¶</a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.dtype">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">dtype</span></span><a class="headerlink" href="#torch_tensorrt.dtype" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specifiy operating precision for engine execution</p>
+<p>Members:</p>
+<blockquote>
+<div><p>float : 32 bit floating point number</p>
+<p>float32 : 32 bit floating point number</p>
+<p>half : 16 bit floating point number</p>
+<p>float16 : 16 bit floating point number</p>
+<p>int8 : 8 bit integer number</p>
+<p>int32 : 32 bit integer number</p>
+<p>long : 64 bit integer number</p>
+<p>int64 : 64 bit integer number</p>
+<p>double : 64 bit floating point number</p>
+<p>float64 : 64 bit floating point number</p>
+<p>bool : Boolean value</p>
+<p>unknown : Unknown data type</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.DeviceType">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">DeviceType</span></span><a class="headerlink" href="#torch_tensorrt.DeviceType" title="Permalink to this definition">¶</a></dt>
+<dd><p>Device types that TensorRT can execute on</p>
+<p>Members:</p>
+<blockquote>
+<div><p>GPU : GPU device</p>
+<p>DLA : DLA core</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.EngineCapability">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">EngineCapability</span></span><a class="headerlink" href="#torch_tensorrt.EngineCapability" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specify engine capability settings (selections of kernels to meet safety requirements)</p>
+<p>Members:</p>
+<blockquote>
+<div><p>safe_gpu : Use safety GPU kernels only</p>
+<p>safe_dla : Use safety DLA kernels only</p>
+<p>default : Use default behavior</p>
+</div></blockquote>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="torch_tensorrt.TensorFormat">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">torch_tensorrt.</span></span><span class="sig-name descname"><span class="pre">TensorFormat</span></span><a class="headerlink" href="#torch_tensorrt.TensorFormat" title="Permalink to this definition">¶</a></dt>
+<dd><p>Enum to specifiy the memory layout of tensors</p>
+<p>Members:</p>
+<blockquote>
+<div><p>contiguous : Contiguous memory layout (NCHW / Linear)</p>
+<p>channels_last : Channels last memory layout (NHWC)</p>
+</div></blockquote>
+</dd></dl>
+
+</section>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this headline">¶</a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="logging.html">torch_tensorrt.logging</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fx.html">torch_tensorrt.fx</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo.html">torch_tensorrt.dynamo</a></li>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="reference internal" href="torch_tensorrt.html">torch_tensorrt</a></li>
+<li class="toctree-l1"><a class="reference internal" href="logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ptq.html">torch_tensorrt.ptq</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo.html">torch_tensorrt.dynamo</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
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+              
+  <section id="torch-tensorrt-ts">
+<span id="torch-tensorrt-ts-py"></span><h1>torch_tensorrt.ts<a class="headerlink" href="#torch-tensorrt-ts" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.ts"></span><section id="functions">
+<h2>Functions<a class="headerlink" href="#functions" title="Permalink to this headline">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.compile">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">compile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">~torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs:</span> <span class="pre">~typing.Optional[~typing.Sequence[torch_tensorrt._Input.Input</span> <span class="pre">|</span> <span class="pre">torch.Tensor]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature:</span> <span class="pre">~typing.Optional[~typing.Tuple[~typing.Union[~torch_tensorrt._Input.Input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">~torch.Tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">~typing.Sequence[~typing.Any]]]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device:</span> <span class="pre">~torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">Device(type=DeviceType.GPU</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gpu_id=0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions:</span> <span class="pre">~typing.Optional[~typing.Set[torch.dtype</span> <span class="pre">|</span> <span class="pre">torch_tensorrt._C.dtype]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability:</span> <span class="pre">~torch_tensorrt._C.EngineCapability</span> <span class="pre">=</span> <span class="pre">&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator:</span> <span class="pre">~typing.Optional[object]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_full_compilation:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_block_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_ops:</span> <span class="pre">~typing.Optional[~typing.List[str]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">torch_executed_modules:</span> <span class="pre">~typing.Optional[~typing.List[str]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_shape_tensors:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#compile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.compile" title="Permalink to this definition">¶</a></dt>
+<dd><p>Compile a TorchScript module for NVIDIA GPUs using TensorRT</p>
+<p>Takes a existing TorchScript module and a set of settings to configure the compiler
+and will convert methods to JIT Graphs which call equivalent TensorRT engines</p>
+<p>Converts specifically the forward method of a TorchScript Module</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>Union</strong> (<em>input_signature</em>) – <p>A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. <strong>This API should be considered beta-level stable and may change in the future</strong></p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input_signature=([
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
+<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
+<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+<li><p><strong>require_full_compilation</strong> (<em>bool</em>) – Require modules to be compiled end to end or return an error as opposed to returning a hybrid graph where operations that cannot be run in TensorRT are run in PyTorch</p></li>
+<li><p><strong>min_block_size</strong> (<em>python:int</em>) – The minimum number of contiguous TensorRT convertable operations in order to run a set of operations in TensorRT</p></li>
+<li><p><strong>torch_executed_ops</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of aten operators that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
+<li><p><strong>torch_executed_modules</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of modules that must be run in PyTorch. An error will be thrown if this list is not empty but <code class="docutils literal notranslate"><span class="pre">require_full_compilation</span></code> is True</p></li>
+<li><p><strong>allow_shape_tensors</strong> – (Experimental) Allow aten::size to output shape tensors using IShapeLayer in TensorRT</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Compiled TorchScript Module, when run it will execute via TensorRT</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.jit.ScriptModule</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.convert_method_to_trt_engine">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">convert_method_to_trt_engine</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module:</span> <span class="pre">~torch.jit._script.ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'forward'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inputs:</span> <span class="pre">~typing.Optional[~typing.Sequence[torch_tensorrt._Input.Input</span> <span class="pre">|</span> <span class="pre">torch.Tensor]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device:</span> <span class="pre">~torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">Device(type=DeviceType.GPU</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gpu_id=0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions:</span> <span class="pre">~typing.Optional[~typing.Set[torch.dtype</span> <span class="pre">|</span> <span class="pre">torch_tensorrt._C.dtype]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability:</span> <span class="pre">~torch_tensorrt._C.EngineCapability</span> <span class="pre">=</span> <span class="pre">&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator:</span> <span class="pre">~typing.Optional[object]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_shape_tensors:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bytes</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#convert_method_to_trt_engine"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.convert_method_to_trt_engine" title="Permalink to this definition">¶</a></dt>
+<dd><p>Convert a TorchScript module method to a serialized TensorRT engine</p>
+<p>Converts a specified method of a module to a serialized TensorRT engine given a dictionary of conversion settings</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to convert</p></li>
+<li><p><strong>Union</strong> (<em>input_signature</em>) – <p>A formatted collection of input specifications for the module. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum to select device type. <strong>This API should be considered beta-level stable and may change in the future</strong></p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input_signature=([
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+], torch.randn((1, 3, 224, 244))) # Use an example tensor and let torch_tensorrt infer settings for input #3
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>dla_sram_size</strong> (<em>python:int</em>) – Fast software managed RAM used by DLA to communicate within a layer.</p></li>
+<li><p><strong>dla_local_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to share intermediate tensor data across operations</p></li>
+<li><p><strong>dla_global_dram_size</strong> (<em>python:int</em>) – Host RAM used by DLA to store weights and metadata for execution</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+<li><p><strong>allow_shape_tensors</strong> – (Experimental) Allow aten::size to output shape tensors using IShapeLayer in TensorRT</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>Serialized TensorRT engine, can either be saved to a file or deserialized via TensorRT APIs</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p><em>bytes</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.check_method_op_support">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">check_method_op_support</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">module</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">ScriptModule</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method_name</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'forward'</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">bool</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#check_method_op_support"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.check_method_op_support" title="Permalink to this definition">¶</a></dt>
+<dd><p>Checks to see if a method is fully supported by torch_tensorrt</p>
+<p>Checks if a method of a TorchScript module can be compiled by torch_tensorrt, if not, a list of operators
+that are not supported are printed out and the function returns false, else true.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>module</strong> (<em>torch.jit.ScriptModule</em>) – Source module, a result of tracing or scripting a PyTorch
+<code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code></p></li>
+<li><p><strong>method_name</strong> (<em>str</em>) – Name of method to check</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>True if supported Method</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p><em>bool</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.embed_engine_in_new_module">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">embed_engine_in_new_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">serialized_engine</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bytes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_binding_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_binding_names</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">List</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt._Device.Device"><span class="pre">Device</span></a></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">Device(type=DeviceType.GPU,</span> <span class="pre">gpu_id=0)</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">ScriptModule</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compiler.html#embed_engine_in_new_module"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.embed_engine_in_new_module" title="Permalink to this definition">¶</a></dt>
+<dd><p>Takes a pre-built serialized TensorRT engine and embeds it within a TorchScript module</p>
+<p>Takes a pre-built serialied TensorRT engine (as bytes) and embeds it within a TorchScript module.
+Registers the forward method to execute the TensorRT engine with the function signature:</p>
+<blockquote>
+<div><p>forward(Tensor[]) -&gt; Tensor[]</p>
+</div></blockquote>
+<dl>
+<dt>TensorRT bindings either be explicitly specified using <code class="docutils literal notranslate"><span class="pre">[in/out]put_binding_names</span></code> or have names with the following format:</dt><dd><ul class="simple">
+<li><p>[symbol].[index in input / output array]</p></li>
+</ul>
+<p>ex.
+- [x.0, x.1, x.2] -&gt; [y.0]</p>
+</dd>
+</dl>
+<p>Module can be save with engine embedded with torch.jit.save and moved / loaded according to torch_tensorrt portability rules</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>serialized_engine</strong> (<em>bytearray</em>) – Serialized TensorRT engine from either torch_tensorrt or TensorRT APIs</p>
+</dd>
+<dt class="field-even">Keyword Arguments</dt>
+<dd class="field-even"><ul class="simple">
+<li><p><strong>input_binding_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of names of TensorRT bindings in order to be passed to the encompassing PyTorch module</p></li>
+<li><p><strong>output_binding_names</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – List of names of TensorRT bindings in order that should be returned from the encompassing PyTorch module</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – Target device to run engine on. Must be compatible with engine provided. Default: Current active device</p></li>
+</ul>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>New TorchScript module with engine embedded</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>torch.jit.ScriptModule</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="torch_tensorrt.ts.TensorRTCompileSpec">
+<span class="sig-prename descclassname"><span class="pre">torch_tensorrt.ts.</span></span><span class="sig-name descname"><span class="pre">TensorRTCompileSpec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">inputs:</span> <span class="pre">~typing.Optional[~typing.List[torch.Tensor</span> <span class="pre">|</span> <span class="pre">torch_tensorrt._Input.Input]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_signature:</span> <span class="pre">~typing.Optional[~typing.Any]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device:</span> <span class="pre">torch.device</span> <span class="pre">|</span> <span class="pre">torch_tensorrt._Device.Device</span> <span class="pre">=</span> <span class="pre">Device(type=DeviceType.GPU</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gpu_id=0)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disable_tf32:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sparse_weights:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enabled_precisions:</span> <span class="pre">~typing.Optional[~typing.Set[torch.dtype</span> <span class="pre">|</span> <span class="pre">torch_tensorrt._C.dtype]]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">refit:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">debug:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">capability:</span> <span class="pre">~torch_tensorrt._C.EngineCapability</span> <span class="pre">=</span> <span class="pre">&lt;EngineCapability.default:</span> <span class="pre">0&gt;</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_avg_timing_iters:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">workspace_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_sram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1048576</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_local_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1073741824</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dla_global_dram_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">536870912</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">truncate_long_and_double:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrator:</span> <span class="pre">~typing.Optional[object]</span> <span class="pre">=</span> <span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">allow_shape_tensors:</span> <span class="pre">bool</span> <span class="pre">=</span> <span class="pre">False</span></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="pre">&lt;torch.ScriptClass</span> <span class="pre">object</span> <span class="pre">at</span> <span class="pre">0x7fb370651af0&gt;</span></span></span><a class="reference internal" href="../_modules/torch_tensorrt/ts/_compile_spec.html#TensorRTCompileSpec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#torch_tensorrt.ts.TensorRTCompileSpec" title="Permalink to this definition">¶</a></dt>
+<dd><p>Utility to create a formated spec dictionary for using the PyTorch TensorRT backend</p>
+<dl class="field-list simple">
+<dt class="field-odd">Keyword Arguments</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>inputs</strong> (<em>List</em><em>[</em><em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Input" title="torch_tensorrt.Input"><em>Input</em></a><em>, </em><em>torch.Tensor</em><em>)</em><em>]</em>) – <p><strong>Required</strong> List of specifications of input shape, dtype and memory layout for inputs to the module. This argument is required. Input Sizes can be specified as torch sizes, tuples or lists. dtypes can be specified using
+torch datatypes or torch_tensorrt datatypes and you can use either torch devices or the torch_tensorrt device type enum
+to select device type.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>input=[
+    torch_tensorrt.Input((1, 3, 224, 224)), # Static NCHW input shape for input #1
+    torch_tensorrt.Input(
+        min_shape=(1, 224, 224, 3),
+        opt_shape=(1, 512, 512, 3),
+        max_shape=(1, 1024, 1024, 3),
+        dtype=torch.int32
+        format=torch.channel_last
+    ), # Dynamic input shape for input #2
+    torch.randn((1, 3, 224, 244)) # Use an example tensor and let torch_tensorrt infer settings
+]
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>device</strong> (<em>Union</em><em>(</em><a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.Device" title="torch_tensorrt.Device"><em>Device</em></a><em>, </em><em>torch.device</em><em>, </em><em>dict</em><em>)</em>) – <p>Target device for TensorRT engines to run on</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">device</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="p">.</span><span class="n">Device</span><span class="p">(</span><span class="s">&quot;dla:1&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="n">True</span><span class="p">)</span>
+</pre></div>
+</div>
+</p></li>
+<li><p><strong>disable_tf32</strong> (<em>bool</em>) – Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas</p></li>
+<li><p><strong>sparse_weights</strong> (<em>bool</em>) – Enable sparsity for convolution and fully connected layers.</p></li>
+<li><p><strong>enabled_precision</strong> (<em>Set</em><em>(</em><em>Union</em><em>(</em><em>torch.dpython:type</em><em>, </em><em>torch_tensorrt.dpython:type</em><em>)</em><em>)</em>) – The set of datatypes that TensorRT can use when selecting kernels</p></li>
+<li><p><strong>refit</strong> (<em>bool</em>) – Enable refitting</p></li>
+<li><p><strong>debug</strong> (<em>bool</em>) – Enable debuggable engine</p></li>
+<li><p><strong>capability</strong> (<a class="reference internal" href="torch_tensorrt.html#torch_tensorrt.EngineCapability" title="torch_tensorrt.EngineCapability"><em>EngineCapability</em></a>) – Restrict kernel selection to safe gpu kernels or safe dla kernels</p></li>
+<li><p><strong>num_avg_timing_iters</strong> (<em>python:int</em>) – Number of averaging timing iterations used to select kernels</p></li>
+<li><p><strong>workspace_size</strong> (<em>python:int</em>) – Maximum size of workspace given to TensorRT</p></li>
+<li><p><strong>truncate_long_and_double</strong> (<em>bool</em>) – Truncate weights provided in int64 or double (float64) to int32 and float32</p></li>
+<li><p><strong>calibrator</strong> (<em>Union</em><em>(</em><em>torch_tensorrt._C.IInt8Calibrator</em><em>, </em><em>tensorrt.IInt8Calibrator</em><em>)</em>) – Calibrator object which will provide data to the PTQ system for INT8 Calibration</p></li>
+<li><p><strong>allow_shape_tensors</strong> – <p>(Experimental) Allow aten::size to output shape tensors using IShapeLayer in TensorRT</p>
+<dl class="simple">
+<dt>Returns:</dt><dd><p>torch.classes.tensorrt.CompileSpec: List of methods and formated spec objects to be provided to <code class="docutils literal notranslate"><span class="pre">torch._C._jit_to_tensorrt</span></code></p>
+</dd>
+</dl>
+</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+</section>
+
+
+             </article>
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<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>
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+                <div class="version">
+                  v2.2.0
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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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+    <span class="s1">&#39;logo_only&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="s1">&#39;display_version&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s1">&#39;prev_next_buttons_location&#39;</span><span class="p">:</span> <span class="s1">&#39;bottom&#39;</span><span class="p">,</span>
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+    <span class="s1">&#39;vcs_pageview_mode&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
+    <span class="c1"># Toc options</span>
+    <span class="s1">&#39;collapse_navigation&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s1">&#39;sticky_navigation&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s1">&#39;navigation_depth&#39;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
+    <span class="s1">&#39;includehidden&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s1">&#39;titles_only&#39;</span><span class="p">:</span> <span class="kc">False</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>The following options are available:</p>
+<section id="base-options">
+<h4>Base options<a class="headerlink" href="#base-options" title="Permalink to this headline">¶</a></h4>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">canonical_url</span></code> String. This will specify a <a class="reference external" href="https://en.wikipedia.org/wiki/Canonical_link_element">canonical url</a>
+to let search engines know they should give higher ranking to latest version of the docs.
+The url points to the root of the documentation and requires a trailing slash.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">analytics_id</span></code> String. Change the Google Analytics ID that is included on pages.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">display_version</span></code> Bool. With this disabled, the version number isn’t shown at the top of the sidebar.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">prev_next_buttons_location</span></code> String. can take the value <code class="docutils literal notranslate"><span class="pre">bottom</span></code>, <code class="docutils literal notranslate"><span class="pre">top</span></code>, <code class="docutils literal notranslate"><span class="pre">both</span></code> , or <code class="docutils literal notranslate"><span class="pre">None</span></code>
+and will display the “Next” and “Previous” buttons accordingly.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">style_external_links</span></code> Bool. Add an icon next to external links. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">vcs_pageview_mode</span></code> String. Changes how to view files when using <cite>display_github</cite>, <cite>display_gitlab</cite>, etc.
+When using Github or Gitlab this can be: <cite>blob</cite> (default), <cite>edit</cite>, or <cite>raw</cite>,
+on Bitbucket, this can be either: <cite>view</cite> (default) or <cite>edit</cite>.</p></li>
+</ul>
+</section>
+<section id="toc-options">
+<h4>TOC Options<a class="headerlink" href="#toc-options" title="Permalink to this headline">¶</a></h4>
+<p>These effect how we display the Table of Contents in the side bar. You can read more about them here: <a class="reference external" href="http://www.sphinx-doc.org/en/stable/templating.html#toctree">http://www.sphinx-doc.org/en/stable/templating.html#toctree</a></p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">collapse_navigation</span></code> Bool. With this enabled, you will lose the <code class="docutils literal notranslate"><span class="pre">[+]</span></code> drop downs next to each section in the sidebar.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">sticky_navigation</span></code> Bool. This causes the sidebar to scroll with the main page content as you scroll the page.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">navigation_depth</span></code> Int. Indicate the max depth of the tree; by default, 4 levels are included;
+set it to -1 to allow unlimited depth.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">includehidden</span></code> Bool. Specifies if the sidebar includes toctrees marked with the <code class="docutils literal notranslate"><span class="pre">:hidden:</span></code> option</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">titles_only</span></code> Bool. If True, removes headers within a page from the sidebar.</p></li>
+</ul>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Setting <code class="docutils literal notranslate"><span class="pre">collapse_navigation</span></code> to False and using a high <code class="docutils literal notranslate"><span class="pre">navigation_depth</span></code>
+can cause projects with many files and a deep file structure to generate HTML files
+that are significantly larger in file size and much longer compilation times.</p>
+</div>
+</section>
+</section>
+<section id="html-context-options">
+<h3>HTML Context Options<a class="headerlink" href="#html-context-options" title="Permalink to this headline">¶</a></h3>
+<p>TODO.</p>
+</section>
+</section>
+<section id="page-level-configuration">
+<h2>Page-level Configuration<a class="headerlink" href="#page-level-configuration" title="Permalink to this headline">¶</a></h2>
+<p>Pages support metadata that changes how the theme renders.
+You can currently add the following:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">:github_url:</span></code> This will force the “Edit on GitHub” to the configured URL</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">:bitbucket_url:</span></code> This will force the “Edit on Bitbucket” to the configured URL</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">:gitlab_url:</span></code> This will force the “Edit on GitLab” to the configured URL</p></li>
+</ul>
+</section>
+<section id="how-the-table-of-contents-builds">
+<h2>How the Table of Contents builds<a class="headerlink" href="#how-the-table-of-contents-builds" title="Permalink to this headline">¶</a></h2>
+<p>Currently the left menu will build based upon any <code class="docutils literal notranslate"><span class="pre">toctree(s)</span></code> defined in your <code class="docutils literal notranslate"><span class="pre">index.rst</span></code> file.
+It outputs 2 levels of depth, which should give your visitors a high level of access to your
+docs. If no toctrees are set the theme reverts to sphinx’s usual local toctree.</p>
+<p>It’s important to note that if you don’t follow the same styling for your rST headers across
+your documents, the toctree will misbuild, and the resulting menu might not show the correct
+depth when it renders.</p>
+<p>Also note that by default the table of contents is set with <code class="docutils literal notranslate"><span class="pre">includehidden=True</span></code>. This allows you
+to set a hidden toc in your index file with the <a class="reference external" href="http://sphinx-doc.org/markup/toctree.html">:hidden:</a> property that will allow you
+to build a toc without it rendering in your index.</p>
+<p>By default, the navigation will “stick” to the screen as you scroll. However if your toc
+is vertically too large, it will revert to static positioning. To disable the sticky nav
+altogether change the setting in <code class="docutils literal notranslate"><span class="pre">conf.py</span></code>.</p>
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\ No newline at end of file
diff --git a/docs/v2.2.0/src/pytorch-sphinx-theme/docs/demo/api.html b/docs/v2.2.0/src/pytorch-sphinx-theme/docs/demo/api.html
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@@ -0,0 +1,798 @@
+
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+
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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+<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>
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+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="mod-test-py-module">
+<h1><a class="toc-backref" href="#id5"><span class="section-number">5. </span><a href="#id1"><span class="problematic" id="id2">:mod:`test_py_module`</span></a></a><a class="headerlink" href="#mod-test-py-module" title="Permalink to this headline">¶</a></h1>
+<div class="contents topic" id="table-of-contents">
+<p class="topic-title">Table of Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#mod-test-py-module" id="id5">:mod:`test_py_module`</a></p>
+<ul>
+<li><p><a class="reference internal" href="#generated-index" id="id6">Generated Index</a></p></li>
+<li><p><a class="reference internal" href="#optional-parameter-args" id="id7">Optional parameter args</a></p></li>
+<li><p><a class="reference internal" href="#data" id="id8">Data</a></p></li>
+</ul>
+</li>
+</ul>
+</div>
+<section id="generated-index">
+<h2><a class="toc-backref" href="#id6"><span class="section-number">5.1. </span>Generated Index</a><a class="headerlink" href="#generated-index" title="Permalink to this headline">¶</a></h2>
+<p>Part of the sphinx build process in generate and index file: <a class="reference internal" href="../../../../genindex.html"><span class="std std-ref">Index</span></a>.</p>
+</section>
+<section id="optional-parameter-args">
+<h2><a class="toc-backref" href="#id7"><span class="section-number">5.2. </span>Optional parameter args</a><a class="headerlink" href="#optional-parameter-args" title="Permalink to this headline">¶</a></h2>
+<p>At this point optional parameters <a class="reference external" href="https://groups.google.com/forum/#!topic/sphinx-users/_qfsVT5Vxpw">cannot be generated from code</a>.
+However, some projects will manually do it, like so:</p>
+<p>This example comes from <a class="reference external" href="http://django-payments.readthedocs.org/en/latest/modules.html#payments.authorizenet.AuthorizeNetProvide">django-payments module docs</a>.</p>
+<dl class="cpp class">
+<dt class="sig sig-object cpp">
+<span class="sig-name descname"><span class="pre">payments.dotpay.DotpayProvider(seller_id,</span> <span class="pre">pin[,</span> <span class="pre">channel=0[,</span> <span class="pre">lock=False],</span> <span class="pre">lang='pl'])</span></span></dt>
+<dd><p>This backend implements payments using a popular Polish gateway, <a class="reference external" href="http://www.dotpay.pl">Dotpay.pl</a>.</p>
+<p>Due to API limitations there is no support for transferring purchased items.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Param seller_id</dt>
+<dd class="field-odd"><p>Seller ID assigned by Dotpay</p>
+</dd>
+<dt class="field-even">Param pin</dt>
+<dd class="field-even"><p>PIN assigned by Dotpay</p>
+</dd>
+<dt class="field-odd">Param channel</dt>
+<dd class="field-odd"><p>Default payment channel (consult reference guide)</p>
+</dd>
+<dt class="field-even">Param lang</dt>
+<dd class="field-even"><p>UI language</p>
+</dd>
+<dt class="field-odd">Param lock</dt>
+<dd class="field-odd"><p>Whether to disable channels other than the default selected above</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="data">
+<h2><a class="toc-backref" href="#id8"><span class="section-number">5.3. </span>Data</a><a class="headerlink" href="#data" title="Permalink to this headline">¶</a></h2>
+<p>Some data link <a href="#id3"><span class="problematic" id="id4">:data:`Data_item_1`</span></a>.</p>
+</section>
+</section>
+
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+<p class="caption" role="heading"><span class="caption-text">Dynamo Frontend</span></p>
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+<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>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="paragraph-level-markup">
+<h1><a class="toc-backref" href="#id33"><span class="section-number">3. </span>Paragraph Level Markup</a><a class="headerlink" href="#paragraph-level-markup" title="Permalink to this headline">¶</a></h1>
+<div class="contents topic" id="table-of-contents">
+<p class="topic-title">Table of Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#paragraph-level-markup" id="id33">Paragraph Level Markup</a></p>
+<ul>
+<li><p><a class="reference internal" href="#inline-markup" id="id34">Inline Markup</a></p></li>
+<li><p><a class="reference internal" href="#math" id="id35">Math</a></p></li>
+<li><p><a class="reference internal" href="#meta" id="id36">Meta</a></p></li>
+<li><p><a class="reference internal" href="#blocks" id="id37">Blocks</a></p>
+<ul>
+<li><p><a class="reference internal" href="#literal-blocks" id="id38">Literal Blocks</a></p></li>
+<li><p><a class="reference internal" href="#line-blocks" id="id39">Line Blocks</a></p></li>
+<li><p><a class="reference internal" href="#block-quotes" id="id40">Block Quotes</a></p></li>
+<li><p><a class="reference internal" href="#doctest-blocks" id="id41">Doctest Blocks</a></p></li>
+<li><p><a class="reference internal" href="#code-blocks" id="id42">Code Blocks</a></p>
+<ul>
+<li><p><a class="reference internal" href="#emphasized-lines-with-line-numbers" id="id43">Emphasized lines with line numbers</a></p></li>
+</ul>
+</li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#sidebar" id="id44">Sidebar</a></p>
+<ul>
+<li><p><a class="reference internal" href="#code-with-sidebar" id="id45">Code with Sidebar</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#references" id="id46">References</a></p>
+<ul>
+<li><p><a class="reference internal" href="#footnotes" id="id47">Footnotes</a></p></li>
+<li><p><a class="reference internal" href="#citations" id="id48">Citations</a></p></li>
+<li><p><a class="reference internal" href="#glossary" id="id49">Glossary</a></p></li>
+<li><p><a class="reference internal" href="#targets" id="id50">Targets</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#directives" id="id51">Directives</a></p>
+<ul>
+<li><p><a class="reference internal" href="#contents" id="id52">Contents</a></p></li>
+<li><p><a class="reference internal" href="#centered-text" id="id53">Centered text</a></p></li>
+<li><p><a class="reference internal" href="#images-figures" id="id54">Images &amp; Figures</a></p>
+<ul>
+<li><p><a class="reference internal" href="#images" id="id55">Images</a></p></li>
+<li><p><a class="reference internal" href="#figures" id="id56">Figures</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#admonitions" id="id57">Admonitions</a></p></li>
+<li><p><a class="reference internal" href="#topics-sidebars-and-rubrics" id="id58">Topics, Sidebars, and Rubrics</a></p></li>
+<li><p><a class="reference internal" href="#target-footnotes" id="id59">Target Footnotes</a></p></li>
+<li><p><a class="reference internal" href="#replacement-text" id="id60">Replacement Text</a></p></li>
+<li><p><a class="reference internal" href="#compound-paragraph" id="id61">Compound Paragraph</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#download-links" id="id62">Download Links</a></p></li>
+</ul>
+</li>
+</ul>
+</div>
+<section id="inline-markup">
+<h2><a class="toc-backref" href="#id34"><span class="section-number">3.1. </span>Inline Markup</a><a class="headerlink" href="#inline-markup" title="Permalink to this headline">¶</a></h2>
+<p>Paragraphs contain text and may contain inline markup: <em>emphasis</em>, <strong>strong emphasis</strong>, <code class="docutils literal notranslate"><span class="pre">inline</span> <span class="pre">literals</span></code>,
+standalone hyperlinks (<a class="reference external" href="http://www.python.org">http://www.python.org</a>), external hyperlinks (<a class="reference external" href="http://www.python.org/">Python</a> <a class="footnote-reference brackets" href="#id29" id="id30">5</a>), internal cross-references (<a class="reference internal" href="#example">example</a>),
+external hyperlinks with embedded URIs (<a class="reference external" href="http://www.python.org">Python web site</a>), footnote references
+(manually numbered <a class="footnote-reference brackets" href="#id6" id="id1">1</a>, anonymous auto-numbered <a class="footnote-reference brackets" href="#id9" id="id2">3</a>, labeled auto-numbered <a class="footnote-reference brackets" href="#label" id="id3">2</a>, or symbolic <a class="footnote-reference brackets" href="#id10" id="id4">*</a>),
+citation references (<a class="footnote-reference brackets" href="#id16" id="id5">12</a>), substitution references (<a class="reference internal" href="../../../../_images/yi_jing_01_chien.jpg"><img alt="EXAMPLE" src="../../../../_images/yi_jing_01_chien.jpg" style="width: 1em;" /></a>), and <span class="target" id="inline-hyperlink-targets">inline hyperlink targets</span>
+(see <a class="reference internal" href="#targets">Targets</a> below for a reference back to here). Character-level inline markup is also possible
+(although exceedingly ugly!) in <em>re</em><code class="docutils literal notranslate"><span class="pre">Structured</span></code><em>Text</em>. Problems are indicated by <a href="#id27"><span class="problematic" id="id28">|problematic|</span></a>
+text (generated by processing errors; this one is intentional).</p>
+<p>Also with <code class="docutils literal notranslate"><span class="pre">sphinx.ext.autodoc</span></code>, which I use in the demo, I can link to <code class="xref cpp cpp-class docutils literal notranslate"><span class="pre">test_py_module.test.Foo</span></code>.
+It will link you right my code documentation for it.</p>
+<p>The default role for interpreted text is <cite>Title Reference</cite>.  Here are some explicit interpreted text roles:
+a PEP reference (<span class="target" id="index-0"></span><a class="pep reference external" href="https://peps.python.org/pep-0287/"><strong>PEP 287</strong></a>); an RFC reference (<span class="target" id="index-1"></span><a class="rfc reference external" href="https://datatracker.ietf.org/doc/html/rfc2822.html"><strong>RFC 2822</strong></a>); a <sub>subscript</sub>; a <sup>superscript</sup>;
+and explicit roles for <em>standard</em> <strong>inline</strong> <code class="docutils literal notranslate"><span class="pre">markup</span></code>.</p>
+<p>GUI labels are a useful way to indicate that <span class="guilabel">Some action</span> is to be taken by the user.
+The GUI label should not run over <code class="docutils literal notranslate"><span class="pre">line-height</span></code> so as not to <span class="guilabel">interfere</span> with text from adjacent lines.</p>
+<p>Key-bindings indicate that the read is to press a button on the keyboard or mouse,
+for example <kbd class="kbd docutils literal notranslate">MMB</kbd> and <kbd class="kbd compound docutils literal notranslate"><kbd class="kbd docutils literal notranslate">Shift</kbd>-<kbd class="kbd docutils literal notranslate">MMB</kbd></kbd>. Another useful markup to indicate a user action
+is to use <code class="docutils literal notranslate"><span class="pre">menuselection</span></code> this can be used to show short and long menus in software.
+For example, and <code class="docutils literal notranslate"><span class="pre">menuselection</span></code> can be seen here that breaks is too long to fit on this line.
+<span class="menuselection">My ‣ Software ‣ Some menu ‣ Some sub menu 1 ‣ sub menu 2</span>.</p>
+<p>Let’s test wrapping and whitespace significance in inline literals:
+<code class="docutils literal notranslate"><span class="pre">This</span> <span class="pre">is</span> <span class="pre">an</span> <span class="pre">example</span> <span class="pre">of</span> <span class="pre">--inline-literal</span> <span class="pre">--text,</span> <span class="pre">--including</span> <span class="pre">some--</span>
+<span class="pre">strangely--hyphenated-words.</span>&#160; <span class="pre">Adjust-the-width-of-your-browser-window</span>
+<span class="pre">to</span> <span class="pre">see</span> <span class="pre">how</span> <span class="pre">the</span> <span class="pre">text</span> <span class="pre">is</span> <span class="pre">wrapped.</span>&#160; <span class="pre">--</span> <span class="pre">----</span> <span class="pre">--------</span>&#160; <span class="pre">Now</span> <span class="pre">note</span>&#160;&#160;&#160; <span class="pre">the</span>
+<span class="pre">spacing</span>&#160;&#160;&#160; <span class="pre">between</span> <span class="pre">the</span>&#160;&#160;&#160; <span class="pre">words</span> <span class="pre">of</span>&#160;&#160;&#160; <span class="pre">this</span> <span class="pre">sentence</span>&#160;&#160;&#160; <span class="pre">(words</span>
+<span class="pre">should</span>&#160;&#160;&#160; <span class="pre">be</span> <span class="pre">grouped</span>&#160;&#160;&#160; <span class="pre">in</span> <span class="pre">pairs).</span></code></p>
+<p>If the <code class="docutils literal notranslate"><span class="pre">--pep-references</span></code> option was supplied, there should be a live link to PEP 258 here.</p>
+</section>
+<section id="math">
+<h2><a class="toc-backref" href="#id35"><span class="section-number">3.2. </span>Math</a><a class="headerlink" href="#math" title="Permalink to this headline">¶</a></h2>
+<p>This is a test. Here is an equation:
+<span class="math notranslate nohighlight">\(X_{0:5} = (X_0, X_1, X_2, X_3, X_4)\)</span>.
+Here is another:</p>
+<div class="math notranslate nohighlight" id="equation-this-is-a-label">
+<span class="eqno">(1)<a class="headerlink" href="#equation-this-is-a-label" title="Permalink to this equation">¶</a></span>\[\nabla^2 f =
+\frac{1}{r^2} \frac{\partial}{\partial r}
+\left( r^2 \frac{\partial f}{\partial r} \right) +
+\frac{1}{r^2 \sin \theta} \frac{\partial f}{\partial \theta}
+\left( \sin \theta \, \frac{\partial f}{\partial \theta} \right) +
+\frac{1}{r^2 \sin^2\theta} \frac{\partial^2 f}{\partial \phi^2}\]</div>
+<p>You can add a link to equations like the one above <a class="reference internal" href="#equation-this-is-a-label">(1)</a> by using <code class="docutils literal notranslate"><span class="pre">:eq:</span></code>.</p>
+</section>
+<section id="meta">
+<h2><a class="toc-backref" href="#id36"><span class="section-number">3.3. </span>Meta</a><a class="headerlink" href="#meta" title="Permalink to this headline">¶</a></h2>
+</section>
+<section id="blocks">
+<h2><a class="toc-backref" href="#id37"><span class="section-number">3.4. </span>Blocks</a><a class="headerlink" href="#blocks" title="Permalink to this headline">¶</a></h2>
+<section id="literal-blocks">
+<h3><a class="toc-backref" href="#id38"><span class="section-number">3.4.1. </span>Literal Blocks</a><a class="headerlink" href="#literal-blocks" title="Permalink to this headline">¶</a></h3>
+<p>Literal blocks are indicated with a double-colon (“::”) at the end of
+the preceding paragraph (over there <code class="docutils literal notranslate"><span class="pre">--&gt;</span></code>).  They can be indented:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>if literal_block:
+    text = &#39;is left as-is&#39;
+    spaces_and_linebreaks = &#39;are preserved&#39;
+    markup_processing = None
+</pre></div>
+</div>
+<p>Or they can be quoted without indentation:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>&gt;&gt; Great idea!
+&gt;
+&gt; Why didn&#39;t I think of that?
+</pre></div>
+</div>
+</section>
+<section id="line-blocks">
+<h3><a class="toc-backref" href="#id39"><span class="section-number">3.4.2. </span>Line Blocks</a><a class="headerlink" href="#line-blocks" title="Permalink to this headline">¶</a></h3>
+<div class="line-block">
+<div class="line">This is a line block.  It ends with a blank line.</div>
+<div class="line-block">
+<div class="line">Each new line begins with a vertical bar (“|”).</div>
+<div class="line">Line breaks and initial indents are preserved.</div>
+</div>
+<div class="line">Continuation lines are wrapped portions of long lines;
+they begin with a space in place of the vertical bar.</div>
+<div class="line-block">
+<div class="line">The left edge of a continuation line need not be aligned with
+the left edge of the text above it.</div>
+</div>
+</div>
+<div class="line-block">
+<div class="line">This is a second line block.</div>
+<div class="line"><br /></div>
+<div class="line">Blank lines are permitted internally, but they must begin with a “|”.</div>
+</div>
+<p>Take it away, Eric the Orchestra Leader!</p>
+<blockquote>
+<div><div class="line-block">
+<div class="line">A one, two, a one two three four</div>
+<div class="line"><br /></div>
+<div class="line">Half a bee, philosophically,</div>
+<div class="line-block">
+<div class="line">must, <em>ipso facto</em>, half not be.</div>
+</div>
+<div class="line">But half the bee has got to be,</div>
+<div class="line-block">
+<div class="line"><em>vis a vis</em> its entity.  D’you see?</div>
+<div class="line"><br /></div>
+</div>
+<div class="line">But can a bee be said to be</div>
+<div class="line-block">
+<div class="line">or not to be an entire bee,</div>
+<div class="line-block">
+<div class="line">when half the bee is not a bee,</div>
+<div class="line-block">
+<div class="line">due to some ancient injury?</div>
+<div class="line"><br /></div>
+</div>
+</div>
+</div>
+<div class="line">Singing…</div>
+</div>
+</div></blockquote>
+</section>
+<section id="block-quotes">
+<h3><a class="toc-backref" href="#id40"><span class="section-number">3.4.3. </span>Block Quotes</a><a class="headerlink" href="#block-quotes" title="Permalink to this headline">¶</a></h3>
+<p>Block quotes consist of indented body elements:</p>
+<blockquote>
+<div><p>My theory by A. Elk.  Brackets Miss, brackets.  This theory goes
+as follows and begins now.  All brontosauruses are thin at one
+end, much much thicker in the middle and then thin again at the
+far end.  That is my theory, it is mine, and belongs to me and I
+own it, and what it is too.</p>
+<p class="attribution">—Anne Elk (Miss)</p>
+</div></blockquote>
+</section>
+<section id="doctest-blocks">
+<h3><a class="toc-backref" href="#id41"><span class="section-number">3.4.4. </span>Doctest Blocks</a><a class="headerlink" href="#doctest-blocks" title="Permalink to this headline">¶</a></h3>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s1">&#39;Python-specific usage examples; begun with &quot;&gt;&gt;&gt;&quot;&#39;</span>
+<span class="go">Python-specific usage examples; begun with &quot;&gt;&gt;&gt;&quot;</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="nb">print</span> <span class="s1">&#39;(cut and pasted from interactive Python sessions)&#39;</span>
+<span class="go">(cut and pasted from interactive Python sessions)</span>
+</pre></div>
+</div>
+</section>
+<section id="code-blocks">
+<h3><a class="toc-backref" href="#id42"><span class="section-number">3.4.5. </span>Code Blocks</a><a class="headerlink" href="#code-blocks" title="Permalink to this headline">¶</a></h3>
+<pre class="literal-block"># parsed-literal test
+curl -O <a class="reference external" href="http://someurl/release">http://someurl/release</a>-v2.2.0.tar-gz</pre>
+<div class="literal-block-wrapper docutils container" id="id23">
+<div class="code-block-caption"><span class="caption-text">Code Blocks can have captions.</span><a class="headerlink" href="#id23" title="Permalink to this code">¶</a></div>
+<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
+<span class="nt">&quot;windows&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
+<span class="w">    </span><span class="p">{</span>
+<span class="w">    </span><span class="nt">&quot;panes&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
+<span class="w">        </span><span class="p">{</span>
+<span class="w">        </span><span class="nt">&quot;shell_command&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
+<span class="w">            </span><span class="s2">&quot;echo &#39;did you know&#39;&quot;</span><span class="p">,</span>
+<span class="w">            </span><span class="s2">&quot;echo &#39;you can inline&#39;&quot;</span>
+<span class="w">        </span><span class="p">]</span>
+<span class="w">        </span><span class="p">},</span>
+<span class="w">        </span><span class="p">{</span>
+<span class="w">        </span><span class="nt">&quot;shell_command&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;echo &#39;single commands&#39;&quot;</span>
+<span class="w">        </span><span class="p">},</span>
+<span class="w">        </span><span class="s2">&quot;echo &#39;for panes&#39;&quot;</span>
+<span class="w">    </span><span class="p">],</span>
+<span class="w">    </span><span class="nt">&quot;window_name&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;long form&quot;</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="p">],</span>
+<span class="nt">&quot;session_name&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;shorthands&quot;</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<section id="emphasized-lines-with-line-numbers">
+<h4><a class="toc-backref" href="#id43"><span class="section-number">3.4.5.1. </span>Emphasized lines with line numbers</a><a class="headerlink" href="#emphasized-lines-with-line-numbers" title="Permalink to this headline">¶</a></h4>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos">1</span><span class="k">def</span> <span class="nf">some_function</span><span class="p">():</span>
+<span class="linenos">2</span>    <span class="n">interesting</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="hll"><span class="linenos">3</span>    <span class="nb">print</span> <span class="s1">&#39;This line is highlighted.&#39;</span>
+</span><span class="linenos">4</span>    <span class="nb">print</span> <span class="s1">&#39;This one is not...&#39;</span>
+<span class="hll"><span class="linenos">5</span>    <span class="nb">print</span> <span class="s1">&#39;...but this one is.&#39;</span>
+</span></pre></div>
+</div>
+</section>
+</section>
+</section>
+<section id="sidebar">
+<h2><a class="toc-backref" href="#id44"><span class="section-number">3.5. </span>Sidebar</a><a class="headerlink" href="#sidebar" title="Permalink to this headline">¶</a></h2>
+<aside class="sidebar">
+<p class="sidebar-title">Ch’ien / The Creative</p>
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<p><em>Above</em> CH’IEN THE CREATIVE, HEAVEN</p>
+<p><em>Below</em> CH’IEN THE CREATIVE, HEAVEN</p>
+</aside>
+<p>The first hexagram is made up of six unbroken lines. These unbroken lines stand for the primal power,
+which is light-giving, active, strong, and of the spirit. The hexagram is consistently strong in character,
+and since it is without weakness, its essence is power or energy. Its image is heaven.
+Its energy is represented as unrestricted by any fixed conditions in space and is therefore conceived of as motion.
+Time is regarded as the basis of this motion.
+Thus the hexagram includes also the power of time and the power of persisting in time, that is, duration.</p>
+<p>The power represented by the hexagram is to be interpreted in a dual sense in terms of its action
+on the universe and of its action on the world of men. In relation to the universe, the hexagram expresses the strong,
+creative action of the Deity. In relation to the human world, it denotes the creative action of the holy man or sage,
+of the ruler or leader of men, who through his power awakens and develops their higher nature.</p>
+<section id="code-with-sidebar">
+<h3><a class="toc-backref" href="#id45"><span class="section-number">3.5.1. </span>Code with Sidebar</a><a class="headerlink" href="#code-with-sidebar" title="Permalink to this headline">¶</a></h3>
+<aside class="sidebar">
+<p class="sidebar-title">A code example</p>
+<p>With a sidebar on the right.</p>
+</aside>
+<div class="literal-block-wrapper docutils container" id="id24">
+<div class="code-block-caption"><span class="caption-text">Literal includes can also have captions.</span><a class="headerlink" href="#id24" title="Permalink to this code">¶</a></div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="linenos"> 2</span><span class="sd">&quot;&quot;&quot;Test Module for sphinx_rtd_theme.&quot;&quot;&quot;</span>
+<span class="linenos"> 3</span>
+<span class="linenos"> 4</span>
+<span class="linenos"> 5</span><span class="k">class</span> <span class="nc">Foo</span><span class="p">:</span>
+<span class="linenos"> 6</span>
+<span class="linenos"> 7</span><span class="w">    </span><span class="sd">&quot;&quot;&quot;Docstring for class Foo.</span>
+<span class="linenos"> 8</span>
+<span class="linenos"> 9</span><span class="sd">    This text tests for the formatting of docstrings generated from output</span>
+<span class="linenos">10</span><span class="sd">    ``sphinx.ext.autodoc``. Which contain reST, but sphinx nests it in the</span>
+<span class="linenos">11</span><span class="sd">    ``&lt;dl&gt;``, and ``&lt;dt&gt;`` tags. Also, ``&lt;tt&gt;`` is used for class, method names</span>
+<span class="linenos">12</span><span class="sd">    and etc, but those will *always* have the ``.descname`` or</span>
+<span class="linenos">13</span><span class="sd">    ``.descclassname`` class.</span>
+<span class="linenos">14</span>
+<span class="linenos">15</span><span class="sd">    Normal ``&lt;tt&gt;`` (like the &lt;tt&gt; I just wrote here) needs to be shown with</span>
+<span class="linenos">16</span><span class="sd">    the same style as anything else with ````this type of markup````.</span>
+<span class="linenos">17</span>
+<span class="linenos">18</span><span class="sd">    It&#39;s common for programmers to give a code example inside of their</span>
+<span class="linenos">19</span><span class="sd">    docstring::</span>
+<span class="linenos">20</span>
+<span class="linenos">21</span><span class="sd">        from test_py_module import Foo</span>
+<span class="linenos">22</span>
+<span class="linenos">23</span><span class="sd">        myclass = Foo()</span>
+<span class="linenos">24</span><span class="sd">        myclass.dothismethod(&#39;with this argument&#39;)</span>
+<span class="linenos">25</span><span class="sd">        myclass.flush()</span>
+<span class="linenos">26</span>
+<span class="linenos">27</span><span class="sd">        print(myclass)</span>
+<span class="linenos">28</span>
+<span class="linenos">29</span>
+<span class="linenos">30</span><span class="sd">    Here is a link to :py:meth:`capitalize`.</span>
+<span class="linenos">31</span><span class="sd">    Here is a link to :py:meth:`__init__`.</span>
+<span class="linenos">32</span>
+<span class="linenos">33</span><span class="sd">    &quot;&quot;&quot;</span>
+<span class="linenos">34</span>
+<span class="linenos">35</span>    <span class="c1">#: Doc comment for class attribute Foo.bar.</span>
+<span class="linenos">36</span>    <span class="c1">#: It can have multiple lines.</span>
+<span class="linenos">37</span>    <span class="n">bar</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="linenos">38</span>
+<span class="linenos">39</span>    <span class="n">flox</span> <span class="o">=</span> <span class="mf">1.5</span>   <span class="c1">#: Doc comment for Foo.flox. One line only.</span>
+<span class="linenos">40</span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+<section id="references">
+<h2><a class="toc-backref" href="#id46"><span class="section-number">3.6. </span>References</a><a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
+<section id="footnotes">
+<h3><a class="toc-backref" href="#id47"><span class="section-number">3.6.1. </span>Footnotes</a><a class="headerlink" href="#footnotes" title="Permalink to this headline">¶</a></h3>
+<dl class="footnote brackets">
+<dt class="label" id="id6"><span class="brackets">1</span><span class="fn-backref">(<a href="#id1">1</a>,<a href="#id7">2</a>)</span></dt>
+<dd><p>A footnote contains body elements, consistently indented by at
+least 3 spaces.</p>
+<p>This is the footnote’s second paragraph.</p>
+</dd>
+<dt class="label" id="label"><span class="brackets">2</span><span class="fn-backref">(<a href="#id3">1</a>,<a href="#id8">2</a>)</span></dt>
+<dd><p>Footnotes may be numbered, either manually (as in <a class="footnote-reference brackets" href="#id6" id="id7">1</a>) or
+automatically using a “#”-prefixed label.  This footnote has a
+label so it can be referred to from multiple places, both as a
+footnote reference (<a class="footnote-reference brackets" href="#label" id="id8">2</a>) and as a hyperlink reference
+(<a class="reference internal" href="#label">label</a>).</p>
+</dd>
+<dt class="label" id="id9"><span class="brackets"><a class="fn-backref" href="#id2">3</a></span></dt>
+<dd><p>This footnote is numbered automatically and anonymously using a
+label of “#” only.</p>
+</dd>
+<dt class="label" id="id10"><span class="brackets"><a class="fn-backref" href="#id4">*</a></span></dt>
+<dd><p>Footnotes may also use symbols, specified with a “*” label.
+Here’s a reference to the next footnote: <a class="footnote-reference brackets" href="#id12" id="id11">†</a>.</p>
+</dd>
+<dt class="label" id="id12"><span class="brackets"><a class="fn-backref" href="#id11">†</a></span></dt>
+<dd><p>This footnote shows the next symbol in the sequence.</p>
+</dd>
+<dt class="label" id="id13"><span class="brackets">4</span></dt>
+<dd><p>Here’s an unreferenced footnote, with a reference to a
+nonexistent footnote: <a href="#id63"><span class="problematic" id="id14">[5]_</span></a>.</p>
+</dd>
+</dl>
+</section>
+<section id="citations">
+<h3><a class="toc-backref" href="#id48"><span class="section-number">3.6.2. </span>Citations</a><a class="headerlink" href="#citations" title="Permalink to this headline">¶</a></h3>
+<dl class="footnote brackets">
+<dt class="label" id="id15"><span class="brackets">11</span></dt>
+<dd><p>This is the citation I made, let’s make this extremely long so that we can tell that it doesn’t follow the normal responsive table stuff.</p>
+</dd>
+<dt class="label" id="id16"><span class="brackets">12</span><span class="fn-backref">(<a href="#id5">1</a>,<a href="#id19">2</a>)</span></dt>
+<dd><p>This citation has some <code class="docutils literal notranslate"><span class="pre">code</span> <span class="pre">blocks</span></code> in it, maybe some <strong>bold</strong> and
+<em>italics</em> too. Heck, lets put a link to a meta citation <a class="footnote-reference brackets" href="#id18" id="id17">13</a> too.</p>
+</dd>
+<dt class="label" id="id18"><span class="brackets"><a class="fn-backref" href="#id17">13</a></span></dt>
+<dd><p>This citation will have two backlinks.</p>
+</dd>
+</dl>
+<p>Here’s a reference to the above, <a class="footnote-reference brackets" href="#id16" id="id19">12</a>, and a <span id="id20">[nonexistent]</span> citation.</p>
+<p>Here is another type of citation: <cite>citation</cite></p>
+</section>
+<section id="glossary">
+<h3><a class="toc-backref" href="#id49"><span class="section-number">3.6.3. </span>Glossary</a><a class="headerlink" href="#glossary" title="Permalink to this headline">¶</a></h3>
+<p>This is a glossary with definition terms for thing like <a class="reference internal" href="#term-Writing"><span class="xref std std-term">Writing</span></a>:</p>
+<dl class="glossary simple">
+<dt id="term-Documentation">Documentation<a class="headerlink" href="#term-Documentation" title="Permalink to this term">¶</a></dt><dd><p>Provides users with the knowledge they need to use something.</p>
+</dd>
+<dt id="term-Reading">Reading<a class="headerlink" href="#term-Reading" title="Permalink to this term">¶</a></dt><dd><p>The process of taking information into ones mind through the use of eyes.</p>
+</dd>
+<dt id="term-Writing">Writing<a class="headerlink" href="#term-Writing" title="Permalink to this term">¶</a></dt><dd><p>The process of putting thoughts into a medium for other people to <a class="reference internal" href="#term-Reading"><span class="xref std std-term">read</span></a>.</p>
+</dd>
+</dl>
+</section>
+<section id="targets">
+<h3><a class="toc-backref" href="#id50"><span class="section-number">3.6.4. </span>Targets</a><a class="headerlink" href="#targets" title="Permalink to this headline">¶</a></h3>
+<p id="example">This paragraph is pointed to by the explicit “example” target.
+A reference can be found under <a class="reference internal" href="#inline-markup">Inline Markup</a>, above. <a class="reference internal" href="#inline-hyperlink-targets">Inline
+hyperlink targets</a> are also possible.</p>
+<p>Section headers are implicit targets, referred to by name. See
+<a class="reference internal" href="#targets">Targets</a>, which is a subsection of <a href="#id64"><span class="problematic" id="id65">`Body Elements`_</span></a>.</p>
+<p>Explicit external targets are interpolated into references such as “<a class="reference external" href="http://www.python.org/">Python</a> <a class="footnote-reference brackets" href="#id29" id="id31">5</a>”.</p>
+<p>Targets may be indirect and anonymous.  Thus <a class="reference internal" href="#targets">this phrase</a> may also
+refer to the <a class="reference internal" href="#targets">Targets</a> section.</p>
+<p>Here’s a <a href="#id66"><span class="problematic" id="id67">`hyperlink reference without a target`_</span></a>, which generates an error.</p>
+</section>
+</section>
+<section id="directives">
+<h2><a class="toc-backref" href="#id51"><span class="section-number">3.7. </span>Directives</a><a class="headerlink" href="#directives" title="Permalink to this headline">¶</a></h2>
+<section id="contents">
+<h3><a class="toc-backref" href="#id52"><span class="section-number">3.7.1. </span>Contents</a><a class="headerlink" href="#contents" title="Permalink to this headline">¶</a></h3>
+<p>These are just a sample of the many reStructuredText Directives. For others, please see:
+<a class="reference external" href="http://docutils.sourceforge.net/docs/ref/rst/directives.html">http://docutils.sourceforge.net/docs/ref/rst/directives.html</a>.</p>
+</section>
+<section id="centered-text">
+<h3><a class="toc-backref" href="#id53"><span class="section-number">3.7.2. </span>Centered text</a><a class="headerlink" href="#centered-text" title="Permalink to this headline">¶</a></h3>
+<p>You can create a statement with centered text with <code class="docutils literal notranslate"><span class="pre">..</span> <span class="pre">centered::</span></code></p>
+<p class="centered">
+<strong>This is centered text!</strong></p></section>
+<section id="images-figures">
+<h3><a class="toc-backref" href="#id54"><span class="section-number">3.7.3. </span>Images &amp; Figures</a><a class="headerlink" href="#images-figures" title="Permalink to this headline">¶</a></h3>
+<section id="images">
+<h4><a class="toc-backref" href="#id55"><span class="section-number">3.7.3.1. </span>Images</a><a class="headerlink" href="#images" title="Permalink to this headline">¶</a></h4>
+<p>An image directive (also clickable – a hyperlink reference):</p>
+<a class="reference internal image-reference" href="#directives"><img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" /></a>
+</section>
+<section id="figures">
+<h4><a class="toc-backref" href="#id56"><span class="section-number">3.7.3.2. </span>Figures</a><a class="headerlink" href="#figures" title="Permalink to this headline">¶</a></h4>
+<figure class="align-default" id="id25">
+<img alt="reStructuredText, the markup syntax" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">A figure is an image with a caption and/or a legend:</span><a class="headerlink" href="#id25" title="Permalink to this image">¶</a></p>
+<div class="legend">
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 20%" />
+<col style="width: 80%" />
+</colgroup>
+<tbody>
+<tr class="row-odd"><td><p>re</p></td>
+<td><p>Revised, revisited, based on ‘re’ module.</p></td>
+</tr>
+<tr class="row-even"><td><p>Structured</p></td>
+<td><p>Structure-enhanced text, structuredtext.</p></td>
+</tr>
+<tr class="row-odd"><td><p>Text</p></td>
+<td><p>Well it is, isn’t it?</p></td>
+</tr>
+</tbody>
+</table>
+<p>This paragraph is also part of the legend.</p>
+</div>
+</figcaption>
+</figure>
+<p>A figure directive with center alignment</p>
+<figure class="align-center" id="id26">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This caption should be centered.</span><a class="headerlink" href="#id26" title="Permalink to this image">¶</a></p>
+</figcaption>
+</figure>
+</section>
+</section>
+<section id="admonitions">
+<h3><a class="toc-backref" href="#id57"><span class="section-number">3.7.4. </span>Admonitions</a><a class="headerlink" href="#admonitions" title="Permalink to this headline">¶</a></h3>
+<div class="admonition attention">
+<p class="admonition-title">Attention</p>
+<p>Directives at large.</p>
+</div>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>Don’t take any wooden nickels.</p>
+</div>
+<div class="admonition danger">
+<p class="admonition-title">Danger</p>
+<p>Mad scientist at work!</p>
+</div>
+<div class="admonition error">
+<p class="admonition-title">Error</p>
+<p>Does not compute.</p>
+</div>
+<div class="admonition hint">
+<p class="admonition-title">Hint</p>
+<p>It’s bigger than a bread box.</p>
+</div>
+<div class="admonition important">
+<p class="admonition-title">Important</p>
+<ul class="simple">
+<li><p>Wash behind your ears.</p></li>
+<li><p>Clean up your room.</p>
+<ul>
+<li><p>Including the closet.</p></li>
+<li><p>The bathroom too.</p>
+<ul>
+<li><p>Take the trash out of the bathroom.</p></li>
+<li><p>Clean the sink.</p></li>
+</ul>
+</li>
+</ul>
+</li>
+<li><p>Call your mother.</p></li>
+<li><p>Back up your data.</p></li>
+</ul>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This is a note.
+Equations within a note:
+<span class="math notranslate nohighlight">\(G_{\mu\nu} = 8 \pi G (T_{\mu\nu}  + \rho_\Lambda g_{\mu\nu})\)</span>.</p>
+</div>
+<div class="admonition tip">
+<p class="admonition-title">Tip</p>
+<p>15% if the service is good.</p>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 100%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Example</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>Thing1</p></td>
+</tr>
+<tr class="row-odd"><td><p>Thing2</p></td>
+</tr>
+<tr class="row-even"><td><p>Thing3</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Strong prose may provoke extreme mental exertion.
+Reader discretion is strongly advised.</p>
+</div>
+<div class="admonition-and-by-the-way admonition">
+<p class="admonition-title">And, by the way…</p>
+<p>You can make up your own admonition too.</p>
+</div>
+</section>
+<section id="topics-sidebars-and-rubrics">
+<h3><a class="toc-backref" href="#id58"><span class="section-number">3.7.5. </span>Topics, Sidebars, and Rubrics</a><a class="headerlink" href="#topics-sidebars-and-rubrics" title="Permalink to this headline">¶</a></h3>
+<aside class="sidebar">
+<p class="sidebar-title">Sidebar Title</p>
+<p class="sidebar-subtitle">Optional Subtitle</p>
+<p>This is a sidebar.  It is for text outside the flow of the main
+text.</p>
+<p class="rubric">This is a rubric inside a sidebar</p>
+<p>Sidebars often appears beside the main text with a border and
+background color.</p>
+</aside>
+<div class="topic">
+<p class="topic-title">Topic Title</p>
+<p>This is a topic.</p>
+</div>
+<p class="rubric">This is a rubric</p>
+</section>
+<section id="target-footnotes">
+<h3><a class="toc-backref" href="#id59"><span class="section-number">3.7.6. </span>Target Footnotes</a><a class="headerlink" href="#target-footnotes" title="Permalink to this headline">¶</a></h3>
+<dl class="footnote brackets">
+<dt class="label" id="id29"><span class="brackets">5</span><span class="fn-backref">(<a href="#id30">1</a>,<a href="#id31">2</a>,<a href="#id32">3</a>)</span></dt>
+<dd><p><a class="reference external" href="http://www.python.org/">http://www.python.org/</a></p>
+</dd>
+</dl>
+</section>
+<section id="replacement-text">
+<h3><a class="toc-backref" href="#id60"><span class="section-number">3.7.7. </span>Replacement Text</a><a class="headerlink" href="#replacement-text" title="Permalink to this headline">¶</a></h3>
+<p>I recommend you try <a class="reference external" href="http://www.python.org/">Python, <em>the</em> best language around</a> <a class="footnote-reference brackets" href="#id29" id="id32">5</a>.</p>
+</section>
+<section id="compound-paragraph">
+<h3><a class="toc-backref" href="#id61"><span class="section-number">3.7.8. </span>Compound Paragraph</a><a class="headerlink" href="#compound-paragraph" title="Permalink to this headline">¶</a></h3>
+<div class="compound">
+<p class="compound-first">This paragraph contains a literal block:</p>
+<div class="compound-middle highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">Connecting</span><span class="p">...</span><span class="w"> </span><span class="n">OK</span>
+<span class="n">Transmitting</span><span class="w"> </span><span class="n">data</span><span class="p">...</span><span class="w"> </span><span class="n">OK</span>
+<span class="n">Disconnecting</span><span class="p">...</span><span class="w"> </span><span class="n">OK</span>
+</pre></div>
+</div>
+<p class="compound-last">and thus consists of a simple paragraph, a literal block, and
+another simple paragraph.  Nonetheless it is semantically <em>one</em>
+paragraph.</p>
+</div>
+<p>This construct is called a <em>compound paragraph</em> and can be produced
+with the “compound” directive.</p>
+</section>
+</section>
+<section id="download-links">
+<h2><a class="toc-backref" href="#id62"><span class="section-number">3.8. </span>Download Links</a><a class="headerlink" href="#download-links" title="Permalink to this headline">¶</a></h2>
+<p><a class="reference download internal" download="" href="../../../../_downloads/26d49aeeb9c710e27197fda28b7c3516/yi_jing_01_chien.jpg"><code class="xref download docutils literal notranslate"><span class="pre">This</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">long</span> <span class="pre">download</span> <span class="pre">link</span> <span class="pre">should</span> <span class="pre">be</span> <span class="pre">blue,</span> <span class="pre">normal</span> <span class="pre">weight</span> <span class="pre">text</span> <span class="pre">with</span> <span class="pre">a</span> <span class="pre">leading</span> <span class="pre">icon,</span> <span class="pre">and</span> <span class="pre">should</span> <span class="pre">wrap</span> <span class="pre">white-spaces</span></code></a></p>
+</section>
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+            <div class="pytorch-side-scroll" id="pytorch-side-scroll-right">
+              <ul>
+<li><a class="reference internal" href="#">3. Paragraph Level Markup</a><ul>
+<li><a class="reference internal" href="#inline-markup">3.1. Inline Markup</a></li>
+<li><a class="reference internal" href="#math">3.2. Math</a></li>
+<li><a class="reference internal" href="#meta">3.3. Meta</a></li>
+<li><a class="reference internal" href="#blocks">3.4. Blocks</a><ul>
+<li><a class="reference internal" href="#literal-blocks">3.4.1. Literal Blocks</a></li>
+<li><a class="reference internal" href="#line-blocks">3.4.2. Line Blocks</a></li>
+<li><a class="reference internal" href="#block-quotes">3.4.3. Block Quotes</a></li>
+<li><a class="reference internal" href="#doctest-blocks">3.4.4. Doctest Blocks</a></li>
+<li><a class="reference internal" href="#code-blocks">3.4.5. Code Blocks</a><ul>
+<li><a class="reference internal" href="#emphasized-lines-with-line-numbers">3.4.5.1. Emphasized lines with line numbers</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li><a class="reference internal" href="#sidebar">3.5. Sidebar</a><ul>
+<li><a class="reference internal" href="#code-with-sidebar">3.5.1. Code with Sidebar</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#references">3.6. References</a><ul>
+<li><a class="reference internal" href="#footnotes">3.6.1. Footnotes</a></li>
+<li><a class="reference internal" href="#citations">3.6.2. Citations</a></li>
+<li><a class="reference internal" href="#glossary">3.6.3. Glossary</a></li>
+<li><a class="reference internal" href="#targets">3.6.4. Targets</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#directives">3.7. Directives</a><ul>
+<li><a class="reference internal" href="#contents">3.7.1. Contents</a></li>
+<li><a class="reference internal" href="#centered-text">3.7.2. Centered text</a></li>
+<li><a class="reference internal" href="#images-figures">3.7.3. Images &amp; Figures</a><ul>
+<li><a class="reference internal" href="#images">3.7.3.1. Images</a></li>
+<li><a class="reference internal" href="#figures">3.7.3.2. Figures</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#admonitions">3.7.4. Admonitions</a></li>
+<li><a class="reference internal" href="#topics-sidebars-and-rubrics">3.7.5. Topics, Sidebars, and Rubrics</a></li>
+<li><a class="reference internal" href="#target-footnotes">3.7.6. Target Footnotes</a></li>
+<li><a class="reference internal" href="#replacement-text">3.7.7. Replacement Text</a></li>
+<li><a class="reference internal" href="#compound-paragraph">3.7.8. Compound Paragraph</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#download-links">3.8. Download Links</a></li>
+</ul>
+</li>
+</ul>
+
+            </div>
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+
+
+
+<!DOCTYPE html>
+<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
+<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
+<head>
+  <meta charset="utf-8">
+  <meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  
+  <title>4. Lists &amp; Tables &mdash; Torch-TensorRT v2.2.0 documentation</title>
+  
+
+  
+  
+  
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+<section id="lists">
+<h2><a class="toc-backref" href="#id18"><span class="section-number">4.1. </span>Lists</a><a class="headerlink" href="#lists" title="Permalink to this headline">¶</a></h2>
+<section id="enumerated-lists">
+<h3><a class="toc-backref" href="#id19"><span class="section-number">4.1.1. </span>Enumerated Lists</a><a class="headerlink" href="#enumerated-lists" title="Permalink to this headline">¶</a></h3>
+<ol class="arabic">
+<li><p>Arabic numerals.</p>
+<ol class="loweralpha simple">
+<li><p>lower alpha)</p>
+<ol class="lowerroman simple">
+<li><p>(lower roman)</p>
+<ol class="upperalpha simple">
+<li><p>upper alpha.</p>
+<ol class="upperroman simple">
+<li><p>upper roman)</p></li>
+</ol>
+</li>
+</ol>
+</li>
+</ol>
+</li>
+</ol>
+</li>
+<li><p>Lists that don’t start at 1:</p>
+<ol class="arabic simple" start="3">
+<li><p>Three</p></li>
+<li><p>Four</p></li>
+</ol>
+<ol class="upperalpha simple" start="3">
+<li><p>C</p></li>
+<li><p>D</p></li>
+</ol>
+<ol class="lowerroman simple" start="3">
+<li><p>iii</p></li>
+<li><p>iv</p></li>
+</ol>
+</li>
+<li><p>List items may also be auto-enumerated.</p></li>
+</ol>
+</section>
+<section id="definition-lists">
+<h3><a class="toc-backref" href="#id20"><span class="section-number">4.1.2. </span>Definition Lists</a><a class="headerlink" href="#definition-lists" title="Permalink to this headline">¶</a></h3>
+<dl>
+<dt>Term</dt><dd><p>Definition</p>
+</dd>
+<dt>Term<span class="classifier">classifier</span></dt><dd><p>Definition paragraph 1.</p>
+<p>Definition paragraph 2.</p>
+</dd>
+<dt>Term</dt><dd><p>Definition</p>
+</dd>
+</dl>
+</section>
+<section id="option-lists">
+<h3><a class="toc-backref" href="#id21"><span class="section-number">4.1.3. </span>Option Lists</a><a class="headerlink" href="#option-lists" title="Permalink to this headline">¶</a></h3>
+<p>For listing command-line options:</p>
+<dl class="option-list">
+<dt><kbd><span class="option">-a</span></kbd></dt>
+<dd><p>command-line option “a”</p>
+</dd>
+<dt><kbd><span class="option">-b <var>file</var></span></kbd></dt>
+<dd><p>options can have arguments
+and long descriptions</p>
+</dd>
+<dt><kbd><span class="option">--long</span></kbd></dt>
+<dd><p>options can be long also</p>
+</dd>
+<dt><kbd><span class="option">--input=<var>file</var></span></kbd></dt>
+<dd><p>long options can also have
+arguments</p>
+</dd>
+<dt><kbd><span class="option">--very-long-option</span></kbd></dt>
+<dd><p>The description can also start on the next line.</p>
+<p>The description may contain multiple body elements,
+regardless of where it starts.</p>
+</dd>
+<dt><kbd><span class="option">-x</span>, <span class="option">-y</span>, <span class="option">-z</span></kbd></dt>
+<dd><p>Multiple options are an “option group”.</p>
+</dd>
+<dt><kbd><span class="option">-v</span>, <span class="option">--verbose</span></kbd></dt>
+<dd><p>Commonly-seen: short &amp; long options.</p>
+</dd>
+<dt><kbd><span class="option">-1 <var>file</var></span>, <span class="option">--one=<var>file</var></span>, <span class="option">--two <var>file</var></span></kbd></dt>
+<dd><p>Multiple options with arguments.</p>
+</dd>
+<dt><kbd><span class="option">/V</span></kbd></dt>
+<dd><p>DOS/VMS-style options too</p>
+</dd>
+</dl>
+<p>There must be at least two spaces between the option and the description.</p>
+</section>
+<section id="field-list">
+<h3><a class="toc-backref" href="#id22"><span class="section-number">4.1.4. </span>Field list</a><a class="headerlink" href="#field-list" title="Permalink to this headline">¶</a></h3>
+<dl class="field-list">
+<dt class="field-odd">Author</dt>
+<dd class="field-odd"><p>David Goodger</p>
+</dd>
+<dt class="field-even">Address</dt>
+<dd class="field-even"><p>123 Example Street
+Example, EX  Canada
+A1B 2C3</p>
+</dd>
+<dt class="field-odd">Contact</dt>
+<dd class="field-odd"><p><a class="reference external" href="mailto:docutils-develop&#37;&#52;&#48;lists&#46;sourceforge&#46;net">docutils-develop<span>&#64;</span>lists<span>&#46;</span>sourceforge<span>&#46;</span>net</a></p>
+</dd>
+<dt class="field-even">Authors</dt>
+<dd class="field-even"><p>Me; Myself; I</p>
+</dd>
+<dt class="field-odd">organization</dt>
+<dd class="field-odd"><p>humankind</p>
+</dd>
+<dt class="field-even">date</dt>
+<dd class="field-even"><p>$Date: 2012-01-03 19:23:53 +0000 (Tue, 03 Jan 2012) $</p>
+</dd>
+<dt class="field-odd">status</dt>
+<dd class="field-odd"><p>This is a “work in progress”</p>
+</dd>
+<dt class="field-even">revision</dt>
+<dd class="field-even"><p>$Revision: 7302 $</p>
+</dd>
+<dt class="field-odd">version</dt>
+<dd class="field-odd"><p>1</p>
+</dd>
+<dt class="field-even">copyright</dt>
+<dd class="field-even"><p>This document has been placed in the public domain. You
+may do with it as you wish. You may copy, modify,
+redistribute, reattribute, sell, buy, rent, lease,
+destroy, or improve it, quote it at length, excerpt,
+incorporate, collate, fold, staple, or mutilate it, or do
+anything else to it that your or anyone else’s heart
+desires.</p>
+</dd>
+<dt class="field-odd">field name</dt>
+<dd class="field-odd"><p>This is a generic bibliographic field.</p>
+</dd>
+<dt class="field-even">field name 2</dt>
+<dd class="field-even"><p>Generic bibliographic fields may contain multiple body elements.</p>
+<p>Like this.</p>
+</dd>
+<dt class="field-odd">Dedication</dt>
+<dd class="field-odd"><p>For Docutils users &amp; co-developers.</p>
+</dd>
+<dt class="field-even">abstract</dt>
+<dd class="field-even"><p>This document is a demonstration of the reStructuredText markup
+language, containing examples of all basic reStructuredText
+constructs and many advanced constructs.</p>
+</dd>
+</dl>
+</section>
+<section id="bullet-lists">
+<h3><a class="toc-backref" href="#id23"><span class="section-number">4.1.5. </span>Bullet Lists</a><a class="headerlink" href="#bullet-lists" title="Permalink to this headline">¶</a></h3>
+<ul>
+<li><p>A bullet list</p>
+<ul class="simple">
+<li><p>Nested bullet list.</p></li>
+<li><p>Nested item 2.</p></li>
+</ul>
+</li>
+<li><p>Item 2.</p>
+<p>Paragraph 2 of item 2.</p>
+<ul class="simple">
+<li><p>Nested bullet list.</p></li>
+<li><p>Nested item 2.</p>
+<ul>
+<li><p>Third level.</p></li>
+<li><p>Item 2.</p></li>
+</ul>
+</li>
+<li><p>Nested item 3.</p></li>
+</ul>
+</li>
+<li><p><code class="docutils literal notranslate"><span class="pre">inline</span> <span class="pre">literall</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">inline</span> <span class="pre">literall</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">inline</span> <span class="pre">literall</span></code></p></li>
+</ul>
+<section id="second-list-level">
+<h4><a class="toc-backref" href="#id24"><span class="section-number">4.1.5.1. </span>Second list level</a><a class="headerlink" href="#second-list-level" title="Permalink to this headline">¶</a></h4>
+<ul>
+<li><p>here is a list in a second-level section.</p></li>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p></li>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<ul>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p></li>
+<li><p>here is an inner bullet <code class="docutils literal notranslate"><span class="pre">oh</span></code></p>
+<ul>
+<li><p>one more <code class="docutils literal notranslate"><span class="pre">with</span> <span class="pre">an</span> <span class="pre">inline</span> <span class="pre">literally</span></code>. <a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<p>heh heh. child. try to beat this embed:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1"># -*- coding: utf-8 -*-</span>
+<span class="linenos"> 2</span><span class="sd">&quot;&quot;&quot;Test Module for sphinx_rtd_theme.&quot;&quot;&quot;</span>
+<span class="linenos"> 3</span>
+<span class="linenos"> 4</span>
+<span class="linenos"> 5</span><span class="k">class</span> <span class="nc">Foo</span><span class="p">:</span>
+<span class="linenos"> 6</span>
+<span class="linenos"> 7</span>    <span class="s2">&quot;&quot;&quot;Docstring for class Foo.</span>
+<span class="linenos"> 8</span>
+<span class="linenos"> 9</span><span class="s2">    This text tests for the formatting of docstrings generated from output</span>
+<span class="linenos">10</span><span class="s2">    ``sphinx.ext.autodoc``. Which contain reST, but sphinx nests it in the</span>
+</pre></div>
+</div>
+</li>
+</ul>
+</li>
+<li><p>and another. <a class="reference external" href="http://www.yahoo.com">yahoo</a></p></li>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">hi</span></code></p></li>
+</ul>
+</li>
+<li><p>and hehe</p></li>
+</ul>
+<section id="but-deeper-down-the-rabbit-hole">
+<h5><a class="toc-backref" href="#id25"><span class="section-number">4.1.5.1.1. </span>But deeper down the rabbit hole</a><a class="headerlink" href="#but-deeper-down-the-rabbit-hole" title="Permalink to this headline">¶</a></h5>
+<ul class="simple">
+<li><p>I kept saying that, “deeper down the rabbit hole”. <a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<ul>
+<li><p>I cackle at night <a class="reference external" href="http://www.yahoo.com">yahoo</a>.</p></li>
+</ul>
+</li>
+<li><p>I’m so lonely here in GZ <code class="docutils literal notranslate"><span class="pre">guangzhou</span></code></p></li>
+<li><p>A man of python destiny, hopes and dreams. <a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<ul>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a></p>
+<ul>
+<li><p><a class="reference external" href="http://www.yahoo.com">yahoo</a> <code class="docutils literal notranslate"><span class="pre">hi</span></code></p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">destiny</span></code></p></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</section>
+</section>
+</section>
+<section id="hlists">
+<h3><a class="toc-backref" href="#id26"><span class="section-number">4.1.6. </span>Hlists</a><a class="headerlink" href="#hlists" title="Permalink to this headline">¶</a></h3>
+<table class="hlist"><tr><td><ul class="simple">
+<li><p>First item</p></li>
+<li><p>Second item</p></li>
+<li><p>Third item</p></li>
+</ul>
+</td><td><ul class="simple">
+<li><p>Forth item</p></li>
+<li><p>Fifth item</p></li>
+<li><p>Sixths item</p></li>
+</ul>
+</td></tr></table>
+<p class="rubric">Hlist with images</p>
+<table class="hlist"><tr><td><ul>
+<li><figure class="align-default" id="id11">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This is a short caption for a figure.</span><a class="headerlink" href="#id11" title="Permalink to this image">¶</a></p>
+</figcaption>
+</figure>
+</li>
+</ul>
+</td><td><ul>
+<li><figure class="align-default" id="id12">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This is a long caption for a figure. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+Donec porttitor dolor in odio posuere, vitae ornare libero mattis. In lobortis justo vestibulum nibh aliquet, non.</span><a class="headerlink" href="#id12" title="Permalink to this image">¶</a></p>
+</figcaption>
+</figure>
+</li>
+</ul>
+</td></tr></table>
+</section>
+<section id="numbered-list">
+<h3><a class="toc-backref" href="#id27"><span class="section-number">4.1.7. </span>Numbered List</a><a class="headerlink" href="#numbered-list" title="Permalink to this headline">¶</a></h3>
+<ol class="arabic simple">
+<li><p>One,</p></li>
+<li><p>Two.</p></li>
+<li><p>Three with long text. Lorem ipsum dolor sit amet, consectetur adipiscing elit.
+Sed feugiat sagittis neque quis eleifend. Duis rutrum lectus sit amet mattis suscipit.</p></li>
+</ol>
+<ul class="simple">
+<li><ol class="upperalpha simple">
+<li><p>Using bullets and letters. (A)</p></li>
+</ol>
+</li>
+<li><ol class="upperalpha simple" start="2">
+<li><p>Using bullets and letters. (B)</p></li>
+</ol>
+</li>
+<li><ol class="upperalpha simple" start="3">
+<li><p>Using bullets and letters. (C)</p></li>
+</ol>
+</li>
+</ul>
+</section>
+</section>
+<section id="tables">
+<h2><a class="toc-backref" href="#id28"><span class="section-number">4.2. </span>Tables</a><a class="headerlink" href="#tables" title="Permalink to this headline">¶</a></h2>
+<section id="grid-tables">
+<h3><a class="toc-backref" href="#id29"><span class="section-number">4.2.1. </span>Grid Tables</a><a class="headerlink" href="#grid-tables" title="Permalink to this headline">¶</a></h3>
+<p>Here’s a grid table followed by a simple table:</p>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 43%" />
+<col style="width: 21%" />
+<col style="width: 18%" />
+<col style="width: 18%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Header row, column 1
+(header rows optional)</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+<th class="head"><p>Header 4</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>body row 1, column 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>column 4</p></td>
+</tr>
+<tr class="row-odd"><td><p>body row 2</p></td>
+<td colspan="3"><p>Cells may span columns.</p></td>
+</tr>
+<tr class="row-even"><td><p>body row 3</p></td>
+<td rowspan="2"><p>Cells may
+span rows.</p></td>
+<td colspan="2" rowspan="2"><ul class="simple">
+<li><p>Table cells</p></li>
+<li><p>contain</p></li>
+<li><p>body elements.</p></li>
+</ul>
+</td>
+</tr>
+<tr class="row-odd"><td><p>body row 4</p></td>
+</tr>
+<tr class="row-even"><td><p>body row 5</p></td>
+<td colspan="2"><p>Cells may also be
+empty: <code class="docutils literal notranslate"><span class="pre">--&gt;</span></code></p></td>
+<td></td>
+</tr>
+</tbody>
+</table>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 31%" />
+<col style="width: 31%" />
+<col style="width: 38%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head" colspan="2"><p>Inputs</p></th>
+<th class="head"><p>Output</p></th>
+</tr>
+<tr class="row-even"><th class="head"><p>A</p></th>
+<th class="head"><p>B</p></th>
+<th class="head"><p>A or B</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-odd"><td><p>False</p></td>
+<td><p>False</p></td>
+<td><p>False</p></td>
+</tr>
+<tr class="row-even"><td><p>True</p></td>
+<td><p>False</p></td>
+<td><p>True</p></td>
+</tr>
+<tr class="row-odd"><td><p>False</p></td>
+<td><p>True</p></td>
+<td><p>True</p></td>
+</tr>
+<tr class="row-even"><td><p>True</p></td>
+<td><p>True</p></td>
+<td><p>True</p></td>
+</tr>
+</tbody>
+</table>
+<section id="giant-tables">
+<h4><a class="toc-backref" href="#id30"><span class="section-number">4.2.1.1. </span>Giant Tables</a><a class="headerlink" href="#giant-tables" title="Permalink to this headline">¶</a></h4>
+<table class="docutils align-default">
+<colgroup>
+<col style="width: 9%" />
+<col style="width: 9%" />
+<col style="width: 8%" />
+<col style="width: 9%" />
+<col style="width: 9%" />
+<col style="width: 8%" />
+<col style="width: 9%" />
+<col style="width: 9%" />
+<col style="width: 8%" />
+<col style="width: 9%" />
+<col style="width: 9%" />
+<col style="width: 8%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+<th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+<th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+<th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+</tr>
+<tr class="row-odd"><td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+</tr>
+<tr class="row-even"><td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+</tr>
+<tr class="row-odd"><td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+<td><p>body row 1</p></td>
+<td><p>column 2</p></td>
+<td><p>column 3</p></td>
+</tr>
+</tbody>
+</table>
+</section>
+</section>
+<section id="list-tables">
+<h3><a class="toc-backref" href="#id31"><span class="section-number">4.2.2. </span>List Tables</a><a class="headerlink" href="#list-tables" title="Permalink to this headline">¶</a></h3>
+<table class="colwidths-given docutils align-default" id="id13">
+<caption><span class="caption-text">List tables can have captions like this one.</span><a class="headerlink" href="#id13" title="Permalink to this table">¶</a></caption>
+<colgroup>
+<col style="width: 13%" />
+<col style="width: 7%" />
+<col style="width: 13%" />
+<col style="width: 67%" />
+</colgroup>
+<thead>
+<tr class="row-odd"><th class="head stub"><p>List table</p></th>
+<th class="head"><p>Header 1</p></th>
+<th class="head"><p>Header 2</p></th>
+<th class="head"><p>Header 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.</p></th>
+</tr>
+</thead>
+<tbody>
+<tr class="row-even"><th class="stub"><p>Stub Row 1</p></th>
+<td><p>Row 1</p></td>
+<td><p>Column 2</p></td>
+<td><p>Column 3 long. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam sit amet mauris arcu.</p></td>
+</tr>
+<tr class="row-odd"><th class="stub"><p>Stub Row 2</p></th>
+<td><p>Row 2</p></td>
+<td><p>Column 2</p></td>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
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+<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>
+</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>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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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>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../cli/torchtrtc.html">torchtrtc</a></li>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+  <section id="long-sticky-nav">
+<h1><a class="toc-backref" href="#id9"><span class="section-number">1. </span>Long Sticky Nav</a><a class="headerlink" href="#long-sticky-nav" title="Permalink to this headline">¶</a></h1>
+<div class="contents topic" id="table-of-contents">
+<p class="topic-title">Table of Contents</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#long-sticky-nav" id="id9">Long Sticky Nav</a></p>
+<ul>
+<li><p><a class="reference internal" href="#example-menu-1" id="id10">Example Menu 1</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-2" id="id11">Example Menu 2</a></p></li>
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+<li><p><a class="reference internal" href="#example-menu-14" id="id23">Example Menu 14</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-15" id="id24">Example Menu 15</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-16" id="id25">Example Menu 16</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-17" id="id26">Example Menu 17</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-18" id="id27">Example Menu 18</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-19" id="id28">Example Menu 19</a></p></li>
+<li><p><a class="reference internal" href="#example-menu-20" id="id29">Example Menu 20</a></p></li>
+<li><p><a class="reference internal" href="#example-submenu-1" id="id30">Example Submenu 1</a></p>
+<ul>
+<li><p><a class="reference internal" href="#submenu-1" id="id31">Submenu 1</a></p>
+<ul>
+<li><p><a class="reference internal" href="#subsubmenu-1" id="id32">Subsubmenu 1</a></p></li>
+<li><p><a class="reference internal" href="#subsubmenu-2" id="id33">Subsubmenu 2</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#submenu-2" id="id34">Submenu 2</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id1" id="id35">Subsubmenu 1</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#submenu-3" id="id36">Submenu 3</a></p></li>
+<li><p><a class="reference internal" href="#submenu-4" id="id37">Submenu 4</a></p></li>
+<li><p><a class="reference internal" href="#submenu-5" id="id38">Submenu 5</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#example-submenu-2" id="id39">Example Submenu 2</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id2" id="id40">Submenu 1</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id3" id="id41">Subsubmenu 1</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#id4" id="id42">Submenu 2</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id5" id="id43">Subsubmenu 1</a></p></li>
+</ul>
+</li>
+<li><p><a class="reference internal" href="#id6" id="id44">Submenu 3</a></p></li>
+<li><p><a class="reference internal" href="#id7" id="id45">Submenu 4</a></p></li>
+<li><p><a class="reference internal" href="#id8" id="id46">Submenu 5</a></p></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</div>
+<p>This section demonstrates how the ‘sticky_navigation’ setting behaves when the menu is very long.
+When this section is selected, it will make the menu and the main area scroll when you are at the top of the page.</p>
+<section id="example-menu-1">
+<h2><a class="toc-backref" href="#id10"><span class="section-number">1.1. </span>Example Menu 1</a><a class="headerlink" href="#example-menu-1" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-2">
+<h2><a class="toc-backref" href="#id11"><span class="section-number">1.2. </span>Example Menu 2</a><a class="headerlink" href="#example-menu-2" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-3">
+<h2><a class="toc-backref" href="#id12"><span class="section-number">1.3. </span>Example Menu 3</a><a class="headerlink" href="#example-menu-3" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-4">
+<h2><a class="toc-backref" href="#id13"><span class="section-number">1.4. </span>Example Menu 4</a><a class="headerlink" href="#example-menu-4" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-5">
+<h2><a class="toc-backref" href="#id14"><span class="section-number">1.5. </span>Example Menu 5</a><a class="headerlink" href="#example-menu-5" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-6">
+<h2><a class="toc-backref" href="#id15"><span class="section-number">1.6. </span>Example Menu 6</a><a class="headerlink" href="#example-menu-6" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-7">
+<h2><a class="toc-backref" href="#id16"><span class="section-number">1.7. </span>Example Menu 7</a><a class="headerlink" href="#example-menu-7" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-8">
+<h2><a class="toc-backref" href="#id17"><span class="section-number">1.8. </span>Example Menu 8</a><a class="headerlink" href="#example-menu-8" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-9">
+<h2><a class="toc-backref" href="#id18"><span class="section-number">1.9. </span>Example Menu 9</a><a class="headerlink" href="#example-menu-9" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-10">
+<h2><a class="toc-backref" href="#id19"><span class="section-number">1.10. </span>Example Menu 10</a><a class="headerlink" href="#example-menu-10" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-11">
+<h2><a class="toc-backref" href="#id20"><span class="section-number">1.11. </span>Example Menu 11</a><a class="headerlink" href="#example-menu-11" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-12">
+<h2><a class="toc-backref" href="#id21"><span class="section-number">1.12. </span>Example Menu 12</a><a class="headerlink" href="#example-menu-12" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+</section>
+<section id="example-menu-13">
+<h2><a class="toc-backref" href="#id22"><span class="section-number">1.13. </span>Example Menu 13</a><a class="headerlink" href="#example-menu-13" title="Permalink to this headline">¶</a></h2>
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+<p>Just a place holder…</p>
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+<h2><a class="toc-backref" href="#id30"><span class="section-number">1.21. </span>Example Submenu 1</a><a class="headerlink" href="#example-submenu-1" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+<section id="submenu-1">
+<h3><a class="toc-backref" href="#id31"><span class="section-number">1.21.1. </span>Submenu 1</a><a class="headerlink" href="#submenu-1" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
+<section id="subsubmenu-1">
+<h4><a class="toc-backref" href="#id32"><span class="section-number">1.21.1.1. </span>Subsubmenu 1</a><a class="headerlink" href="#subsubmenu-1" title="Permalink to this headline">¶</a></h4>
+<p>Just a place holder…</p>
+</section>
+<section id="subsubmenu-2">
+<h4><a class="toc-backref" href="#id33"><span class="section-number">1.21.1.2. </span>Subsubmenu 2</a><a class="headerlink" href="#subsubmenu-2" title="Permalink to this headline">¶</a></h4>
+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<h4><a class="toc-backref" href="#id35"><span class="section-number">1.21.2.1. </span>Subsubmenu 1</a><a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h4>
+<p>Just a place holder…</p>
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+<h3><a class="toc-backref" href="#id36"><span class="section-number">1.21.3. </span>Submenu 3</a><a class="headerlink" href="#submenu-3" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
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+<section id="submenu-4">
+<h3><a class="toc-backref" href="#id37"><span class="section-number">1.21.4. </span>Submenu 4</a><a class="headerlink" href="#submenu-4" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<section id="example-submenu-2">
+<h2><a class="toc-backref" href="#id39"><span class="section-number">1.22. </span>Example Submenu 2</a><a class="headerlink" href="#example-submenu-2" title="Permalink to this headline">¶</a></h2>
+<p>Just a place holder…</p>
+<section id="id2">
+<h3><a class="toc-backref" href="#id40"><span class="section-number">1.22.1. </span>Submenu 1</a><a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
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+<h4><a class="toc-backref" href="#id41"><span class="section-number">1.22.1.1. </span>Subsubmenu 1</a><a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h4>
+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<p>Just a place holder…</p>
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+<h3><a class="toc-backref" href="#id44"><span class="section-number">1.22.3. </span>Submenu 3</a><a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h3>
+<p>Just a place holder…</p>
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+<section id="id7">
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+<p>Just a place holder…</p>
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+<section id="id8">
+<h3><a class="toc-backref" href="#id46"><span class="section-number">1.22.5. </span>Submenu 5</a><a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h3>
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diff --git a/docs/v2.2.0/src/pytorch-sphinx-theme/docs/demo/structure.html b/docs/v2.2.0/src/pytorch-sphinx-theme/docs/demo/structure.html
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@@ -0,0 +1,877 @@
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+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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+<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>
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+<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>
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+<p class="caption" role="heading"><span class="caption-text">FX Frontend</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../fx/getting_started_with_fx_path.html">Torch-TensorRT (FX Frontend) User Guide</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../../contributors/writing_dynamo_aten_lowering_passes.html">Writing Dynamo ATen Lowering Passes</a></li>
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+<li><p><a class="reference internal" href="#document-paragraph" id="id8">Document Paragraph</a></p></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
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+</ul>
+</li>
+<li><p><a class="reference internal" href="#structural-elements-2" id="id9">Structural Elements 2</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id1" id="id10">Document Section</a></p>
+<ul>
+<li><p><a class="reference internal" href="#id2" id="id11">Document Subsection</a></p></li>
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+<section id="document-section">
+<h2><a class="toc-backref" href="#id5"><span class="section-number">1.1. </span>Document Section</a><a class="headerlink" href="#document-section" title="Permalink to this headline">¶</a></h2>
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+<section id="document-subsection">
+<h3><a class="toc-backref" href="#id6"><span class="section-number">1.1.1. </span>Document Subsection</a><a class="headerlink" href="#document-subsection" title="Permalink to this headline">¶</a></h3>
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+<section id="document-subsubsection">
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+<section id="document-paragraph">
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+</section>
+<section id="structural-elements-2">
+<h1><a class="toc-backref" href="#id9"><span class="section-number">2. </span>Structural Elements 2</a><a class="headerlink" href="#structural-elements-2" title="Permalink to this headline">¶</a></h1>
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+Pellentesque dictum dui sem, non placerat tortor rhoncus in. Sed placerat nulla at rhoncus iaculis.</p>
+<section id="id1">
+<h2><a class="toc-backref" href="#id10"><span class="section-number">2.1. </span>Document Section</a><a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
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+<h3><a class="toc-backref" href="#id11"><span class="section-number">2.1.1. </span>Document Subsection</a><a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
+<figure class="align-right" id="id3" style="width: 200px">
+<img alt="../../../../_images/yi_jing_01_chien.jpg" src="../../../../_images/yi_jing_01_chien.jpg" />
+<figcaption>
+<p><span class="caption-text">This is a caption for a figure. Text should wrap around the caption.</span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
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+In hac habitasse platea dictumst. Sed sit amet imperdiet purus.</p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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+<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>
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+<p class="caption" role="heading"><span class="caption-text">TorchScript Frontend</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../../ts/creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
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+<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>
+</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>
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+<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/ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
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diff --git a/docs/v2.2.0/src/pytorch-sphinx-theme/docs/installing.html b/docs/v2.2.0/src/pytorch-sphinx-theme/docs/installing.html
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diff --git a/docs/v2.2.0/ts/creating_torchscript_module_in_python.html b/docs/v2.2.0/ts/creating_torchscript_module_in_python.html
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+<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>
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+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="#working-with-torchscript-in-python">Working with TorchScript in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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/logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</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/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
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+              
+  <section id="creating-a-torchscript-module">
+<span id="creating-a-ts-mod"></span><h1>Creating a TorchScript Module<a class="headerlink" href="#creating-a-torchscript-module" title="Permalink to this headline">¶</a></h1>
+<p>TorchScript is a way to create serializable and optimizable models from PyTorch code.
+PyTorch has detailed documentation on how to do this <a class="reference external" href="https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html">https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html</a> but briefly here is the
+here is key background information and the process:</p>
+<p>PyTorch programs are based around <code class="docutils literal notranslate"><span class="pre">Module</span></code> s which can be used to compose higher level modules. <code class="docutils literal notranslate"><span class="pre">Modules</span></code> contain a constructor to set up the modules, parameters and sub-modules
+and a forward function which describes how to use the parameters and submodules when the module is invoked.</p>
+<p>For example, we can define a LeNet module like this:</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">import</span> <span class="nn">torch.nn</span> <span class="k">as</span> <span class="nn">nn</span>
+<span class="linenos"> 2</span><span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
+<span class="linenos"> 3</span>
+<span class="linenos"> 4</span>
+<span class="linenos"> 5</span><span class="k">class</span> <span class="nc">LeNetFeatExtractor</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="linenos"> 6</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos"> 7</span>        <span class="nb">super</span><span class="p">(</span><span class="n">LeNetFeatExtractor</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos"> 8</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="linenos"> 9</span>        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+<span class="linenos">10</span>
+<span class="linenos">11</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">12</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="linenos">13</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">max_pool2d</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">x</span><span class="p">)),</span> <span class="mi">2</span><span class="p">)</span>
+<span class="linenos">14</span>        <span class="k">return</span> <span class="n">x</span>
+<span class="linenos">15</span>
+<span class="linenos">16</span>
+<span class="linenos">17</span><span class="k">class</span> <span class="nc">LeNetClassifier</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="linenos">18</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos">19</span>        <span class="nb">super</span><span class="p">(</span><span class="n">LeNetClassifier</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos">20</span>        <span class="bp">self</span><span class="o">.</span><span class="n">fc1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">16</span> <span class="o">*</span> <span class="mi">6</span> <span class="o">*</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">120</span><span class="p">)</span>
+<span class="linenos">21</span>        <span class="bp">self</span><span class="o">.</span><span class="n">fc2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">120</span><span class="p">,</span> <span class="mi">84</span><span class="p">)</span>
+<span class="linenos">22</span>        <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">84</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+<span class="linenos">23</span>
+<span class="linenos">24</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">25</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+<span class="linenos">26</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc1</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">27</span>        <span class="n">x</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fc2</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+<span class="linenos">28</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fc3</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">29</span>        <span class="k">return</span> <span class="n">x</span>
+<span class="linenos">30</span>
+<span class="linenos">31</span>
+<span class="linenos">32</span><span class="k">class</span> <span class="nc">LeNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="linenos">33</span>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="linenos">34</span>        <span class="nb">super</span><span class="p">(</span><span class="n">LeNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+<span class="linenos">35</span>        <span class="bp">self</span><span class="o">.</span><span class="n">feat</span> <span class="o">=</span> <span class="n">LeNetFeatExtractor</span><span class="p">()</span>
+<span class="linenos">36</span>        <span class="bp">self</span><span class="o">.</span><span class="n">classifer</span> <span class="o">=</span> <span class="n">LeNetClassifier</span><span class="p">()</span>
+<span class="linenos">37</span>
+<span class="linenos">38</span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+<span class="linenos">39</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feat</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">40</span>        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">classifer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="linenos">41</span>        <span class="k">return</span> <span class="n">x</span>
+</pre></div>
+</div>
+<p>.</p>
+<blockquote>
+<div><p>Obviously you may want to consolidate such a simple model into a single module but we can see the composability of PyTorch here</p>
+</div></blockquote>
+<p>From here are two pathways for going from PyTorch Python code to TorchScript code: Tracing and Scripting.</p>
+<p>Tracing follows the path of execution when the module is called and records what happens.
+To trace an instance of our LeNet module, we can call <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code> with an example input.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">])</span>
+<span class="n">traced_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">input_data</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Scripting actually inspects your code with a compiler and generates an equivalent TorchScript program. The difference is that since tracing
+is following the execution of your module, it cannot pick up control flow for instance. By working from the Python code, the compiler can
+include these components. We can run the script compiler on our LeNet module by calling <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
+<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>There are reasons to use one path or another, the PyTorch documentation has information on how to choose. From a Torch-TensorRT prespective, there is
+better support (i.e your module is more likely to compile) for traced modules because it doesn’t include all the complexities of a complete
+programming language, though both paths supported.</p>
+<p>After scripting or tracing your module, you are given back a TorchScript Module. This contains the code and parameters used to run the module stored
+in a intermediate representation that Torch-TensorRT can consume.</p>
+<p>Here is what the LeNet traced module IR looks like:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self.1 : __torch__.___torch_mangle_10.LeNet,
+    %input.1 : Float(1, 1, 32, 32)):
+    %129 : __torch__.___torch_mangle_9.LeNetClassifier = prim::GetAttr[name=&quot;classifer&quot;](%self.1)
+    %119 : __torch__.___torch_mangle_5.LeNetFeatExtractor = prim::GetAttr[name=&quot;feat&quot;](%self.1)
+    %137 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%119, %input.1)
+    %138 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%129, %137)
+    return (%138)
+</pre></div>
+</div>
+<p>and the LeNet scripted module IR:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self : __torch__.LeNet,
+    %x.1 : Tensor):
+    %2 : __torch__.LeNetFeatExtractor = prim::GetAttr[name=&quot;feat&quot;](%self)
+    %x.3 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%2, %x.1) # x.py:38:12
+    %5 : __torch__.LeNetClassifier = prim::GetAttr[name=&quot;classifer&quot;](%self)
+    %x.5 : Tensor = prim::CallMethod[name=&quot;forward&quot;](%5, %x.3) # x.py:39:12
+    return (%x.5)
+</pre></div>
+</div>
+<p>You can see that the IR preserves the module structure we have in our python code.</p>
+</section>
+<section id="working-with-torchscript-in-python">
+<span id="ts-in-py"></span><h1>Working with TorchScript in Python<a class="headerlink" href="#working-with-torchscript-in-python" title="Permalink to this headline">¶</a></h1>
+<p>TorchScript Modules are run the same way you run normal PyTorch modules. You can run the forward pass using the
+<code class="docutils literal notranslate"><span class="pre">forward</span></code> method or just calling the module <code class="docutils literal notranslate"><span class="pre">torch_scirpt_module(in_tensor)</span></code> The JIT compiler will compile
+and optimize the module on the fly and then returns the results.</p>
+</section>
+<section id="saving-torchscript-module-to-disk">
+<h1>Saving TorchScript Module to Disk<a class="headerlink" href="#saving-torchscript-module-to-disk" title="Permalink to this headline">¶</a></h1>
+<p>For either traced or scripted modules, you can save the module to disk with the following command</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">LeNet</span><span class="p">()</span>
+<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<span class="n">script_model</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;lenet_scripted.ts&quot;</span><span class="p">)</span>
+</pre></div>
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diff --git a/docs/v2.2.0/ts/getting_started_with_cpp_api.html b/docs/v2.2.0/ts/getting_started_with_cpp_api.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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 class="current">
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="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="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="getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Using Torch-TensorRT in  C++</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend</a></li>
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+</ul>
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+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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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+              
+  <section id="using-torch-tensorrt-in-c">
+<span id="getting-started-cpp"></span><h1>Using Torch-TensorRT in  C++<a class="headerlink" href="#using-torch-tensorrt-in-c" title="Permalink to this headline">¶</a></h1>
+<p>If you haven’t already, acquire a tarball of the library by following the instructions in <a class="reference internal" href="../getting_started/installation.html#installation"><span class="std std-ref">Installation</span></a></p>
+<section id="id1">
+<h2>Using Torch-TensorRT in C++<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<p>Torch-TensorRT C++ API accepts TorchScript modules (generated either from <code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.jit.trace</span></code>) as an input and returns
+a Torchscript module (optimized using TensorRT), Dynamo compilation workflows will not be supported in the C++ API however, execution of
+torch.jit.trace’d compiled FX GraphModules is supported for FX and Dyanmo workflows.</p>
+<p>Please refer to <a class="reference external" href="https://nvidia.github.io/Torch-TensorRT/tutorials/creating_torchscript_module_in_python.html">Creating TorchScript modules in Python</a> section to generate torchscript graphs.</p>
+<section id="torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">
+<span id="torch-tensorrt-quickstart"></span><h3>[Torch-TensorRT Quickstart] Compiling TorchScript Modules with <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code><a class="headerlink" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc" title="Permalink to this headline">¶</a></h3>
+<p>An easy way to get started with Torch-TensorRT and to check if your model can be supported without extra work is to run it through
+<code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code>, which supports almost all features of the compiler from the command line including post training quantization
+(given a previously created calibration cache). For example we can compile our lenet model by setting our preferred operating
+precision and input size. This new TorchScript file can be loaded into Python (note: you need to <code class="docutils literal notranslate"><span class="pre">import</span> <span class="pre">torch_tensorrt</span></code> before loading
+these compiled modules because the compiler extends the PyTorch the deserializer and runtime to execute compiled modules).</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>❯<span class="w"> </span>torchtrtc<span class="w"> </span>-p<span class="w"> </span>f16<span class="w"> </span>lenet_scripted.ts<span class="w"> </span>trt_lenet_scripted.ts<span class="w"> </span><span class="s2">&quot;(1,1,32,32)&quot;</span>
+
+❯<span class="w"> </span>python3
+Python<span class="w"> </span><span class="m">3</span>.6.9<span class="w"> </span><span class="o">(</span>default,<span class="w"> </span>Apr<span class="w"> </span><span class="m">18</span><span class="w"> </span><span class="m">2020</span>,<span class="w"> </span><span class="m">01</span>:56:04<span class="o">)</span>
+<span class="o">[</span>GCC<span class="w"> </span><span class="m">8</span>.4.0<span class="o">]</span><span class="w"> </span>on<span class="w"> </span>linux
+Type<span class="w"> </span><span class="s2">&quot;help&quot;</span>,<span class="w"> </span><span class="s2">&quot;copyright&quot;</span>,<span class="w"> </span><span class="s2">&quot;credits&quot;</span><span class="w"> </span>or<span class="w"> </span><span class="s2">&quot;license&quot;</span><span class="w"> </span><span class="k">for</span><span class="w"> </span>more<span class="w"> </span>information.
+&gt;&gt;&gt;<span class="w"> </span>import<span class="w"> </span>torch
+&gt;&gt;&gt;<span class="w"> </span>import<span class="w"> </span>torch_tensorrt
+&gt;&gt;&gt;<span class="w"> </span><span class="nv">ts_model</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>torch.jit.load<span class="o">(</span>“trt_lenet_scripted.ts”<span class="o">)</span>
+&gt;&gt;&gt;<span class="w"> </span>ts_model<span class="o">(</span>torch.randn<span class="o">((</span><span class="m">1</span>,1,32,32<span class="o">))</span>.to<span class="o">(</span>“cuda”<span class="o">)</span>.half<span class="o">())</span>
+</pre></div>
+</div>
+<p>You can learn more about <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> usage here: <a class="reference internal" href="../cli/torchtrtc.html#torchtrtc"><span class="std std-ref">torchtrtc</span></a></p>
+</section>
+<section id="working-with-torchscript-in-c">
+<span id="ts-in-cc"></span><h3>Working with TorchScript in C++<a class="headerlink" href="#working-with-torchscript-in-c" title="Permalink to this headline">¶</a></h3>
+<p>If we are developing an application to deploy with C++, we can save either our traced or scripted module using <code class="docutils literal notranslate"><span class="pre">torch.jit.save</span></code>
+which will serialize the TorchScript code, weights and other information into a package. This is also where our dependency on Python ends.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">torch_script_module</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;lenet.jit.pt&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>From here we can now load our TorchScript module in C++</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;torch/script.h&gt;</span><span class="c1"> // One-stop header.</span>
+
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;iostream&gt;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;memory&gt;</span>
+
+<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="k">module</span><span class="p">;</span>
+<span class="w">    </span><span class="k">try</span><span class="w"> </span><span class="p">{</span>
+<span class="w">        </span><span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
+<span class="w">        </span><span class="k">module</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TS MOD&gt;&quot;</span><span class="p">);</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="w">    </span><span class="k">catch</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span><span class="w"> </span><span class="n">e</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;error loading the model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
+<span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span>
+<span class="w">    </span><span class="p">}</span>
+
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;ok</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>You can do full training and inference in C++ with PyTorch / LibTorch if you would like, you can even define your modules in C++ and
+have access to the same powerful tensor library that backs PyTorch. (For more information: <a class="reference external" href="https://pytorch.org/cppdocs/">https://pytorch.org/cppdocs/</a>).
+For instance we can do inference with our LeNet module like this:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">});</span>
+<span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+</pre></div>
+</div>
+<p>and to run on the GPU:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+<span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+</pre></div>
+</div>
+<p>As you can see it is pretty similar to the Python API. When you call the <code class="docutils literal notranslate"><span class="pre">forward</span></code> method, you invoke the PyTorch JIT compiler, which will optimize and run your TorchScript code.</p>
+</section>
+<section id="compiling-with-torch-tensorrt-in-c">
+<span id="compile-cpp"></span><h3>Compiling with Torch-TensorRT in C++<a class="headerlink" href="#compiling-with-torch-tensorrt-in-c" title="Permalink to this headline">¶</a></h3>
+<p>We are also at the point were we can compile and optimize our module with Torch-TensorRT, but instead of in a JIT fashion we must do it ahead-of-time (AOT) i.e. before we start doing actual inference work
+since it takes a bit of time to optimize the module, it would not make sense to do this every time you run the module or even the first time you run it.</p>
+<p>With our module loaded, we can feed it into the Torch-TensorRT compiler. When we do so we must provide some information on the expected input size and also configure any additional settings.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span>
+<span class="p">...</span>
+
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">});</span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">{{</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()}});</span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span>
+</pre></div>
+</div>
+<p>Thats it! Now the graph runs primarily not with the JIT compiler but using TensorRT (though we execute the graph using the JIT runtime).</p>
+<p>We can also set settings like operating precision to run in FP16.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span>
+<span class="p">...</span>
+
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">input_sizes</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span>
+<span class="w">    </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="w"> </span><span class="nf">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span>
+<span class="w">    </span><span class="n">info</span><span class="p">.</span><span class="n">enable_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="n">info</span><span class="p">);</span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">trt_mod</span><span class="p">.</span><span class="n">forward</span><span class="p">({</span><span class="n">in</span><span class="p">});</span>
+</pre></div>
+</div>
+<p>And now we are running the module in FP16 precision. You can then save the module to load later.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">trt_mod</span><span class="p">.</span><span class="n">save</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TRT/TS MOD&gt;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Torch-TensorRT compiled TorchScript modules are loaded in the same way as normal TorchScript module. Make sure your deployment application is linked against <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code></p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span>
+
+<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">(</span><span class="kt">int</span><span class="w"> </span><span class="n">argc</span><span class="p">,</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="kt">char</span><span class="o">*</span><span class="w"> </span><span class="n">argv</span><span class="p">[])</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Module</span><span class="w"> </span><span class="k">module</span><span class="p">;</span>
+<span class="w">    </span><span class="k">try</span><span class="w"> </span><span class="p">{</span>
+<span class="w">        </span><span class="c1">// Deserialize the ScriptModule from a file using torch::jit::load().</span>
+<span class="w">        </span><span class="k">module</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">load</span><span class="p">(</span><span class="s">&quot;&lt;PATH TO SAVED TRT/TS MOD&gt;&quot;</span><span class="p">);</span>
+<span class="w">    </span><span class="p">}</span>
+<span class="w">    </span><span class="k">catch</span><span class="w"> </span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">c10</span><span class="o">::</span><span class="n">Error</span><span class="o">&amp;</span><span class="w"> </span><span class="n">e</span><span class="p">)</span><span class="w"> </span><span class="p">{</span>
+<span class="w">        </span><span class="n">std</span><span class="o">::</span><span class="n">cerr</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;error loading the model</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
+<span class="w">        </span><span class="k">return</span><span class="w"> </span><span class="mi">-1</span><span class="p">;</span>
+<span class="w">    </span><span class="p">}</span>
+
+<span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">out</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">mod</span><span class="p">.</span><span class="n">forward</span><span class="p">(</span><span class="n">in</span><span class="p">);</span>
+
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">cout</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="s">&quot;ok</span><span class="se">\n</span><span class="s">&quot;</span><span class="p">;</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>If you want to save the engine produced by Torch-TensorRT to use in a TensorRT application you can use the <code class="docutils literal notranslate"><span class="pre">ConvertGraphToTRTEngine</span></code> API.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span>
+<span class="p">...</span>
+
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">to</span><span class="p">(</span><span class="n">at</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">);</span>
+<span class="w">    </span><span class="n">mod</span><span class="p">.</span><span class="n">eval</span><span class="p">();</span>
+
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">randn</span><span class="p">({</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">},</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kCUDA</span><span class="p">}).</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">input_sizes</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">InputRange</span><span class="o">&gt;</span><span class="p">({</span><span class="n">in</span><span class="p">.</span><span class="n">sizes</span><span class="p">()});</span>
+<span class="w">    </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="w"> </span><span class="nf">info</span><span class="p">(</span><span class="n">input_sizes</span><span class="p">);</span>
+<span class="w">    </span><span class="n">info</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kHALF</span><span class="p">);</span>
+<span class="w">    </span><span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ConvertGraphToTRTEngine</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="s">&quot;forward&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">info</span><span class="p">);</span>
+<span class="w">    </span><span class="n">std</span><span class="o">::</span><span class="n">ofstream</span><span class="w"> </span><span class="n">out</span><span class="p">(</span><span class="s">&quot;/tmp/engine_converted_from_jit.trt&quot;</span><span class="p">);</span>
+<span class="w">    </span><span class="n">out</span><span class="w"> </span><span class="o">&lt;&lt;</span><span class="w"> </span><span class="n">engine</span><span class="p">;</span>
+<span class="w">    </span><span class="n">out</span><span class="p">.</span><span class="n">close</span><span class="p">();</span>
+</pre></div>
+</div>
+</section>
+<section id="under-the-hood">
+<span id="id2"></span><h3>Under The Hood<a class="headerlink" href="#under-the-hood" title="Permalink to this headline">¶</a></h3>
+<p>When a module is provided to Torch-TensorRT, the compiler starts by mapping a graph like you saw above to a graph like this:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%input.2 : Tensor):
+    %2 : Float(84, 10) = prim::Constant[value=&lt;Tensor&gt;]()
+    %3 : Float(120, 84) = prim::Constant[value=&lt;Tensor&gt;]()
+    %4 : Float(576, 120) = prim::Constant[value=&lt;Tensor&gt;]()
+    %5 : int = prim::Constant[value=-1]() # x.py:25:0
+    %6 : int[] = prim::Constant[value=annotate(List[int], [])]()
+    %7 : int[] = prim::Constant[value=[2, 2]]()
+    %8 : int[] = prim::Constant[value=[0, 0]]()
+    %9 : int[] = prim::Constant[value=[1, 1]]()
+    %10 : bool = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %11 : int = prim::Constant[value=1]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %12 : bool = prim::Constant[value=0]() # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %self.classifer.fc3.bias : Float(10) = prim::Constant[value= 0.0464  0.0383  0.0678  0.0932  0.1045 -0.0805 -0.0435 -0.0818  0.0208 -0.0358 [ CUDAFloatType{10} ]]()
+    %self.classifer.fc2.bias : Float(84) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.classifer.fc1.bias : Float(120) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv2.weight : Float(16, 6, 3, 3) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv2.bias : Float(16) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv1.weight : Float(6, 1, 3, 3) = prim::Constant[value=&lt;Tensor&gt;]()
+    %self.feat.conv1.bias : Float(6) = prim::Constant[value= 0.0530 -0.1691  0.2802  0.1502  0.1056 -0.1549 [ CUDAFloatType{6} ]]()
+    %input0.4 : Tensor = aten::_convolution(%input.2, %self.feat.conv1.weight, %self.feat.conv1.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %input0.5 : Tensor = aten::relu(%input0.4) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %input1.2 : Tensor = aten::max_pool2d(%input0.5, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %input0.6 : Tensor = aten::_convolution(%input1.2, %self.feat.conv2.weight, %self.feat.conv2.bias, %9, %8, %9, %12, %8, %11, %12, %12, %10) # ~/.local/lib/python3.6/site-packages/torch/nn/modules/conv.py:346:0
+    %input2.1 : Tensor = aten::relu(%input0.6) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %x.1 : Tensor = aten::max_pool2d(%input2.1, %7, %6, %8, %9, %12) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:539:0
+    %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0
+    %27 : Tensor = aten::matmul(%input.1, %4)
+    %28 : Tensor = trt::const(%self.classifer.fc1.bias)
+    %29 : Tensor = aten::add_(%28, %27, %11)
+    %input0.2 : Tensor = aten::relu(%29) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %31 : Tensor = aten::matmul(%input0.2, %3)
+    %32 : Tensor = trt::const(%self.classifer.fc2.bias)
+    %33 : Tensor = aten::add_(%32, %31, %11)
+    %input1.1 : Tensor = aten::relu(%33) # ~/.local/lib/python3.6/site-packages/torch/nn/functional.py:1063:0
+    %35 : Tensor = aten::matmul(%input1.1, %2)
+    %36 : Tensor = trt::const(%self.classifer.fc3.bias)
+    %37 : Tensor = aten::add_(%36, %35, %11)
+    return (%37)
+(CompileGraph)
+</pre></div>
+</div>
+<p>The graph has now been transformed from a collection of modules, each managing their own parameters into a single graph with the parameters inlined
+into the graph and all of the operations laid out. Torch-TensorRT has also executed a number of optimizations and mappings to make the graph easier to translate to TensorRT.
+From here the compiler can assemble the TensorRT engine by following the dataflow through the graph.</p>
+<p>When the graph construction phase is complete, Torch-TensorRT produces a serialized TensorRT engine. From here depending on the API, this engine is returned
+to the user or moves into the graph construction phase. Here Torch-TensorRT creates a JIT Module to execute the TensorRT engine which will be instantiated and managed
+by the Torch-TensorRT runtime.</p>
+<p>Here is the graph that you get back after compilation is complete:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>graph(%self_1 : __torch__.lenet, %input_0 : Tensor):
+    %1 : ...trt.Engine = prim::GetAttr[name=&quot;lenet&quot;](%self_1)
+    %3 : Tensor[] = prim::ListConstruct(%input_0)
+    %4 : Tensor[] = trt::execute_engine(%3, %1)
+    %5 : Tensor = prim::ListUnpack(%4)
+    return (%5)
+</pre></div>
+</div>
+<p>You can see the call where the engine is executed, after extracting the attribute containing the engine and constructing a list of inputs, then returns the tensors back to the user.</p>
+</section>
+<section id="working-with-unsupported-operators">
+<span id="unsupported-ops"></span><h3>Working with Unsupported Operators<a class="headerlink" href="#working-with-unsupported-operators" title="Permalink to this headline">¶</a></h3>
+<p>Torch-TensorRT is a new library and the PyTorch operator library is quite large, so there will be ops that aren’t supported natively by the compiler. You can either use the composition techinques
+shown above to make modules are fully Torch-TensorRT supported and ones that are not and stitch the modules together in the deployment application or you can register converters for missing ops.</p>
+<blockquote>
+<div><p>You can check support without going through the full compilation pipleine using the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::CheckMethodOperatorSupport(const</span> <span class="pre">torch::jit::Module&amp;</span> <span class="pre">module,</span> <span class="pre">std::string</span> <span class="pre">method_name)</span></code> api
+to see what operators are not supported. <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code> automatically checks modules with this method before starting compilation and will print out a list of operators that are not supported.</p>
+</div></blockquote>
+<section id="registering-custom-converters">
+<span id="custom-converters"></span><h4>Registering Custom Converters<a class="headerlink" href="#registering-custom-converters" title="Permalink to this headline">¶</a></h4>
+<p>Operations are mapped to TensorRT through the use of modular converters, a function that takes a node from a the JIT graph and produces an equivalent layer or subgraph in TensorRT.
+Torch-TensorRT ships with a library of these converters stored in a registry, that will be executed depending on the node being parsed. For instance a <code class="docutils literal notranslate"><span class="pre">aten::relu(%input0.4)</span></code> instruction will trigger
+the relu converter to be run on it, producing an activation layer in the TensorRT graph. But since this library is not exhaustive you may need to write your own to get Torch-TensorRT
+to support your module.</p>
+<p>Shipped with the Torch-TensorRT distribution are the internal core API headers. You can therefore access the converter registry and add a converter for the op you need.</p>
+<p>For example, if we try to compile a graph with a build of Torch-TensorRT that doesn’t support the flatten operation (<code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code>) you may see this error:</p>
+<div class="highlight-none notranslate"><div class="highlight"><pre><span></span>terminate called after throwing an instance of &#39;torch_tensorrt::Error&#39;
+what():  [enforce fail at core/conversion/conversion.cpp:109] Expected converter to be true but got false
+Unable to convert node: %input.1 : Tensor = aten::flatten(%x.1, %11, %5) # x.py:25:0 (conversion.AddLayer)
+Schema: aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)
+Converter for aten::flatten requested, but no such converter was found.
+If you need a converter for this operator, you can try implementing one yourself
+or request a converter: https://www.github.com/NVIDIA/Torch-TensorRT/issues
+</pre></div>
+</div>
+<p>We can register a converter for this operator in our application. All of the tools required to build a converter can be imported by including <code class="docutils literal notranslate"><span class="pre">torch_tensorrt/core/conversion/converters/converters.h</span></code>.
+We start by creating an instance of the self-registering class <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::core::conversion::converters::RegisterNodeConversionPatterns()</span></code> which will register converters
+in the global converter registry, associating a function schema like <code class="docutils literal notranslate"><span class="pre">aten::flatten.using_ints(Tensor</span> <span class="pre">self,</span> <span class="pre">int</span> <span class="pre">start_dim=0,</span> <span class="pre">int</span> <span class="pre">end_dim=-1)</span> <span class="pre">-&gt;</span> <span class="pre">(Tensor)</span></code> with a lambda that
+will take the state of the conversion, the node/operation in question to convert and all of the inputs to the node and produces as a side effect a new layer in the TensorRT network.
+Arguments are passed as a vector of inspectable unions of TensorRT <code class="docutils literal notranslate"><span class="pre">ITensors</span></code> and Torch <code class="docutils literal notranslate"><span class="pre">IValues</span></code> in the order arguments are listed in the schema.</p>
+<p>Below is a implementation of a <code class="docutils literal notranslate"><span class="pre">aten::flatten</span></code> converter that we can use in our application. You have full access to the Torch and TensorRT libraries in the converter implementation. So
+for example we can quickly get the output size by just running the operation in PyTorch instead of implementing the full calculation outself like we do below for this flatten converter.</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/script.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/torch_tensorrt.h&quot;</span>
+<span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/core/conversion/converters/converters.h&quot;</span>
+
+<span class="k">static</span><span class="w"> </span><span class="k">auto</span><span class="w"> </span><span class="n">flatten_converter</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">RegisterNodeConversionPatterns</span><span class="p">()</span>
+<span class="w">    </span><span class="p">.</span><span class="n">pattern</span><span class="p">({</span>
+<span class="w">        </span><span class="s">&quot;aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -&gt; (Tensor)&quot;</span><span class="p">,</span>
+<span class="w">        </span><span class="p">[](</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">ConversionCtx</span><span class="o">*</span><span class="w"> </span><span class="n">ctx</span><span class="p">,</span>
+<span class="w">           </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">jit</span><span class="o">::</span><span class="n">Node</span><span class="o">*</span><span class="w"> </span><span class="n">n</span><span class="p">,</span>
+<span class="w">           </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">conversion</span><span class="o">::</span><span class="n">converters</span><span class="o">::</span><span class="n">args</span><span class="o">&amp;</span><span class="w"> </span><span class="n">args</span><span class="p">)</span><span class="w"> </span><span class="o">-&gt;</span><span class="w"> </span><span class="kt">bool</span><span class="w"> </span><span class="p">{</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">in</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">].</span><span class="n">ITensor</span><span class="p">();</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">start_dim</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">1</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">end_dim</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">args</span><span class="p">[</span><span class="mi">2</span><span class="p">].</span><span class="n">unwrapToInt</span><span class="p">();</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">in_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toVec</span><span class="p">(</span><span class="n">in</span><span class="o">-&gt;</span><span class="n">getDimensions</span><span class="p">());</span>
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">out_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">flatten</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">rand</span><span class="p">(</span><span class="n">in_shape</span><span class="p">),</span><span class="w"> </span><span class="n">start_dim</span><span class="p">,</span><span class="w"> </span><span class="n">end_dim</span><span class="p">).</span><span class="n">sizes</span><span class="p">();</span>
+
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">shuffle</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">net</span><span class="o">-&gt;</span><span class="n">addShuffle</span><span class="p">(</span><span class="o">*</span><span class="n">in</span><span class="p">);</span>
+<span class="w">            </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setReshapeDimensions</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">toDims</span><span class="p">(</span><span class="n">out_shape</span><span class="p">));</span>
+<span class="w">            </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">setName</span><span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">core</span><span class="o">::</span><span class="n">util</span><span class="o">::</span><span class="n">node_info</span><span class="p">(</span><span class="n">n</span><span class="p">).</span><span class="n">c_str</span><span class="p">());</span>
+
+<span class="w">            </span><span class="k">auto</span><span class="w"> </span><span class="n">out_tensor</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">ctx</span><span class="o">-&gt;</span><span class="n">AssociateValueAndTensor</span><span class="p">(</span><span class="n">n</span><span class="o">-&gt;</span><span class="n">outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span><span class="w"> </span><span class="n">shuffle</span><span class="o">-&gt;</span><span class="n">getOutput</span><span class="p">(</span><span class="mi">0</span><span class="p">));</span>
+<span class="w">            </span><span class="k">return</span><span class="w"> </span><span class="nb">true</span><span class="p">;</span>
+<span class="w">        </span><span class="p">}</span>
+<span class="w">    </span><span class="p">});</span>
+
+<span class="kt">int</span><span class="w"> </span><span class="nf">main</span><span class="p">()</span><span class="w"> </span><span class="p">{</span>
+<span class="w">    </span><span class="p">...</span>
+</pre></div>
+</div>
+<p>To use this converter in Python, it is recommended to use PyTorch’s <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_extension.html#custom-c-and-cuda-extensions">C++ / CUDA Extention</a>
+template to wrap your library of converters into a <code class="docutils literal notranslate"><span class="pre">.so</span></code> that you can load with <code class="docutils literal notranslate"><span class="pre">ctypes.CDLL()</span></code> in your Python application.</p>
+<p>You can find more information on all the details of writing converters in the contributors documentation (<span class="xref std std-ref">writing_converters</span>).
+If you find yourself with a large library of converter implementations, do consider upstreaming them, PRs are welcome and it would be great for the community to benefit as well.</p>
+</section>
+</section>
+</section>
+</section>
+
+
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+              <ul>
+<li><a class="reference internal" href="#">Using Torch-TensorRT in  C++</a><ul>
+<li><a class="reference internal" href="#id1">Using Torch-TensorRT in C++</a><ul>
+<li><a class="reference internal" href="#torch-tensorrt-quickstart-compiling-torchscript-modules-with-torchtrtc">[Torch-TensorRT Quickstart] Compiling TorchScript Modules with <code class="docutils literal notranslate"><span class="pre">torchtrtc</span></code></a></li>
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+              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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 class="current">
+<li class="toctree-l1"><a class="reference internal" href="creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="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="creating_torchscript_module_in_python.html#saving-torchscript-module-to-disk">Saving TorchScript Module to Disk</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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/logging.html">torch_tensorrt.logging</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/ptq.html">torch_tensorrt.ptq</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/ts.html">torch_tensorrt.ts</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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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+  <section id="using-torch-tensorrt-in-python">
+<span id="getting-started-with-python-api"></span><h1>Using Torch-TensorRT in Python<a class="headerlink" href="#using-torch-tensorrt-in-python" title="Permalink to this headline">¶</a></h1>
+<p>The Torch-TensorRT Python API supports a number of unique usecases compared to the CLI and C++ APIs which solely support TorchScript compilation.</p>
+<p>Torch-TensorRT Python API can accept a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code>, <code class="docutils literal notranslate"><span class="pre">torch.jit.ScriptModule</span></code>, or <code class="docutils literal notranslate"><span class="pre">torch.fx.GraphModule</span></code> as an input.
+Depending on what is provided one of the two frontends (TorchScript or FX) will be selected to compile the module. Provided the
+module type is supported, users may explicitly set which frontend they would like to use using the <code class="docutils literal notranslate"><span class="pre">ir</span></code> flag for <code class="docutils literal notranslate"><span class="pre">compile</span></code>.
+If given a <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> and the <code class="docutils literal notranslate"><span class="pre">ir</span></code> flag is set to either <code class="docutils literal notranslate"><span class="pre">default</span></code> or <code class="docutils literal notranslate"><span class="pre">torchscript</span></code> the module will be run through
+<code class="docutils literal notranslate"><span class="pre">torch.jit.script</span></code> to convert the input module into a  TorchScript module.</p>
+<p>To compile your input <code class="docutils literal notranslate"><span class="pre">torch.nn.Module</span></code> with Torch-TensorRT, all you need to do is provide the module and inputs
+to Torch-TensorRT and you will be returned an optimized TorchScript module to run or add into another PyTorch module. Inputs
+is a list of <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input</span></code> classes which define input Tensors’ shape, datatype and memory format. Alternatively, if your input is a more complex data type, such as a tuple or list of Tensors, you can use the <code class="docutils literal notranslate"><span class="pre">input_signature</span></code> argument to specify a collection-based input, such as <code class="docutils literal notranslate"><span class="pre">(List[Tensor],</span> <span class="pre">Tuple[Tensor,</span> <span class="pre">Tensor])</span></code>. See the second sample below for an example. You can also specify settings such as operating precision for the engine or target device. After compilation you can save the module just like any other module
+to load in a deployment application. In order to load a TensorRT/TorchScript module, make sure you first import <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code>.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="o">...</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>  <span class="c1"># torch module needs to be in eval (not training) mode</span>
+
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span>
+        <span class="n">min_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">16</span><span class="p">],</span>
+        <span class="n">opt_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">],</span>
+        <span class="n">max_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span>
+    <span class="p">)</span>
+<span class="p">]</span>
+<span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">}</span>  <span class="c1"># Run with fp16</span>
+
+<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+    <span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span>
+<span class="p">)</span>
+
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ts_module</span><span class="p">,</span> <span class="s2">&quot;trt_ts_module.ts&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Sample using collection-based inputs via the input_signature argument</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="o">...</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+
+<span class="c1"># input_signature expects a tuple of individual input arguments to the module</span>
+<span class="c1"># The module below, for example, would have a docstring of the form:</span>
+<span class="c1"># def forward(self, input0: List[torch.Tensor], input1: Tuple[torch.Tensor, torch.Tensor])</span>
+<span class="n">input_signature</span> <span class="o">=</span> <span class="p">(</span>
+    <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">),</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">)],</span>
+    <span class="p">(</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">),</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">)),</span>
+<span class="p">)</span>
+<span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">}</span>
+
+<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+    <span class="n">model</span><span class="p">,</span> <span class="n">input_signature</span><span class="o">=</span><span class="n">input_signature</span><span class="p">,</span> <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span>
+<span class="p">)</span>
+
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ts_module</span><span class="p">,</span> <span class="s2">&quot;trt_ts_module.ts&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Deployment application</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">trt_ts_module</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;trt_ts_module.ts&quot;</span><span class="p">)</span>
+<span class="n">input_data</span> <span class="o">=</span> <span class="n">input_data</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span>
+<span class="n">result</span> <span class="o">=</span> <span class="n">trt_ts_module</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Torch-TensorRT Python API also provides <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.ts.compile</span></code> which accepts a TorchScript module as input and <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.fx.compile</span></code> which accepts a FX GraphModule as input.</p>
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diff --git a/docs/v2.2.0/ts/torchscript_frontend_from_pytorch.html b/docs/v2.2.0/ts/torchscript_frontend_from_pytorch.html
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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="creating_torchscript_module_in_python.html">Creating a TorchScript Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="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="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="getting_started_with_python_api.html">Using Torch-TensorRT in Python</a></li>
+<li class="toctree-l1"><a class="reference internal" href="getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</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>
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+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</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/logging.html">torch_tensorrt.logging</a></li>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</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>
+</ul>
+<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="using-torch-tensorrt-torchscript-frontend-directly-from-pytorch">
+<span id="use-from-pytorch"></span><h1>Using Torch-TensorRT TorchScript Frontend Directly From PyTorch<a class="headerlink" href="#using-torch-tensorrt-torchscript-frontend-directly-from-pytorch" title="Permalink to this headline">¶</a></h1>
+<p>You will now be able to directly access TensorRT from PyTorch APIs. The process to use this feature
+is very similar to the compilation workflow described in <a class="reference internal" href="getting_started_with_python_api.html#getting-started-with-python-api"><span class="std std-ref">Using Torch-TensorRT in Python</span></a></p>
+<p>Start by loading <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> into your application.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+</pre></div>
+</div>
+<p>Then given a TorchScript module, you can compile it with TensorRT using the <code class="docutils literal notranslate"><span class="pre">torch._C._jit_to_backend(&quot;tensorrt&quot;,</span> <span class="pre">...)</span></code> API.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">mobilenet_v2</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="n">script_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">script</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Unlike the <code class="docutils literal notranslate"><span class="pre">compile</span></code> API in Torch-TensorRT which assumes you are trying to compile the <code class="docutils literal notranslate"><span class="pre">forward</span></code> function of a module
+or the <code class="docutils literal notranslate"><span class="pre">convert_method_to_trt_engine</span></code> which converts a specified function to a TensorRT engine, the backend API
+will take a dictionary which maps names of functions to compile to Compilation Spec objects which wrap the same
+sort of dictionary you would provide to <code class="docutils literal notranslate"><span class="pre">compile</span></code>. For more information on the compile spec dictionary take a look
+at the documentation for the Torch-TensorRT <code class="docutils literal notranslate"><span class="pre">TensorRTCompileSpec</span></code> API.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">spec</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;forward&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ts</span><span class="o">.</span><span class="n">TensorRTCompileSpec</span><span class="p">(</span>
+        <span class="o">**</span><span class="p">{</span>
+            <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">])],</span>
+            <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">},</span>
+            <span class="s2">&quot;refit&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+            <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+            <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="p">{</span>
+                <span class="s2">&quot;device_type&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">,</span>
+                <span class="s2">&quot;gpu_id&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                <span class="s2">&quot;dla_core&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                <span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+            <span class="p">},</span>
+            <span class="s2">&quot;capability&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">EngineCapability</span><span class="o">.</span><span class="n">default</span><span class="p">,</span>
+            <span class="s2">&quot;num_avg_timing_iters&quot;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="p">}</span>
+    <span class="p">)</span>
+<span class="p">}</span>
+</pre></div>
+</div>
+<p>Now to compile with Torch-TensorRT, provide the target module objects and the spec dictionary to <code class="docutils literal notranslate"><span class="pre">torch._C._jit_to_backend(&quot;tensorrt&quot;,</span> <span class="pre">...)</span></code></p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">trt_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">_C</span><span class="o">.</span><span class="n">_jit_to_backend</span><span class="p">(</span><span class="s2">&quot;tensorrt&quot;</span><span class="p">,</span> <span class="n">script_model</span><span class="p">,</span> <span class="n">spec</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>To run explicitly call the function of the method you want to run (vs. how you can just call on the module itself in standard PyTorch)</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">300</span><span class="p">,</span> <span class="mi">300</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">trt_model</span><span class="o">.</span><span class="n">forward</span><span class="p">(</span><span class="nb">input</span><span class="p">))</span>
+</pre></div>
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+<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>
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+<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>
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+  <section id="dynamo-torch-compile">
+<span id="torch-compile"></span><span id="sphx-glr-tutorials-rendered-examples-dynamo"></span><h1>Dynamo / <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code><a class="headerlink" href="#dynamo-torch-compile" title="Permalink to this headline">¶</a></h1>
+<p>Torch-TensorRT provides a backend for the new <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> API released in PyTorch 2.0. In the following examples we describe
+a number of ways you can leverage this backend to accelerate inference.</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="torch_compile_resnet_example.html#torch-compile-resnet"><span class="std std-ref">Compiling ResNet using the Torch-TensorRT torch.compile Backend</span></a>: Compiling a ResNet model using the Torch Compile Frontend for <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile</span></code></p></li>
+<li><p><a class="reference internal" href="torch_compile_transformers_example.html#torch-compile-transformer"><span class="std std-ref">Compiling a Transformer using torch.compile and TensorRT</span></a>: Compiling a Transformer model using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></p></li>
+<li><p><a class="reference internal" href="torch_compile_advanced_usage.html#torch-compile-advanced-usage"><span class="std std-ref">Torch Compile Advanced Usage</span></a>: Advanced usage including making a custom backend to use directly with the <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> API</p></li>
+<li><p><a class="reference internal" href="torch_compile_stable_diffusion.html#torch-compile-stable-diffusion"><span class="std std-ref">Torch Compile Stable Diffusion</span></a>: Compiling a Stable Diffusion model using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></p></li>
+</ul>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi..."><img alt="" src="../../../_images/sphx_glr_torch_compile_stable_diffusion_thumb.png" />
+<p><a class="reference internal" href="torch_compile_stable_diffusion.html#sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-stable-diffusion-py"><span class="std std-ref">Torch Compile Stable Diffusion</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Torch Compile Stable Diffusion</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi..."><img alt="" src="../../../_images/sphx_glr_torch_compile_resnet_example_thumb.png" />
+<p><a class="reference internal" href="torch_compile_resnet_example.html#sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-resnet-example-py"><span class="std std-ref">Compiling ResNet using the Torch-TensorRT torch.compile Backend</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Compiling ResNet using the Torch-TensorRT torch.compile Backend</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi..."><img alt="" src="../../../_images/sphx_glr_torch_compile_transformers_example_thumb.png" />
+<p><a class="reference internal" href="torch_compile_transformers_example.html#sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-transformers-example-py"><span class="std std-ref">Compiling a Transformer using torch.compile and TensorRT</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Compiling a Transformer using torch.compile and TensorRT</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as an overview of the process by which torch_tensorrt.compi..."><img alt="" src="../../../_images/sphx_glr_torch_compile_advanced_usage_thumb.png" />
+<p><a class="reference internal" href="torch_compile_advanced_usage.html#sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-advanced-usage-py"><span class="std std-ref">Torch Compile Advanced Usage</span></a></p>
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diff --git a/docs/v2.2.0/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html b/docs/v2.2.0/tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage.html
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+              
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-tutorials-rendered-examples-dynamo-torch-compile-advanced-usage-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="torch-compile-advanced-usage">
+<span id="sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-advanced-usage-py"></span><span id="id1"></span><h1>Torch Compile Advanced Usage<a class="headerlink" href="#torch-compile-advanced-usage" title="Permalink to this headline">¶</a></h1>
+<p>This interactive script is intended as an overview of the process by which <cite>torch_tensorrt.compile(…, ir=”torch_compile”, …)</cite> works, and how it integrates with the <cite>torch.compile</cite> API.</p>
+<section id="imports-and-model-definition">
+<h2>Imports and Model Definition<a class="headerlink" href="#imports-and-model-definition" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># We begin by defining a model</span>
+<span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">relu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="n">x_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">y_out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>
+        <span class="n">x_y_out</span> <span class="o">=</span> <span class="n">x_out</span> <span class="o">+</span> <span class="n">y_out</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">x_y_out</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="compilation-with-torch-compile-using-default-settings">
+<h2>Compilation with <cite>torch.compile</cite> Using Default Settings<a class="headerlink" href="#compilation-with-torch-compile-using-default-settings" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define sample float inputs and initialize model</span>
+<span class="n">sample_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span> <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()]</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Next, we compile the model using torch.compile</span>
+<span class="c1"># For the default settings, we can simply call torch.compile</span>
+<span class="c1"># with the backend &quot;torch_tensorrt&quot;, and run the model on an</span>
+<span class="c1"># input to cause compilation, as so:</span>
+<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span> <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">sample_inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="compilation-with-torch-compile-using-custom-settings">
+<h2>Compilation with <cite>torch.compile</cite> Using Custom Settings<a class="headerlink" href="#compilation-with-torch-compile-using-custom-settings" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># First, we use Torch utilities to clean up the workspace</span>
+<span class="c1"># after the previous compile invocation</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+
+<span class="c1"># Define sample half inputs and initialize model</span>
+<span class="n">sample_inputs_half</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span><span class="o">.</span><span class="n">half</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">rand</span><span class="p">((</span><span class="mi">5</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span><span class="o">.</span><span class="n">half</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">(),</span>
+<span class="p">]</span>
+<span class="n">model_half</span> <span class="o">=</span> <span class="n">Model</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># If we want to customize certain options in the backend,</span>
+<span class="c1"># but still use the torch.compile call directly, we can provide</span>
+<span class="c1"># custom options to the backend via the &quot;options&quot; keyword</span>
+<span class="c1"># which takes in a dictionary mapping options to values.</span>
+<span class="c1">#</span>
+<span class="c1"># For accepted backend options, see the CompilationSettings dataclass:</span>
+<span class="c1"># py/torch_tensorrt/dynamo/_settings.py</span>
+<span class="n">backend_kwargs</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">},</span>
+    <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="p">{</span><span class="s2">&quot;torch.ops.aten.sub.Tensor&quot;</span><span class="p">},</span>
+    <span class="s2">&quot;optimization_level&quot;</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
+    <span class="s2">&quot;use_python_runtime&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="c1"># Run the model on an input to cause compilation, as so:</span>
+<span class="n">optimized_model_custom</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+    <span class="n">model_half</span><span class="p">,</span>
+    <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span>
+    <span class="n">options</span><span class="o">=</span><span class="n">backend_kwargs</span><span class="p">,</span>
+    <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">optimized_model_custom</span><span class="p">(</span><span class="o">*</span><span class="n">sample_inputs_half</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="cleanup">
+<h2>Cleanup<a class="headerlink" href="#cleanup" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Finally, we use Torch utilities to clean up the workspace</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="cuda-driver-error-note">
+<h2>Cuda Driver Error Note<a class="headerlink" href="#cuda-driver-error-note" title="Permalink to this headline">¶</a></h2>
+<p>Occasionally, upon exiting the Python runtime after Dynamo compilation with <cite>torch_tensorrt</cite>,
+one may encounter a Cuda Driver Error. This issue is related to <a class="reference external" href="https://github.com/NVIDIA/TensorRT/issues/2052">https://github.com/NVIDIA/TensorRT/issues/2052</a>
+and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>if __name__ == &#39;__main__&#39;:
+    compile_engine_and_infer()
+</pre></div>
+</div>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
+<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-tutorials-rendered-examples-dynamo-torch-compile-advanced-usage-py">
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+              <ul>
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+<li><a class="reference internal" href="#compilation-with-torch-compile-using-default-settings">Compilation with <cite>torch.compile</cite> Using Default Settings</a></li>
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diff --git a/docs/v2.2.0/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html b/docs/v2.2.0/tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html
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@@ -0,0 +1,859 @@
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+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<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>
+</ul>
+<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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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-tutorials-rendered-examples-dynamo-torch-compile-resnet-example-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="compiling-resnet-using-the-torch-tensorrt-torch-compile-backend">
+<span id="torch-compile-resnet"></span><span id="sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-resnet-example-py"></span><h1>Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend<a class="headerlink" href="#compiling-resnet-using-the-torch-tensorrt-torch-compile-backend" title="Permalink to this headline">¶</a></h1>
+<p>This interactive script is intended as a sample of the Torch-TensorRT workflow with <cite>torch.compile</cite> on a ResNet model.</p>
+<section id="imports-and-model-definition">
+<h2>Imports and Model Definition<a class="headerlink" href="#imports-and-model-definition" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="kn">import</span> <span class="nn">torchvision.models</span> <span class="k">as</span> <span class="nn">models</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize model with half precision and sample inputs</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">resnet18</span><span class="p">(</span><span class="n">pretrained</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">half</span><span class="p">()]</span>
+</pre></div>
+</div>
+</section>
+<section id="optional-input-arguments-to-torch-tensorrt-compile">
+<h2>Optional Input Arguments to <cite>torch_tensorrt.compile</cite><a class="headerlink" href="#optional-input-arguments-to-torch-tensorrt-compile" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Enabled precision for TensorRT optimization</span>
+<span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">}</span>
+
+<span class="c1"># Whether to print verbose logs</span>
+<span class="n">debug</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Workspace size for TensorRT</span>
+<span class="n">workspace_size</span> <span class="o">=</span> <span class="mi">20</span> <span class="o">&lt;&lt;</span> <span class="mi">30</span>
+
+<span class="c1"># Maximum number of TRT Engines</span>
+<span class="c1"># (Lower value allows more graph segmentation)</span>
+<span class="n">min_block_size</span> <span class="o">=</span> <span class="mi">7</span>
+
+<span class="c1"># Operations to Run in Torch, regardless of converter support</span>
+<span class="n">torch_executed_ops</span> <span class="o">=</span> <span class="p">{}</span>
+</pre></div>
+</div>
+</section>
+<section id="compilation-with-torch-tensorrt-compile">
+<h2>Compilation with <cite>torch_tensorrt.compile</cite><a class="headerlink" href="#compilation-with-torch-tensorrt-compile" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build and compile the model with torch.compile, using Torch-TensorRT backend</span>
+<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+    <span class="n">model</span><span class="p">,</span>
+    <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;torch_compile&quot;</span><span class="p">,</span>
+    <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span>
+    <span class="n">enabled_precisions</span><span class="o">=</span><span class="n">enabled_precisions</span><span class="p">,</span>
+    <span class="n">debug</span><span class="o">=</span><span class="n">debug</span><span class="p">,</span>
+    <span class="n">workspace_size</span><span class="o">=</span><span class="n">workspace_size</span><span class="p">,</span>
+    <span class="n">min_block_size</span><span class="o">=</span><span class="n">min_block_size</span><span class="p">,</span>
+    <span class="n">torch_executed_ops</span><span class="o">=</span><span class="n">torch_executed_ops</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+<p>Equivalently, we could have run the above via the torch.compile frontend, as so:
+<cite>optimized_model = torch.compile(model, backend=”torch_tensorrt”, options={“enabled_precisions”: enabled_precisions, …}); optimized_model(*inputs)</cite></p>
+</section>
+<section id="inference">
+<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Does not cause recompilation (same batch size as input)</span>
+<span class="n">new_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span><span class="o">.</span><span class="n">half</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)]</span>
+<span class="n">new_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">new_inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Does cause recompilation (new batch size)</span>
+<span class="n">new_batch_size_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span><span class="o">.</span><span class="n">half</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)]</span>
+<span class="n">new_batch_size_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">new_batch_size_inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="cleanup">
+<h2>Cleanup<a class="headerlink" href="#cleanup" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Finally, we use Torch utilities to clean up the workspace</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="cuda-driver-error-note">
+<h2>Cuda Driver Error Note<a class="headerlink" href="#cuda-driver-error-note" title="Permalink to this headline">¶</a></h2>
+<p>Occasionally, upon exiting the Python runtime after Dynamo compilation with <cite>torch_tensorrt</cite>,
+one may encounter a Cuda Driver Error. This issue is related to <a class="reference external" href="https://github.com/NVIDIA/TensorRT/issues/2052">https://github.com/NVIDIA/TensorRT/issues/2052</a>
+and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>if __name__ == &#39;__main__&#39;:
+    compile_engine_and_infer()
+</pre></div>
+</div>
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+              <ul>
+<li><a class="reference internal" href="#">Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend</a><ul>
+<li><a class="reference internal" href="#imports-and-model-definition">Imports and Model Definition</a></li>
+<li><a class="reference internal" href="#optional-input-arguments-to-torch-tensorrt-compile">Optional Input Arguments to <cite>torch_tensorrt.compile</cite></a></li>
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+<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>
+</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>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../../user_guide/using_dla.html">DLA</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../../serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend</a></li>
+<li class="toctree-l1"><a class="reference internal" href="torch_compile_transformers_example.html">Compiling a Transformer using torch.compile and TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Torch Compile Stable Diffusion</a></li>
+</ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../cli/torchtrtc.html">torchtrtc</a></li>
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+<ul>
+<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>
+</ul>
+<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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+          
+          <div class="rst-content">
+          
+            <div role="main" class="main-content" itemscope="itemscope" itemtype="http://schema.org/Article">
+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-tutorials-rendered-examples-dynamo-torch-compile-stable-diffusion-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="torch-compile-stable-diffusion">
+<span id="sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-stable-diffusion-py"></span><span id="id1"></span><h1>Torch Compile Stable Diffusion<a class="headerlink" href="#torch-compile-stable-diffusion" title="Permalink to this headline">¶</a></h1>
+<p>This interactive script is intended as a sample of the Torch-TensorRT workflow with <cite>torch.compile</cite> on a Stable Diffusion model. A sample output is featured below:</p>
+<a class="reference internal image-reference" href="../../../_images/majestic_castle.png"><img alt="../../../_images/majestic_castle.png" class="align-right" src="../../../_images/majestic_castle.png" style="width: 256.0px; height: 256.0px;" /></a>
+<section id="imports-and-model-definition">
+<h2>Imports and Model Definition<a class="headerlink" href="#imports-and-model-definition" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">diffusers</span> <span class="kn">import</span> <span class="n">DiffusionPipeline</span>
+
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">model_id</span> <span class="o">=</span> <span class="s2">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
+<span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cuda:0&quot;</span>
+
+<span class="c1"># Instantiate Stable Diffusion Pipeline with FP16 weights</span>
+<span class="n">pipe</span> <span class="o">=</span> <span class="n">DiffusionPipeline</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span>
+    <span class="n">model_id</span><span class="p">,</span> <span class="n">revision</span><span class="o">=</span><span class="s2">&quot;fp16&quot;</span><span class="p">,</span> <span class="n">torch_dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float16</span>
+<span class="p">)</span>
+<span class="n">pipe</span> <span class="o">=</span> <span class="n">pipe</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+
+<span class="n">backend</span> <span class="o">=</span> <span class="s2">&quot;torch_tensorrt&quot;</span>
+
+<span class="c1"># Optimize the UNet portion with Torch-TensorRT</span>
+<span class="n">pipe</span><span class="o">.</span><span class="n">unet</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+    <span class="n">pipe</span><span class="o">.</span><span class="n">unet</span><span class="p">,</span>
+    <span class="n">backend</span><span class="o">=</span><span class="n">backend</span><span class="p">,</span>
+    <span class="n">options</span><span class="o">=</span><span class="p">{</span>
+        <span class="s2">&quot;truncate_long_and_double&quot;</span><span class="p">:</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="s2">&quot;precision&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">float16</span><span class="p">,</span>
+    <span class="p">},</span>
+    <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="inference">
+<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">prompt</span> <span class="o">=</span> <span class="s2">&quot;a majestic castle in the clouds&quot;</span>
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diff --git a/docs/v2.2.0/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html b/docs/v2.2.0/tutorials/_rendered_examples/dynamo/torch_compile_transformers_example.html
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+  <div class="sphx-glr-download-link-note admonition note">
+<p class="admonition-title">Note</p>
+<p><a class="reference internal" href="#sphx-glr-download-tutorials-rendered-examples-dynamo-torch-compile-transformers-example-py"><span class="std std-ref">Go to the end</span></a>
+to download the full example code</p>
+</div>
+<section class="sphx-glr-example-title" id="compiling-a-transformer-using-torch-compile-and-tensorrt">
+<span id="torch-compile-transformer"></span><span id="sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-transformers-example-py"></span><h1>Compiling a Transformer using torch.compile and TensorRT<a class="headerlink" href="#compiling-a-transformer-using-torch-compile-and-tensorrt" title="Permalink to this headline">¶</a></h1>
+<p>This interactive script is intended as a sample of the Torch-TensorRT workflow with <cite>torch.compile</cite> on a transformer-based model.</p>
+<section id="imports-and-model-definition">
+<h2>Imports and Model Definition<a class="headerlink" href="#imports-and-model-definition" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="kn">from</span> <span class="nn">transformers</span> <span class="kn">import</span> <span class="n">BertModel</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Initialize model with float precision and sample inputs</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">BertModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;bert-base-uncased&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">),</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">),</span>
+<span class="p">]</span>
+</pre></div>
+</div>
+</section>
+<section id="optional-input-arguments-to-torch-tensorrt-compile">
+<h2>Optional Input Arguments to <cite>torch_tensorrt.compile</cite><a class="headerlink" href="#optional-input-arguments-to-torch-tensorrt-compile" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Enabled precision for TensorRT optimization</span>
+<span class="n">enabled_precisions</span> <span class="o">=</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">}</span>
+
+<span class="c1"># Whether to print verbose logs</span>
+<span class="n">debug</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Workspace size for TensorRT</span>
+<span class="n">workspace_size</span> <span class="o">=</span> <span class="mi">20</span> <span class="o">&lt;&lt;</span> <span class="mi">30</span>
+
+<span class="c1"># Maximum number of TRT Engines</span>
+<span class="c1"># (Lower value allows more graph segmentation)</span>
+<span class="n">min_block_size</span> <span class="o">=</span> <span class="mi">7</span>
+
+<span class="c1"># Operations to Run in Torch, regardless of converter support</span>
+<span class="n">torch_executed_ops</span> <span class="o">=</span> <span class="p">{}</span>
+</pre></div>
+</div>
+</section>
+<section id="compilation-with-torch-compile">
+<h2>Compilation with <cite>torch.compile</cite><a class="headerlink" href="#compilation-with-torch-compile" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Define backend compilation keyword arguments</span>
+<span class="n">compilation_kwargs</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;enabled_precisions&quot;</span><span class="p">:</span> <span class="n">enabled_precisions</span><span class="p">,</span>
+    <span class="s2">&quot;debug&quot;</span><span class="p">:</span> <span class="n">debug</span><span class="p">,</span>
+    <span class="s2">&quot;workspace_size&quot;</span><span class="p">:</span> <span class="n">workspace_size</span><span class="p">,</span>
+    <span class="s2">&quot;min_block_size&quot;</span><span class="p">:</span> <span class="n">min_block_size</span><span class="p">,</span>
+    <span class="s2">&quot;torch_executed_ops&quot;</span><span class="p">:</span> <span class="n">torch_executed_ops</span><span class="p">,</span>
+<span class="p">}</span>
+
+<span class="c1"># Build and compile the model with torch.compile, using Torch-TensorRT backend</span>
+<span class="n">optimized_model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span>
+    <span class="n">model</span><span class="p">,</span>
+    <span class="n">backend</span><span class="o">=</span><span class="s2">&quot;torch_tensorrt&quot;</span><span class="p">,</span>
+    <span class="n">dynamic</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">options</span><span class="o">=</span><span class="n">compilation_kwargs</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Equivalently, we could have run the above via the convenience frontend, as so:
+<cite>torch_tensorrt.compile(model, ir=”torch_compile”, inputs=inputs, **compilation_kwargs)</cite></p>
+</section>
+<section id="inference">
+<h2>Inference<a class="headerlink" href="#inference" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Does not cause recompilation (same batch size as input)</span>
+<span class="n">new_inputs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">),</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="n">new_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">new_inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Does cause recompilation (new batch size)</span>
+<span class="n">new_inputs</span> <span class="o">=</span> <span class="p">[</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">),</span>
+    <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">),</span>
+<span class="p">]</span>
+<span class="n">new_outputs</span> <span class="o">=</span> <span class="n">optimized_model</span><span class="p">(</span><span class="o">*</span><span class="n">new_inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="cleanup">
+<h2>Cleanup<a class="headerlink" href="#cleanup" title="Permalink to this headline">¶</a></h2>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Finally, we use Torch utilities to clean up the workspace</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">_dynamo</span><span class="o">.</span><span class="n">reset</span><span class="p">()</span>
+</pre></div>
+</div>
+</section>
+<section id="cuda-driver-error-note">
+<h2>Cuda Driver Error Note<a class="headerlink" href="#cuda-driver-error-note" title="Permalink to this headline">¶</a></h2>
+<p>Occasionally, upon exiting the Python runtime after Dynamo compilation with <cite>torch_tensorrt</cite>,
+one may encounter a Cuda Driver Error. This issue is related to <a class="reference external" href="https://github.com/NVIDIA/TensorRT/issues/2052">https://github.com/NVIDIA/TensorRT/issues/2052</a>
+and can be resolved by wrapping the compilation/inference in a function and using a scoped call, as in:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>if __name__ == &#39;__main__&#39;:
+    compile_engine_and_infer()
+</pre></div>
+</div>
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+              <ul>
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+<li><a class="reference internal" href="#imports-and-model-definition">Imports and Model Definition</a></li>
+<li><a class="reference internal" href="#optional-input-arguments-to-torch-tensorrt-compile">Optional Input Arguments to <cite>torch_tensorrt.compile</cite></a></li>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo/torch_compile_transformers_example.html">Compiling a Transformer using torch.compile and TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo/torch_compile_advanced_usage.html">Torch Compile Advanced Usage</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dynamo/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../_cpp_api/torch_tensort_cpp.html">Torch-TensorRT C++ API</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
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+              
+  <section id="torch-tensorrt-tutorials">
+<span id="id1"></span><h1>Torch-TensorRT Tutorials<a class="headerlink" href="#torch-tensorrt-tutorials" title="Permalink to this headline">¶</a></h1>
+<p>The user guide covers the basic concepts and usage of Torch-TensorRT.
+We also provide a number of tutorials to explore specific usecases and advanced concepts</p>
+<div class="sphx-glr-thumbnails"></div><section id="dynamo-torch-compile">
+<h2>Dynamo / <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code><a class="headerlink" href="#dynamo-torch-compile" title="Permalink to this headline">¶</a></h2>
+<p>Torch-TensorRT provides a backend for the new <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> API released in PyTorch 2.0. In the following examples we describe
+a number of ways you can leverage this backend to accelerate inference.</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="dynamo/torch_compile_resnet_example.html#torch-compile-resnet"><span class="std std-ref">Compiling ResNet using the Torch-TensorRT torch.compile Backend</span></a>: Compiling a ResNet model using the Torch Compile Frontend for <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile</span></code></p></li>
+<li><p><a class="reference internal" href="dynamo/torch_compile_transformers_example.html#torch-compile-transformer"><span class="std std-ref">Compiling a Transformer using torch.compile and TensorRT</span></a>: Compiling a Transformer model using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></p></li>
+<li><p><a class="reference internal" href="dynamo/torch_compile_advanced_usage.html#torch-compile-advanced-usage"><span class="std std-ref">Torch Compile Advanced Usage</span></a>: Advanced usage including making a custom backend to use directly with the <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code> API</p></li>
+<li><p><a class="reference internal" href="dynamo/torch_compile_stable_diffusion.html#torch-compile-stable-diffusion"><span class="std std-ref">Torch Compile Stable Diffusion</span></a>: Compiling a Stable Diffusion model using <code class="docutils literal notranslate"><span class="pre">torch.compile</span></code></p></li>
+</ul>
+<div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi..."><img alt="" src="../../_images/sphx_glr_torch_compile_stable_diffusion_thumb.png" />
+<p><a class="reference internal" href="dynamo/torch_compile_stable_diffusion.html#sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-stable-diffusion-py"><span class="std std-ref">Torch Compile Stable Diffusion</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Torch Compile Stable Diffusion</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi..."><img alt="" src="../../_images/sphx_glr_torch_compile_resnet_example_thumb.png" />
+<p><a class="reference internal" href="dynamo/torch_compile_resnet_example.html#sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-resnet-example-py"><span class="std std-ref">Compiling ResNet using the Torch-TensorRT torch.compile Backend</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Compiling ResNet using the Torch-TensorRT torch.compile Backend</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as a sample of the Torch-TensorRT workflow with torch.compi..."><img alt="" src="../../_images/sphx_glr_torch_compile_transformers_example_thumb.png" />
+<p><a class="reference internal" href="dynamo/torch_compile_transformers_example.html#sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-transformers-example-py"><span class="std std-ref">Compiling a Transformer using torch.compile and TensorRT</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Compiling a Transformer using torch.compile and TensorRT</div>
+</div><div class="sphx-glr-thumbcontainer" tooltip="This interactive script is intended as an overview of the process by which torch_tensorrt.compi..."><img alt="" src="../../_images/sphx_glr_torch_compile_advanced_usage_thumb.png" />
+<p><a class="reference internal" href="dynamo/torch_compile_advanced_usage.html#sphx-glr-tutorials-rendered-examples-dynamo-torch-compile-advanced-usage-py"><span class="std std-ref">Torch Compile Advanced Usage</span></a></p>
+  <div class="sphx-glr-thumbnail-title">Torch Compile Advanced Usage</div>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="serving_torch_tensorrt_with_triton.html">Serving a Torch-TensorRT model with Triton</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <cite>torch.compile</cite> Backend</a></li>
+<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_compile_transformers_example.html">Compiling a Transformer using torch.compile and TensorRT</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="_rendered_examples/dynamo/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../py_api/fx.html">torch_tensorrt.fx</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
+<ul>
+<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>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
+</ul>
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="example-notebooks">
+<span id="notebooks"></span><h1>Example notebooks<a class="headerlink" href="#example-notebooks" title="Permalink to this headline">¶</a></h1>
+<p>There exists a number of notebooks which cover specific using specific features and models
+with Torch-TensorRT</p>
+<section id="id1">
+<h2>Notebooks<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
+<section id="compiling-citrinet-with-torch-tensorrt">
+<h3>Compiling CitriNet with Torch-TensorRT<a class="headerlink" href="#compiling-citrinet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>Citrinet is an acoustic model used for the speech to text recognition task. It is a version
+of QuartzNet that extends ContextNet, utilizing subword encoding (via Word Piece tokenization)
+and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models. CitriNet
+models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.</p>
+<p>This notebook demonstrates the steps for optimizing a pretrained CitriNet model with Torch-TensorRT,
+and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/CitriNet-example.ipynb">Torch-TensorRT Getting Started - CitriNet</a></p></li>
+</ul>
+</section>
+<section id="compiling-efficentnet-with-torch-tensorrt">
+<h3>Compiling EfficentNet with Torch-TensorRT<a class="headerlink" href="#compiling-efficentnet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>EfficentNet is a feedforward CNN designed to achieve better performance and accuracy than alternative architectures
+by using a “scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient”.</p>
+<p>This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
+and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/EfficientNet-example.ipynb">Torch-TensorRT Getting Started - EfficientNet-B0</a></p></li>
+</ul>
+</section>
+<section id="masked-language-modeling-mlm-with-hugging-face-bert-transformer-accelerated-by-torch-tensorrt">
+<h3>Masked Language Modeling (MLM) with Hugging Face BERT Transformer accelerated by Torch-TensorRT<a class="headerlink" href="#masked-language-modeling-mlm-with-hugging-face-bert-transformer-accelerated-by-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>“BERT is a transformer model pretrained on a large corpus of English data in a self-supervised fashion.
+This way, the model learns an inner representation of the English language that can then be used to extract
+features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train
+a standard classifier using the features produced by the BERT model as inputs.” (<a class="reference external" href="https://huggingface.co/bert-base-uncased">https://huggingface.co/bert-base-uncased</a>)</p>
+<p>This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
+and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Hugging-Face-BERT.ipynb">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></p></li>
+</ul>
+</section>
+<section id="serving-a-model-in-c-using-torch-tensorrt">
+<h3>Serving a model in C++ using Torch-TensorRT<a class="headerlink" href="#serving-a-model-in-c-using-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>This example shows how you can load a pretrained ResNet-50 model, convert it to a Torch-TensorRT
+optimized model (via the Torch-TensorRT Python API), save the model as a torchscript module, and
+then finally load and serve the model with the PyTorch C++ API.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-CPP.ipynb">ResNet C++ Serving Example</a></p></li>
+</ul>
+</section>
+<section id="compiling-resnet50-with-torch-tensorrt">
+<h3>Compiling ResNet50 with Torch-TensorRT<a class="headerlink" href="#compiling-resnet50-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a
+pretrained ResNet-50 network, and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-example.ipynb">Torch-TensorRT Getting Started - ResNet 50</a></p></li>
+</ul>
+</section>
+<section id="using-dynamic-shapes-with-torch-tensorrt">
+<h3>Using Dynamic Shapes with Torch-TensorRT<a class="headerlink" href="#using-dynamic-shapes-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>Making use of Dynamic Shaped Tensors in Torch TensorRT is quite simple. Let’s say you are
+using the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile(...)</span></code> function  to compile a torchscript module. One
+of the args in this function in this function is <code class="docutils literal notranslate"><span class="pre">input</span></code>: which defines an input to a
+module in terms of expected shape, data type and tensor format: <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input.</span></code></p>
+<p>For the purposes of this walkthrough we just need three kwargs: <cite>min_shape</cite>, <cite>opt_shape`</cite> and <cite>max_shape</cite>.</p>
+<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span>
+        <span class="n">min_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+        <span class="n">opt_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+        <span class="n">max_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
+        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span>
+        <span class="nb">format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channel_last</span>
+    <span class="p">)</span>
+<span class="o">...</span>
+</pre></div>
+</div>
+<p>In this example, we are going to use a simple ResNet model to demonstrate the use of the API.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/dynamic-shapes.ipynb">Torch-TensorRT - Using Dynamic Shapes</a></p></li>
+</ul>
+</section>
+<section id="using-the-fx-frontend-with-torch-tensorrt">
+<h3>Using the FX Frontend with Torch-TensorRT<a class="headerlink" href="#using-the-fx-frontend-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>The purpose of this example is to demostrate the overall flow of lowering a PyTorch model to TensorRT
+conveniently with using FX.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb">Using the FX Frontend with Torch-TensorRT</a></p></li>
+</ul>
+</section>
+<section id="compiling-a-pytorch-model-using-fx-frontend-with-torch-tensorrt">
+<h3>Compiling a PyTorch model using FX Frontend with Torch-TensorRT<a class="headerlink" href="#compiling-a-pytorch-model-using-fx-frontend-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>The purpose of this example is to demonstrate the overall flow of lowering a PyTorch
+model to TensorRT via FX with existing FX based tooling</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_module.ipynb">Compiling a PyTorch model using FX Frontend with Torch-TensorRT</a></p></li>
+</ul>
+</section>
+<section id="compiling-lenet-with-torch-tensorrt">
+<h3>Compiling LeNet with Torch-TensorRT<a class="headerlink" href="#compiling-lenet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/lenet-getting-started.ipynb">Torch-TensorRT Getting Started - LeNet</a></p></li>
+</ul>
+</section>
+<section id="accelerate-deep-learning-models-using-quantization-in-torch-tensorrt">
+<h3>Accelerate Deep Learning Models using Quantization in Torch-TensorRT<a class="headerlink" href="#accelerate-deep-learning-models-using-quantization-in-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>Model Quantization is a popular way of optimization which reduces the size of models thereby
+accelerating inference, also opening up the possibilities of deployments on devices with lower
+computation power such as Jetson. Simply put, quantization is a process of mapping input values</p>
+<blockquote>
+<div><p>from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
+that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
+you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
+called Imagenette which has 10 classes.</p>
+</div></blockquote>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/qat-ptq-workflow.ipynb">Accelerate Deep Learning Models using Quantization in Torch-TensorRT</a></p></li>
+</ul>
+</section>
+<section id="object-detection-with-torch-tensorrt-ssd">
+<h3>Object Detection with Torch-TensorRT (SSD)<a class="headerlink" href="#object-detection-with-torch-tensorrt-ssd" title="Permalink to this headline">¶</a></h3>
+<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/ssd-object-detection-demo.ipynb">Object Detection with Torch-TensorRT (SSD)</a></p></li>
+</ul>
+</section>
+<section id="deploying-quantization-aware-trained-models-in-int8-using-torch-tensorrt">
+<h3>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a class="headerlink" href="#deploying-quantization-aware-trained-models-in-int8-using-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
+<p>Quantization Aware training (QAT) simulates quantization during training by
+quantizing weights and activation layers. This will help to reduce the loss in
+accuracy when we convert the network trained in FP32 to INT8 for faster inference.
+QAT introduces additional nodes in the graph which will be used to learn the dynamic
+ranges of weights and activation layers. In this notebook, we illustrate the following
+steps from training to inference of a QAT model in Torch-TensorRT.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/vgg-qat.ipynb">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></p></li>
+</ul>
+</section>
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+<li><a class="reference internal" href="#">Example notebooks</a><ul>
+<li><a class="reference internal" href="#id1">Notebooks</a><ul>
+<li><a class="reference internal" href="#compiling-citrinet-with-torch-tensorrt">Compiling CitriNet with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#compiling-efficentnet-with-torch-tensorrt">Compiling EfficentNet with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#masked-language-modeling-mlm-with-hugging-face-bert-transformer-accelerated-by-torch-tensorrt">Masked Language Modeling (MLM) with Hugging Face BERT Transformer accelerated by Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#serving-a-model-in-c-using-torch-tensorrt">Serving a model in C++ using Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#compiling-resnet50-with-torch-tensorrt">Compiling ResNet50 with Torch-TensorRT</a></li>
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+<li><a class="reference internal" href="#compiling-a-pytorch-model-using-fx-frontend-with-torch-tensorrt">Compiling a PyTorch model using FX Frontend with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#compiling-lenet-with-torch-tensorrt">Compiling LeNet with Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#accelerate-deep-learning-models-using-quantization-in-torch-tensorrt">Accelerate Deep Learning Models using Quantization in Torch-TensorRT</a></li>
+<li><a class="reference internal" href="#object-detection-with-torch-tensorrt-ssd">Object Detection with Torch-TensorRT (SSD)</a></li>
+<li><a class="reference internal" href="#deploying-quantization-aware-trained-models-in-int8-using-torch-tensorrt">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></li>
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diff --git a/docs/v2.2.0/tutorials/serving_torch_tensorrt_with_triton.html b/docs/v2.2.0/tutorials/serving_torch_tensorrt_with_triton.html
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+<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</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">User Guide</span></p>
+<ul>
+<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/ptq.html">Post Training Quantization (PTQ)</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Tutorials</span></p>
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+<p class="caption" role="heading"><span class="caption-text">C++ API Documenation</span></p>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
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+              
+  <section id="serving-a-torch-tensorrt-model-with-triton">
+<span id="serving-torch-tensorrt-with-triton"></span><h1>Serving a Torch-TensorRT model with Triton<a class="headerlink" href="#serving-a-torch-tensorrt-model-with-triton" title="Permalink to this headline">¶</a></h1>
+<p>Optimization and deployment go hand in hand in a discussion about Machine
+Learning infrastructure. Once network level optimzation are done
+to get the maximum performance, the next step would be to deploy it.</p>
+<p>However, serving this optimized model comes with it’s own set of considerations
+and challenges like: building an infrastructure to support concorrent model
+executions, supporting clients over HTTP or gRPC and more.</p>
+<p>The <a class="reference external" href="https://github.com/triton-inference-server/server">Triton Inference Server</a>
+solves the aforementioned and more. Let’s discuss step-by-step, the process of
+optimizing a model with Torch-TensorRT, deploying it on Triton Inference
+Server, and building a client to query the model.</p>
+<section id="step-1-optimize-your-model-with-torch-tensorrt">
+<h2>Step 1: Optimize your model with Torch-TensorRT<a class="headerlink" href="#step-1-optimize-your-model-with-torch-tensorrt" title="Permalink to this headline">¶</a></h2>
+<p>Most Torch-TensorRT users will be familiar with this step. For the purpose of
+this demonstration, we will be using a ResNet50 model from Torchhub.</p>
+<p>Let’s first pull the <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch">NGC PyTorch Docker container</a>. You may need to create
+an account and get the API key from <a class="reference external" href="https://ngc.nvidia.com/setup/">here</a>.
+Sign up and login with your key (follow the instructions
+<a class="reference external" href="https://ngc.nvidia.com/setup/api-key">here</a> after signing up).</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># &lt;xx.xx&gt; is the yy:mm for the publishing tag for NVIDIA&#39;s Pytorch</span>
+<span class="cp"># container; eg. 22.04</span>
+
+<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">-</span><span class="n">it</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="n">$</span><span class="p">{</span><span class="n">PWD</span><span class="p">}</span><span class="o">:/</span><span class="n">scratch_space</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">pytorch</span><span class="o">:&lt;</span><span class="n">xx</span><span class="p">.</span><span class="n">xx</span><span class="o">&gt;-</span><span class="n">py3</span>
+<span class="n">cd</span><span class="w"> </span><span class="o">/</span><span class="n">scratch_space</span>
+</pre></div>
+</div>
+<p>Once inside the container, we can proceed to download a ResNet model from
+Torchhub and optimize it with Torch-TensorRT.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>import torch
+import torch_tensorrt
+torch.hub._validate_not_a_forked_repo=lambda a,b,c: True
+
+# load model
+model = torch.hub.load(&#39;pytorch/vision:v0.10.0&#39;, &#39;resnet50&#39;, pretrained=True).eval().to(&quot;cuda&quot;)
+
+# Compile with Torch TensorRT;
+trt_model = torch_tensorrt.compile(model,
+    inputs= [torch_tensorrt.Input((1, 3, 224, 224))],
+    enabled_precisions= { torch.half} # Run with FP32
+)
+
+# Save the model
+torch.jit.save(trt_model, &quot;model.pt&quot;)
+</pre></div>
+</div>
+<p>After copying the model, exit the container. The next step in the process
+is to set up a Triton Inference Server.</p>
+</section>
+<section id="step-2-set-up-triton-inference-server">
+<h2>Step 2: Set Up Triton Inference Server<a class="headerlink" href="#step-2-set-up-triton-inference-server" title="Permalink to this headline">¶</a></h2>
+<p>If you are new to the Triton Inference Server and want to learn more, we
+highly recommend to checking our <a class="reference external" href="https://github.com/triton-inference-server">Github
+Repository</a>.</p>
+<p>To use Triton, we need to make a model repository. A model repository, as the
+name suggested, is a repository of the models the Inference server hosts. While
+Triton can serve models from multiple repositories, in this example, we will
+discuss the simplest possible form of the model repository.</p>
+<p>The structure of this repository should look something like this:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">model_repository</span>
+<span class="o">|</span>
+<span class="o">+--</span><span class="w"> </span><span class="n">resnet50</span>
+<span class="w">    </span><span class="o">|</span>
+<span class="w">    </span><span class="o">+--</span><span class="w"> </span><span class="n">config</span><span class="p">.</span><span class="n">pbtxt</span>
+<span class="w">    </span><span class="o">+--</span><span class="w"> </span><span class="mi">1</span>
+<span class="w">        </span><span class="o">|</span>
+<span class="w">        </span><span class="o">+--</span><span class="w"> </span><span class="n">model</span><span class="p">.</span><span class="n">pt</span>
+</pre></div>
+</div>
+<p>There are two files that Triton requires to serve the model: the model itself
+and a model configuration file which is typically provided in <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code>.
+For the model we prepared in step 1, the following configuration can be used:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;resnet50&quot;</span>
+<span class="nl">platform</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;pytorch_libtorch&quot;</span>
+<span class="nl">max_batch_size</span><span class="w"> </span><span class="p">:</span><span class="w"> </span><span class="mi">0</span>
+<span class="n">input</span><span class="w"> </span><span class="p">[</span>
+<span class="w">  </span><span class="p">{</span>
+<span class="w">    </span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;input__0&quot;</span>
+<span class="w">    </span><span class="nl">data_type</span><span class="p">:</span><span class="w"> </span><span class="n">TYPE_FP32</span>
+<span class="w">    </span><span class="nl">dims</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="w"> </span><span class="p">]</span>
+<span class="w">    </span><span class="n">reshape</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">shape</span><span class="o">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="p">,</span><span class="w"> </span><span class="mi">224</span><span class="w"> </span><span class="p">]</span><span class="w"> </span><span class="p">}</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="p">]</span>
+<span class="n">output</span><span class="w"> </span><span class="p">[</span>
+<span class="w">  </span><span class="p">{</span>
+<span class="w">    </span><span class="nl">name</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;output__0&quot;</span>
+<span class="w">    </span><span class="nl">data_type</span><span class="p">:</span><span class="w"> </span><span class="n">TYPE_FP32</span>
+<span class="w">    </span><span class="nl">dims</span><span class="p">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1000</span><span class="w"> </span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1</span><span class="p">]</span>
+<span class="w">    </span><span class="n">reshape</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">shape</span><span class="o">:</span><span class="w"> </span><span class="p">[</span><span class="w"> </span><span class="mi">1</span><span class="p">,</span><span class="w"> </span><span class="mi">1000</span><span class="w"> </span><span class="p">]</span><span class="w"> </span><span class="p">}</span>
+<span class="w">  </span><span class="p">}</span>
+<span class="p">]</span>
+</pre></div>
+</div>
+<p>The <code class="docutils literal notranslate"><span class="pre">config.pbtxt</span></code> file is used to describe the exact model configuration
+with details like the names and shapes of the input and output layer(s),
+datatypes, scheduling and batching details and more. If you are new to Triton,
+we highly encourage you to check out this <a class="reference external" href="https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md">section of our
+documentation</a>
+for more details.</p>
+<p>With the model repository setup, we can proceed to launch the Triton server
+with the docker command below. Refer <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver">this page</a> for the pull tag for the container.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Make sure that the TensorRT version in the Triton container</span>
+<span class="cp"># and TensorRT version in the environment used to optimize the model</span>
+<span class="cp"># are the same.</span>
+
+<span class="n">docker</span><span class="w"> </span><span class="n">run</span><span class="w"> </span><span class="o">--</span><span class="n">gpus</span><span class="w"> </span><span class="n">all</span><span class="w"> </span><span class="o">--</span><span class="n">rm</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8000</span><span class="o">:</span><span class="mi">8000</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8001</span><span class="o">:</span><span class="mi">8001</span><span class="w"> </span><span class="o">-</span><span class="n">p</span><span class="w"> </span><span class="mi">8002</span><span class="o">:</span><span class="mi">8002</span><span class="w"> </span><span class="o">-</span><span class="n">v</span><span class="w"> </span><span class="o">/</span><span class="n">full</span><span class="o">/</span><span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">the_model_repository</span><span class="o">/</span><span class="n">model_repository</span><span class="o">:/</span><span class="n">models</span><span class="w"> </span><span class="n">nvcr</span><span class="p">.</span><span class="n">io</span><span class="o">/</span><span class="n">nvidia</span><span class="o">/</span><span class="n">tritonserver</span><span class="o">:&lt;</span><span class="n">xx</span><span class="p">.</span><span class="n">yy</span><span class="o">&gt;-</span><span class="n">py3</span><span class="w"> </span><span class="n">tritonserver</span><span class="w"> </span><span class="o">--</span><span class="n">model</span><span class="o">-</span><span class="n">repository</span><span class="o">=/</span><span class="n">models</span>
+</pre></div>
+</div>
+<p>This should spin up a Triton Inference server. Next step, building a simple
+http client to query the server.</p>
+</section>
+<section id="step-3-building-a-triton-client-to-query-the-server">
+<h2>Step 3: Building a Triton Client to Query the Server<a class="headerlink" href="#step-3-building-a-triton-client-to-query-the-server" title="Permalink to this headline">¶</a></h2>
+<p>Before proceeding, make sure to have a sample image on hand. If you don’t
+have one, download an example image to test inference. In this section, we
+will be going over a very basic client. For a variety of more fleshed out
+examples, refer to the <a class="reference external" href="https://github.com/triton-inference-server/client/tree/main/src/python/examples">Triton Client Repository</a></p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">wget</span><span class="w">  </span><span class="o">-</span><span class="n">O</span><span class="w"> </span><span class="n">img1</span><span class="p">.</span><span class="n">jpg</span><span class="w"> </span><span class="s">&quot;https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg&quot;</span>
+</pre></div>
+</div>
+<p>We then need to install dependencies for building a python client. These will
+change from client to client. For a full list of all languages supported by Triton,
+please refer to <a class="reference external" href="https://github.com/triton-inference-server/client">Triton’s client repository</a>.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">torchvision</span>
+<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">attrdict</span>
+<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">nvidia</span><span class="o">-</span><span class="n">pyindex</span>
+<span class="n">pip</span><span class="w"> </span><span class="n">install</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">[</span><span class="n">all</span><span class="p">]</span>
+</pre></div>
+</div>
+<p>Let’s jump into the client. Firstly, we write a small preprocessing function to
+resize and normalize the query image.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="k">import</span><span class="w"> </span><span class="n">numpy</span><span class="w"> </span><span class="n">as</span><span class="w"> </span><span class="n">np</span>
+<span class="n">from</span><span class="w"> </span><span class="n">torchvision</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">transforms</span>
+<span class="n">from</span><span class="w"> </span><span class="n">PIL</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">Image</span>
+<span class="k">import</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">.</span><span class="n">http</span><span class="w"> </span><span class="n">as</span><span class="w"> </span><span class="n">httpclient</span>
+<span class="n">from</span><span class="w"> </span><span class="n">tritonclient</span><span class="p">.</span><span class="n">utils</span><span class="w"> </span><span class="k">import</span><span class="w"> </span><span class="n">triton_to_np_dtype</span>
+
+<span class="cp"># preprocessing function</span>
+<span class="n">def</span><span class="w"> </span><span class="n">rn50_preprocess</span><span class="p">(</span><span class="n">img_path</span><span class="o">=</span><span class="s">&quot;img1.jpg&quot;</span><span class="p">)</span><span class="o">:</span>
+<span class="w">    </span><span class="n">img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Image</span><span class="p">.</span><span class="n">open</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span>
+<span class="w">    </span><span class="n">preprocess</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">transforms</span><span class="p">.</span><span class="n">Compose</span><span class="p">([</span>
+<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">Resize</span><span class="p">(</span><span class="mi">256</span><span class="p">),</span>
+<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">CenterCrop</span><span class="p">(</span><span class="mi">224</span><span class="p">),</span>
+<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">ToTensor</span><span class="p">(),</span>
+<span class="w">        </span><span class="n">transforms</span><span class="p">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">mean</span><span class="o">=</span><span class="p">[</span><span class="mf">0.485</span><span class="p">,</span><span class="w"> </span><span class="mf">0.456</span><span class="p">,</span><span class="w"> </span><span class="mf">0.406</span><span class="p">],</span><span class="w"> </span><span class="n">std</span><span class="o">=</span><span class="p">[</span><span class="mf">0.229</span><span class="p">,</span><span class="w"> </span><span class="mf">0.224</span><span class="p">,</span><span class="w"> </span><span class="mf">0.225</span><span class="p">]),</span>
+<span class="w">    </span><span class="p">])</span>
+<span class="w">    </span><span class="k">return</span><span class="w"> </span><span class="n">preprocess</span><span class="p">(</span><span class="n">img</span><span class="p">).</span><span class="n">numpy</span><span class="p">()</span>
+
+<span class="n">transformed_img</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">rn50_preprocess</span><span class="p">()</span>
+</pre></div>
+</div>
+<p>Building a client requires three basic points. Firstly, we setup a connection
+with the Triton Inference Server.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="cp"># Setting up client</span>
+<span class="n">client</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferenceServerClient</span><span class="p">(</span><span class="n">url</span><span class="o">=</span><span class="s">&quot;localhost:8000&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Secondly, we specify the names of the input and output layer(s) of our model.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span><span class="n">inputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferInput</span><span class="p">(</span><span class="s">&quot;input__0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">transformed_img</span><span class="p">.</span><span class="n">shape</span><span class="p">,</span><span class="w"> </span><span class="n">datatype</span><span class="o">=</span><span class="s">&quot;FP32&quot;</span><span class="p">)</span>
+<span class="n">inputs</span><span class="p">.</span><span class="n">set_data_from_numpy</span><span class="p">(</span><span class="n">transformed_img</span><span class="p">,</span><span class="w"> </span><span class="n">binary_data</span><span class="o">=</span><span class="n">True</span><span class="p">)</span>
+
+<span class="n">outputs</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">httpclient</span><span class="p">.</span><span class="n">InferRequestedOutput</span><span class="p">(</span><span class="s">&quot;output__0&quot;</span><span class="p">,</span><span class="w"> </span><span class="n">binary_data</span><span class="o">=</span><span class="n">True</span><span class="p">,</span><span class="w"> </span><span class="n">class_count</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>Lastly, we send an inference request to the Triton Inference Server.</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span># Querying the server
+results = client.infer(model_name=&quot;resnet50&quot;, inputs=[inputs], outputs=[outputs])
+inference_output = results.as_numpy(&#39;output__0&#39;)
+print(inference_output[:5])
+</pre></div>
+</div>
+<p>The output of the same should look like below:</p>
+<div class="highlight-cpp notranslate"><div class="highlight"><pre><span></span>[b&#39;12.468750:90&#39; b&#39;11.523438:92&#39; b&#39;9.664062:14&#39; b&#39;8.429688:136&#39;
+ b&#39;8.234375:11&#39;]
+</pre></div>
+</div>
+<p>The output format here is <code class="docutils literal notranslate"><span class="pre">&lt;confidence_score&gt;:&lt;classification_index&gt;</span></code>.
+To learn how to map these to the label names and more, refer to Triton Inference Server’s
+<a class="reference external" href="https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_classification.md">documentation</a>.</p>
+</section>
+</section>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+              
+  <section id="dynamic-shapes-with-torch-tensorrt">
+<span id="dynamic-shapes"></span><h1>Dynamic shapes with Torch-TensorRT<a class="headerlink" href="#dynamic-shapes-with-torch-tensorrt" title="Permalink to this headline">¶</a></h1>
+<p>By default, you can run a pytorch model with varied input shapes and the output shapes are determined eagerly.
+However, Torch-TensorRT is an AOT compiler which requires some prior information about the input shapes to compile and optimize the model.
+In the case of dynamic input shapes, we must provide the (min_shape, opt_shape, max_shape) arguments so that the model can be optimized for
+these range of input shapes. An example usage of static and dynamic shapes is as follows.</p>
+<p>NOTE: The following code uses Dynamo Frontend. Incase of Torchscript Frontend, please swap out <code class="docutils literal notranslate"><span class="pre">ir=dynamo</span></code> with <code class="docutils literal notranslate"><span class="pre">ir=ts</span></code> and the behavior is exactly the same.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="c1"># Compile with static shapes</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="c1"># or compile with dynamic shapes</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">min_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">],</span>
+                              <span class="n">opt_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">],</span>
+                              <span class="n">max_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">],</span>
+                              <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">trt_gm</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;dynamo&quot;</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+<section id="under-the-hood">
+<h2>Under the hood<a class="headerlink" href="#under-the-hood" title="Permalink to this headline">¶</a></h2>
+<p>There are two phases of compilation when we use <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile</span></code> API with <code class="docutils literal notranslate"><span class="pre">ir=dynamo</span></code> (default).</p>
+<ul class="simple">
+<li><p>aten_tracer.trace (which uses torch.export to trace the graph with the given inputs)</p></li>
+</ul>
+<p>In the tracing phase, we use torch.export along with the constraints. In the case of
+dynamic shaped inputs, the range can be provided to the tracing via constraints. Please
+refer to this <a class="reference external" href="https://github.com/pytorch/pytorch/blob/5dcee01c2b89f6bedeef9dd043fd8d6728286582/torch/export/__init__.py#L372-L434">docstring</a>
+for detailed information on how to set constraints. In short, we create new inputs for
+torch.export tracing and provide constraints on the min and max values(provided by the user), a particular dimension can take.
+Please take a look at <code class="docutils literal notranslate"><span class="pre">aten_tracer.py</span></code> file to understand how this works under the hood.</p>
+<ul class="simple">
+<li><p>dynamo.compile (which compiles a torch.fx.GraphModule object using TensorRT)</p></li>
+</ul>
+<p>In the conversion to TensorRT, we use the user provided dynamic shape inputs.
+We perform shape analysis using dummy inputs (across min, opt and max shapes) and store the
+intermediate output shapes which can be used in case the graph has a mix of Pytorch
+and TensorRT submodules.</p>
+</section>
+<section id="custom-constraints">
+<h2>Custom Constraints<a class="headerlink" href="#custom-constraints" title="Permalink to this headline">¶</a></h2>
+<p>Given an input <code class="docutils literal notranslate"><span class="pre">x</span> <span class="pre">=</span> <span class="pre">torch_tensorrt.Input(min_shape,</span> <span class="pre">opt_shape,</span> <span class="pre">max_shape,</span> <span class="pre">dtype)</span></code>,
+Torch-TensorRT automatically sets the constraints during <code class="docutils literal notranslate"><span class="pre">torch.export</span></code> tracing as follows</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">constraint_dims</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">min_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">constraints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">min_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">dynamic_dim</span><span class="p">(</span><span class="n">trace_input</span><span class="p">,</span> <span class="n">dim</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">max_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">constraints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dynamic_dim</span><span class="p">(</span><span class="n">trace_input</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span> <span class="o">&lt;=</span> <span class="n">max_shape</span><span class="p">[</span><span class="n">dim</span><span class="p">])</span>
+</pre></div>
+</div>
+<p>Sometimes, we might need to set additional constraints and Torchdynamo errors out if we don’t specify them.
+For example, in the case of BERT model compilation, there are two inputs and a constraint has to be set involving the sequence length size of these two inputs.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">constraints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dynamic_dim</span><span class="p">(</span><span class="n">trace_inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="n">dynamic_dim</span><span class="p">(</span><span class="n">trace_inputs</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">0</span><span class="p">))</span>
+</pre></div>
+</div>
+<p>If you have to provide any custom constraints to your model, the overall workflow for model compilation using <code class="docutils literal notranslate"><span class="pre">ir=dynamo</span></code> would involve a few steps.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="kn">from</span> <span class="nn">torch_tensorrt.dynamo.lowering</span> <span class="kn">import</span> <span class="n">apply_lowering_passes</span><span class="p">,</span> <span class="n">get_decompositions</span>
+<span class="c1"># Assume the model has two inputs</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span>
+<span class="n">torch_input_1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">torch_input_2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+
+<span class="n">dynamic_inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">min_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">],</span>
+                    <span class="n">opt_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">14</span><span class="p">],</span>
+                    <span class="n">max_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">14</span><span class="p">],</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span>
+                  <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">min_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">],</span>
+                    <span class="n">opt_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">14</span><span class="p">],</span>
+                    <span class="n">max_shape</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">14</span><span class="p">],</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)]</span>
+
+<span class="c1"># Export the model with additional constraints</span>
+<span class="n">constraints</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="c1"># The following constraints are automatically added by Torch-TensorRT in the</span>
+<span class="c1"># general case when you call torch_tensorrt.compile directly on MyModel()</span>
+<span class="n">constraints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dynamic_dim</span><span class="p">(</span><span class="n">torch_input_1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">8</span><span class="p">)</span>
+<span class="n">constraints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dynamic_dim</span><span class="p">(</span><span class="n">torch_input_2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">8</span><span class="p">)</span>
+<span class="c1"># This is an additional constraint as instructed by Torchdynamo</span>
+<span class="n">constraints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dynamic_dim</span><span class="p">(</span><span class="n">torch_input_1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> <span class="o">==</span> <span class="n">dynamic_dim</span><span class="p">(</span><span class="n">torch_input_2</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+<span class="k">with</span> <span class="n">unittest</span><span class="o">.</span><span class="n">mock</span><span class="o">.</span><span class="n">patch</span><span class="p">(</span>
+    <span class="s2">&quot;torch._export.DECOMP_TABLE&quot;</span><span class="p">,</span> <span class="n">get_decompositions</span><span class="p">(</span><span class="n">experimental_decompositions</span><span class="p">)</span>
+<span class="p">):</span>
+    <span class="n">graph_module</span> <span class="o">=</span> <span class="n">export</span><span class="p">(</span>
+        <span class="n">model</span><span class="p">,</span> <span class="p">(</span><span class="n">torch_input_1</span><span class="p">,</span> <span class="n">torch_input_2</span><span class="p">),</span> <span class="n">constraints</span><span class="o">=</span><span class="n">constraints</span>
+    <span class="p">)</span><span class="o">.</span><span class="n">module</span><span class="p">()</span>
+
+<span class="c1"># Use the dynamo.compile API</span>
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">dynamo</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">graph_module</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">dynamic_inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">compile_spec</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="limitations">
+<h2>Limitations<a class="headerlink" href="#limitations" title="Permalink to this headline">¶</a></h2>
+<p>If there are operations in the graph that use the dynamic dimension of the input, Pytorch
+introduces <code class="docutils literal notranslate"><span class="pre">torch.ops.aten.sym_size.int</span></code> ops in the graph. Currently, we cannot handle these operators and
+the compilation results in undefined behavior. We plan to add support for these operators and implement
+robust support for shape tensors in the next release. Here is an example of the limitation described above</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="k">class</span> <span class="nc">MyModule</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">avgpool</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">AdaptiveAvgPool2d</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">avgpool</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="c1"># Compile with dynamic shapes</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="n">min_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+                     <span class="n">opt_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+                     <span class="n">max_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span>
+                     <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">trt_gm</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;dynamo&quot;</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>The traced graph of <cite>MyModule()</cite> looks as follows</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">Post</span> <span class="n">export</span> <span class="n">graph</span><span class="p">:</span> <span class="n">graph</span><span class="p">():</span>
+<span class="o">%</span><span class="n">arg0_1</span> <span class="p">:</span> <span class="p">[</span><span class="n">num_users</span><span class="o">=</span><span class="mi">2</span><span class="p">]</span> <span class="o">=</span> <span class="n">placeholder</span><span class="p">[</span><span class="n">target</span><span class="o">=</span><span class="n">arg0_1</span><span class="p">]</span>
+<span class="o">%</span><span class="n">mean</span> <span class="p">:</span> <span class="p">[</span><span class="n">num_users</span><span class="o">=</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">call_function</span><span class="p">[</span><span class="n">target</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">mean</span><span class="o">.</span><span class="n">dim</span><span class="p">](</span><span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="o">%</span><span class="n">arg0_1</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="kc">True</span><span class="p">),</span> <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{})</span>
+<span class="o">%</span><span class="n">sym_size</span> <span class="p">:</span> <span class="p">[</span><span class="n">num_users</span><span class="o">=</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">call_function</span><span class="p">[</span><span class="n">target</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">sym_size</span><span class="o">.</span><span class="n">int</span><span class="p">](</span><span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="o">%</span><span class="n">arg0_1</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{})</span>
+<span class="o">%</span><span class="n">view</span> <span class="p">:</span> <span class="p">[</span><span class="n">num_users</span><span class="o">=</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">call_function</span><span class="p">[</span><span class="n">target</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">ops</span><span class="o">.</span><span class="n">aten</span><span class="o">.</span><span class="n">view</span><span class="o">.</span><span class="n">default</span><span class="p">](</span><span class="n">args</span> <span class="o">=</span> <span class="p">(</span><span class="o">%</span><span class="n">mean</span><span class="p">,</span> <span class="p">[</span><span class="o">%</span><span class="n">sym_size</span><span class="p">,</span> <span class="mi">512</span><span class="p">]),</span> <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{})</span>
+<span class="k">return</span> <span class="p">(</span><span class="n">view</span><span class="p">,)</span>
+</pre></div>
+</div>
+<p>Here the <code class="docutils literal notranslate"><span class="pre">%sym_size</span></code> node captures the dynamic batch and uses it in the <code class="docutils literal notranslate"><span class="pre">aten.view</span></code> layer. This requires shape tensors support
+which would be a part of our next release.</p>
+</section>
+<section id="workaround-bert-static-compilation-example">
+<h2>Workaround (BERT static compilation example)<a class="headerlink" href="#workaround-bert-static-compilation-example" title="Permalink to this headline">¶</a></h2>
+<p>In the case where you encounter the issues mentioned in the <strong>Limitations</strong> section,
+you can compile the model (static mode) with max input size that can be provided. In the cases of smaller inputs,
+we can pad them accordingly. This is only a workaround until we address the limitations.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+<span class="kn">from</span> <span class="nn">transformers.utils.fx</span> <span class="kn">import</span> <span class="n">symbolic_trace</span> <span class="k">as</span> <span class="n">transformers_trace</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">BertModel</span><span class="o">.</span><span class="n">from_pretrained</span><span class="p">(</span><span class="s2">&quot;bert-base-uncased&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+
+<span class="c1"># Input sequence length is 20.</span>
+<span class="n">input1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">input2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">20</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">transformers_trace</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">input_names</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;input_ids&quot;</span><span class="p">,</span> <span class="s2">&quot;attention_mask&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">],</span> <span class="o">**</span><span class="n">compile_spec</span><span class="p">)</span>
+<span class="n">model_outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">input2</span><span class="p">)</span>
+
+<span class="c1"># If you have a sequence of length 14, pad 6 zero tokens and run inference</span>
+<span class="c1"># or recompile for sequence length of 14.</span>
+<span class="n">input1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">input2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">14</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cuda&quot;</span><span class="p">)</span>
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">input1</span><span class="p">,</span> <span class="n">input2</span><span class="p">],</span> <span class="o">**</span><span class="n">compile_spec</span><span class="p">)</span>
+<span class="n">model_outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">input2</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="dynamic-shapes-with-ir-torch-compile">
+<h2>Dynamic shapes with ir=torch_compile<a class="headerlink" href="#dynamic-shapes-with-ir-torch-compile" title="Permalink to this headline">¶</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile(model,</span> <span class="pre">inputs,</span> <span class="pre">ir=&quot;torch_compile&quot;)</span></code> returns a torch.compile boxed function with the backend
+configured to Tensorrt. In the case of <code class="docutils literal notranslate"><span class="pre">ir=torch_compile</span></code>, when the input size changes, Dynamo will trigger a recompilation
+of the TensorRT engine automatically giving dynamic shape behavior similar to native PyTorch eager however with the cost of rebuilding
+TRT engine. This limitation will be addressed in future versions of Torch-TensorRT.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">trt_gm</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;torch_compile&quot;</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+<span class="c1"># Compilation happens when you call the model</span>
+<span class="n">trt_gm</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
+
+<span class="c1"># Recompilation happens with modified batch size</span>
+<span class="n">inputs_bs2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">trt_gm</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;torch_compile&quot;</span><span class="p">,</span> <span class="n">inputs_bs2</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+
+
+             </article>
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diff --git a/docs/v2.2.0/user_guide/ptq.html b/docs/v2.2.0/user_guide/ptq.html
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+</ul>
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+  <section id="post-training-quantization-ptq">
+<span id="ptq"></span><h1>Post Training Quantization (PTQ)<a class="headerlink" href="#post-training-quantization-ptq" title="Permalink to this headline">¶</a></h1>
+<p>Post Training Quantization (PTQ) is a technique to reduce the required computational resources for inference
+while still preserving the accuracy of your model by mapping the traditional FP32 activation space to a reduced
+INT8 space. TensorRT uses a calibration step which executes your model with sample data from the target domain
+and track the activations in FP32 to calibrate a mapping to INT8 that minimizes the information loss between
+FP32 inference and INT8 inference.</p>
+<p>Users writing TensorRT applications are required to setup a calibrator class which will provide sample data to
+the TensorRT calibrator. With Torch-TensorRT we look to leverage existing infrastructure in PyTorch to make implementing
+calibrators easier.</p>
+<p>LibTorch provides a <code class="docutils literal notranslate"><span class="pre">DataLoader</span></code> and <code class="docutils literal notranslate"><span class="pre">Dataset</span></code> API which steamlines preprocessing and batching input data.
+These APIs are exposed via both C++ and Python interface which makes it easier for the end user.
+For C++ interface, we use <code class="docutils literal notranslate"><span class="pre">torch::Dataset</span></code> and <code class="docutils literal notranslate"><span class="pre">torch::data::make_data_loader</span></code> objects to construct and perform pre-processing on datasets.
+The equivalent functionality in python interface uses <code class="docutils literal notranslate"><span class="pre">torch.utils.data.Dataset</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.utils.data.DataLoader</span></code>.
+This section of the PyTorch documentation has more information <a class="reference external" href="https://pytorch.org/tutorials/advanced/cpp_frontend.html#loading-data">https://pytorch.org/tutorials/advanced/cpp_frontend.html#loading-data</a> and <a class="reference external" href="https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html">https://pytorch.org/tutorials/recipes/recipes/loading_data_recipe.html</a>.
+Torch-TensorRT uses Dataloaders as the base of a generic calibrator implementation. So you will be able to reuse or quickly
+implement a <code class="docutils literal notranslate"><span class="pre">torch::Dataset</span></code> for your target domain, place it in a DataLoader and create a INT8 Calibrator
+which you can provide to Torch-TensorRT to run INT8 Calibration during compliation of your module.</p>
+<section id="how-to-create-your-own-ptq-application-in-c">
+<span id="writing-ptq-cpp"></span><h2>How to create your own PTQ application in C++<a class="headerlink" href="#how-to-create-your-own-ptq-application-in-c" title="Permalink to this headline">¶</a></h2>
+<p>Here is an example interface of a <code class="docutils literal notranslate"><span class="pre">torch::Dataset</span></code> class for CIFAR10:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="c1">//cpp/ptq/datasets/cifar10.h</span>
+<span class="linenos"> 2</span><span class="cp">#pragma once</span>
+<span class="linenos"> 3</span>
+<span class="linenos"> 4</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/data/datasets/base.h&quot;</span>
+<span class="linenos"> 5</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/data/example.h&quot;</span>
+<span class="linenos"> 6</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch/types.h&quot;</span>
+<span class="linenos"> 7</span>
+<span class="linenos"> 8</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;cstddef&gt;</span>
+<span class="linenos"> 9</span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&lt;string&gt;</span>
+<span class="linenos">10</span>
+<span class="linenos">11</span><span class="k">namespace</span><span class="w"> </span><span class="nn">datasets</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">12</span><span class="c1">// The CIFAR10 Dataset</span>
+<span class="linenos">13</span><span class="k">class</span><span class="w"> </span><span class="nc">CIFAR10</span><span class="w"> </span><span class="o">:</span><span class="w"> </span><span class="k">public</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">datasets</span><span class="o">::</span><span class="n">Dataset</span><span class="o">&lt;</span><span class="n">CIFAR10</span><span class="o">&gt;</span><span class="w"> </span><span class="p">{</span>
+<span class="linenos">14</span><span class="k">public</span><span class="o">:</span>
+<span class="linenos">15</span><span class="w">    </span><span class="c1">// The mode in which the dataset is loaded</span>
+<span class="linenos">16</span><span class="w">    </span><span class="k">enum</span><span class="w"> </span><span class="k">class</span><span class="w"> </span><span class="nc">Mode</span><span class="w"> </span><span class="p">{</span><span class="w"> </span><span class="n">kTrain</span><span class="p">,</span><span class="w"> </span><span class="n">kTest</span><span class="w"> </span><span class="p">};</span>
+<span class="linenos">17</span>
+<span class="linenos">18</span><span class="w">    </span><span class="c1">// Loads CIFAR10 from un-tarred file</span>
+<span class="linenos">19</span><span class="w">    </span><span class="c1">// Dataset can be found https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz</span>
+<span class="linenos">20</span><span class="w">    </span><span class="c1">// Root path should be the directory that contains the content of tarball</span>
+<span class="linenos">21</span><span class="w">    </span><span class="k">explicit</span><span class="w"> </span><span class="n">CIFAR10</span><span class="p">(</span><span class="k">const</span><span class="w"> </span><span class="n">std</span><span class="o">::</span><span class="n">string</span><span class="o">&amp;</span><span class="w"> </span><span class="n">root</span><span class="p">,</span><span class="w"> </span><span class="n">Mode</span><span class="w"> </span><span class="n">mode</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">Mode</span><span class="o">::</span><span class="n">kTrain</span><span class="p">);</span>
+<span class="linenos">22</span>
+<span class="linenos">23</span><span class="w">    </span><span class="c1">// Returns the pair at index in the dataset</span>
+<span class="linenos">24</span><span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">Example</span><span class="o">&lt;&gt;</span><span class="w"> </span><span class="n">get</span><span class="p">(</span><span class="kt">size_t</span><span class="w"> </span><span class="n">index</span><span class="p">)</span><span class="w"> </span><span class="k">override</span><span class="p">;</span>
+<span class="linenos">25</span>
+<span class="linenos">26</span><span class="w">    </span><span class="c1">// The size of the dataset</span>
+<span class="linenos">27</span><span class="w">    </span><span class="n">c10</span><span class="o">::</span><span class="n">optional</span><span class="o">&lt;</span><span class="kt">size_t</span><span class="o">&gt;</span><span class="w"> </span><span class="n">size</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">override</span><span class="p">;</span>
+<span class="linenos">28</span>
+<span class="linenos">29</span><span class="w">    </span><span class="c1">// The mode the dataset is in</span>
+<span class="linenos">30</span><span class="w">    </span><span class="kt">bool</span><span class="w"> </span><span class="nf">is_train</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="w"> </span><span class="k">noexcept</span><span class="p">;</span>
+<span class="linenos">31</span>
+<span class="linenos">32</span><span class="w">    </span><span class="c1">// Returns all images stacked into a single tensor</span>
+<span class="linenos">33</span><span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span><span class="w"> </span><span class="nf">images</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
+<span class="linenos">34</span>
+<span class="linenos">35</span><span class="w">    </span><span class="c1">// Returns all targets stacked into a single tensor</span>
+<span class="linenos">36</span><span class="w">    </span><span class="k">const</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="o">&amp;</span><span class="w"> </span><span class="nf">targets</span><span class="p">()</span><span class="w"> </span><span class="k">const</span><span class="p">;</span>
+<span class="linenos">37</span>
+<span class="linenos">38</span><span class="w">    </span><span class="c1">// Trims the dataset to the first n pairs</span>
+<span class="linenos">39</span><span class="w">    </span><span class="n">CIFAR10</span><span class="o">&amp;&amp;</span><span class="w"> </span><span class="nf">use_subset</span><span class="p">(</span><span class="kt">int64_t</span><span class="w"> </span><span class="n">new_size</span><span class="p">);</span>
+<span class="linenos">40</span>
+<span class="linenos">41</span>
+<span class="linenos">42</span><span class="k">private</span><span class="o">:</span>
+<span class="linenos">43</span><span class="w">    </span><span class="n">Mode</span><span class="w"> </span><span class="n">mode_</span><span class="p">;</span>
+<span class="linenos">44</span><span class="w">    </span><span class="n">torch</span><span class="o">::</span><span class="n">Tensor</span><span class="w"> </span><span class="n">images_</span><span class="p">,</span><span class="w"> </span><span class="n">targets_</span><span class="p">;</span>
+<span class="linenos">45</span><span class="p">};</span>
+<span class="linenos">46</span><span class="p">}</span><span class="w"> </span><span class="c1">// namespace datasets</span>
+</pre></div>
+</div>
+<p>This class’s implementation reads from the binary distribution of the CIFAR10 dataset and builds two tensors which hold the images and labels.</p>
+<p>We use a subset of the dataset to use for calibration, since we don’t need the the full dataset for effective calibration and calibration does
+some take time, then define the preprocessing to apply to the images in the dataset and create a DataLoader from the dataset which will batch the data:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="k">auto</span><span class="w"> </span><span class="n">calibration_dataset</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">datasets</span><span class="o">::</span><span class="n">CIFAR10</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span><span class="w"> </span><span class="n">datasets</span><span class="o">::</span><span class="n">CIFAR10</span><span class="o">::</span><span class="n">Mode</span><span class="o">::</span><span class="n">kTest</span><span class="p">)</span>
+<span class="w">                                    </span><span class="p">.</span><span class="n">use_subset</span><span class="p">(</span><span class="mi">320</span><span class="p">)</span>
+<span class="w">                                    </span><span class="p">.</span><span class="n">map</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">transforms</span><span class="o">::</span><span class="n">Normalize</span><span class="o">&lt;&gt;</span><span class="p">({</span><span class="mf">0.4914</span><span class="p">,</span><span class="w"> </span><span class="mf">0.4822</span><span class="p">,</span><span class="w"> </span><span class="mf">0.4465</span><span class="p">},</span>
+<span class="w">                                                                            </span><span class="p">{</span><span class="mf">0.2023</span><span class="p">,</span><span class="w"> </span><span class="mf">0.1994</span><span class="p">,</span><span class="w"> </span><span class="mf">0.2010</span><span class="p">}))</span>
+<span class="w">                                    </span><span class="p">.</span><span class="n">map</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">transforms</span><span class="o">::</span><span class="n">Stack</span><span class="o">&lt;&gt;</span><span class="p">());</span>
+<span class="k">auto</span><span class="w"> </span><span class="n">calibration_dataloader</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">make_data_loader</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">calibration_dataset</span><span class="p">),</span>
+<span class="w">                                                            </span><span class="n">torch</span><span class="o">::</span><span class="n">data</span><span class="o">::</span><span class="n">DataLoaderOptions</span><span class="p">().</span><span class="n">batch_size</span><span class="p">(</span><span class="mi">32</span><span class="p">)</span>
+<span class="w">                                                                                            </span><span class="p">.</span><span class="n">workers</span><span class="p">(</span><span class="mi">2</span><span class="p">));</span>
+</pre></div>
+</div>
+<p>Next we create a calibrator from the <code class="docutils literal notranslate"><span class="pre">calibration_dataloader</span></code> using the calibrator factory (found in <code class="docutils literal notranslate"><span class="pre">torch_tensorrt/ptq.h</span></code>):</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="cp">#include</span><span class="w"> </span><span class="cpf">&quot;torch_tensorrt/ptq.h&quot;</span>
+<span class="p">...</span>
+
+<span class="k">auto</span><span class="w"> </span><span class="n">calibrator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ptq</span><span class="o">::</span><span class="n">make_int8_calibrator</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">calibration_dataloader</span><span class="p">),</span><span class="w"> </span><span class="n">calibration_cache_file</span><span class="p">,</span><span class="w"> </span><span class="nb">true</span><span class="p">);</span>
+</pre></div>
+</div>
+<p>Here we also define a location to write a calibration cache file to which we can use to reuse the calibration data without needing the dataset and whether or not
+we should use the cache file if it exists. There also exists a <code class="docutils literal notranslate"><span class="pre">torch_tensorrt::ptq::make_int8_cache_calibrator</span></code> factory which creates a calibrator that uses the cache
+only for cases where you may do engine building on a machine that has limited storage (i.e. no space for a full dataset) or to have a simpiler deployment application.</p>
+<p>The calibrator factories create a calibrator that inherits from a <code class="docutils literal notranslate"><span class="pre">nvinfer1::IInt8Calibrator</span></code> virtual class (<code class="docutils literal notranslate"><span class="pre">nvinfer1::IInt8EntropyCalibrator2</span></code> by default) which
+defines the calibration algorithm used when calibrating. You can explicitly make the selection of calibration algorithm like this:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="c1">// MinMax Calibrator is geared more towards NLP tasks</span>
+<span class="k">auto</span><span class="w"> </span><span class="n">calibrator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">ptq</span><span class="o">::</span><span class="n">make_int8_calibrator</span><span class="o">&lt;</span><span class="n">nvinfer1</span><span class="o">::</span><span class="n">IInt8MinMaxCalibrator</span><span class="o">&gt;</span><span class="p">(</span><span class="n">std</span><span class="o">::</span><span class="n">move</span><span class="p">(</span><span class="n">calibration_dataloader</span><span class="p">),</span><span class="w"> </span><span class="n">calibration_cache_file</span><span class="p">,</span><span class="w"> </span><span class="nb">true</span><span class="p">);</span>
+</pre></div>
+</div>
+<p>Then all thats required to setup the module for INT8 calibration is to set the following compile settings in the <cite>torch_tensorrt::CompileSpec</cite> struct and compiling the module:</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">input_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{{</span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">}};</span>
+<span class="c1">/// Configure settings for compilation</span>
+<span class="k">auto</span><span class="w"> </span><span class="n">compile_spec</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="p">({</span><span class="n">input_shape</span><span class="p">});</span>
+<span class="c1">/// Set operating precision to INT8</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kF16</span><span class="p">);</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="p">.</span><span class="n">insert</span><span class="p">(</span><span class="n">torch</span><span class="o">::</span><span class="n">kI8</span><span class="p">);</span>
+<span class="c1">/// Use the TensorRT Entropy Calibrator</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">ptq_calibrator</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">calibrator</span><span class="p">;</span>
+
+<span class="k">auto</span><span class="w"> </span><span class="n">trt_mod</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileGraph</span><span class="p">(</span><span class="n">mod</span><span class="p">,</span><span class="w"> </span><span class="n">compile_spec</span><span class="p">);</span>
+</pre></div>
+</div>
+<p>If you have an existing Calibrator implementation for TensorRT you may directly set the <code class="docutils literal notranslate"><span class="pre">ptq_calibrator</span></code> field with a pointer to your calibrator and it will work as well.
+From here not much changes in terms of how to execution works. You are still able to fully use LibTorch as the sole interface for inference. Data should remain
+in FP32 precision when it’s passed into <cite>trt_mod.forward</cite>. There exists an example application in the Torch-TensorRT demo that takes you from training a VGG16 network on
+CIFAR10 to deploying in INT8 with Torch-TensorRT here: <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/master/cpp/ptq">https://github.com/pytorch/TensorRT/tree/master/cpp/ptq</a></p>
+</section>
+<section id="how-to-create-your-own-ptq-application-in-python">
+<span id="writing-ptq-python"></span><h2>How to create your own PTQ application in Python<a class="headerlink" href="#how-to-create-your-own-ptq-application-in-python" title="Permalink to this headline">¶</a></h2>
+<p>Torch-TensorRT Python API provides an easy and convenient way to use pytorch dataloaders with TensorRT calibrators. <code class="docutils literal notranslate"><span class="pre">DataLoaderCalibrator</span></code> class can be used to create
+a TensorRT calibrator by providing desired configuration. The following code demonstrates an example on how to use it</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">testing_dataset</span> <span class="o">=</span> <span class="n">torchvision</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">CIFAR10</span><span class="p">(</span>
+    <span class="n">root</span><span class="o">=</span><span class="s2">&quot;./data&quot;</span><span class="p">,</span>
+    <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">transform</span><span class="o">=</span><span class="n">transforms</span><span class="o">.</span><span class="n">Compose</span><span class="p">(</span>
+        <span class="p">[</span>
+            <span class="n">transforms</span><span class="o">.</span><span class="n">ToTensor</span><span class="p">(),</span>
+            <span class="n">transforms</span><span class="o">.</span><span class="n">Normalize</span><span class="p">((</span><span class="mf">0.4914</span><span class="p">,</span> <span class="mf">0.4822</span><span class="p">,</span> <span class="mf">0.4465</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.2023</span><span class="p">,</span> <span class="mf">0.1994</span><span class="p">,</span> <span class="mf">0.2010</span><span class="p">)),</span>
+        <span class="p">]</span>
+    <span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">testing_dataloader</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">DataLoader</span><span class="p">(</span>
+    <span class="n">testing_dataset</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">1</span>
+<span class="p">)</span>
+<span class="n">calibrator</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ptq</span><span class="o">.</span><span class="n">DataLoaderCalibrator</span><span class="p">(</span>
+    <span class="n">testing_dataloader</span><span class="p">,</span>
+    <span class="n">cache_file</span><span class="o">=</span><span class="s2">&quot;./calibration.cache&quot;</span><span class="p">,</span>
+    <span class="n">use_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">algo_type</span><span class="o">=</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ptq</span><span class="o">.</span><span class="n">CalibrationAlgo</span><span class="o">.</span><span class="n">ENTROPY_CALIBRATION_2</span><span class="p">,</span>
+    <span class="n">device</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:0&quot;</span><span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">))],</span>
+                                    <span class="n">enabled_precisions</span><span class="o">=</span><span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">},</span>
+                                    <span class="n">calibrator</span><span class="o">=</span><span class="n">calibrator</span><span class="p">,</span>
+                                    <span class="n">device</span><span class="o">=</span><span class="p">{</span>
+                                         <span class="s2">&quot;device_type&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">DeviceType</span><span class="o">.</span><span class="n">GPU</span><span class="p">,</span>
+                                         <span class="s2">&quot;gpu_id&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                                         <span class="s2">&quot;dla_core&quot;</span><span class="p">:</span> <span class="mi">0</span><span class="p">,</span>
+                                         <span class="s2">&quot;allow_gpu_fallback&quot;</span><span class="p">:</span> <span class="kc">False</span><span class="p">,</span>
+                                         <span class="s2">&quot;disable_tf32&quot;</span><span class="p">:</span> <span class="kc">False</span>
+                                     <span class="p">})</span>
+</pre></div>
+</div>
+<p>In the cases where there is a pre-existing calibration cache file that users want to use, <code class="docutils literal notranslate"><span class="pre">CacheCalibrator</span></code> can be used without any dataloaders. The following example demonstrates how
+to use <code class="docutils literal notranslate"><span class="pre">CacheCalibrator</span></code> to use in INT8 mode.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">calibrator</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">ptq</span><span class="o">.</span><span class="n">CacheCalibrator</span><span class="p">(</span><span class="s2">&quot;./calibration.cache&quot;</span><span class="p">)</span>
+
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">])],</span>
+                                      <span class="n">enabled_precisions</span><span class="o">=</span><span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">},</span>
+                                      <span class="n">calibrator</span><span class="o">=</span><span class="n">calibrator</span><span class="p">)</span>
+</pre></div>
+</div>
+<p>If you already have an existing calibrator class (implemented directly using TensorRT API), you can directly set the calibrator field to your class which can be very convenient.
+For a demo on how PTQ can be performed on a VGG network using Torch-TensorRT API, you can refer to <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_dataloader_calibrator.py">https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_dataloader_calibrator.py</a>
+and <a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_trt_calibrator.py">https://github.com/pytorch/TensorRT/blob/master/tests/py/test_ptq_trt_calibrator.py</a></p>
+<section id="citations">
+<h3>Citations<a class="headerlink" href="#citations" title="Permalink to this headline">¶</a></h3>
+<p>Krizhevsky, A., &amp; Hinton, G. (2009). Learning multiple layers of features from tiny images.</p>
+<p>Simonyan, K., &amp; Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.</p>
+</section>
+</section>
+</section>
+
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+              <ul>
+<li><a class="reference internal" href="#">Post Training Quantization (PTQ)</a><ul>
+<li><a class="reference internal" href="#how-to-create-your-own-ptq-application-in-c">How to create your own PTQ application in C++</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
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+<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>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
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+<li class="toctree-l1 current"><a class="current reference internal" href="#">Deploying Torch-TensorRT Programs</a></li>
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+<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>
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+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
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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>
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+<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="deploying-torch-tensorrt-programs">
+<span id="runtime"></span><h1>Deploying Torch-TensorRT Programs<a class="headerlink" href="#deploying-torch-tensorrt-programs" title="Permalink to this headline">¶</a></h1>
+<p>After compiling and saving Torch-TensorRT programs there is no longer a strict dependency on the full
+Torch-TensorRT library. All that is required to run a compiled program is the runtime. There are therfore a couple
+options to deploy your programs other than shipping the full Torch-TensorRT compiler with your applications.</p>
+<section id="torch-tensorrt-package-libtorchtrt-so">
+<h2>Torch-TensorRT package / libtorchtrt.so<a class="headerlink" href="#torch-tensorrt-package-libtorchtrt-so" title="Permalink to this headline">¶</a></h2>
+<p>Once a program is compiled, you run it using the standard PyTorch APIs. All that is required is that the package
+must be imported in python or linked in C++.</p>
+</section>
+<section id="runtime-library">
+<h2>Runtime Library<a class="headerlink" href="#runtime-library" title="Permalink to this headline">¶</a></h2>
+<p>Distributed with the C++ distribution is <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code>. This library only contains the components
+necessary to run Torch-TensorRT programs. Instead of linking <code class="docutils literal notranslate"><span class="pre">libtorchtrt.so</span></code> or importing <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> you can
+link <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code> in your deployment programs or use <code class="docutils literal notranslate"><span class="pre">DL_OPEN</span></code> or <code class="docutils literal notranslate"><span class="pre">LD_PRELOAD</span></code>. For python
+you can load the runtime with <code class="docutils literal notranslate"><span class="pre">torch.ops.load_library(&quot;libtorchtrt_runtime.so&quot;)</span></code>. You can then continue to use
+programs just as you would otherwise via PyTorch API.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If you are using the standard distribution of PyTorch in Python on x86, likely you will need the pre-cxx11-abi variant of <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code>, check <a class="reference internal" href="../getting_started/installation.html#installation"><span class="std std-ref">Installation</span></a> documentation for more details.</p>
+</div>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If you are linking <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code>, likely using the following flags will help <code class="docutils literal notranslate"><span class="pre">-Wl,--no-as-needed</span> <span class="pre">-ltorchtrt</span> <span class="pre">-Wl,--as-needed</span></code> as theres no direct symbol dependency to anything in the Torch-TensorRT runtime for most Torch-TensorRT runtime applications</p>
+</div>
+<p>An example of how to use <code class="docutils literal notranslate"><span class="pre">libtorchtrt_runtime.so</span></code> can be found here: <a class="reference external" href="https://github.com/pytorch/TensorRT/tree/master/examples/torchtrt_runtime_example">https://github.com/pytorch/TensorRT/tree/master/examples/torchtrt_runtime_example</a></p>
+</section>
+<section id="plugin-library">
+<h2>Plugin Library<a class="headerlink" href="#plugin-library" title="Permalink to this headline">¶</a></h2>
+<p>In the case you use Torch-TensorRT as a converter to a TensorRT engine and your engine uses plugins provided by Torch-TensorRT, Torch-TensorRT
+ships the library <code class="docutils literal notranslate"><span class="pre">libtorchtrt_plugins.so</span></code> which contains the implementation of the TensorRT plugins used by Torch-TensorRT during
+compilation. This library can be <code class="docutils literal notranslate"><span class="pre">DL_OPEN</span></code> or <code class="docutils literal notranslate"><span class="pre">LD_PRELOAD</span></code> similar to other TensorRT plugin libraries.</p>
+</section>
+<section id="multi-device-safe-mode">
+<h2>Multi Device Safe Mode<a class="headerlink" href="#multi-device-safe-mode" title="Permalink to this headline">¶</a></h2>
+<p>Multi-device safe mode is a setting in Torch-TensorRT which allows the user to determine whether
+the runtime checks for device consistency prior to every inference call.</p>
+<p>There is a non-negligible, fixed cost per-inference call when multi-device safe mode is enabled, which is why
+it is now disabled by default. It can be controlled via the following convenience function which
+doubles as a context manager.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Enables Multi Device Safe Mode</span>
+<span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">set_multi_device_safe_mode</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Disables Multi Device Safe Mode [Default Behavior]</span>
+<span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">set_multi_device_safe_mode</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="c1"># Enables Multi Device Safe Mode, then resets the safe mode to its prior setting</span>
+<span class="k">with</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">runtime</span><span class="o">.</span><span class="n">set_multi_device_safe_mode</span><span class="p">(</span><span class="kc">True</span><span class="p">):</span>
+    <span class="o">...</span>
+</pre></div>
+</div>
+<p>TensorRT requires that each engine be associated with the CUDA context in the active thread from which it is invoked.
+Therefore, if the device were to change in the active thread, which may be the case when invoking
+engines on multiple GPUs from the same Python process, safe mode will cause Torch-TensorRT to display
+an alert and switch GPUs accordingly. If safe mode were not enabled, there could be a mismatch in the engine
+device and CUDA context device, which could lead the program to crash.</p>
+<p>One technique for managing multiple TRT engines on different GPUs while not sacrificing performance for
+multi-device safe mode is to use Python threads. Each thread is responsible for all of the TRT engines
+on a single GPU, and the default CUDA device on each thread corresponds to the GPU for which it is
+responsible (can be set via <code class="docutils literal notranslate"><span class="pre">torch.cuda.set_device(...)</span></code>). In this way, multiple threads can be used in the same
+Python script without needing to switch CUDA contexts and incur performance overhead.</p>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../ts/getting_started_with_cpp_api.html">Using Torch-TensorRT in  C++</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>
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+<p class="caption" role="heading"><span class="caption-text">User Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Saving models compiled with Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="runtime.html">Deploying Torch-TensorRT Programs</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../contributors/dynamo_converters.html">Writing Dynamo Converters</a></li>
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+              
+  <section id="saving-models-compiled-with-torch-tensorrt">
+<span id="saving-models"></span><h1>Saving models compiled with Torch-TensorRT<a class="headerlink" href="#saving-models-compiled-with-torch-tensorrt" title="Permalink to this headline">¶</a></h1>
+<span class="target" id="module-torch_tensorrt.dynamo"></span><p>Saving models compiled with Torch-TensorRT varies slightly with the <cite>ir</cite> that has been used for compilation.</p>
+<section id="dynamo-ir">
+<h2>Dynamo IR<a class="headerlink" href="#dynamo-ir" title="Permalink to this headline">¶</a></h2>
+<p>The output type of <cite>ir=dynamo</cite> compilation of Torch-TensorRT is <cite>torch.export.ExportedProgram</cite> object by default.
+In addition, we provide a new parameter <cite>output_format</cite> in the <cite>CompilationSetting</cite> object provided before compilation.
+The <cite>output_format</cite> can take the following options</p>
+<ul class="simple">
+<li><p><cite>exported_program</cite> (or) <cite>ep</cite> : This is the default. Returns an ExportedProgram</p></li>
+<li><p><cite>torchscript</cite> (or) <cite>ts</cite> : This returns a TorchScript module</p></li>
+<li><p><cite>graph_module</cite> (or) <cite>fx</cite> : This returns a torch.fx.GraphModule which can be traced into Torchscript to save to disk.</p></li>
+</ul>
+<section id="a-torchscript">
+<h3>a) Torchscript<a class="headerlink" href="#a-torchscript" title="Permalink to this headline">¶</a></h3>
+<p>If you set the <cite>output_format=”torchscript”</cite>, this will return a <cite>ScriptModule</cite> which can be serialized via torch.jit.save</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()]</span>
+<span class="c1"># trt_ts is a torch.jit.ScriptModule object</span>
+<span class="n">trt_ts</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;dynamo&quot;</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">output_format</span><span class="o">=</span><span class="s2">&quot;torchscript&quot;</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ts</span><span class="p">,</span> <span class="s2">&quot;trt_model.ts&quot;</span><span class="p">)</span>
+
+<span class="c1"># Later, you can load it and run inference</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;trt_model.ts&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="b-exportedprogram">
+<h3>b) ExportedProgram<a class="headerlink" href="#b-exportedprogram" title="Permalink to this headline">¶</a></h3>
+<p><cite>torch.export.ExportedProgram</cite>, a new format introduced in Pytorch 2.X is the default return type of Torch-TensorRT compilation.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()]</span>
+<span class="c1"># trt_ep is a torch.export.ExportedProgram object</span>
+<span class="n">trt_ep</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;dynamo&quot;</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ep</span><span class="p">,</span> <span class="s2">&quot;trt_model.ep&quot;</span><span class="p">)</span>
+
+<span class="c1"># Later, you can load it and run inference</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">export</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;trt_model.ep&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+<section id="c-graphmodule">
+<h3>c) GraphModule<a class="headerlink" href="#c-graphmodule" title="Permalink to this headline">¶</a></h3>
+<p>We can also return a <cite>torch.fx.GraphModule</cite> object as the output of Torch-TensorRT compilation by setting <cite>output_format=”graph_module”</cite>.
+Internally, partitioning, lowering, conversion phases operate using GraphModule objects. These can be either traced into a Torchscript modules or
+exported into <cite>ExportedProgram</cite> objects</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()]</span>
+<span class="c1"># trt_gm is a torch.fx.GraphModule object</span>
+<span class="n">trt_gm</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;dynamo&quot;</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">output_format</span><span class="o">=</span><span class="s2">&quot;graph_module&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
+</section>
+<section id="torchscript-ir">
+<h2>Torchscript IR<a class="headerlink" href="#torchscript-ir" title="Permalink to this headline">¶</a></h2>
+<p>In Torch-TensorRT 1.X versions, the primary way to compile and run inference with Torch-TensorRT is using Torchscript IR.
+For <cite>ir=ts</cite>, this behavior stays the same in 2.X versions as well.</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch_tensorrt</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">MyModel</span><span class="p">()</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">inputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span><span class="o">.</span><span class="n">cuda</span><span class="p">()]</span>
+<span class="n">trt_ts</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ir</span><span class="o">=</span><span class="s2">&quot;ts&quot;</span><span class="p">,</span> <span class="n">inputs</span><span class="p">)</span> <span class="c1"># Output is a ScriptModule object</span>
+<span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">trt_ts</span><span class="p">,</span> <span class="s2">&quot;trt_model.ts&quot;</span><span class="p">)</span>
+
+<span class="c1"># Later, you can load it and run inference</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">jit</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;trt_model.ts&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
+<span class="n">model</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">)</span>
+</pre></div>
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+<li class="toctree-l1"><a class="reference internal" href="../getting_started/installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html">Building Torch-TensorRT on Windows</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../getting_started/getting_started_with_windows.html#building-with-visual-studio-code">Building With Visual Studio Code</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>
+</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">User Guide</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="dynamic_shapes.html">Dynamic shapes with Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="ptq.html">Post Training Quantization (PTQ)</a></li>
+<li class="toctree-l1"><a class="reference internal" href="saving_models.html">Saving models compiled with Torch-TensorRT</a></li>
+<li class="toctree-l1"><a class="reference internal" href="runtime.html">Deploying Torch-TensorRT Programs</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">DLA</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/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/notebooks.html">Example notebooks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/_rendered_examples/dynamo/torch_compile_resnet_example.html">Compiling ResNet using the Torch-TensorRT <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 a Transformer using torch.compile and TensorRT</a></li>
+<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/torch_compile_stable_diffusion.html">Torch Compile Stable Diffusion</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Python API Documenation</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../py_api/torch_tensorrt.html">torch_tensorrt</a></li>
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+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">CLI Documenation</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../cli/torchtrtc.html">torchtrtc</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Contributor Documentation</span></p>
+<ul>
+<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>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Indices</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../indices/supported_ops.html">Operators Supported</a></li>
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+             <article itemprop="articleBody" id="pytorch-article" class="pytorch-article">
+              
+  <section id="dla">
+<span id="using-dla"></span><h1>DLA<a class="headerlink" href="#dla" title="Permalink to this headline">¶</a></h1>
+<p><code class="docutils literal notranslate"><span class="pre">DLA</span></code> NVIDIA Deep Learning Accelerator is a fixed-function accelerator engine targeted for deep learning operations. DLA is designed to do full hardware acceleration of convolutional neural networks. DLA supports various layers such as convolution, deconvolution, fully-connected, activation, pooling, batch normalization, etc. <code class="docutils literal notranslate"><span class="pre">torch_tensorrt</span></code> supports compilation of TorchScript Module and deployment pipeline on the DLA hardware available on NVIDIA embedded platforms.</p>
+<p>NOTE: DLA supports fp16 and int8 precision only.</p>
+<p>Using DLA with torchtrtc</p>
+<div class="highlight-shell notranslate"><div class="highlight"><pre><span></span>torchtrtc<span class="w"> </span><span class="o">[</span>input_file_path<span class="o">]</span><span class="w"> </span><span class="o">[</span>output_file_path<span class="o">]</span><span class="w"> </span><span class="o">[</span>input_shapes...<span class="o">]</span><span class="w"> </span>-p<span class="w"> </span>f16<span class="w"> </span>-d<span class="w"> </span>dla<span class="w"> </span><span class="o">{</span>OPTIONS<span class="o">}</span>
+</pre></div>
+</div>
+<p>Using DLA in a C++ application</p>
+<div class="highlight-c++ notranslate"><div class="highlight"><pre><span></span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="n">std</span><span class="o">::</span><span class="n">vector</span><span class="o">&lt;</span><span class="kt">int64_t</span><span class="o">&gt;&gt;</span><span class="w"> </span><span class="n">input_shape</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{{</span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">3</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">,</span><span class="w"> </span><span class="mi">32</span><span class="p">}};</span>
+<span class="k">auto</span><span class="w"> </span><span class="n">compile_spec</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="p">({</span><span class="n">input_shape</span><span class="p">});</span>
+
+<span class="cp"># Set a precision. DLA supports fp16 or int8 only</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">enabled_precisions</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="p">{</span><span class="n">torch</span><span class="o">::</span><span class="n">kF16</span><span class="p">};</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">device_type</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="n">torch_tensorrt</span><span class="o">::</span><span class="n">CompileSpec</span><span class="o">::</span><span class="n">DeviceType</span><span class="o">::</span><span class="n">kDLA</span><span class="p">;</span>
+
+<span class="cp"># Make sure the gpu id is set to Xavier id for DLA</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">gpu_id</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">0</span><span class="p">;</span>
+
+<span class="cp"># Set the DLA core id</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">dla_core</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="mi">1</span><span class="p">;</span>
+
+<span class="cp"># If a layer fails to run on DLA it will fallback to GPU</span>
+<span class="n">compile_spec</span><span class="p">.</span><span class="n">device</span><span class="p">.</span><span class="n">allow_gpu_fallback</span><span class="w"> </span><span class="o">=</span><span class="w"> </span><span class="nb">true</span><span class="p">;</span>
+</pre></div>
+</div>
+<p>Using DLA in a python application</p>
+<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">compile_spec</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;inputs&quot;</span><span class="p">:</span> <span class="p">[</span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input</span><span class="o">.</span><span class="n">shape</span><span class="p">)],</span>
+    <span class="s2">&quot;device&quot;</span><span class="p">:</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Device</span><span class="p">(</span><span class="s2">&quot;dla:0&quot;</span><span class="p">,</span> <span class="n">allow_gpu_fallback</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+    <span class="s2">&quot;enalbed_precisions&quot;</span><span class="p">:</span> <span class="p">{</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">},</span>
+<span class="p">}</span>
+
+<span class="n">trt_mod</span> <span class="o">=</span> <span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scripted_model</span><span class="p">,</span> <span class="n">compile_spec</span><span class="p">)</span>
+</pre></div>
+</div>
+</section>
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diff --git a/py/ci/Dockerfile.ci b/py/ci/Dockerfile.ci
index 96f2b90200..b4692d394e 100644
--- a/py/ci/Dockerfile.ci
+++ b/py/ci/Dockerfile.ci
@@ -3,8 +3,8 @@ ARG trt_version
 
 RUN echo -e "Installing with TensorRT ${trt_version}"
 
-RUN yum install -y ninja-build tensorrt-${trt_version}.*
-
+RUN yum-config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/cuda-rhel7.repo
+RUN yum install -y ninja-build gettext tensorrt-${trt_version}.*
 RUN wget https://github.com/bazelbuild/bazelisk/releases/download/v1.17.0/bazelisk-linux-amd64 \
     && mv bazelisk-linux-amd64 /usr/bin/bazel \
     && chmod +x /usr/bin/bazel
diff --git a/py/ci/soname_excludes.params b/py/ci/soname_excludes.params
index a5eecb7c9a..477852c48a 100644
--- a/py/ci/soname_excludes.params
+++ b/py/ci/soname_excludes.params
@@ -19,6 +19,9 @@
 --exclude libnvToolsExt-847d78f2.so
 --exclude libcudart-e409450e.so.11.0
 --exclude libcudart-e409450e.so
+--exclude libcudart-24af1308.so.12.1.55
+--exclude libcudart-24af1308.so.12.1
+--exclude libcudart-24af1308.so
 --exclude libgomp-a34b3233.so.1
 --exclude libgomp-a34b3233.so
 --exclude libcudart.so.11
diff --git a/toolchains/ci_workspaces/WORKSPACE.x86_64.cu118.release.rhel b/toolchains/ci_workspaces/WORKSPACE.x86_64.cu118.release.rhel
index 79c7575d2b..fd368f4dd4 100644
--- a/toolchains/ci_workspaces/WORKSPACE.x86_64.cu118.release.rhel
+++ b/toolchains/ci_workspaces/WORKSPACE.x86_64.cu118.release.rhel
@@ -58,17 +58,17 @@ new_local_repository(
 http_archive(
     name = "libtorch",
     build_file = "@//third_party/libtorch:BUILD",
-    sha256 = "174579a7ee2a506d063714160c5fc57da428f7935311ef511c8f19820eb14c86",
+    sha256 = "a2b0f51ff59ef2787a82c36bba67f7380236a6384dbbd2459c558989af27184f",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/nightly/cu121/libtorch-cxx11-abi-shared-with-deps-2.1.0.dev20230731%2Bcu121.zip"],
+    urls = ["https://download.pytorch.org/libtorch/cu118/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcu118.zip"],
 )
 
 http_archive(
     name = "libtorch_pre_cxx11_abi",
     build_file = "@//third_party/libtorch:BUILD",
-    sha256 = "532217063c65354d5534211badadc9c370d889cb1c3fdb295c9b3d0f181bc0ba",
+    sha256 = "f9c887085207f9500357cae4324a53c3010b8890397db915d7dbefb9183c7964",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/nightly/cu121/libtorch-shared-with-deps-2.1.0.dev20230731%2Bcu121.zip"],
+    urls = ["https://download.pytorch.org/libtorch/cu118/libtorch-shared-with-deps-2.2.0%2Bcu118.zip"],
 )
 
 ####################################################################################
diff --git a/toolchains/ci_workspaces/WORKSPACE.x86_64.cu121.release.rhel b/toolchains/ci_workspaces/WORKSPACE.x86_64.cu121.release.rhel
index 2fc09e8219..045862c578 100644
--- a/toolchains/ci_workspaces/WORKSPACE.x86_64.cu121.release.rhel
+++ b/toolchains/ci_workspaces/WORKSPACE.x86_64.cu121.release.rhel
@@ -58,17 +58,17 @@ new_local_repository(
 http_archive(
     name = "libtorch",
     build_file = "@//third_party/libtorch:BUILD",
-    sha256 = "174579a7ee2a506d063714160c5fc57da428f7935311ef511c8f19820eb14c86",
+    sha256 = "1fcf1b4b841f5bb677d3bd4ee7ac72d2c4f630447cafc41067e8316de8dfe061",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/nightly/cu121/libtorch-cxx11-abi-shared-with-deps-2.1.0.dev20230731%2Bcu121.zip"],
+    urls = ["https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.2.0%2Bcu121.zip"],
 )
 
 http_archive(
     name = "libtorch_pre_cxx11_abi",
     build_file = "@//third_party/libtorch:BUILD",
-    sha256 = "532217063c65354d5534211badadc9c370d889cb1c3fdb295c9b3d0f181bc0ba",
+    sha256 = "0a1a034b1980199543ec5cbc8d42215f55b188ac188b3dac42d83aeb449922bb",
     strip_prefix = "libtorch",
-    urls = ["https://download.pytorch.org/libtorch/nightly/cu121/libtorch-shared-with-deps-2.1.0.dev20230731%2Bcu121.zip"],
+    urls = ["https://download.pytorch.org/libtorch/cu121/libtorch-shared-with-deps-2.2.0%2Bcu121.zip"],
 )
 
 ####################################################################################