docs: [Automated] Regenerating documenation for 919b9e9

Signed-off-by: Torch-TensorRT Github Bot <torch-tensorrt.github.bot@nvidia.com>

docs/_notebooks/CitriNet-example.html

@@ -857,7 +857,7 @@
         </div>
        </div>
        <p>
-        <img alt="16b97c6c0c8746a9ad5b95a20bbb48b7" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="06dbf5b7da64440da85bb0a3a1fa0d39" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
        </p>
        <section id="Torch-TensorRT-Getting-Started---CitriNet">
         <h1 id="notebooks-citrinet-example--page-root">

docs/_notebooks/EfficientNet-example.html

@@ -857,7 +857,7 @@
         </div>
        </div>
        <p>
-        <img alt="2eb6e25acb6241e5a7fb71e7bbe4a514" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="014c3196047e4928abffdab4c053b10c" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
        </p>
        <section id="Torch-TensorRT-Getting-Started---EfficientNet-B0">
         <h1 id="notebooks-efficientnet-example--page-root">

docs/_notebooks/Hugging-Face-BERT.html

@@ -809,7 +809,7 @@
         </div>
        </div>
        <p>
-        <img alt="01cc7685bbcc49698ca422d848cd686b" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png"/>
+        <img alt="ebee052e327d47b19490e23186c1589c" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png"/>
        </p>
        <section id="Masked-Language-Modeling-(MLM)-with-Hugging-Face-BERT-Transformer">
         <h1 id="notebooks-hugging-face-bert--page-root">

docs/_notebooks/Resnet50-example.html

@@ -857,7 +857,7 @@
         </div>
        </div>
        <p>
-        <img alt="4bc581df48894031abeaeade842f63e6" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png"/>
+        <img alt="6a06541e0a714d89adfee0ad1925a35b" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png"/>
        </p>
        <section id="Torch-TensorRT-Getting-Started---ResNet-50">
         <h1 id="notebooks-resnet50-example--page-root">

docs/_notebooks/lenet-getting-started.html

@@ -895,7 +895,7 @@
         </div>
        </div>
        <p>
-        <img alt="95168dd749c543dea8d29528fa4769a5" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="0b18389c4b4949eba10f5d6e5d2beac1" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
        </p>
        <section id="Torch-TensorRT-Getting-Started---LeNet">
         <h1 id="notebooks-lenet-getting-started--page-root">

docs/_notebooks/ssd-object-detection-demo.html

@@ -915,7 +915,7 @@
         </div>
        </div>
        <p>
-        <img alt="93aa88092fbf4efeb380fcbe20133f1a" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png"/>
+        <img alt="89d2ee3efaf040128b4ca16a897787e3" src="https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_ssd_notebook.png"/>
        </p>
        <section id="Object-Detection-with-Torch-TensorRT-(SSD)">
         <h1 id="notebooks-ssd-object-detection-demo--page-root">

docs/_notebooks/vgg-qat.html

@@ -922,7 +922,7 @@ <h2 id="Overview">
          </div>
          <p>
           ## 2. VGG16 Overview ### Very Deep Convolutional Networks for Large-Scale Image Recognition VGG is one of the earliest family of image classification networks that first used small (3x3) convolution filters and achieved significant improvements on ImageNet recognition challenge. The network architecture looks as follows
-          <img alt="ba2d50f0e2f940918ef09b03f7da5774" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png"/>
+          <img alt="108b3227d92f48dfac1e036b6a225fd3" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png"/>
          </p>
          <p>
           ## 3. Training a baseline VGG16 model We train VGG16 on CIFAR10 dataset. Define training and testing datasets and dataloaders. This will download the CIFAR 10 data in your

docs/_sources/contributors/conversion.rst.txt

@@ -19,7 +19,7 @@ inputs and assemble an array of resources to pass to the converter. Inputs can b
 
 *  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 to the ``value_tensor_map``,
+   *  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
@@ -32,6 +32,7 @@ inputs and assemble an array of resources to pass to the converter. Inputs can b
       static value has been evaluated
 
 *  The input is from a node that has not been converted
+
    *  Torch-TensorRT will error out here
 
 Node Evaluation

docs/_sources/contributors/lowering.rst.txt

@@ -134,7 +134,7 @@ Removes _all_ tuples and raises an error if some cannot be removed, this is used
 Module Fallback
 *****************
 
-    `Torch-TensorRT/core/lowering/passes/module_fallback.cpp <https://github.com/nvidia/Torch-TensorRT/blob/master/core/lowering/passes/module_fallback.cpp>`
+    `Torch-TensorRT/core/lowering/passes/module_fallback.cpp <https://github.com/nvidia/Torch-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.

docs/_sources/contributors/partitioning.rst.txt

@@ -3,6 +3,6 @@
 Partitioning Phase
 ====================
 
-The phase is optional and enabled by the user. It instructs the compiler to seperate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
-Criteria for seperation 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.
+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.

docs/_sources/contributors/phases.rst.txt

@@ -21,10 +21,10 @@ TensorRT.
 
 Partitioning
 ^^^^^^^^^^^^^
-:ref:`partitioning
+:ref:`partitioning`
 
-The phase is optional and enabled by the user. It instructs the compiler to seperate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
-Criteria for seperation 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
+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

docs/_sources/contributors/system_overview.rst.txt

@@ -23,7 +23,7 @@ The repository is structured into:
 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. The there are the three main phases of the
+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

docs/contributors/conversion.html

@@ -532,7 +532,7 @@ <h1 id="contributors-conversion--page-root">
           <ul>
            <li>
             <p>
-             In this case the ITensor of the output has added to the to the
+             In this case the ITensor of the output has added to the
              <code class="docutils literal notranslate">
               <span class="pre">
                value_tensor_map
@@ -570,8 +570,14 @@ <h1 id="contributors-conversion--page-root">
          <li>
           <p>
            The input is from a node that has not been converted
-*  Torch-TensorRT will error out here
           </p>
+          <ul>
+           <li>
+            <p>
+             Torch-TensorRT will error out here
+            </p>
+           </li>
+          </ul>
          </li>
         </ul>
         <section id="node-evaluation">

docs/contributors/lowering.html

@@ -912,9 +912,9 @@ <h3 id="module-fallback">
           <blockquote>
            <div>
             <p>
-             <cite>
-              Torch-TensorRT/core/lowering/passes/module_fallback.cpp &lt;https://github.com/nvidia/Torch-TensorRT/blob/master/core/lowering/passes/module_fallback.cpp&gt;
-             </cite>
+             <a class="reference external" href="https://github.com/nvidia/Torch-TensorRT/blob/master/core/lowering/passes/module_fallback.cpp">
+              Torch-TensorRT/core/lowering/passes/module_fallback.cpp
+             </a>
             </p>
            </div>
           </blockquote>

docs/contributors/partitioning.html

@@ -468,8 +468,8 @@ <h1 id="contributors-partitioning--page-root">
          </a>
         </h1>
         <p>
-         The phase is optional and enabled by the user. It instructs the compiler to seperate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
-Criteria for seperation 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
+         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>

docs/contributors/phases.html

@@ -520,17 +520,15 @@ <h2 id="partitioning">
           </a>
          </h2>
          <p>
-          :ref:
-          <a href="#id1">
-           <span class="problematic" id="id2">
-            `
+          <a class="reference internal" href="partitioning.html#partitioning">
+           <span class="std std-ref">
+            Partitioning Phase
            </span>
           </a>
-          partitioning
          </p>
          <p>
-          The phase is optional and enabled by the user. It instructs the compiler to seperate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
-Criteria for seperation 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
+          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>

docs/contributors/system_overview.html

@@ -617,7 +617,7 @@ <h1 id="contributors-system-overview--page-root">
         </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. The there are the three main phases of the
+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">
@@ -657,17 +657,15 @@ <h3 id="partitioning">
            </a>
           </h3>
           <p>
-           :ref:
-           <a href="#id2">
-            <span class="problematic" id="id3">
-             `
+           <a class="reference internal" href="partitioning.html#partitioning">
+            <span class="std std-ref">
+             Partitioning Phase
             </span>
            </a>
-           partitioning
           </p>
           <p>
-           The phase is optional and enabled by the user. It instructs the compiler to seperate nodes into ones that should run in PyTorch and ones that should run in TensorRT.
-Criteria for seperation 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
+           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>

docs/py_api/ts.html

@@ -2066,7 +2066,7 @@ <h2 id="functions">
               at
              </span>
              <span class="pre">
-              0x7fc5e1feb7f0&gt;
+              0x7f24b4674ef0&gt;
              </span>
             </span>
            </span>