docs: [Automated] Regenerating documenation for 7861ab3

Signed-off-by: TRTorch Github Bot <[email protected]>

Author: TRTorch Github Bot

docs/_notebooks/Resnet50-example.html

@@ -714,7 +714,7 @@
         </div>
        </div>
        <p>
-        <img alt="eac40fcdfb9d444fafdced53ee615533" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="8806a948f32744bc9364d1d91489d05d" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
        </p>
        <section id="TRTorch-Getting-Started---ResNet-50">
         <h1 id="notebooks-resnet50-example--page-root">

docs/_notebooks/lenet-getting-started.html

@@ -808,7 +808,7 @@
         </div>
        </div>
        <p>
-        <img alt="d612ebd4ae644e6f90790f015213aa9f" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="5a196ac0b5e945e69ff2d98c8de96e9c" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
        </p>
        <section id="TRTorch-Getting-Started---LeNet">
         <h1 id="notebooks-lenet-getting-started--page-root">

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

@@ -828,7 +828,7 @@
         </div>
        </div>
        <p>
-        <img alt="5877b75a445b497ba8c7b47e376ed418" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="d195cbfd5c804f06956a87f57ca4ef17" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
        </p>
        <section id="Object-Detection-with-TRTorch-(SSD)">
         <h1 id="notebooks-ssd-object-detection-demo--page-root">

docs/_sources/tutorials/trtorchc.rst.txt

@@ -39,7 +39,14 @@ to standard TorchScript. Load with ``torch.jit.load()`` and run like you would r
           --allow-gpu-fallback              (Only used when targeting DLA
                                             (device-type)) Lets engine run layers on
                                             GPU if they are not supported on DLA
-          --disable-tf32                    Prevent Float32 layers from using the
+
+         --allow-torch-fallback            Enable layers to run in torch
+                                          if they are not supported in TensorRT
+         --ffo,
+         --forced-fallback-ops             List of operators in the graph that
+                                          should be forced to fallback to Pytorch for execution
+                                          
+         --disable-tf32                    Prevent Float32 layers from using the
                                             TF32 data format
           -p[precision...],
           --enabled-precison=[precision...] (Repeatable) Enabling an operating
@@ -48,6 +55,7 @@ to standard TorchScript. Load with ``torch.jit.load()`` and run like you would r
                                             calibration-cache argument) [ float |
                                             float32 | f32 | half | float16 | f16 |
                                             int8 | i8 ] (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

docs/py_api/trtorch.html

@@ -1129,7 +1129,7 @@ <h2 id="functions">
            <span class="sig-paren">
             )
            </span>
-           → &lt;torch._C.ScriptClass object at 0x7f4d69cff630&gt;
+           → &lt;torch._C.ScriptClass object at 0x7f4c257c0cb0&gt;
            <a class="headerlink" href="#trtorch.TensorRTCompileSpec" title="Permalink to this definition">
             ¶
            </a>

docs/searchindex.js

@@ -447,7 +447,14 @@ <h1 id="tutorials-trtorchc--page-root">
       --allow-gpu-fallback              (Only used when targeting DLA
                                         (device-type)) Lets engine run layers on
                                         GPU if they are not supported on DLA
-      --disable-tf32                    Prevent Float32 layers from using the
+
+     --allow-torch-fallback            Enable layers to run in torch
+                                      if they are not supported in TensorRT
+     --ffo,
+     --forced-fallback-ops             List of operators in the graph that
+                                      should be forced to fallback to Pytorch for execution
+
+     --disable-tf32                    Prevent Float32 layers from using the
                                         TF32 data format
       -p[precision...],
       --enabled-precison=[precision...] (Repeatable) Enabling an operating
@@ -456,6 +463,7 @@ <h1 id="tutorials-trtorchc--page-root">
                                         calibration-cache argument) [ float |
                                         float32 | f32 | half | float16 | f16 |
                                         int8 | i8 ] (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