docs: [Automated] Regenerating documenation for 2918675

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

Author: TRTorch Github Bot

docs/_notebooks/Resnet50-example.html

@@ -722,7 +722,7 @@
         </div>
        </div>
        <p>
-        <img alt="c663cd868ad041eca14dbccadec5e94f" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="b97ec936abed43db979023efd9a3470c" 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

@@ -816,7 +816,7 @@
         </div>
        </div>
        <p>
-        <img alt="6f93c858f8694b589446f1535fbd52b7" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="b5ec674148924b12989dc0891fbbb9f2" 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

@@ -836,7 +836,7 @@
         </div>
        </div>
        <p>
-        <img alt="ab20ffc8371040b9a0601624d65ecbc6" src="http://developer.download.nvidia.com/compute/machine-learning/frameworks/nvidia_logo.png"/>
+        <img alt="633e3c0e65e64de4965400f3b384e4d2" 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/_notebooks/vgg-qat.html

@@ -808,7 +808,7 @@ <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="3100e8214a1b48288545c2a2900a7db0" src="https://neurohive.io/wp-content/uploads/2018/11/vgg16-1-e1542731207177.png"/>
+          <img alt="b8f75b050c8b44a29989f7f224b20627" 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/py_api/trtorch.html

@@ -1153,7 +1153,7 @@ <h2 id="functions">
            <span class="sig-paren">
             )
            </span>
-           → &lt;torch._C.ScriptClass object at 0x7f737ea6c8b0&gt;
+           → &lt;torch._C.ScriptClass object at 0x7efd42976970&gt;
            <a class="headerlink" href="#trtorch.TensorRTCompileSpec" title="Permalink to this definition">
             ¶
            </a>