original_docstring: 
      Load Pretrained VGG Model into TensorFlow.
      :param sess: TensorFlow Session
      :param vgg_path: Path to vgg folder, containing "variables/" and "saved_model.pb"
      :return: Tuple of Tensors from VGG model (image_input, keep_prob, layer3_out, layer4_out, layer7_out)
      
docstring: Load Pretrained VGG Model into TensorFlow.
docstring_tokens: Load Pretrained VGG Model into TensorFlow .
code: def load_vgg(sess, vgg_path):
      """
      Load Pretrained VGG Model into TensorFlow.
      :param sess: TensorFlow Session
      :param vgg_path: Path to vgg folder, containing "variables/" and "saved_model.pb"
      :return: Tuple of Tensors from VGG model (image_input, keep_prob, layer3_out, layer4_out, layer7_out)
      """
      # TODO: Implement function
      #   Use tf.saved_model.loader.load to load the model and weights
      vgg_tag = 'vgg16'
      vgg_input_tensor_name = 'image_input:0'
      vgg_keep_prob_tensor_name = 'keep_prob:0'
      vgg_layer3_out_tensor_name = 'layer3_out:0'
      vgg_layer4_out_tensor_name = 'layer4_out:0'
      vgg_layer7_out_tensor_name = 'layer7_out:0'
  
      tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)
      graph = tf.get_default_graph()
      input_img = graph.get_tensor_by_name(vgg_input_tensor_name)
      keep = graph.get_tensor_by_name(vgg_keep_prob_tensor_name)
      layer3 = graph.get_tensor_by_name(vgg_layer3_out_tensor_name)
      layer4 = graph.get_tensor_by_name(vgg_layer4_out_tensor_name)
      layer7 = graph.get_tensor_by_name(vgg_layer7_out_tensor_name)
  
      return input_img, keep, layer3, layer4, layer7
code_tokens: def load_vgg ( sess , vgg_path ) : vgg_tag = 'vgg16' vgg_input_tensor_name = 'image_input:0' vgg_keep_prob_tensor_name = 'keep_prob:0' vgg_layer3_out_tensor_name = 'layer3_out:0' vgg_layer4_out_tensor_name = 'layer4_out:0' vgg_layer7_out_tensor_name = 'layer7_out:0' tf . saved_model . loader . load ( sess , [ vgg_tag ] , vgg_path ) graph = tf . get_default_graph ( ) input_img = graph . get_tensor_by_name ( vgg_input_tensor_name ) keep = graph . get_tensor_by_name ( vgg_keep_prob_tensor_name ) layer3 = graph . get_tensor_by_name ( vgg_layer3_out_tensor_name ) layer4 = graph . get_tensor_by_name ( vgg_layer4_out_tensor_name ) layer7 = graph . get_tensor_by_name ( vgg_layer7_out_tensor_name ) return input_img , keep , layer3 , layer4 , layer7
short_docstring: Load Pretrained VGG Model into TensorFlow.
short_docstring_tokens: Load Pretrained VGG Model into TensorFlow .
comment: """
      Load Pretrained VGG Model into TensorFlow.
      :param sess: TensorFlow Session
      :param vgg_path: Path to vgg folder, containing "variables/" and "saved_model.pb"
      :return: Tuple of Tensors from VGG model (image_input, keep_prob, layer3_out, layer4_out, layer7_out)
      """$SEP$# TODO: Implement function$SEP$#   Use tf.saved_model.loader.load to load the model and weights