code: def truncated_vgg_16(inputs, is_training=True, scope="vgg_16"):
      """Oxford Net VGG 16-Layers version D Example.
  
      For use in SSD object detection network, which has this particular
      truncated version of VGG16 detailed in its paper.
  
      Args:
        inputs: a tensor of size [batch_size, height, width, channels].
        scope: Optional scope for the variables.
  
      Returns:
        the last op containing the conv5 tensor and end_points dict.
      """
      with variable_scope.variable_scope(
          scope, "vgg_16", [inputs]
      ) as sc:
          end_points_collection = sc.original_name_scope + "_end_points"
          # Collect outputs for conv2d, fully_connected and max_pool2d.
          with arg_scope(
              [
                  layers.conv2d,
                  layers_lib.fully_connected,
                  layers_lib.max_pool2d,
              ],
              outputs_collections=end_points_collection,
          ):
              net = layers_lib.repeat(
                  inputs, 2, layers.conv2d, 64, [3, 3], scope="conv1"
              )
              net = layers_lib.max_pool2d(net, [2, 2], scope="pool1")
              net = layers_lib.repeat(
                  net, 2, layers.conv2d, 128, [3, 3], scope="conv2"
              )
              net = layers_lib.max_pool2d(net, [2, 2], scope="pool2")
              net = layers_lib.repeat(
                  net, 3, layers.conv2d, 256, [3, 3], scope="conv3"
              )
              net = layers_lib.max_pool2d(net, [2, 2], scope="pool3")
              net = layers_lib.repeat(
                  net, 3, layers.conv2d, 512, [3, 3], scope="conv4"
              )
              net = layers_lib.max_pool2d(net, [2, 2], scope="pool4")
              net = layers_lib.repeat(
                  net, 3, layers.conv2d, 512, [3, 3], scope="conv5"
              )
              # Convert end_points_collection into a end_point dict.
              end_points = utils.convert_collection_to_dict(
                  end_points_collection
              )
              return net, end_points
  
file_path: luminoth/luminoth/models/base/truncated_vgg.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensorflow.contrib import layers
  from tensorflow.contrib.framework.python.ops import arg_scope
  from tensorflow.contrib.layers.python.layers import layers
  from tensorflow.contrib.layers.python.layers import regularizers
  from tensorflow.contrib.layers.python.layers import utils
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import nn_ops
  from tensorflow.python.ops import variable_scope
label_desc: Use image node_id 2 for calling a global function with example usage: truncated_vgg_16(inputs, is_training, scope) and returns: net,  end_points