class_docstr: VGG, see https://arxiv.org/abs/1409.1556
  
  Args:
      depth (int): the VGG net depth (16 or 19)
      normalizations (list): params list of init scale in l2 norm, skip init
          scale if param is -1.
      with_extra_blocks (bool): whether or not extra blocks should be added
      extra_block_filters (list): in each extra block, params:
          [in_channel, out_channel, padding_size, stride_size, filter_size]
      class_dim (int): number of class while classification
code: def _extra_block(
      self,
      input,
      num_filters1,
      num_filters2,
      padding_size,
      stride_size,
      filter_size,
      name=None,
  ):
      # 1x1 conv
      conv_1 = self._conv_layer(
          input=input,
          num_filters=int(num_filters1),
          filter_size=1,
          stride=1,
          act="relu",
          padding=0,
          name=name + "1",
      )
  
      # 3x3 conv
      conv_2 = self._conv_layer(
          input=conv_1,
          num_filters=int(num_filters2),
          filter_size=filter_size,
          stride=stride_size,
          act="relu",
          padding=padding_size,
          name=name + "2",
      )
      return conv_2
  
file_path: PaddleHub/modules/image/classification/vgg16_imagenet/vgg.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from paddle import fluid
  from paddle.fluid.param_attr import ParamAttr
label_desc: Use image node_id 6 for calling the VGG obj's underlying member method code with example usage: obj._extra_block(input, num_filters1, num_filters2, padding_size, stride_size, filter_size, name) and returns: conv_2