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 _add_extras_block(self, input):
      cfg = self.extra_block_filters
      conv = input
      layers = []
      for k, v in enumerate(cfg):
          assert len(v) == 5, "extra_block_filters size not fix"
          conv = self._extra_block(
              conv,
              v[0],
              v[1],
              v[2],
              v[3],
              v[4],
              name="conv{}_".format(6 + k),
          )
          layers.append(conv)
  
      return layers
  
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 4 for calling the VGG obj's underlying member method code with example usage: obj._add_extras_block(input) and returns: layers