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 __call__(self, input):
      layers = []
      layers += self._vgg_block(input)
  
      if not self.with_extra_blocks:
          return layers[-1]
  
      layers += self._add_extras_block(layers[-1])
      norm_cfg = self.normalizations
      for k, v in enumerate(layers):
          if not norm_cfg[k] == -1:
              layers[k] = self._l2_norm_scale(v, init_scale=norm_cfg[k])
  
      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 2 for calling the VGG obj's underlying member method code with example usage: obj.__call__(input) and returns: layers, layers