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 _conv_block(self, input, num_filter, groups, name=None):
      conv = input
      for i in range(groups):
          conv = self._conv_layer(
              input=conv,
              num_filters=num_filter,
              filter_size=3,
              stride=1,
              padding=1,
              act="relu",
              name=name + str(i + 1),
          )
      return conv
  
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 5 for calling the VGG obj's underlying member method code with example usage: obj._conv_block(input, num_filter, groups, name) and returns: conv