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 _pooling_block(
      self, conv, pool_size, pool_stride, pool_padding=0, ceil_mode=True
  ):
      pool = fluid.layers.pool2d(
          input=conv,
          pool_size=pool_size,
          pool_type="max",
          pool_stride=pool_stride,
          pool_padding=pool_padding,
          ceil_mode=ceil_mode,
      )
      return pool
  
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 8 for calling the VGG obj's underlying member method code with example usage: obj._pooling_block(conv, pool_size, pool_stride, pool_padding, ceil_mode) and returns: pool