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_layer(
      self,
      input,
      num_filters,
      filter_size,
      stride,
      padding,
      dilation=1,
      act="relu",
      use_cudnn=True,
      name=None,
  ):
      conv = fluid.layers.conv2d(
          input=input,
          num_filters=num_filters,
          filter_size=filter_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          act=act,
          use_cudnn=use_cudnn,
          param_attr=ParamAttr(name=name + "_weights"),
          bias_attr=ParamAttr(name=name + "_biases")
          if self.with_extra_blocks
          else False,
          name=name + ".conv2d.output.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 7 for calling the VGG obj's underlying member method code with example usage: obj._conv_layer(input, num_filters, filter_size, stride, padding, dilation, act, use_cudnn, name) and returns: conv