code: def conv_bn_layer(
      self,
      input,
      filter_size,
      num_filters,
      stride,
      padding,
      channels=None,
      num_groups=1,
      act="relu",
      use_cudnn=True,
  ):
      conv = fluid.layers.conv2d(
          input=input,
          num_filters=num_filters,
          filter_size=filter_size,
          stride=stride,
          padding=padding,
          groups=num_groups,
          act=None,
          use_cudnn=use_cudnn,
          param_attr=ParamAttr(initializer=MSRA()),
          bias_attr=False,
      )
      return fluid.layers.batch_norm(input=conv, act=act)
  
file_path: AdvBox/tutorials/image_classification/mobilenet.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from builtins import range
  from builtins import object
  import paddle.fluid
  from paddle.fluid.initializer import MSRA
  from paddle.fluid.param_attr import ParamAttr
label_desc: Use image node_id 3 for calling the MobileNet obj's underlying member method code with example usage: obj.conv_bn_layer(input, filter_size, num_filters, stride, padding, channels, num_groups, act, use_cudnn) and returns: fluid