code: def conv_bn(
      input, filter, ksize, stride, padding, act="relu", bias_attr=False
  ):
      conv = fluid.layers.conv2d(
          input=input,
          filter_size=ksize,
          num_filters=filter,
          stride=stride,
          padding=padding,
          act=None,
          bias_attr=bias_attr,
      )
      return fluid.layers.batch_norm(input=conv, act=act)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import six
  import paddle.fluid
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Xavier
  from paddle.fluid.initializer import Constant
  from paddle.fluid.initializer import Bilinear
  from paddle.fluid.regularizer import L2Decay
label_desc: Use image node_id 1 for calling a global function with example usage: conv_bn(input, filter, ksize, stride, padding, act, bias_attr) and returns: fluid