code: def conv_bn_layer(
      self,
      input,
      num_filters,
      filter_size,
      stride=1,
      groups=1,
      act=None,
  ):
      conv = fluid.layers.conv2d(
          input=input,
          num_filters=num_filters,
          filter_size=filter_size,
          stride=stride,
          padding=old_div((filter_size - 1), 2),
          groups=groups,
          act=None,
          bias_attr=False,
      )
      return fluid.layers.batch_norm(input=conv, act=act)
  
imports: from __future__ import division
  from builtins import range
  from builtins import object
  from past.utils import old_div
  import paddle.fluid
  import math
label_desc: Use image node_id 3 for calling the ResNet obj's underlying member method code with example usage: obj.conv_bn_layer(input, num_filters, filter_size, stride, groups, act) and returns: fluid