code: def conv_bn(
      input,
      filter_size,
      num_filters,
      strides=(1, 1),
      init=he_normal(),
      bn_init_scale=1,
  ):
      c = Convolution(
          filter_size,
          num_filters,
          activation=None,
          init=init,
          pad=True,
          strides=strides,
          bias=False,
      )(input)
      r = BatchNormalization(
          map_rank=1,
          normalization_time_constant=4096,
          use_cntk_engine=False,
          init_scale=bn_init_scale,
          disable_regularization=True,
      )(c)
      return r
  
file_path: CNTK/Examples/Image/Classification/ResNet/Python/resnet_models.py
imports: import numpy
  from cntk.initializer import he_normal, normal
  from cntk.layers import AveragePooling, MaxPooling, BatchNormalization, Convolution, Dense
  from cntk.ops import element_times, relu
label_desc: Use image node_id 1 for calling a global function with example usage: conv_bn(input, filter_size, num_filters, strides, init, bn_init_scale) and returns: r