code: def conv_bn_relu_layer(
      input,
      num_filters,
      filter_size,
      strides=(1, 1),
      pad=True,
      bnTimeConst=4096,
      init=he_normal(),
  ):
      conv = Convolution(
          filter_size,
          num_filters,
          activation=None,
          init=init,
          pad=pad,
          strides=strides,
          bias=False,
      )(input)
      bn = BatchNormalization(
          map_rank=1,
          normalization_time_constant=bnTimeConst,
          use_cntk_engine=False,
      )(conv)
      return relu(bn)
  
file_path: CNTK/Examples/Image/Classification/GoogLeNet/BN-Inception/Python/BN_Inception.py
imports: from cntk.initializer import he_normal
  from cntk.layers import AveragePooling, MaxPooling, BatchNormalization, Convolution, Dense
  from cntk.ops import element_times, relu, splice
label_desc: Use image node_id 1 for calling a global function with example usage: conv_bn_relu_layer(input, num_filters, filter_size, strides, pad, bnTimeConst, init) and returns: relu