code: def bn_inception_model(input, labelDim, bnTimeConst):
      # 224 x 224 x 3
      conv1 = conv_bn_relu_layer(
          input, 64, (7, 7), (2, 2), True, bnTimeConst
      )
      # 112 x 112 x 64
      pool1 = MaxPooling(filter_shape=(3, 3), strides=(2, 2), pad=True)(
          conv1
      )
      # 56 x 56 x 64
      conv2a = conv_bn_relu_layer(
          pool1, 64, (1, 1), (1, 1), True, bnTimeConst
      )
      # 56 x 56 x 64
      conv2b = conv_bn_relu_layer(
          conv2a, 192, (3, 3), (1, 1), True, bnTimeConst
      )
      # 56 x 56 x 192
      pool2 = MaxPooling(filter_shape=(3, 3), strides=(2, 2), pad=True)(
          conv2b
      )
  
      # Inception Blocks
      # 28 x 28 x 192
      inception3a = inception_block_with_avgpool(
          pool2, 64, 64, 64, 64, 96, 32, bnTimeConst
      )
      # 28 x 28 x 256
      inception3b = inception_block_with_avgpool(
          inception3a, 64, 64, 96, 64, 96, 64, bnTimeConst
      )
      # 28 x 28 x 320
      inception3c = inception_block_pass_through(
          inception3b, 0, 128, 160, 64, 96, 0, bnTimeConst
      )
      # 14 x 14 x 576
      inception4a = inception_block_with_avgpool(
          inception3c, 224, 64, 96, 96, 128, 128, bnTimeConst
      )
      # 14 x 14 x 576
      inception4b = inception_block_with_avgpool(
          inception4a, 192, 96, 128, 96, 128, 128, bnTimeConst
      )
      # 14 x 14 x 576
      inception4c = inception_block_with_avgpool(
          inception4b, 160, 128, 160, 128, 160, 128, bnTimeConst
      )
      # 14 x 14 x 576
      inception4d = inception_block_with_avgpool(
          inception4c, 96, 128, 192, 160, 192, 128, bnTimeConst
      )
      # 14 x 14 x 576
      inception4e = inception_block_pass_through(
          inception4d, 0, 128, 192, 192, 256, 0, bnTimeConst
      )
      # 7 x 7 x 1024
      inception5a = inception_block_with_avgpool(
          inception4e, 352, 192, 320, 160, 224, 128, bnTimeConst
      )
      # 7 x 7 x 1024
      inception5b = inception_block_with_maxpool(
          inception5a, 352, 192, 320, 192, 224, 128, bnTimeConst
      )
  
      # Global Average
      # 7 x 7 x 1024
      pool3 = AveragePooling(filter_shape=(7, 7))(inception5b)
      # 1 x 1 x 1024
      z = Dense(labelDim, init=he_normal())(pool3)
  
      return z
  
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 5 for calling a global function with example usage: bn_inception_model(input, labelDim, bnTimeConst) and returns: z