code: def inception_block_1(
      input, num1x1, num5x5, num3x3dbl, numPool, bnTimeConst
  ):
      # 1x1 Convolution
      branch1x1 = conv_bn_relu_layer(
          input, num1x1, (1, 1), (1, 1), True, bnTimeConst
      )
  
      # 5x5 Convolution
      branch5x5_1 = conv_bn_relu_layer(
          input, num5x5[0], (1, 1), (1, 1), True, bnTimeConst
      )
      branch5x5 = conv_bn_relu_layer(
          branch5x5_1, num5x5[1], (5, 5), (1, 1), True, bnTimeConst
      )
  
      # Double 3x3 Convolution
      branch3x3dbl_1 = conv_bn_relu_layer(
          input, num3x3dbl[0], (1, 1), (1, 1), True, bnTimeConst
      )
      branch3x3dbl_2 = conv_bn_relu_layer(
          branch3x3dbl_1,
          num3x3dbl[1],
          (3, 3),
          (1, 1),
          True,
          bnTimeConst,
      )
      branch3x3dbl = conv_bn_relu_layer(
          branch3x3dbl_2,
          num3x3dbl[2],
          (3, 3),
          (1, 1),
          True,
          bnTimeConst,
      )
  
      # Average Pooling
      branchPool_avgpool = AveragePooling(
          (3, 3), strides=(1, 1), pad=True
      )(input)
      branchPool = conv_bn_relu_layer(
          branchPool_avgpool, numPool, (1, 1), (1, 1), True, bnTimeConst
      )
  
      out = splice(
          branch1x1, branch5x5, branch3x3dbl, branchPool, axis=0
      )
  
      return out
  
file_path: CNTK/Examples/Image/Classification/GoogLeNet/InceptionV3/Python/InceptionV3.py
imports: from cntk.initializer import he_normal
  from cntk.layers import AveragePooling, MaxPooling, BatchNormalization, Convolution, Dense, Dropout
  from cntk.ops import minus, element_times, relu, splice
label_desc: Use image node_id 2 for calling a global function with example usage: inception_block_1(input, num1x1, num5x5, num3x3dbl, numPool, bnTimeConst) and returns: out