code: def inception_block_2(input, num3x3, num3x3dbl, bnTimeConst):
      # 3x3 Convolution
      branch3x3 = conv_bn_relu_layer(
          input, num3x3, (3, 3), (2, 2), False, 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),
          (2, 2),
          False,
          bnTimeConst,
      )
  
      # Max Pooling
      branchPool = MaxPooling((3, 3), strides=(2, 2), pad=False)(input)
  
      out = splice(branch3x3, 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 3 for calling a global function with example usage: inception_block_2(input, num3x3, num3x3dbl, bnTimeConst) and returns: out