code: def inception_block_with_maxpool(
      input,
      num1x1,
      num3x3r,
      num3x3,
      num3x3dblr,
      num3x3dbl,
      numPool,
      bnTimeConst,
  ):
      # 1x1 Convolution
      branch1x1 = conv_bn_relu_layer(
          input, num1x1, (1, 1), (1, 1), True, bnTimeConst
      )
  
      # 3x3 Convolution
      branch3x3_reduce = conv_bn_relu_layer(
          input, num3x3r, (1, 1), (1, 1), True, bnTimeConst
      )
      branch3x3 = conv_bn_relu_layer(
          branch3x3_reduce, num3x3, (3, 3), (1, 1), True, bnTimeConst
      )
  
      # Double 3x3 Convolution
      branch3x3dbl_reduce = conv_bn_relu_layer(
          input, num3x3dblr, (1, 1), (1, 1), True, bnTimeConst
      )
      branch3x3dbl_conv = conv_bn_relu_layer(
          branch3x3dbl_reduce,
          num3x3dbl,
          (3, 3),
          (1, 1),
          True,
          bnTimeConst,
      )
      branch3x3dbl = conv_bn_relu_layer(
          branch3x3dbl_conv,
          num3x3dbl,
          (3, 3),
          (1, 1),
          True,
          bnTimeConst,
      )
  
      # Max Pooling
      branchPool_maxpool = MaxPooling((3, 3), strides=(1, 1), pad=True)(
          input
      )
      branchPool = conv_bn_relu_layer(
          branchPool_maxpool, numPool, (1, 1), (1, 1), True, bnTimeConst
      )
  
      out = splice(
          branch1x1, branch3x3, branch3x3dbl, branchPool, axis=0
      )
  
      return out
  
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 3 for calling a global function with example usage: inception_block_with_maxpool(input, num1x1, num3x3r, num3x3, num3x3dblr, num3x3dbl, numPool, bnTimeConst) and returns: out