code: def inception_v3_norm_model(input, labelDim, dropRate, bnTimeConst):
      # Normalize inputs to -1 and 1.
      featMean = 128
      featScale = 1 / 128
      input_subtracted = minus(input, featMean)
      input_scaled = element_times(input_subtracted, featScale)
  
      return inception_v3_model(
          input_scaled, labelDim, dropRate, bnTimeConst
      )
  
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 7 for calling a global function with example usage: inception_v3_norm_model(input, labelDim, dropRate, bnTimeConst) and returns: inception_v3_model