code: def create_inception_v3():
      # Input variables denoting the features and label data
      feature_var = C.ops.input_variable(
          (NUM_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH)
      )
      label_var = C.ops.input_variable((NUM_CLASSES))
  
      drop_rate = 0.2
      bn_time_const = 4096
      out = inception_v3_norm_model(
          feature_var, NUM_CLASSES, drop_rate, bn_time_const
      )
  
      # loss and metric
      aux_weight = 0.3
      ce_aux = C.losses.cross_entropy_with_softmax(
          out["aux"], label_var
      )
      ce_z = C.losses.cross_entropy_with_softmax(out["z"], label_var)
      ce = C.ops.plus(C.ops.element_times(ce_aux, aux_weight), ce_z)
      pe = C.metrics.classification_error(out["z"], label_var)
      pe5 = C.metrics.classification_error(out["z"], label_var, topN=5)
  
      C.logging.log_number_of_parameters(out["z"])
      print()
  
      return {
          "feature": feature_var,
          "label": label_var,
          "ce": ce,
          "pe": pe,
          "pe5": pe5,
          "output": out["z"],
          "outputAux": out["aux"],
      }
  
file_path: CNTK/Examples/Image/Classification/GoogLeNet/InceptionV3/Python/InceptionV3_ImageNet.py
imports: import os
  import math
  import argparse
  import numpy
  import cntk
  from InceptionV3 import inception_v3_norm_model
label_desc: Use image node_id 2 for calling a global function with example usage: create_inception_v3() and returns: dict