code: def create_bn_inception():
      # Input variables denoting the features and label data
      feature_var = input_variable(
          (NUM_CHANNELS, IMAGE_HEIGHT, IMAGE_WIDTH)
      )
      label_var = input_variable((NUM_CLASSES))
  
      bn_time_const = 4096
      z = bn_inception_model(feature_var, NUM_CLASSES, bn_time_const)
  
      # loss and metric
      ce = cross_entropy_with_softmax(z, label_var)
      pe = classification_error(z, label_var)
      pe5 = classification_error(z, label_var, topN=5)
  
      log_number_of_parameters(z)
      print()
  
      return {
          "feature": feature_var,
          "label": label_var,
          "ce": ce,
          "pe": pe,
          "pe5": pe5,
          "output": z,
      }
  
imports: from __future__ import print_function
  from __future__ import division
  import os
  import math
  import argparse
  import numpy
  import cntk
  import _cntk_py
  import cntk.io.transforms
  from cntk.debugging import start_profiler, stop_profiler, enable_profiler
  from cntk.io import ImageDeserializer, MinibatchSource, StreamDef, StreamDefs, FULL_DATA_SWEEP
  from cntk.learners import learning_parameter_schedule, momentum_schedule, momentum_sgd
  from cntk.logging import ProgressPrinter, log_number_of_parameters
  from cntk.losses import cross_entropy_with_softmax
  from cntk.metrics import classification_error
  from cntk.ops import input_variable
  from cntk.train import Trainer
  from BN_Inception import bn_inception_model
label_desc: Use image node_id 2 for calling a global function with example usage: create_bn_inception() and returns: dict