code: def inception_v3_train_and_eval(
      train_data,
      test_data,
      minibatch_size=32,
      epoch_size=1281167,
      max_epochs=300,
      restore=True,
      log_to_file=None,
      num_mbs_per_log=100,
      gen_heartbeat=False,
      profiler_dir=None,
      testing_parameters=(5000, 32),
  ):
      C.debugging.set_computation_network_trace_level(1)
  
      progress_printer = C.logging.ProgressPrinter(
          freq=num_mbs_per_log,
          tag="Training",
          log_to_file=log_to_file,
          gen_heartbeat=gen_heartbeat,
          num_epochs=max_epochs,
      )
  
      network = create_inception_v3()
      trainer = create_trainer(
          network, epoch_size, max_epochs, minibatch_size
      )
      train_source = create_image_mb_source(
          train_data,
          True,
          total_number_of_samples=max_epochs * epoch_size,
      )
      test_source = create_image_mb_source(
          test_data, False, total_number_of_samples=C.io.FULL_DATA_SWEEP
      )
      return train_and_test(
          network,
          trainer,
          train_source,
          test_source,
          progress_printer,
          max_epochs,
          minibatch_size,
          epoch_size,
          restore,
          profiler_dir,
          testing_parameters,
      )
  
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 5 for calling a global function with example usage: inception_v3_train_and_eval(train_data, test_data, minibatch_size, epoch_size, max_epochs, restore, log_to_file, num_mbs_per_log, gen_heartbeat, profiler_dir, testing_parameters) and returns: train_and_test