code: def bn_inception_train_and_eval(
      train_data,
      test_data,
      mean_data,
      minibatch_size=128,
      epoch_size=50000,
      max_epochs=200,
      restore=True,
      log_to_file=None,
      num_mbs_per_log=100,
      gen_heartbeat=False,
      profiler_dir=None,
      testing_parameters=(10000, 128),
  ):
      _cntk_py.set_computation_network_trace_level(0)
  
      progress_writers = [
          ProgressPrinter(
              freq=num_mbs_per_log,
              tag="Training",
              log_to_file=log_to_file,
              gen_heartbeat=gen_heartbeat,
              num_epochs=max_epochs,
          )
      ]
  
      network = create_bn_inception()
      trainer = create_trainer(
          network,
          epoch_size,
          max_epochs,
          minibatch_size,
          progress_writers,
      )
      train_source = create_image_mb_source(
          train_data,
          mean_data,
          True,
          total_number_of_samples=max_epochs * epoch_size,
      )
      test_source = create_image_mb_source(
          test_data,
          mean_data,
          False,
          total_number_of_samples=FULL_DATA_SWEEP,
      )
      return train_and_test(
          network,
          trainer,
          train_source,
          test_source,
          max_epochs,
          minibatch_size,
          epoch_size,
          restore,
          profiler_dir,
          progress_writers[0],
          testing_parameters,
      )
  
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 training_session, CheckpointConfig, TestConfig, Trainer
  from BN_Inception import bn_inception_cifar_model
label_desc: Use image node_id 5 for calling a global function with example usage: bn_inception_train_and_eval(train_data, test_data, mean_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