code: def vgg19_train_and_eval(
      train_data,
      test_data,
      num_quantization_bits=32,
      minibatch_size=128,
      epoch_size=1281167,
      max_epochs=80,
      restore=True,
      log_to_file=None,
      num_mbs_per_log=None,
      gen_heartbeat=False,
      testing=False,
  ):
      _cntk_py.set_computation_network_trace_level(0)
  
      progress_printer = ProgressPrinter(
          freq=num_mbs_per_log,
          tag="Training",
          log_to_file=log_to_file,
          rank=Communicator.rank(),
          gen_heartbeat=gen_heartbeat,
          num_epochs=max_epochs,
      )
  
      network = create_vgg19()
      trainer = create_trainer(
          network, epoch_size, num_quantization_bits, progress_printer
      )
      train_source = create_image_mb_source(
          train_data,
          True,
          total_number_of_samples=max_epochs * epoch_size,
      )
  
      if testing:
          # reduce number of samples for validation when testing
          num_of_validation_samples = max_epochs * epoch_size * 10
      else:
          num_of_validation_samples = FULL_DATA_SWEEP
  
      test_source = create_image_mb_source(
          test_data,
          False,
          total_number_of_samples=num_of_validation_samples,
      )
      train_and_test(
          network,
          trainer,
          train_source,
          test_source,
          minibatch_size,
          epoch_size,
          restore,
      )
  
file_path: CNTK/Examples/Image/Classification/VGG/Python/VGG19_ImageNet_Distributed.py
imports: from __future__ import print_function
  import os
  import math
  import argparse
  import numpy
  import cntk
  import _cntk_py
  import cntk.io.transforms
  from cntk.logging import *
  from cntk.ops import placeholder, minus, constant, relu
  from cntk.train.distributed import data_parallel_distributed_learner, Communicator
  from cntk.io import ImageDeserializer, MinibatchSource, StreamDef, StreamDefs, FULL_DATA_SWEEP
  from cntk.layers import Convolution2D, Activation, MaxPooling, Dense, Dropout, default_options, Sequential, For
  from cntk.initializer import normal
  from cntk.train.training_session import *
label_desc: Use image node_id 5 for calling a global function with example usage: vgg19_train_and_eval(train_data, test_data, num_quantization_bits, minibatch_size, epoch_size, max_epochs, restore, log_to_file, num_mbs_per_log, gen_heartbeat, testing) without return types