code: def create_image_mb_source(
      map_file, is_training, total_number_of_samples
  ):
      if not os.path.exists(map_file):
          raise RuntimeError("File '%s' does not exist." % (map_file))
  
      # transformation pipeline for the features has jitter/crop only when training
      transforms = []
      if is_training:
          transforms += [
              C.io.transforms.crop(
                  crop_type="randomarea",
                  area_ratio=(0.05, 1.0),
                  aspect_ratio=(0.75, 1.0),
                  jitter_type="uniratio",
              ),  # train uses jitter
              C.io.transforms.scale(
                  width=IMAGE_WIDTH,
                  height=IMAGE_HEIGHT,
                  channels=NUM_CHANNELS,
                  interpolations="linear",
              ),
              C.io.transforms.color(
                  brightness_radius=0.125,
                  contrast_radius=0.5,
                  saturation_radius=0.5,
              ),
          ]
      else:
          transforms += [
              C.io.transforms.crop(
                  crop_type="center", side_ratio=0.875
              ),  # test has no jitter
              C.io.transforms.scale(
                  width=IMAGE_WIDTH,
                  height=IMAGE_HEIGHT,
                  channels=NUM_CHANNELS,
                  interpolations="linear",
              ),
          ]
  
      # deserializer
      return C.io.MinibatchSource(
          C.io.ImageDeserializer(
              map_file,
              C.io.StreamDefs(
                  features=C.io.StreamDef(
                      field="image", transforms=transforms
                  ),  # first column in map file is referred to as 'image'
                  labels=C.io.StreamDef(
                      field="label", shape=NUM_CLASSES
                  ),
              ),
          ),  # and second as 'label'
          randomize=is_training,
          max_samples=total_number_of_samples,
          multithreaded_deserializer=True,
      )
  
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 1 for calling a global function with example usage: create_image_mb_source(map_file, is_training, total_number_of_samples) and returns: C