code: def data_cifar10(
      train_start=0, train_end=50000, test_start=0, test_end=10000
  ):
      """
      Preprocess CIFAR10 dataset
      :return:
      """
  
      # These values are specific to CIFAR10
      img_rows = 32
      img_cols = 32
      nb_classes = 10
  
      # the data, shuffled and split between train and test sets
      (x_train, y_train), (x_test, y_test) = cifar10.load_data()
  
      if tf.keras.backend.image_data_format() == "channels_first":
          x_train = x_train.reshape(
              x_train.shape[0], 3, img_rows, img_cols
          )
          x_test = x_test.reshape(
              x_test.shape[0], 3, img_rows, img_cols
          )
      else:
          x_train = x_train.reshape(
              x_train.shape[0], img_rows, img_cols, 3
          )
          x_test = x_test.reshape(
              x_test.shape[0], img_rows, img_cols, 3
          )
      x_train = x_train.astype("float32")
      x_test = x_test.astype("float32")
      x_train /= 255
      x_test /= 255
      print("x_train shape:", x_train.shape)
      print(x_train.shape[0], "train samples")
      print(x_test.shape[0], "test samples")
  
      # convert class vectors to binary class matrices
      y_train = np_utils.to_categorical(y_train, nb_classes)
      y_test = np_utils.to_categorical(y_test, nb_classes)
  
      x_train = x_train[train_start:train_end, :, :, :]
      y_train = y_train[train_start:train_end, :]
      x_test = x_test[test_start:test_end, :]
      y_test = y_test[test_start:test_end, :]
  
      return x_train, y_train, x_test, y_test
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import array
  import functools
  import gzip
  import operator
  import os
  import struct
  import tempfile
  import sys
  import warnings
  import numpy
  import tensorflow
  from cleverhans import utils
label_desc: Use image node_id 4 for calling a global function with example usage: data_cifar10(train_start, train_end, test_start, test_end) and returns: x_train,  y_train,  x_test,  y_test