code: def ld_cifar10(test_only=False):
      """Load the original CIFAR10 data."""
  
      # Define files to be downloaded
      file_urls = [
          "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
      ]
  
      # Maybe download data and retrieve local storage urls
      local_urls = maybe_download(file_urls, FLAGS.data_dir)
  
      # Extract archives and return different sets
      dataset = extract_cifar10(local_urls[0], FLAGS.data_dir)
  
      # Unpack tuple
      train_data, train_labels, test_data, test_labels = dataset
  
      # Apply whitening to input data
      train_data = image_whitening(train_data)
      test_data = image_whitening(test_data)
  
      if test_only:
          return test_data, test_labels
      else:
          return train_data, train_labels, test_data, test_labels
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gzip
  import math
  import os
  import sys
  import tarfile
  import numpy
  from scipy.io import loadmat
  from six.moves import cPickle
  from six.moves import urllib
  from six.moves import xrange
  import tensorflow.compat.v1
label_desc: Use image node_id 10 for calling a global function with example usage: ld_cifar10(test_only) and returns: test_data,  test_labels, train_data,  train_labels,  test_data,  test_labels