class_docstr: The CIFAR-10 dataset [#]_ consists of 60000 32x32 color images. Target variable is one amongst 10 classes. The dataset has
  6000 images per class. There are 50000 training images and 10000 test images. The classes are: airplane, automobile,
  bird, cat, deer, dog, frog, horse, ship ,truck. We further divide the training set into train1 (30000 samples) and
  train2 (20,000 samples). For ProfWt, the complex model is trained on train1 while the simple model is trained on train2.
  
  References:
      .. [#] `Krizhevsky, Hinton. Learning multiple layers of features from tiny images. Technical Report, University of
         Toronto 1 (4), 7. 2009 <https://www.cs.toronto.edu/~kriz/cifar.html>`_
class_docstr_tok: ["The","CIFAR-10","dataset","[","#","]","_","consists","of","60000","32x32","color","images",".","Target","variable","is","one","amongst","10","classes",".","The","dataset","has","6000","images","per","class",".","There","are","50000","training","images","and","10000","test","images",".","The","classes","are",":","airplane",",","automobile",",","bird",",","cat",",","deer",",","dog",",","frog",",","horse",",","ship",",","truck",".","We","further","divide","the","training","set","into","train1","(","30000","samples",")","and","train2","(","20,000","samples",")",".","For","ProfWt",",","the","complex","model","is","trained","on","train1","while","the","simple","model","is","trained","on","train2",".","References",":","..","[","#","]","`","Krizhevsky",",","Hinton",".","Learning","multiple","layers","of","features","from","tiny","images",".","Technical","Report",",","University","of","Toronto","1","(","4",")",",","7",".","2009","<","https",":","\/\/www.cs.toronto.edu\/~kriz\/cifar.html",">","`","_"]
code: def _download_data(self):
      name = "cifar-10-python.tar.gz"
      json_file_name = "cifar-10-train1-image.json"
      full_name = os.path.join(self._dirpath, name)
      if not os.path.exists(
          os.path.join(self._dirpath, json_file_name)
      ):
          if not os.path.exists(full_name):
              print("retrieving file", name)
              urllib.request.urlretrieve(
                  "https://www.cs.toronto.edu/~kriz/" + name, full_name
              )
              print("retrieved")
          # now extract the files
          # print("extracting files")
          tar = tarfile.open(full_name, "r:gz")
          tar.extractall(self._dirpath)
          tar.close()
          # print("extracted files")
  
          self._process_data()
  
          # now cleanup
          if os.path.exists(full_name):
              os.remove(full_name)
  
file_path: AIX360/aix360/datasets/cifar_dataset.py
imports: import numpy
  import json
  import sys, os
  import urllib.request
  import tarfile
  import pickle
  from sklearn.preprocessing import OneHotEncoder
  import shutil
label_desc: Use image node_id 2 for calling the CIFARDataset obj's underlying member method code with example usage: obj._download_data() without return types