class_docstr: The CIFAR-10 dataset
code: def __init__(
      self,
      train_start=0,
      train_end=60000,
      test_start=0,
      test_end=10000,
      center=False,
      max_val=1.0,
  ):
      kwargs = locals()
      if "__class__" in kwargs:
          del kwargs["__class__"]
      super(CIFAR10, self).__init__(kwargs)
      packed = data_cifar10(
          train_start=train_start,
          train_end=train_end,
          test_start=test_start,
          test_end=test_end,
      )
      x_train, y_train, x_test, y_test = packed
  
      if center:
          x_train = x_train * 2.0 - 1.0
          x_test = x_test * 2.0 - 1.0
      x_train *= max_val
      x_test *= max_val
  
      self.x_train = x_train
      self.y_train = y_train
      self.x_test = x_test
      self.y_test = y_test
      self.max_val = max_val
  
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 1 to create a new CIFAR10 object from inherited base classes: Dataset with example: obj = CIFAR10(train_start, train_end, test_start, test_end, center, max_val)