class_docstr: An object summarizing the accuracy results for experiments involving
  training on clean examples or adversarial examples, then evaluating
  on clean or adversarial examples.
class_docstr_tok: ["An","object","summarizing","the","accuracy","results","for","experiments","involving","training","on","clean","examples","or","adversarial","examples",",","then","evaluating","on","clean","or","adversarial","examples","."]
code: def __init__(self):
      self.clean_train_clean_eval = 0.0
      self.clean_train_adv_eval = 0.0
      self.adv_train_clean_eval = 0.0
      self.adv_train_adv_eval = 0.0
  
      # Training data accuracy results to be used by tutorials
      self.train_clean_train_clean_eval = 0.0
      self.train_clean_train_adv_eval = 0.0
      self.train_adv_train_clean_eval = 0.0
      self.train_adv_train_adv_eval = 0.0
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from collections import OrderedDict
  import warnings
  import logging
  import os
  import re
  import subprocess
  import numpy
  from six.moves import xrange
label_desc: Use image node_id 1 to create a new AccuracyReport object from inherited base classes: object with example: obj = AccuracyReport()