class_docstr: A data structure reporting how much confidence a model assigned to its
  predictions on each example and whether those predictions were correct.
  This class is just a dictionary with some type checks.
  It maps string data type names (like "clean" for clean data or "Semantic"
  for semantic adversarial examples) to ConfidenceReportEntry instances.
  
  :param iterable: optional iterable containing (key, value) tuples
class_docstr_tok: ["A","data","structure","reporting","how","much","confidence","a","model","assigned","to","its","predictions","on","each","example","and","whether","those","predictions","were","correct",".","This","class","is","just","a","dictionary","with","some","type","checks",".","It","maps","string","data","type","names","(","like","``","clean","''","for","clean","data","or","``","Semantic","''","for","semantic","adversarial","examples",")","to","ConfidenceReportEntry","instances",".",":","param","iterable",":","optional","iterable","containing","(","key",",","value",")","tuples"]
code: def __init__(self, iterable=None):
      super(ConfidenceReport, self).__init__()
      # This field tracks whether the report is completed.
      # It's important e.g. for reports that are made by bundlers and repeatedly
      # written to disk during the process. This field makes it possible to tell
      # whether a report on disk is complete or whether the bundling process
      # got killed (e.g. due to VM migration)
      self.completed = False
      if iterable is not None:
          # pickle sometimes wants to use this interface to unpickle the OrderedDict
          for key, value in iterable:
              self[key] = value
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from collections import OrderedDict
  import logging
  import time
  import warnings
  import numpy
  import six
  import tensorflow
  from cleverhans.attacks import MaxConfidence
  from cleverhans.attacks import Semantic
  from cleverhans.evaluation import correctness_and_confidence
  from cleverhans.evaluation import run_attack
  from cleverhans.utils import set_log_level
  from cleverhans.serial import load, save
  from cleverhans.utils_tf import infer_devices
label_desc: Use image node_id 1 to create a new ConfidenceReport object from inherited base classes: OrderedDict with example: obj = ConfidenceReport(iterable)