class_docstr: The Momentum Iterative Fast Gradient Sign Method (Dong et al. 2017).
  This method won the first places in NIPS 2017 Non-targeted Adversarial
  Attacks and Targeted Adversarial Attacks. The original paper used
  hard labels for this attack; no label smoothing. inf norm.
  Paper link: https://arxiv.org/pdf/1710.06081.pdf
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docstr: :param model(model): The model to be attacked.
  :param support_targeted(bool): Does this attack method support targeted.
docstr_tok: [":","param","model","(","model",")",":","The","model","to","be","attacked",".",":","param","support_targeted","(","bool",")",":","Does","this","attack","method","support","targeted","."]
code: def __init__(self, model, support_targeted=True):
      """
      :param model(model): The model to be attacked.
      :param support_targeted(bool): Does this attack method support targeted.
      """
      super(MomentumIteratorAttack, self).__init__(model)
      self.support_targeted = support_targeted
  
imports: from __future__ import division
  from builtins import range
  import logging
  from collections import Iterable
  import numpy
  from .base import Attack
  from functools import reduce
label_desc: Use image node_id 1 to create a new MomentumIteratorAttack object from inherited base classes: GradientMethodAttack with example: obj = MomentumIteratorAttack(model, support_targeted)