class_docstr: DeepFool is an untargeted & iterative attack which is based on an
  iterative linearization of the classifier. The implementation here
  is w.r.t. the L2 norm.
  Paper link: "https://arxiv.org/pdf/1511.04599.pdf"
  
  :param model: cleverhans.model.Model
  :param sess: tf.Session
  :param dtypestr: dtype of the data
  :param kwargs: passed through to super constructor
docstr: :param nb_candidate: The number of classes to test against, i.e.,
                       deepfool only consider nb_candidate classes when
                       attacking(thus accelerate speed). The nb_candidate
                       classes are chosen according to the prediction
                       confidence during implementation.
  :param overshoot: A termination criterion to prevent vanishing updates
  :param max_iter: Maximum number of iteration for deepfool
  :param clip_min: Minimum component value for clipping
  :param clip_max: Maximum component value for clipping
docstr_tok: [":","param","nb_candidate",":","The","number","of","classes","to","test","against",",","i.e.",",","deepfool","only","consider","nb_candidate","classes","when","attacking","(","thus","accelerate","speed",")",".","The","nb_candidate","classes","are","chosen","according","to","the","prediction","confidence","during","implementation",".",":","param","overshoot",":","A","termination","criterion","to","prevent","vanishing","updates",":","param","max_iter",":","Maximum","number","of","iteration","for","deepfool",":","param","clip_min",":","Minimum","component","value","for","clipping",":","param","clip_max",":","Maximum","component","value","for","clipping"]
code: def parse_params(
      self,
      nb_candidate=10,
      overshoot=0.02,
      max_iter=50,
      clip_min=0.0,
      clip_max=1.0,
      **kwargs,
  ):
      """
      :param nb_candidate: The number of classes to test against, i.e.,
                           deepfool only consider nb_candidate classes when
                           attacking(thus accelerate speed). The nb_candidate
                           classes are chosen according to the prediction
                           confidence during implementation.
      :param overshoot: A termination criterion to prevent vanishing updates
      :param max_iter: Maximum number of iteration for deepfool
      :param clip_min: Minimum component value for clipping
      :param clip_max: Maximum component value for clipping
      """
      self.nb_candidate = nb_candidate
      self.overshoot = overshoot
      self.max_iter = max_iter
      self.clip_min = clip_min
      self.clip_max = clip_max
      if len(kwargs.keys()) > 0:
          warnings.warn(
              "kwargs is unused and will be removed on or after "
              "2019-04-26."
          )
  
      return True
  
file_path: cleverhans/cleverhans_v3.1.0/cleverhans/attacks/deep_fool.py
imports: import copy
  import logging
  import warnings
  import numpy
  import tensorflow
  from cleverhans.attacks.attack import Attack
  from cleverhans.model import Model, wrapper_warning_logits, CallableModelWrapper
  from cleverhans import utils
  from cleverhans import utils_tf
label_desc: Use image node_id 3 for calling the DeepFool obj's underlying member method code with example usage: obj.parse_params(nb_candidate, overshoot, max_iter, clip_min, clip_max) and returns: True