class_docstr: The MaxConfidence attack.
  
  An attack designed for use against models that use confidence thresholding
  as a defense.
  If the underlying optimizer is optimal, this attack procedure gives the
  optimal failure rate for every confidence threshold t > 0.5.
  
  Publication: https://openreview.net/forum?id=H1g0piA9tQ
  
  :param model: cleverhans.model.Model
  :param sess: optional tf.session.Session
  :param base_attacker: cleverhans.attacks.Attack
code: def parse_params(self, y=None, nb_classes=10, **kwargs):
      self.y = y
      self.nb_classes = nb_classes
      self.params = kwargs
      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/max_confidence.py
imports: import warnings
  import tensorflow
  from cleverhans.attacks.attack import Attack
  from cleverhans.attacks.projected_gradient_descent import ProjectedGradientDescent
  from cleverhans.model import Model
label_desc: Use image node_id 3 for calling the MaxConfidence obj's underlying member method code with example usage: obj.parse_params(y, nb_classes) and returns: True