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
docstr: Generate symbolic graph for adversarial examples and return.
  
  :param x: The model's symbolic inputs.
  :param kwargs: Keyword arguments for the base attacker
code: def generate(self, x, **kwargs):
      """
      Generate symbolic graph for adversarial examples and return.
  
      :param x: The model's symbolic inputs.
      :param kwargs: Keyword arguments for the base attacker
      """
  
      assert self.parse_params(**kwargs)
      labels, _nb_classes = self.get_or_guess_labels(x, kwargs)
      adv_x = self.attack(x, labels)
  
      return adv_x
  
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 2 for calling the MaxConfidence obj's underlying member method code with example usage: obj.generate(x) and returns: adv_x