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: Run the attack on a specific target class.
  :param x: tf Tensor. The input example.
  :param target_y: tf Tensor. The attacker's desired target class.
  Returns:
    A targeted adversarial example, intended to be classified as the target class.
code: def attack_class(self, x, target_y):
      """
      Run the attack on a specific target class.
      :param x: tf Tensor. The input example.
      :param target_y: tf Tensor. The attacker's desired target class.
      Returns:
        A targeted adversarial example, intended to be classified as the target class.
      """
      adv = self.base_attacker.generate(
          x, y_target=target_y, **self.params
      )
      return adv
  
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 5 for calling the MaxConfidence obj's underlying member method code with example usage: obj.attack_class(x, target_y) and returns: adv