code: def deepfool_batch(
      sess,
      x,
      pred,
      logits,
      grads,
      X,
      nb_candidate,
      overshoot,
      max_iter,
      clip_min,
      clip_max,
      nb_classes,
      feed=None,
  ):
      """
      Applies DeepFool to a batch of inputs
      :param sess: TF session
      :param x: The input placeholder
      :param pred: The model's sorted symbolic output of logits, only the top
                   nb_candidate classes are contained
      :param logits: The model's unnormalized output tensor (the input to
                     the softmax layer)
      :param grads: Symbolic gradients of the top nb_candidate classes, procuded
                    from gradient_graph
      :param X: Numpy array with sample inputs
      :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 value for components of the example returned
      :param clip_max: Maximum value for components of the example returned
      :param nb_classes: Number of model output classes
      :return: Adversarial examples
      """
      X_adv = deepfool_attack(
          sess,
          x,
          pred,
          logits,
          grads,
          X,
          nb_candidate,
          overshoot,
          max_iter,
          clip_min,
          clip_max,
          feed=feed,
      )
  
      return np.asarray(X_adv, dtype=np_dtype)
  
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 1 for calling a global function with example usage: deepfool_batch(sess, x, pred, logits, grads, X, nb_candidate, overshoot, max_iter, clip_min, clip_max, nb_classes, feed) and returns: np