class_docstr: Implementation of the attack framework proposed by Inkawhich et al. (2018). Prioritizes the frame of a sequential
  input to be adversarially perturbed based on the saliency score of each frame.
  
  | Paper link: https://arxiv.org/abs/1811.11875
class_docstr_tok: ["Implementation","of","the","attack","framework","proposed","by","Inkawhich","et","al",".","(","2018",")",".","Prioritizes","the","frame","of","a","sequential","input","to","be","adversarially","perturbed","based","on","the","saliency","score","of","each","frame",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1811.11875"]
code: def _compute_attack_failure_array(
      self, x: np.ndarray, targets: np.ndarray, x_adv: np.ndarray
  ) -> np.ndarray:
      attack_success = compute_success_array(
          self.attacker.estimator, x, targets, x_adv, self.attacker.targeted  # type: ignore
      )
      return np.invert(attack_success)
  
file_path: adversarial-robustness-toolbox/art/attacks/evasion/frame_saliency.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, TYPE_CHECKING
  import numpy
  from tqdm.auto import trange
  from art.config import ART_NUMPY_DTYPE
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassGradientsMixin
  from art.attacks.attack import EvasionAttack
  from art.utils import compute_success_array, get_labels_np_array, check_and_transform_label_format
label_desc: Use image node_id 3 for calling the FrameSaliencyAttack obj's underlying member method code with example usage: obj._compute_attack_failure_array(x, targets, x_adv) and returns: np