class_docstr: Implementation of the HopSkipJump attack from Jianbo et al. (2019). This is a powerful black-box attack that
  only requires final class prediction, and is an advanced version of the boundary attack.
  
  | Paper link: https://arxiv.org/abs/1904.02144
docstr: Check whether an image is adversarial.
  
  :param samples: A batch of examples.
  :param target: The target label.
  :param clip_min: Minimum value of an example.
  :param clip_max: Maximum value of an example.
  :return: An array of 0/1.
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code: def _adversarial_satisfactory(
      self,
      samples: np.ndarray,
      target: int,
      clip_min: float,
      clip_max: float,
  ) -> np.ndarray:
      """
      Check whether an image is adversarial.
  
      :param samples: A batch of examples.
      :param target: The target label.
      :param clip_min: Minimum value of an example.
      :param clip_max: Maximum value of an example.
      :return: An array of 0/1.
      """
      samples = np.clip(samples, clip_min, clip_max)
      preds = np.argmax(
          self.estimator.predict(samples, batch_size=self.batch_size),
          axis=1,
      )
  
      if self.targeted:
          result = preds == target
      else:
          result = preds != target
  
      return result
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from tqdm.auto import tqdm
  from art.config import ART_NUMPY_DTYPE
  from art.attacks.attack import EvasionAttack
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification import ClassifierMixin
  from art.utils import compute_success, to_categorical, check_and_transform_label_format, get_labels_np_array
label_desc: Use image node_id 9 for calling the HopSkipJump obj's underlying member method code with example usage: obj._adversarial_satisfactory(samples, target, clip_min, clip_max) and returns: result