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: Compute the delta parameter.
  
  :param current_sample: Current adversarial example.
  :param original_sample: The original input.
  :param clip_min: Minimum value of an example.
  :param clip_max: Maximum value of an example.
  :return: Delta value.
docstr_tok: ["Compute","the","delta","parameter",".",":","param","current_sample",":","Current","adversarial","example",".",":","param","original_sample",":","The","original","input",".",":","param","clip_min",":","Minimum","value","of","an","example",".",":","param","clip_max",":","Maximum","value","of","an","example",".",":","return",":","Delta","value","."]
code: def _compute_delta(
      self,
      current_sample: np.ndarray,
      original_sample: np.ndarray,
      clip_min: float,
      clip_max: float,
  ) -> float:
      """
      Compute the delta parameter.
  
      :param current_sample: Current adversarial example.
      :param original_sample: The original input.
      :param clip_min: Minimum value of an example.
      :param clip_max: Maximum value of an example.
      :return: Delta value.
      """
      # Note: This is a bit different from the original paper, instead we keep those that are
      # implemented in the original source code of the authors
      if self.curr_iter == 0:
          return 0.1 * (clip_max - clip_min)
  
      if self.norm == 2:
          dist = np.linalg.norm(original_sample - current_sample)
          delta = (
              np.sqrt(np.prod(self.estimator.input_shape))
              * self.theta
              * dist
          )
      else:
          dist = np.max(abs(original_sample - current_sample))
          delta = (
              np.prod(self.estimator.input_shape) * self.theta * dist
          )
  
      return delta
  
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 7 for calling the HopSkipJump obj's underlying member method code with example usage: obj._compute_delta(current_sample, original_sample, clip_min, clip_max) and returns: delta, unknown