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
code: def _check_params(self) -> None:
      # Check if order of the norm is acceptable given current implementation
      if self.norm not in [2, np.inf, "inf"]:
          raise ValueError(
              'Norm order must be either 2, `np.inf` or "inf".'
          )
  
      if not isinstance(self.max_iter, int) or self.max_iter < 0:
          raise ValueError(
              "The number of iterations must be a non-negative integer."
          )
  
      if not isinstance(self.max_eval, int) or self.max_eval <= 0:
          raise ValueError(
              "The maximum number of evaluations must be a positive integer."
          )
  
      if not isinstance(self.init_eval, int) or self.init_eval <= 0:
          raise ValueError(
              "The initial number of evaluations must be a positive integer."
          )
  
      if self.init_eval > self.max_eval:
          raise ValueError(
              "The maximum number of evaluations must be larger than the initial number of evaluations."
          )
  
      if not isinstance(self.init_size, int) or self.init_size <= 0:
          raise ValueError(
              "The number of initial trials must be a positive integer."
          )
  
      if not isinstance(self.verbose, bool):
          raise ValueError(
              "The argument `verbose` has to be of type bool."
          )
  
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 11 for calling the HopSkipJump obj's underlying member method code with example usage: obj._check_params() without return types