class_docstr: The black-box zeroth-order optimization attack from Pin-Yu Chen et al. (2018). This attack is a variant of the
  C&W attack which uses ADAM coordinate descent to perform numerical estimation of gradients.
  
  | Paper link: https://arxiv.org/abs/1708.03999
class_docstr_tok: ["The","black-box","zeroth-order","optimization","attack","from","Pin-Yu","Chen","et","al",".","(","2018",")",".","This","attack","is","a","variant","of","the","C","&","W","attack","which","uses","ADAM","coordinate","descent","to","perform","numerical","estimation","of","gradients",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1708.03999"]
code: def _max_pooling(image: np.ndarray, kernel_size: int) -> np.ndarray:
      img_pool = np.copy(image)
      for i in range(0, image.shape[1], kernel_size):
          for j in range(0, image.shape[2], kernel_size):
              img_pool[
                  :, i : i + kernel_size, j : j + kernel_size
              ] = np.max(
                  image[:, i : i + kernel_size, j : j + kernel_size],
                  axis=(1, 2),
                  keepdims=True,
              )
  
      return img_pool
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, Any, TYPE_CHECKING
  import numpy
  from scipy.ndimage import zoom
  from tqdm.auto import trange
  from art.config import ART_NUMPY_DTYPE
  from art.attacks.attack import EvasionAttack
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import compute_success, get_labels_np_array, check_and_transform_label_format
label_desc: Use image node_id 13 for calling the ZooAttack obj's underlying member method code with example usage: obj._max_pooling(image, kernel_size) and returns: img_pool