class_docstr: Implementation of the adversarial patch attack for square and rectangular images and videos in Numpy.
  
  | Paper link: https://arxiv.org/abs/1712.09665
class_docstr_tok: ["Implementation","of","the","adversarial","patch","attack","for","square","and","rectangular","images","and","videos","in","Numpy",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1712.09665"]
code: def _check_params(self) -> None:
      if not isinstance(self.rotation_max, (float, int)):
          raise ValueError(
              "The maximum rotation of the random patches must be of type float."
          )
      if self.rotation_max < 0 or self.rotation_max > 180.0:
          raise ValueError(
              "The maximum rotation of the random patches must be between 0 and 180 degrees."
          )
  
      if not isinstance(self.scale_min, float):
          raise ValueError(
              "The minimum scale of the random patched must be of type float."
          )
      if self.scale_min < 0 or self.scale_min > self.scale_max:
          raise ValueError(
              "The minimum scale of the random patched must be greater than 0 and less than the maximum scaling."
          )
  
      if not isinstance(self.scale_max, float):
          raise ValueError(
              "The maximum scale of the random patched must be of type float."
          )
      if self.scale_max > 1:
          raise ValueError(
              "The maximum scale of the random patched must not be greater than 1."
          )
  
      if not isinstance(self.learning_rate, float):
          raise ValueError("The learning rate must be of type float.")
      if self.learning_rate <= 0.0:
          raise ValueError(
              "The learning rate must be greater than 0.0."
          )
  
      if not isinstance(self.max_iter, int):
          raise ValueError(
              "The number of optimization steps must be of type int."
          )
      if self.max_iter <= 0:
          raise ValueError(
              "The number of optimization steps must be greater than 0."
          )
  
      if not isinstance(self.batch_size, int):
          raise ValueError("The batch size must be of type int.")
      if self.batch_size <= 0:
          raise ValueError("The batch size must be greater than 0.")
  
      if not isinstance(self.targeted, bool) and not self.targeted:
          raise ValueError(
              "The argument `targeted` has to be of type bool. Currently AdversarialPatchNumpy only supports targeted"
              "attacks."
          )
  
      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
  import math
  from typing import Optional, Union, Tuple, TYPE_CHECKING
  import random
  import numpy
  from scipy.ndimage import rotate, shift, zoom
  from tqdm.auto import trange
  from art.attacks.attack import EvasionAttack
  from art.attacks.evasion.adversarial_patch.utils import insert_transformed_patch
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import check_and_transform_label_format
label_desc: Use image node_id 4 for calling the AdversarialPatchNumpy obj's underlying member method code with example usage: obj._check_params() without return types