class_docstr: Implementation of the adversarial patch attack for square and rectangular images and videos.
  
  | Paper link: https://arxiv.org/abs/1712.09665
class_docstr_tok: ["Implementation","of","the","adversarial","patch","attack","for","square","and","rectangular","images","and","videos",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1712.09665"]
docstr: Reset the adversarial patch.
  
  :param initial_patch_value: Patch value to use for resetting the patch.
docstr_tok: ["Reset","the","adversarial","patch",".",":","param","initial_patch_value",":","Patch","value","to","use","for","resetting","the","patch","."]
code: def reset_patch(
      self, initial_patch_value: Optional[Union[float, np.ndarray]]
  ) -> None:
      """
      Reset the adversarial patch.
  
      :param initial_patch_value: Patch value to use for resetting the patch.
      """
      self._attack.reset_patch(initial_patch_value=initial_patch_value)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  from art.attacks.evasion.adversarial_patch.adversarial_patch_numpy import AdversarialPatchNumpy
  from art.attacks.evasion.adversarial_patch.adversarial_patch_tensorflow import AdversarialPatchTensorFlowV2
  from art.attacks.evasion.adversarial_patch.adversarial_patch_pytorch import AdversarialPatchPyTorch
  from art.estimators.estimator import BaseEstimator, NeuralNetworkMixin
  from art.estimators.classification.classifier import ClassifierMixin
  from art.estimators.classification import TensorFlowV2Classifier, PyTorchClassifier
  from art.attacks.attack import EvasionAttack
label_desc: Use image node_id 4 for calling the AdversarialPatch obj's underlying member method code with example usage: obj.reset_patch(initial_patch_value) without return types