class_docstr: Implementation of the adversarial patch attack for square and rectangular images and videos in PyTorch.
  
  | 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,
  ) -> None:
      """
      Reset the adversarial patch.
  
      :param initial_patch_value: Patch value to use for resetting the patch.
      """
      import torch
  
      if initial_patch_value is None:
          self._patch.data = torch.Tensor(self._initial_value).double()
      elif isinstance(initial_patch_value, float):
          initial_value = (
              np.ones(self.patch_shape) * initial_patch_value
          )
          self._patch.data = torch.Tensor(initial_value).double()
      elif self._patch.shape == initial_patch_value.shape:
          self._patch.data = torch.Tensor(initial_patch_value).double()
      else:
          raise ValueError("Unexpected value for initial_patch_value.")
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import math
  from typing import Any, Optional, Tuple, Union, TYPE_CHECKING
  import numpy
  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.utils import check_and_transform_label_format, is_probability, to_categorical
  from art.summary_writer import SummaryWriter
label_desc: Use image node_id 10 for calling the AdversarialPatchPyTorch obj's underlying member method code with example usage: obj.reset_patch(initial_patch_value) without return types