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
code: def _predictions(
      self,
      images: "torch.Tensor",
      mask: Optional["torch.Tensor"],
      target: "torch.Tensor",
  ) -> Tuple["torch.Tensor", "torch.Tensor"]:
      import torch
  
      patched_input = self._random_overlay(
          images, self._patch, mask=mask
      )
      patched_input = torch.clamp(
          patched_input,
          min=self.estimator.clip_values[0],
          max=self.estimator.clip_values[1],
      )
  
      predictions, target = self.estimator._predict_framework(
          patched_input, target
      )  # pylint: disable=W0212
  
      return predictions, target
  
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 3 for calling the AdversarialPatchPyTorch obj's underlying member method code with example usage: obj._predictions(images, mask, target) and returns: predictions,  target