class_docstr: Implementation of the adversarial patch attack for square and rectangular images and videos in TensorFlow v2.
  
  | Paper link: https://arxiv.org/abs/1712.09665
code: def _predictions(
      self, images: "tf.Tensor", mask: Optional["tf.Tensor"]
  ) -> "tf.Tensor":
      import tensorflow as tf
  
      patched_input = self._random_overlay(
          images, self._patch, mask=mask
      )
  
      patched_input = tf.clip_by_value(
          patched_input,
          clip_value_min=self.estimator.clip_values[0],
          clip_value_max=self.estimator.clip_values[1],
      )
  
      predictions = self.estimator._predict_framework(
          patched_input
      )  # pylint: disable=W0212
  
      return predictions
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import math
  from typing import 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.estimators.classification.classifier import ClassifierMixin
  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 AdversarialPatchTensorFlowV2 obj's underlying member method code with example usage: obj._predictions(images, mask) and returns: predictions