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 _train_step(
      self,
      images: "tf.Tensor",
      target: Optional["tf.Tensor"] = None,
      mask: Optional["tf.Tensor"] = None,
  ) -> "tf.Tensor":
      import tensorflow as tf
  
      with tf.GradientTape() as tape:
          tape.watch(self._patch)
          loss = self._loss(images, target, mask)
  
      gradients = tape.gradient(loss, [self._patch])
  
      if (
          (not self.targeted and self._optimizer_string != "pgd")
          or self.targeted
          and self._optimizer_string == "pgd"
      ):
          gradients = [-g for g in gradients]
  
      if self._optimizer_string == "pgd":
          gradients = tf.sign(gradients) * self.learning_rate
  
          self._patch = self._patch + tf.squeeze(gradients)
  
          self._patch = tf.clip_by_value(
              self._patch,
              clip_value_min=self.estimator.clip_values[0],
              clip_value_max=self.estimator.clip_values[1],
          )
  
      else:
          self._optimizer.apply_gradients(zip(gradients, [self._patch]))
  
      return loss
  
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 2 for calling the AdversarialPatchTensorFlowV2 obj's underlying member method code with example usage: obj._train_step(images, target, mask) and returns: loss