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 _train_step(
      self,
      images: "torch.Tensor",
      target: "torch.Tensor",
      mask: Optional["torch.Tensor"] = None,
  ) -> "torch.Tensor":
      import torch
  
      self.estimator.model.zero_grad()
      loss = self._loss(images, target, mask)
      loss.backward(retain_graph=True)
  
      if self._optimizer_string == "pgd":
          if self._patch.grad is not None:
              gradients = self._patch.grad.sign() * self.learning_rate
          else:
              raise ValueError(
                  "Gradient term in PyTorch model is `None`."
              )
  
          with torch.no_grad():
              self._patch[:] = torch.clamp(
                  self._patch + gradients,
                  min=self.estimator.clip_values[0],
                  max=self.estimator.clip_values[1],
              )
      else:
          self._optimizer.step()
  
          with torch.no_grad():
              self._patch[:] = torch.clamp(
                  self._patch,
                  min=self.estimator.clip_values[0],
                  max=self.estimator.clip_values[1],
              )
  
      return loss
  
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 2 for calling the AdversarialPatchPyTorch obj's underlying member method code with example usage: obj._train_step(images, target, mask) and returns: loss