class_docstr: Implement the Cutout data augmentation defence approach in PyTorch.
  
  | Paper link: https://arxiv.org/abs/1708.04552
  
  | Please keep in mind the limitations of defences. For more information on the limitations of this defence,
      see https://arxiv.org/abs/1803.09868 . For details on how to evaluate classifier security in general, see
      https://arxiv.org/abs/1902.06705
docstr: Apply Cutout data augmentation to sample `x`.
  
  :param x: Sample to cut out with shape of `NCHW`, `NHWC`, `NCFHW` or `NFHWC`.
            `x` values are expected to be in the data range [0, 1] or [0, 255].
  :param y: Labels of the sample `x`. This function does not affect them in any way.
  :return: Data augmented sample.
code: def forward(
      self, x: "torch.Tensor", y: Optional["torch.Tensor"] = None
  ) -> Tuple["torch.Tensor", Optional["torch.Tensor"]]:
      """
      Apply Cutout data augmentation to sample `x`.
  
      :param x: Sample to cut out with shape of `NCHW`, `NHWC`, `NCFHW` or `NFHWC`.
                `x` values are expected to be in the data range [0, 1] or [0, 255].
      :param y: Labels of the sample `x`. This function does not affect them in any way.
      :return: Data augmented sample.
      """
      import torch  # lgtm [py/repeated-import]
  
      x_ndim = len(x.shape)
  
      # NHWC/NCFHW/NFHWC --> NCHW.
      if x_ndim == 4:
          if self.channels_first:
              # NCHW
              x_nchw = x
          else:
              # NHWC --> NCHW
              x_nchw = x.permute(0, 3, 1, 2)
      elif x_ndim == 5:
          if self.channels_first:
              # NCFHW --> NFCHW --> NCHW
              nb_clips, channels, clip_size, height, width = x.shape
              x_nchw = x.permute(0, 2, 1, 3, 4).reshape(
                  nb_clips * clip_size, channels, height, width
              )
          else:
              # NFHWC --> NHWC --> NCHW
              nb_clips, clip_size, height, width, channels = x.shape
              x_nchw = x.reshape(
                  nb_clips * clip_size, height, width, channels
              ).permute(0, 3, 1, 2)
      else:
          raise ValueError(
              "Unrecognized input dimension. Cutout can only be applied to image and video data."
          )
  
      n, _, height, width = x_nchw.shape
      x_nchw = x_nchw.clone()
  
      # generate a random bounding box per image
      for idx in trange(n, desc="Cutout", disable=not self.verbose):
          # uniform sampling
          center_x = torch.randint(0, height, (1,))
          center_y = torch.randint(0, width, (1,))
          bby1 = torch.clamp(center_y - self.length // 2, 0, height)
          bbx1 = torch.clamp(center_x - self.length // 2, 0, width)
          bby2 = torch.clamp(center_y + self.length // 2, 0, height)
          bbx2 = torch.clamp(center_x + self.length // 2, 0, width)
  
          # zero out the bounding box
          x_nchw[idx, :, bbx1:bbx2, bby1:bby2] = 0  # type: ignore
  
      # NHWC/NCFHW/NFHWC <-- NCHW.
      if x_ndim == 4:
          if self.channels_first:
              # NCHW
              x_aug = x_nchw
          else:
              # NHWC <-- NCHW
              x_aug = x_nchw.permute(0, 2, 3, 1)
      elif x_ndim == 5:  # lgtm [py/redundant-comparison]
          if self.channels_first:
              # NCFHW <-- NFCHW <-- NCHW
              x_nfchw = x_nchw.reshape(
                  nb_clips, clip_size, channels, height, width
              )
              x_aug = x_nfchw.permute(0, 2, 1, 3, 4)
          else:
              # NFHWC <-- NHWC <-- NCHW
              x_nhwc = x_nchw.permute(0, 2, 3, 1)
              x_aug = x_nhwc.reshape(
                  nb_clips, clip_size, height, width, channels
              )
  
      return x_aug, y
  
file_path: adversarial-robustness-toolbox/art/defences/preprocessor/cutout/cutout_pytorch.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Tuple, TYPE_CHECKING
  from tqdm.auto import trange
  from art.defences.preprocessor.preprocessor import PreprocessorPyTorch
label_desc: Use image node_id 2 for calling the CutoutPyTorch obj's underlying member method code with example usage: obj.forward(x, y) and returns: x_aug,  y