class_docstr: This module implements square padding for images and object detection bounding boxes in PyTorch.
docstr: Square pad `x` and adjust bounding boxes for labels `y` accordingly.
  
  :param x: Input samples. A list of samples is also supported.
  :param y: Label of the samples `x`.
  :return: Transformed samples and labels.
code: def forward(
      self,
      x: "torch.Tensor",
      y: Optional[
          Union["torch.Tensor", List[Dict[str, "torch.Tensor"]]]
      ] = None,
  ) -> Tuple[
      "torch.Tensor",
      Optional[Union["torch.Tensor", List[Dict[str, "torch.Tensor"]]]],
  ]:
      """
      Square pad `x` and adjust bounding boxes for labels `y` accordingly.
  
      :param x: Input samples. A list of samples is also supported.
      :param y: Label of the samples `x`.
      :return: Transformed samples and labels.
      """
      import torch
  
      x_preprocess = []
      y_preprocess: Optional[
          Union[torch.Tensor, List[Dict[str, torch.Tensor]]]
      ]
      if y is not None and self.label_type == "object_detection":
          y_preprocess = []
      else:
          y_preprocess = y
  
      for i, x_i in enumerate(
          tqdm(
              x, desc="ImageSquarePadPyTorch", disable=not self.verbose
          )
      ):
          if not self.channels_first:
              x_i = torch.permute(x_i, (2, 0, 1))
  
          # Calculate padding
          _, height, width = x_i.shape
          if height > width:
              pad_left = int(np.floor((height - width) / 2))
              pad_right = int(np.ceil((height - width) / 2))
              pad_top = 0
              pad_bottom = 0
          else:
              pad_left = 0
              pad_right = 0
              pad_top = int(np.floor((width - height) / 2))
              pad_bottom = int(np.ceil((width - height) / 2))
  
          # Pad image to square size
          padding = [pad_left, pad_right, pad_top, pad_bottom]
          x_pad = torch.nn.functional.pad(
              x_i, padding, mode=self.pad_mode, **self.pad_kwargs
          )
  
          if not self.channels_first:
              x_pad = torch.permute(x_pad, (1, 2, 0))
  
          if self.clip_values is not None:
              x_pad = torch.clamp(x_pad, self.clip_values[0], self.clip_values[1])  # type: ignore
  
          x_preprocess.append(x_pad)
  
          if self.label_type == "object_detection" and y is not None:
              y_pad: Dict[str, torch.Tensor] = {}
  
              # Copy labels and ensure types
              if isinstance(y, list) and isinstance(y_preprocess, list):
                  y_i = y[i]
                  if isinstance(y_i, dict):
                      y_pad = {
                          k: torch.clone(v) for k, v in y_i.items()
                      }
                  else:
                      raise TypeError(
                          "Wrong type for `y` and label_type=object_detection."
                      )
              else:
                  raise TypeError(
                      "Wrong type for `y` and label_type=object_detection."
                  )
  
              # Shift bounding boxes
              y_pad["boxes"][:, 0] += pad_left
              y_pad["boxes"][:, 1] += pad_top
              y_pad["boxes"][:, 2] += pad_left
              y_pad["boxes"][:, 3] += pad_top
  
              y_preprocess.append(y_pad)
  
      if isinstance(x, torch.Tensor):
          return torch.stack(x_preprocess, dim=0), y_preprocess
  
      return x_preprocess, y_preprocess
  
file_path: adversarial-robustness-toolbox/art/preprocessing/image/image_square_pad/pytorch.py
imports: import logging
  from typing import Dict, List, Any, Optional, TYPE_CHECKING, Tuple, Union
  import numpy
  from tqdm.auto import tqdm
  from art.preprocessing.preprocessing import PreprocessorPyTorch
label_desc: Use image node_id 2 for calling the ImageSquarePadPyTorch obj's underlying member method code with example usage: obj.forward(x, y) and returns: x_preprocess,  y_preprocess, torch,  y_preprocess