def forward(
        self,
        x: "torch.Tensor",
        y: "torch.Tensor" | list[dict[str, "torch.Tensor"]] | None = None,
    ) -> tuple["torch.Tensor", "torch.Tensor" | list[dict[str, "torch.Tensor"]] | None]:
        """
        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: torch.Tensor | list[dict[str, torch.Tensor]] | None
        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

        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.
        
art/preprocessing/image/image_square_pad/pytorch.py