def _transform(
        self, x: "torch.Tensor", y: "torch.Tensor" | list[dict[str, "torch.Tensor"]] | None, **kwargs
    ) -> tuple["torch.Tensor", "torch.Tensor" | list[dict[str, "torch.Tensor"]] | None]:
        """
        Transformation of an input image and its label by randomly sampled rotation.

        :param x: Input samples.
        :param y: Label of the samples `x`.
        :return: Transformed samples and labels.
        """
        import torch
        import torchvision

        if isinstance(self.angles, list):
            angles = np.random.choice(self.angles).item()
        else:
            angles = np.random.uniform(low=self.angles_range[0], high=self.angles_range[1])

        # Ensure channels-first
        channels_first = True
        if x.shape[-1] in [1, 3]:
            x = torch.permute(x, (0, 3, 1, 2))
            channels_first = False

        expand = self.label_type == "object_detection"

        x_preprocess = torchvision.transforms.functional.rotate(
            img=x,
            angle=angles,
            interpolation=torchvision.transforms.functional.InterpolationMode.NEAREST,
            expand=expand,
        )

        x_preprocess = torch.clamp(
            input=x_preprocess,
            min=-self.clip_values[0],
            max=self.clip_values[1],
        )

        y_preprocess: "torch.Tensor" | list[dict[str, "torch.Tensor"]] | None

        if self.label_type == "object_detection" and y is not None:

            y_od: list[dict[str, "torch.Tensor"]] = [{}]

            if isinstance(y, list):
                if isinstance(y[0], dict):
                    y_od[0]["boxes"] = torch.clone(y[0]["boxes"])
                    y_od[0]["labels"] = torch.clone(y[0]["labels"])
                else:
                    raise TypeError("Wrong type for `y` and label_type=object_detection.")
            else:
                raise TypeError("Wrong type for `y` and label_type=object_detection.")

            y_b = y[0]["boxes"]
            image_width = x.shape[2]
            image_height = x.shape[1]
            x_1_arr = y_b[:, 0]
            y_1_arr = y_b[:, 1]
            x_2_arr = y_b[:, 2]
            y_2_arr = y_b[:, 3]
            box_width = x_2_arr - x_1_arr
            box_height = y_2_arr - y_1_arr

            if angles == 0:
                x_1_new = x_1_arr
                y_1_new = y_1_arr
                x_2_new = x_2_arr
                y_2_new = y_2_arr

            elif angles == 90:
                x_1_new = y_1_arr
                y_1_new = image_width - x_1_arr - box_width
                x_2_new = y_1_arr + box_height
                y_2_new = image_width - x_1_arr

            elif angles == 180:
                x_1_new = image_width - x_2_arr
                y_1_new = image_height - y_2_arr
                x_2_new = x_1_new + box_width
                y_2_new = y_1_new + box_height

            elif angles == 270:
                x_1_new = image_height - y_1_arr - box_height
                y_1_new = x_1_arr
                x_2_new = image_height - y_1_arr
                y_2_new = x_1_arr + box_width

            else:
                raise ValueError("The angle is not supported for object detection.")

            y_od[0]["boxes"][:, 0] = x_1_new
            y_od[0]["boxes"][:, 1] = y_1_new
            y_od[0]["boxes"][:, 2] = x_2_new
            y_od[0]["boxes"][:, 3] = y_2_new

            y_preprocess = y_od

        else:

            y_preprocess = y

        if not channels_first:
            x_preprocess = torch.permute(x_preprocess, (0, 2, 3, 1))

        return x_preprocess, y_preprocess

        Transformation of an input image and its label by randomly sampled rotation.

        :param x: Input samples.
        :param y: Label of the samples `x`.
        :return: Transformed samples and labels.
        
art/preprocessing/expectation_over_transformation/image_rotation/pytorch.py