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]:
        """
        Center crop an input image and its labels by randomly sampled crop size.

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

        size = np.random.randint(low=self.size_range[0], high=self.size_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

        x_preprocess = torchvision.transforms.functional.resized_crop(
            img=x,
            top=size,
            left=size,
            height=x.shape[-2] - 2 * size,
            width=x.shape[-1] - 2 * size,
            size=x.shape[-2:-1],
            interpolation=torchvision.transforms.functional.InterpolationMode.NEAREST,
        )

        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.")

            ratio_h = x.shape[-2] / (x.shape[-2] - 2 * size)
            ratio_w = x.shape[-1] / (x.shape[-1] - 2 * size)

            # top-left corner

            y_od[0]["boxes"][:, 0] -= size
            y_od[0]["boxes"][:, 1] -= size

            y_od[0]["boxes"][:, 0] = y_od[0]["boxes"][:, 0] * ratio_h
            y_od[0]["boxes"][:, 1] = y_od[0]["boxes"][:, 1] * ratio_w

            y_od[0]["boxes"][:, 0] = torch.maximum(torch.tensor(0), y_od[0]["boxes"][:, 0]).int()
            y_od[0]["boxes"][:, 1] = torch.maximum(torch.tensor(0), y_od[0]["boxes"][:, 1]).int()

            # bottom-right corner

            y_od[0]["boxes"][:, 2] -= size
            y_od[0]["boxes"][:, 3] -= size

            y_od[0]["boxes"][:, 2] = y_od[0]["boxes"][:, 2] * ratio_h
            y_od[0]["boxes"][:, 3] = y_od[0]["boxes"][:, 3] * ratio_w

            y_od[0]["boxes"][:, 2] = torch.minimum(y_od[0]["boxes"][:, 2], torch.tensor(x.shape[-2])).int()
            y_od[0]["boxes"][:, 3] = torch.minimum(y_od[0]["boxes"][:, 3], torch.tensor(x.shape[-1])).int()

            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

        Center crop an input image and its labels by randomly sampled crop size.

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