def __init__(
        self,
        model,
        input_shape: tuple[int, ...],
        clip_values: "CLIP_VALUES_TYPE" | None = None,
        channels_first: bool | None = None,
        preprocessing_defences: "Preprocessor" | list["Preprocessor"] | None = None,
        postprocessing_defences: "Postprocessor" | list["Postprocessor"] | None = None,
        preprocessing: "PREPROCESSING_TYPE" = None,
        device_type: str = "gpu",
    ):
        """
        Initialization.

        :param model: GOTURN model.
        :param input_shape: Shape of one input sample as expected by the model, e.g. input_shape=(3, 227, 227).
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
               maximum values allowed for features. If floats are provided, these will be used as the range of all
               features. If arrays are provided, each value will be considered the bound for a feature, thus
               the shape of clip values needs to match the total number of features.
        :param channels_first: Set channels first or last.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
               used for data preprocessing. The first value will be subtracted from the input. The input will then
               be divided by the second one.
        :param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU
                            if available otherwise run on CPU.
        """
        import torch

        # Set device
        self._device: torch.device
        if device_type == "cpu" or not torch.cuda.is_available():
            self._device = torch.device("cpu")
        else:  # pragma: no cover
            cuda_idx = torch.cuda.current_device()
            self._device = torch.device(f"cuda:{cuda_idx}")

        model.to(self._device)

        super().__init__(
            model=model,
            clip_values=clip_values,
            channels_first=channels_first,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
            device_type=device_type,
        )
        # got-10k toolkit
        # Tracker.__init__(self, name="PyTorchGoturn", is_deterministic=True)
        self.name = "PyTorchGoturn"
        self.is_deterministic = True

        self._input_shape = input_shape

        if self.clip_values is not None:
            if self.clip_values[0] != 0:  # pragma: no cover
                raise ValueError("This classifier requires un-normalized input images with clip_vales=(0, 255).")
            if self.clip_values[1] not in [1, 255]:  # pragma: no cover
                raise ValueError(
                    "This classifier requires un-normalized input images with clip_vales=(0, 1) or "
                    "clip_vales=(0, 255)."
                )

        if self.postprocessing_defences is not None:  # pragma: no cover
            raise ValueError("This estimator does not support `postprocessing_defences`.")

        self.attack_losses: tuple[str, ...] = ("torch.nn.L1Loss",)

        Initialization.

        :param model: GOTURN model.
        :param input_shape: Shape of one input sample as expected by the model, e.g. input_shape=(3, 227, 227).
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
               maximum values allowed for features. If floats are provided, these will be used as the range of all
               features. If arrays are provided, each value will be considered the bound for a feature, thus
               the shape of clip values needs to match the total number of features.
        :param channels_first: Set channels first or last.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :param preprocessing: Tuple of the form `(subtrahend, divisor)` of floats or `np.ndarray` of values to be
               used for data preprocessing. The first value will be subtracted from the input. The input will then
               be divided by the second one.
        :param device_type: Type of device to be used for model and tensors, if `cpu` run on CPU, if `gpu` run on GPU
                            if available otherwise run on CPU.
        
art/estimators/object_tracking/pytorch_goturn.py