def __init__(self, device_type: str = "gpu", **kwargs) -> None:
        """
        Estimator class for PyTorch models.

        :param channels_first: Set channels first or last.
        :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 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 on which the classifier is run, either `gpu` or `cpu`.
        """
        import torch

        preprocessing = kwargs.get("preprocessing")
        if isinstance(preprocessing, tuple):
            from art.preprocessing.standardisation_mean_std.pytorch import StandardisationMeanStdPyTorch

            kwargs["preprocessing"] = StandardisationMeanStdPyTorch(
                mean=preprocessing[0], std=preprocessing[1], device_type=device_type
            )

        super().__init__(**kwargs)

        self._device_type = device_type

        # Set 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}")

        PyTorchEstimator._check_params(self)

        Estimator class for PyTorch models.

        :param channels_first: Set channels first or last.
        :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 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 on which the classifier is run, either `gpu` or `cpu`.
        
art/estimators/pytorch.py