def __init__(
        self,
        classifier: "CLASSIFIER_LOSS_GRADIENTS_TYPE",
        augmentations: "Preprocessor" | list["Preprocessor"],
        noise: Literal["gaussian", "laplacian", "exponential"] = "laplacian",
        loc: int | float = 0.0,
        scale: int | float = 0.03,
        clip_values: "CLIP_VALUES_TYPE" = (0.0, 1.0),
    ):
        """
        Create an :class:`.DPInstaHideTrainer` instance.

        :param classifier: The model to train using the protocol.
        :param augmentations: The preprocessing data augmentation defence(s) to be applied.
        :param noise: The type of additive noise to use: 'gaussian' | 'laplacian' | 'exponential'.
        :param loc: The location or mean parameter of the distribution to sample.
        :param scale: The scale or standard deviation parameter of the distribution to sample.
        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
               for features.
        """
        from art.defences.preprocessor import Preprocessor

        super().__init__(classifier)
        if isinstance(augmentations, Preprocessor):
            self.augmentations = [augmentations]
        else:
            self.augmentations = augmentations
        self.noise = noise
        self.loc = loc
        self.scale = scale
        self.clip_values = clip_values

        Create an :class:`.DPInstaHideTrainer` instance.

        :param classifier: The model to train using the protocol.
        :param augmentations: The preprocessing data augmentation defence(s) to be applied.
        :param noise: The type of additive noise to use: 'gaussian' | 'laplacian' | 'exponential'.
        :param loc: The location or mean parameter of the distribution to sample.
        :param scale: The scale or standard deviation parameter of the distribution to sample.
        :param clip_values: Tuple of the form `(min, max)` representing the minimum and maximum values allowed
               for features.
        
art/defences/trainer/dp_instahide_trainer.py