def __init__(
        self,
        model: "torch.nn.Module",
        loss: "torch.nn.modules.loss._Loss",
        input_shape: tuple[int, ...],
        nb_classes: int,
        optimizer: "torch.optim.Optimizer" | None = None,
        channels_first: bool = True,
        clip_values: "CLIP_VALUES_TYPE" | None = None,
        preprocessing_defences: "Preprocessor" | list["Preprocessor"] | None = None,
        postprocessing_defences: "Postprocessor" | list["Postprocessor"] | None = None,
        preprocessing: "PREPROCESSING_TYPE" = (0.0, 1.0),
        device_type: str = "gpu",
        sample_size: int = 32,
        scale: float = 0.1,
        alpha: float = 0.001,
        beta: float = 16.0,
        gamma: float = 8.0,
        lmbda: float = 12.0,
        gaussian_samples: int = 16,
    ) -> None:
        """
        Create a MACER classifier.

        :param model: PyTorch model. The output of the model can be logits, probabilities or anything else. Logits
               output should be preferred where possible to ensure attack efficiency.
        :param loss: The loss function for which to compute gradients for training. The target label must be raw
               categorical, i.e. not converted to one-hot encoding.
        :param input_shape: The shape of one input instance.
        :param nb_classes: The number of classes of the model.
        :param optimizer: The optimizer used to train the classifier.
        :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`.
        :param sample_size: Number of samples for smoothing.
        :param scale: Standard deviation of Gaussian noise added.
        :param alpha: The failure probability of smoothing.
        :param beta: The inverse temperature.
        :param gamma: The hinge factor.
        :param lmbda: The trade-off factor.
        :param gaussian_samples: The number of gaussian samples per input.
        """
        super().__init__(
            model=model,
            loss=loss,
            input_shape=input_shape,
            nb_classes=nb_classes,
            optimizer=optimizer,
            channels_first=channels_first,
            clip_values=clip_values,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
            device_type=device_type,
            sample_size=sample_size,
            scale=scale,
            alpha=alpha,
        )
        self.beta = beta
        self.gamma = gamma
        self.lmbda = lmbda
        self.gaussian_samples = gaussian_samples

        Create a MACER classifier.

        :param model: PyTorch model. The output of the model can be logits, probabilities or anything else. Logits
               output should be preferred where possible to ensure attack efficiency.
        :param loss: The loss function for which to compute gradients for training. The target label must be raw
               categorical, i.e. not converted to one-hot encoding.
        :param input_shape: The shape of one input instance.
        :param nb_classes: The number of classes of the model.
        :param optimizer: The optimizer used to train the classifier.
        :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`.
        :param sample_size: Number of samples for smoothing.
        :param scale: Standard deviation of Gaussian noise added.
        :param alpha: The failure probability of smoothing.
        :param beta: The inverse temperature.
        :param gamma: The hinge factor.
        :param lmbda: The trade-off factor.
        :param gaussian_samples: The number of gaussian samples per input.
        
art/estimators/certification/randomized_smoothing/macer/pytorch.py