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",
        concrete_to_zonotope: Callable | None = None,
    ):
        """
        Create a certifier based on the zonotope domain.

        :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 concrete_to_zonotope:  Optional argument. Function which takes in a concrete data point and the bound
                                      and converts the datapoint to the zonotope domain via:

                                                processed_sample, eps_bound = concrete_to_zonotope(sample, bound)

                                      where processed_sample is the zonotope bias term, and eps_bound are the
                                      associated error terms.
                                      If left as None, by default we apply the bound to every feature equally and
                                      adjust the zonotope such that it remains in the 0 - 1 range.
        """

        warnings.warn(
            "\nThis estimator does not support networks which have dense layers before convolutional. "
            "We currently infer a reshape when a neural network goes from convolutional layers to "
            "dense layers. If your use case does not fall into this pattern then consider "
            "directly building a certifier network with the "
            "custom layers found in art.estimators.certification.deepz.deep_z.py\n"
        )
        converted_model = ConvertedModel(model, channels_first, input_shape)

        if TYPE_CHECKING:
            converted_optimizer: torch.optim.Adam | torch.optim.SGD | None

        if optimizer is not None:
            opt_state_dict = optimizer.state_dict()
            if isinstance(optimizer, torch.optim.Adam):
                logging.info("Converting Adam Optimiser")
                converted_optimizer = torch.optim.Adam(converted_model.parameters(), lr=1e-4)
            elif isinstance(optimizer, torch.optim.SGD):
                logging.info("Converting SGD Optimiser")
                converted_optimizer = torch.optim.SGD(converted_model.parameters(), lr=1e-4)
            else:
                raise ValueError("Optimiser not supported for conversion")

            converted_optimizer.load_state_dict(opt_state_dict)
        else:
            converted_optimizer = None

        self.concrete_to_zonotope = concrete_to_zonotope

        super().__init__(
            model=converted_model,
            loss=loss,
            input_shape=input_shape,
            nb_classes=nb_classes,
            optimizer=converted_optimizer,
            channels_first=channels_first,
            clip_values=clip_values,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
            device_type=device_type,
        )

        Create a certifier based on the zonotope domain.

        :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 concrete_to_zonotope:  Optional argument. Function which takes in a concrete data point and the bound
                                      and converts the datapoint to the zonotope domain via:

                                                processed_sample, eps_bound = concrete_to_zonotope(sample, bound)

                                      where processed_sample is the zonotope bias term, and eps_bound are the
                                      associated error terms.
                                      If left as None, by default we apply the bound to every feature equally and
                                      adjust the zonotope such that it remains in the 0 - 1 range.
        
art/estimators/certification/deep_z/pytorch.py