def predict_zonotopes(
        self, cent: np.ndarray, bound: float, training_mode: bool = True, **kwargs
    ) -> tuple[list[np.ndarray], list[np.ndarray]]:
        """

        :param cent: The datapoint, representing the zonotope center.
        :param bound: The perturbation range for the zonotope.
        :param training_mode: `True` for model set to training mode and `False` for model set to evaluation mode.
        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
               and providing it takes no effect.
        """
        x_preprocessed, _ = self._apply_preprocessing(cent, y=None, fit=False)
        self._model.train(mode=training_mode)

        bias_results_list = []
        eps_results_list = []

        for sample in x_preprocessed:
            if self.concrete_to_zonotope is None:
                if sys.version_info >= (3, 8):
                    eps_bound = np.eye(math.prod(self.input_shape)) * bound
                else:
                    eps_bound = np.eye(reduce(lambda x, y: x * y, self.input_shape)) * bound

                processed_sample, eps_bound = self.pre_process(cent=np.copy(sample), eps=eps_bound)
                processed_sample = np.expand_dims(processed_sample, axis=0)
            else:
                processed_sample, eps_bound = self.concrete_to_zonotope(sample, bound)

            bias, eps = self.model.forward(eps=eps_bound, cent=processed_sample)
            bias = bias.detach().cpu().numpy()
            eps = eps.detach().cpu().numpy()

            bias_results_list.append(np.expand_dims(bias, axis=0))
            eps_results_list.append(eps)

        return bias_results_list, eps_results_list


        :param cent: The datapoint, representing the zonotope center.
        :param bound: The perturbation range for the zonotope.
        :param training_mode: `True` for model set to training mode and `False` for model set to evaluation mode.
        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
               and providing it takes no effect.
        
art/estimators/certification/deep_z/pytorch.py