def METHOD_NAME(self, previous_model_params):
    # add a random mask in [-1, 1]
    for k in previous_model_params.keys():
        if is_weight_param(k):
            previous_model_params[k] = (
                torch.from_numpy(
                    2
                    * np.random.random_sample(size=previous_model_params[k].size())
                    - 1
                    + previous_model_params[k]
                )
                .float()
                .to(self.device)
            )
    return previous_model_params