def save_model(self, save_path, save_name):
        """
        Save the model parameters to a torch file.
        :param save_path: the path where the model will be saved
        :param save_name: the name for the saved file
        :return:
        """
        # grab parameters for saving
        d = dict()
        d["muK"] = self.muK.cpu()
        d["cK"] = self.cK.cpu()
        d["Sigma"] = self.Sigma.cpu()
        d["num_updates"] = self.num_updates

        # save model out
        torch.save(d, os.path.join(save_path, save_name + ".pth"))

        Save the model parameters to a torch file.
        :param save_path: the path where the model will be saved
        :param save_name: the name for the saved file
        :return:
        
avalanche/training/supervised/deep_slda.py