def load_model(self, save_path, save_name):
        """
        Load the model parameters into StreamingLDA object.
        :param save_path: the path where the model is saved
        :param save_name: the name of the saved file
        :return:
        """
        # load parameters
        d = torch.load(os.path.join(save_path, save_name + ".pth"))
        self.muK = d["muK"].to(self.device)
        self.cK = d["cK"].to(self.device)
        self.Sigma = d["Sigma"].to(self.device)
        self.num_updates = d["num_updates"]

        Load the model parameters into StreamingLDA object.
        :param save_path: the path where the model is saved
        :param save_name: the name of the saved file
        :return:
        
avalanche/training/supervised/deep_slda.py