How to use the megnet.models.MEGNetModel function in megnet

#  Load model from file
            if learning_rate is None:
                full_model = load_model(
                    model_file,
                    custom_objects={
                        "softplus2": softplus2,
                        "Set2Set": Set2Set,
                        "mean_squared_error_with_scale": mean_squared_error_with_scale,
                        "MEGNetLayer": MEGNetLayer,
                    },
                )

                learning_rate = K.get_value(full_model.optimizer.lr)
            # Set up model
            model = MEGNetModel(
                100,
                2,
                nblocks=args.n_blocks,
                nvocal=95,
                npass=args.n_pass,
                lr=learning_rate,
                loss=args.loss,
                graph_convertor=cg,
                is_classification=True if args.type == "classification" else False,
                nfeat_node=None if embedding_file is None else 16,
            )
            model.load_weights(model_file)
            initial_epoch = int(model_list[-1].split("_")[2])
            print(
                "warm start from : {}, \nlearning_rate is {}.".format(
                    model_file, learning_rate

y_scaler = StandardScaler()
            train_targets = y_scaler.fit_transform(
                np.array(train_targets).reshape(-1, 1)
            ).ravel()
            val_targets = y_scaler.transform(
                np.array(val_targets).reshape((-1, 1))
            ).ravel()
        else:
            y_scaler = None

        # Initialize model
        if warm_start is None:
            #  Set up model
            if learning_rate is None:
                learning_rate = 1e-3
            model = MEGNetModel(
                100,
                2,
                nblocks=args.n_blocks,
                nvocal=95,
                npass=args.n_pass,
                lr=learning_rate,
                loss=args.loss,
                graph_convertor=cg,
                is_classification=True if args.type == "classification" else False,
                nfeat_node=None if embedding_file is None else 16,
            )

            initial_epoch = 0
        else:
            # Model file
            model_list = [