How to use the megnet.layers.MEGNetLayer function in megnet

def one_block(a, b, c, has_ff=True):
        if has_ff:
            x1_ = ff(a)
            x2_ = ff(b)
            x3_ = ff(c)
        else:
            x1_ = a
            x2_ = b
            x3_ = c
        out = MEGNetLayer(
            [n1, n1, n2], [n1, n1, n2], [n1, n1, n2],
            pool_method='mean', activation=act, kernel_regularizer=reg)(
            [x1_, x2_, x3_, x4, x5, x6, x7])

        x1_temp = out[0]
        x2_temp = out[1]
        x3_temp = out[2]
        if dropout:
            x1_temp = Dropout(dropout)(x1_temp, training=dropout_training)
            x2_temp = Dropout(dropout)(x2_temp, training=dropout_training)
            x3_temp = Dropout(dropout)(x3_temp, training=dropout_training)
        return x1_temp, x2_temp, x3_temp

def one_block(a, b, c, has_ff=True, block_index=0):
        if has_ff:
            x1_ = ff(a, name_prefix='block_%d_atom_ff' % block_index)
            x2_ = ff(b, name_prefix='block_%d_bond_ff' % block_index)
            x3_ = ff(c, name_prefix='block_%d_state_ff' % block_index)
        else:
            x1_ = a
            x2_ = b
            x3_ = c
        out = MEGNetLayer(
            [n1, n1, n2], [n1, n1, n2], [n1, n1, n2],
            pool_method='mean', activation=act, kernel_regularizer=reg, name='megnet_%d' % block_index)(
            [x1_, x2_, x3_, x4, x5, x6, x7])

        x1_temp = out[0]
        x2_temp = out[1]
        x3_temp = out[2]
        if dropout:
            x1_temp = Dropout(dropout, name='dropout_atom_%d' % block_index)(x1_temp, training=dropout_training)
            x2_temp = Dropout(dropout, name='dropout_bond_%d' % block_index)(x2_temp, training=dropout_training)
            x3_temp = Dropout(dropout, name='dropout_state_%d' % block_index)(x3_temp, training=dropout_training)
        return x1_temp, x2_temp, x3_temp

reverse=True,
                )

            model_file = os.path.join(
                warm_start, "kfold_{}".format(fold), "model", model_list[-1]
            )

            #  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,