def METHOD_NAME(parser, program, model, test_exe, graph, y_true, split_idx):
    feed_dict=model.gw.to_feed(graph)
    if parser.use_label_e:
        feed_dict['label']=y_true
        feed_dict['label_idx']=split_idx['train']
        feed_dict['attn_drop']=-1
    avg_cost_np = test_exe.run(
            program=program,
            feed=feed_dict,
            fetch_list=[model.out_feat])
    y_pred=avg_cost_np[0].argmax(axis=-1)
    y_pred=np.expand_dims(y_pred, 1)
    train_acc = evaluator.eval({
        'y_true': y_true[split_idx['train']],
        'y_pred': y_pred[split_idx['train']],
    })['acc']
    val_acc = evaluator.eval({
        'y_true': y_true[split_idx['valid']],
        'y_pred': y_pred[split_idx['valid']],
    })['acc']
    test_acc = evaluator.eval({
        'y_true': y_true[split_idx['test']],
        'y_pred': y_pred[split_idx['test']],
    })['acc']
    return train_acc, val_acc, test_acc