def METHOD_NAME():
    seg_num = 8
    target_size = 224
    video_files = [FLAGS.data + '/' + f for f in os.listdir(FLAGS.data)]
    pipeline = create_video_pipe(video_files, seg_num, target_size, FLAGS.stride)
    video_loader = DALIGenericIterator(
        pipeline, ['image'], reader_name="Reader", dynamic_shape=True)
    exe = fluid.Executor(fluid.CUDAPlace(0))
    startup_prog = fluid.Program()
    eval_prog = fluid.Program()
    with fluid.program_guard(eval_prog, startup_prog):
        with fluid.unique_name.guard():
            fetch_list = build(seg_num, target_size)
    exe.run(startup_prog)
    compiled_eval_prog = fluid.CompiledProgram(eval_prog)
    load_weights(exe, eval_prog, PRETRAIN_WEIGHTS)
    labels = json.load(open("kinetics_labels.json"))
    for idx, batch in enumerate(video_loader):
        fetches = exe.run(
            compiled_eval_prog, feed=batch, fetch_list=fetch_list)
        pred = fetches[0][0]
        topk_indices = pred.argsort()[0 - FLAGS.topk:]
        topk_labels = [labels[i] for i in topk_indices]
        filename = video_files[idx]
        print("prediction for {} is: {}".format(filename, topk_labels))