def METHOD_NAME(model):
    """
    Custom evaluate function to inference the model for specified metric on validation dataset.
    Args:
        model (tf.keras.Model): The input model will be the objection of tf.keras.Model.
    Returns:
        accuracy (float): evaluation result, the larger is better.
    """
    postprocess = LabelShift(label_shift=1)
    from neural_compressor import METRICS
    metrics = METRICS('tensorflow')
    metric = metrics['topk']()
    latency_list = []
    def eval_func(dataloader, metric):
        warmup = 5
        iteration = None
        if FLAGS.benchmark and FLAGS.mode == 'performance':
            iteration = FLAGS.iters
        for idx, (inputs, labels) in enumerate(dataloader):
            start = time.time()
            predictions = model.predict_on_batch(inputs)
            end = time.time()
            latency_list.append(end - start)
            predictions, labels = postprocess((predictions, labels))
            metric.update(predictions, labels)
            if iteration and idx >= iteration:
                break
        latency = np.array(latency_list[warmup:]).mean() / eval_dataloader.batch_size
        return latency
    latency = eval_func(eval_dataloader, metric)
    if FLAGS.benchmark:
        logger.info("\n{} mode benchmark result:".format(FLAGS.mode))
        for i, res in enumerate(latency_list):
            logger.debug("Iteration {} result {}:".format(i, res))
    if FLAGS.benchmark and FLAGS.mode == 'performance':
        logger.info("Batch size = {}".format(eval_dataloader.batch_size))
        logger.info("Latency: {:.3f} ms".format(latency * 1000))
        logger.info("Throughput: {:.3f} images/sec".format(1. / latency))
    acc = metric.result()
    return acc