def METHOD_NAME(dataloader):
    latency_list = []
    for idx, (inputs, labels) in enumerate(dataloader):
        # dataloader should keep the order and len of inputs same with input_tensor
        inputs = np.array([inputs])
        feed_dict = dict(zip(input_tensor, inputs))
        start = time.time()
        predictions = model.sess.run(output_tensor, feed_dict)
        end = time.time()
        metric.update(predictions, labels)
        latency_list.append(end-start)
        if idx + 1 == iteration:
            break
    latency = np.array(latency_list).mean() / args.batch_size
    return latency