def METHOD_NAME(model):
    """Custom evaluate function to estimate the accuracy of the model.
    Args:
        model (tf.Graph_def): The input model graph
    Returns:
        accuracy (float): evaluation result, the larger is better.
    """
    from neural_compressor.model import Model
    model = Model(model)
    output_tensor = model.output_tensor if len(model.output_tensor)>1 else \
                        model.output_tensor[0]
    iteration = -1              
    minibatch = prepare_Dataset()      
    if args.benchmark and args.mode == 'performance':
        iteration = args.iters
    #output_tensor = model.sess.graph.get_tensor_by_name('Sigmoid:0')
    def eval_func(size, output_tensor, minibatch, test):
        t_test = time.time()
        val_losses = []
        val_preds = []
        labels = []
        iter_num = 0
        finished = False
        total_time = 0
        while not finished:
            feed_dict_val, batch_labels, finished, _ = minibatch.incremental_node_val_feed_dict(size, iter_num, test=True)
            tf_logging.warn('\n---> Start iteration {0}'.format(str(iter_num)))
            start_time = time.time()
            node_outs_val = model.sess.run([output_tensor],feed_dict=feed_dict_val)
            time_consume = time.time() - start_time
            val_preds.append(node_outs_val[0])
            labels.append(batch_labels)
            iter_num += 1
            total_time += time_consume
            if iteration and iter_num >= iteration:
                break
        tf_logging.warn('\n---> Stop iteration {0}'.format(str(iter_num)))
        val_preds = np.vstack(val_preds)
        labels = np.vstack(labels)
        f1_scores = utils.calc_f1(labels, val_preds)
        time_average = total_time / iter_num
        return f1_scores, (time.time() - t_test)/iter_num, time_average
    test_f1_micro, duration, time_average = eval_func(args.batch_size, output_tensor, minibatch, test=True)
    if args.benchmark and args.mode == 'performance':
        latency = time_average / args.batch_size
        print("Batch size = {}".format(args.batch_size))
        print("Latency: {:.3f} ms".format(latency * 1000))
        print("Throughput: {:.3f} images/sec".format(1. / latency))
    return test_f1_micro