def METHOD_NAME(test_reader, flods, flags, net, args):
    net.eval()
    featureLs = None
    featureRs = None
    for idx, data in enumerate(test_reader()):
        data_list = [[] for _ in range(4)]
        for _ in range(len(data)):
            data_list[0].append(data[_][0])
            data_list[1].append(data[_][1])
            data_list[2].append(data[_][2])
            data_list[3].append(data[_][3])
        res = [net(paddle.to_tensor(np.array(d))).numpy() for d in data_list]
        featureL = np.concatenate((res[0], res[1]), 1)
        featureR = np.concatenate((res[2], res[3]), 1)
        if featureLs is None:
            featureLs = featureL
        else:
            featureLs = np.concatenate((featureLs, featureL), 0)
        if featureRs is None:
            featureRs = featureR
        else:
            featureRs = np.concatenate((featureRs, featureR), 0)
    result = {'fl': featureLs, 'fr': featureRs, 'fold': flods, 'flag': flags}
    scipy.io.savemat(args.feature_save_dir, result)
    ACCs = evaluation_10_fold(args.feature_save_dir)
    for i in range(len(ACCs)):
        print('{}    {:.2f}'.format(i + 1, ACCs[i] * 100))
    print('--------')
    print('AVE    {:.2f}'.format(np.mean(ACCs) * 100))