def METHOD_NAME(self):
    steps = 10
    np_pred = np.zeros((0), dtype=int)
    np_label = np.zeros((0), dtype=int)
    for i in range(steps):
        numpy.random.seed(i)
        label, pred, cur_np_label, cur_np_pred, average_type, pos_label = self.get_multi_labels_random_case()
        np_label = np.concatenate((np_label, cur_np_label))
        np_pred = np.concatenate((np_pred, cur_np_pred))
        precision, recall, f, _ = precision_recall_fscore_support(
            np_label, np_pred, average=average_type, pos_label=pos_label
        )
        args = self.metrics.compute(paddle.to_tensor(pred), paddle.to_tensor(label))
        self.metrics.update(args)
        result = self.metrics.accumulate(average=average_type, pos_label=pos_label)
        self.assertEqual(precision, result[0])
        self.assertEqual(recall, result[1])
        self.assertEqual(f, result[2])