def METHOD_NAME(self):
    label = np.random.randint(self.cls_num, size=self.label_shape).astype("int64")
    pred = np.random.uniform(0.1, 1.0, self.shape).astype(paddle.get_default_dtype())
    np_label = label.reshape(-1)
    np_pred = pred.reshape(-1, self.cls_num).argmax(axis=1)
    average_type = ["micro", "macro", "weighted", None]
    pos_label = np.random.randint(0, self.cls_num)
    return label, pred, np_label, np_pred, average_type[np.random.randint(0, 3)], pos_label