def METHOD_NAME(self, doc_ids: List[str], search_scores: List[float]):
    # one classifier
    if len(self.clf_type) == 1:
        self._set_classifier(self.clf_type[0])
        return self._rerank_with_classifier(doc_ids, search_scores)
    # two classifier with FusionMethod.AVG
    doc_score_dict = {}
    for i in range(2):
        self._set_classifier(self.clf_type[i])
        i_scores, i_doc_ids = self._rerank_with_classifier(doc_ids, search_scores)
        for score, doc_id in zip(i_scores, i_doc_ids):
            if doc_id not in doc_score_dict:
                doc_score_dict[doc_id] = set()
            doc_score_dict[doc_id].add(score)
    r_scores, r_doc_ids = [], []
    for doc_id, score in doc_score_dict.items():
        avg = sum(score) / len(score)
        r_doc_ids.append(doc_id)
        r_scores.append(avg)
    return r_scores, r_doc_ids