How to use the pandasticsearch.queries.Agg._process_agg function in pandasticsearch

def explain_result(self, result=None):
        super(Agg, self).explain_result(result)
        tuples = list(Agg._process_agg(self._result_dict['aggregations']))
        assert len(tuples) > 0
        self._index_names = list(tuples[0][0])
        self._values = []
        self._indexes = []
        for t in tuples:
            _, index, row = t
            self._values.append(row)
            if len(index) > 0:
                self._indexes.append(index)

Recursively extract agg values
        :param bucket: a bucket contains either sub-buckets or a bunch of aggregated values
        :return: a list of tuples: (index_name, index_tuple, row)
        """
        # for each agg, yield a row
        row = {}
        for k, v in bucket.items():
            if isinstance(v, dict):
                if 'buckets' in v:
                    for sub_bucket in v['buckets']:

                        if 'key_as_string' in sub_bucket:
                            key = sub_bucket['key_as_string']
                        else:
                            key = sub_bucket['key']
                        for x in Agg._process_agg(sub_bucket,
                                                  indexes + (key,),
                                                  names + (k,)):
                            yield x
                elif 'value' in v:
                    row[k] = v['value']
                elif 'values' in v:  # percentiles
                    row = v['values']
                else:
                    row.update(v)  # stats
            else:
                if k == 'doc_count':  # count docs
                    row['doc_count'] = v

        if len(row) > 0:
            yield (names, indexes, row)