def METHOD_NAME(ibs, aid_list, config2_=None, **kwargs):
    """
    very hacky, but cute way to cache keypoint distinctivness
    Args:
        ibs (IBEISController):  wbia controller object
        aid_list (list):
        dstncvs_normer (None):
    Returns:
        list: dstncvs_list
    CommandLine:
        python -m wbia.control.manual_wbiacontrol_funcs --test-get_annot_kpts_distinctiveness
    Example:
        >>> # SLOW_DOCTEST
        >>> # xdoctest: +SKIP
        >>> from wbia.control.manual_wbiacontrol_funcs import *  # NOQA
        >>> from wbia.algo.hots import distinctiveness_normalizer
        >>> import wbia
        >>> import numpy as np
        >>> config2_ = None
        >>> # build test data
        >>> ibs = wbia.opendb('testdb1')
        >>> aid_list = ibs.get_valid_aids(species=const.TEST_SPECIES.ZEB_PLAIN)
        >>> # execute function
        >>> aid_list1 = aid_list[::2]
        >>> aid_list2 = aid_list[1::3]
        >>> dstncvs_list1 = get_annot_kpts_distinctiveness(ibs, aid_list1)
        >>> dstncvs_list2 = get_annot_kpts_distinctiveness(ibs, aid_list2)
        >>> dstncvs_list = get_annot_kpts_distinctiveness(ibs, aid_list)
        >>> print(ut.depth_profile(dstncvs_list1))
        >>> stats_dict = ut.dict_stack([ut.get_stats(dstncvs) for dstncvs in dstncvs_list])
        >>> print(ut.repr2(stats_dict))
        >>> assert np.all(np.array(stats_dict['min']) >= 0), 'distinctiveness was out of bounds'
        >>> assert np.all(np.array(stats_dict['max']) <= 1), 'distinctiveness was out of bounds'
    """
    from wbia.algo.hots import distinctiveness_normalizer as dcvs_normer
    # per-species disinctivness wrapper around wbia cached function
    # get feature rowids
    aid_list = np.array(aid_list)
    fid_list = np.array(
        ibs.get_annot_feat_rowids(
            aid_list, ensure=True, eager=True, nInput=None, config2_=config2_
        )
    )
    species_rowid_list = np.array(ibs.get_annot_species_rowids(aid_list))
    # Compute distinctivness separately for each species
    unique_sids, groupxs = vt.group_indices(species_rowid_list)
    fids_groups = vt.apply_grouping(fid_list, groupxs)
    species_text_list = ibs.get_species_texts(unique_sids)
    # Map distinctivness computation
    normer_list = [
        dcvs_normer.request_species_distinctiveness_normalizer(species)
        for species in species_text_list
    ]
    # Reduce to get results
    dstncvs_groups = [
        get_feat_kpts_distinctiveness(
            ibs, fids, dstncvs_normer=dstncvs_normer, species_rowid=sid, **kwargs
        )
        for dstncvs_normer, fids, sid in zip(normer_list, fids_groups, unique_sids)
    ]
    dstncvs_list = vt.invert_apply_grouping(dstncvs_groups, groupxs)
    return dstncvs_list