How to use the gnomad.utils.filtering.filter_to_autosomes function in gnomad

:param bi_allelic_only: If set, only bi-allelic sites are used for the computation
    :param autosomes_only: If set, only autosomal intervals are used.
    :param matches: If set, returns all intervals in intervals_ht that overlap the locus in the input MT.
    :return: Callrate MT
    """
    logger.info("Computing call rate MatrixTable")

    if len(intervals_ht.key) != 1 or not isinstance(
        intervals_ht.key[0], hl.expr.IntervalExpression
    ):
        logger.warning(
            f"Call rate matrix computation expects `intervals_ht` with a key of type Interval. Found: {intervals_ht.key}"
        )

    if autosomes_only:
        callrate_mt = filter_to_autosomes(mt)

    if bi_allelic_only:
        callrate_mt = callrate_mt.filter_rows(bi_allelic_expr(callrate_mt))

    intervals_ht = intervals_ht.annotate(_interval_key=intervals_ht.key)
    callrate_mt = callrate_mt.annotate_rows(
        _interval_key=intervals_ht.index(
            callrate_mt.locus, all_matches=match
        )._interval_key
    )

    if match:
        callrate_mt = callrate_mt.explode_rows("_interval_key")

    callrate_mt = callrate_mt.filter_rows(
        hl.is_defined(callrate_mt._interval_key.interval)

table will be excluded from the PCA.

    The loadings Table returned also contains a `pca_af` annotation which is the allele frequency
    used for PCA. This is useful to project other samples in the PC space.

    :param qc_mt: Input QC MT
    :param related_samples_to_drop: Optional table of related samples to drop
    :param n_pcs: Number of PCs to compute
    :param autosomes_only: Whether to run the analysis on autosomes only
    :return: eigenvalues, scores and loadings
    """

    unrelated_mt = qc_mt.persist()

    if autosomes_only:
        unrelated_mt = filter_to_autosomes(unrelated_mt)

    if related_samples_to_drop:
        unrelated_mt = qc_mt.filter_cols(
            hl.is_missing(related_samples_to_drop[qc_mt.col_key])
        )

    pca_evals, pca_scores, pca_loadings = hl.hwe_normalized_pca(
        unrelated_mt.GT, k=n_pcs, compute_loadings=True
    )
    pca_af_ht = unrelated_mt.annotate_rows(
        pca_af=hl.agg.mean(unrelated_mt.GT.n_alt_alleles()) / 2
    ).rows()
    pca_loadings = pca_loadings.annotate(
        pca_af=pca_af_ht[pca_loadings.key].pca_af
    )  # TODO: Evaluate if needed to write results at this point if relateds or not

Default function to run `generate_trio_stats_expr` to get trio stats stratified by raw and adj

    .. note::

        Expects that `mt` is it a trio matrix table that was annotated with adj and if dealing with
        a sparse MT `hl.experimental.densify` must be run first.

        By default this pipeline function will filter `mt` to only autosomes and bi-allelic sites.

    :param mt: A Trio Matrix Table returned from `hl.trio_matrix`. Must be dense
    :param autosomes_only: If set, only autosomal intervals are used.
    :param bi_allelic_only: If set, only bi-allelic sites are used for the computation
    :return: Table with trio stats
    """
    if autosomes_only:
        mt = filter_to_autosomes(mt)
    if bi_allelic_only:
        mt = mt.filter_rows(bi_allelic_expr(mt))

    logger.info(f"Generating trio stats using {mt.count_cols()} trios.")
    trio_adj = mt.proband_entry.adj & mt.father_entry.adj & mt.mother_entry.adj

    ht = mt.select_rows(
        **generate_trio_stats_expr(
            mt,
            transmitted_strata={"raw": True, "adj": trio_adj},
            de_novo_strata={"raw": True, "adj": trio_adj},
            ac_strata={"raw": True, "adj": trio_adj},
        )
    ).rows()

    return ht

.. note::

        By default this pipeline function will filter `mt` to only autosomes and bi-allelic sites.

    :param mt: Input Matrix table
    :param relatedness_ht: Input relationship table
    :param i_col: Column containing the 1st sample of the pair in the relationship table
    :param j_col: Column containing the 2nd sample of the pair in the relationship table
    :param relationship_col: Column containing the relationship for the sample pair as defined in this module constants.
    :param autosomes_only: If set, only autosomal intervals are used.
    :param bi_allelic_only: If set, only bi-allelic sites are used for the computation
    :return: A Table with the sibling shared variant counts
    """
    if autosomes_only:
        mt = filter_to_autosomes(mt)
    if bi_allelic_only:
        mt = mt.filter_rows(bi_allelic_expr(mt))

    sib_ht = relatedness_ht.filter(relatedness_ht[relationship_col] == SIBLINGS)
    s_to_keep = sib_ht.aggregate(
        hl.agg.explode(
            lambda s: hl.agg.collect_as_set(s), [sib_ht[i_col].s, sib_ht[j_col].s]
        ),
        _localize=False,
    )
    mt = mt.filter_cols(s_to_keep.contains(mt.s))
    if "adj" not in mt.entry:
        mt = annotate_adj(mt)

    sib_stats_ht = mt.select_rows(
        **generate_sib_stats_expr(