def METHOD_NAME(ds: Dataset) -> Table:
    """Build table with DALYs for mental health causes."""
    # Reset index
    # We are only interested in mental health causes
    causes_mental_health = [
        "anxiety_disorders__both_sexes__age_standardized",
        "bipolar_disorder__both_sexes__age_standardized",
        "depressive_disorders__both_sexes__age_standardized",
        "eating_disorders__both_sexes__age_standardized",
        "schizophrenia__both_sexes__age_standardized",
    ]
    tb = Table()
    for cause in causes_mental_health:
        tb_cause = ds[cause].reset_index()
        tb_cause = tb_cause.drop(columns=["sex", "age", "cause", "rei"], errors="ignore")
        tb_cause = tb_cause.dropna(axis=1, how="all")
        if tb.shape == (0, 0):
            tb = tb_cause
        else:
            tb = pr.merge(tb, tb_cause, on=["country", "year"])
    # Filter dimensions
    tb = tb[(tb["country"] == "World")].drop(columns=["country"])
    cause_rename = {
        "dalys_from_anxiety_disorders_per_100_000_people_in__both_sexes_aged_age_standardized": "Anxiety disorders",
        "dalys_from_bipolar_disorder_per_100_000_people_in__both_sexes_aged_age_standardized": "Bipolar disorder",
        "dalys_from_depressive_disorders_per_100_000_people_in__both_sexes_aged_age_standardized": "Depressive disorders",
        "dalys_from_eating_disorders_per_100_000_people_in__both_sexes_aged_age_standardized": "Eating disorders",
        "dalys_from_schizophrenia_per_100_000_people_in__both_sexes_aged_age_standardized": "Schizophrenia",
    }
    tb = tb.melt(id_vars=["year"])
    list_keys = list(cause_rename.keys())
    tb = tb[tb["variable"].isin(list_keys)]
    assert len(tb["variable"].drop_duplicates()) == len(cause_rename)
    tb["variable"] = tb["variable"].replace(cause_rename)
    tb = tb.rename(columns={"variable": "cause", "value": "dalys_rate"})
    tb = tb.set_index(["cause", "year"], verify_integrity=True).sort_index()
    return tb