def METHOD_NAME():
    id = "Gamma_K17"
    description = "Deleterious Gamma DFE"
    long_description = """
    Return neutral and negative MutationType()s representing a human DFE.
    Kim et al. (2017), https://doi.org/10.1534/genetics.116.197145
    The DFE was inferred assuming synonymous variants are neutral and a relative
    mutation rate ratio of 2.31 nonsynonymous to 1 synonymous mutation
    (Huber et al. 2016). Gamma parameters are given as the shape and the mean
    selection coefficient (E[s]) escaled, such that E[s] = -shape*scale*2/(2Na),
    where Na is the ancestral population size (12378) as in Kim et al. (2017).
    """
    citations = [
        stdpopsim.Citation(
            author="Kim et al.",
            year=2017,
            doi="https://doi.org/10.1534/genetics.116.197145",
            reasons={stdpopsim.CiteReason.DFE},  # include the dfe_model reason
        )
    ]
    neutral = stdpopsim.MutationType()
    Na = 12378
    gamma_scale = 875
    gamma_shape = 0.186  # shape
    # Extra factor of 2 in mean is to account for difference between tools
    # in how fitness is defined
    # (1+s for homozygote in SLiM versus 1+2s in dadi)
    gamma_mean = (-gamma_shape * gamma_scale * 2) / (2 * Na)  # expected value
    h = 0.5  # dominance coefficient
    negative = stdpopsim.MutationType(
        dominance_coeff=h,
        distribution_type="g",  # gamma distribution
        distribution_args=[gamma_mean, gamma_shape],
    )
    return stdpopsim.DFE(
        id=id,
        description=description,
        long_description=long_description,
        mutation_types=[neutral, negative],
        proportions=[0.3, 0.7],
        citations=citations,
    )