def METHOD_NAME(
    false_positive_rate: np.float64,
) -> Dict[str, Dict[str, Number]]:
    normal_mean: int = 5000
    normal_stdev: int = 1000
    normal_lower_quantile: Number = stats.norm.ppf(
        q=false_positive_rate / 2, loc=normal_mean, scale=normal_stdev
    )
    normal_upper_quantile: Number = stats.norm.ppf(
        q=1 - false_positive_rate / 2, loc=normal_mean, scale=normal_stdev
    )
    uniform_lower_bound: int = 4000
    uniform_scale: int = 6000
    uniform_lower_quantile: Number = stats.uniform.ppf(
        q=false_positive_rate / 2, loc=uniform_lower_bound, scale=uniform_scale
    )
    uniform_upper_quantile: Number = stats.uniform.ppf(
        q=1 - false_positive_rate / 2, loc=uniform_lower_bound, scale=uniform_scale
    )
    bimodal_mean_1: int = 4000
    bimodal_stdev_1: int = 500
    bimodal_mean_2: int = 6000
    bimodal_stdev_2: int = 500
    # 10,000 samples taken from a bimodal mixture of equally weighted normal distributions.
    # This approximation is being used due to the lack of functionality in scipy.stats regarding a
    # percent point functipn (ppf) for gaussian mixtures.
    # For a look at a similar statistical representation and implementation in R see:
    # https://stats.stackexchange.com/questions/390931/compute-quantile-function-from-a-mixture-of-normal-distribution
    bimodal_approximation = np.concatenate(
        [
            stats.norm.rvs(
                loc=bimodal_mean_1,
                scale=bimodal_stdev_1,
                size=5000,
                random_state=RANDOM_STATE,
            ),
            stats.norm.rvs(
                loc=bimodal_mean_2,
                scale=bimodal_stdev_2,
                size=5000,
                random_state=RANDOM_STATE,
            ),
        ]
    )
    bimodal_lower_quantile = np.quantile(
        a=bimodal_approximation, q=false_positive_rate / 2
    )
    bimodal_upper_quantile = np.quantile(
        a=bimodal_approximation, q=1 - false_positive_rate / 2
    )
    exponential_shape: float = 1.5
    exponential_lower_bound: int = 5000
    exponential_scale: int = 1000
    exponential_lower_quantile: Number = stats.gamma.ppf(
        q=false_positive_rate / 2,
        a=exponential_shape,
        loc=exponential_lower_bound,
        scale=exponential_scale,
    )
    exponential_upper_quantile: Number = stats.gamma.ppf(
        q=1 - false_positive_rate / 2,
        a=exponential_shape,
        loc=exponential_lower_bound,
        scale=exponential_scale,
    )
    return {
        "normal": {
            "mean": normal_mean,
            "stdev": normal_stdev,
            "lower_quantile": normal_lower_quantile,
            "upper_quantile": normal_upper_quantile,
        },
        "uniform": {
            "lower_bound": uniform_lower_bound,
            "scale": uniform_scale,
            "lower_quantile": uniform_lower_quantile,
            "upper_quantile": uniform_upper_quantile,
        },
        "bimodal": {
            "mean_1": bimodal_mean_1,
            "stdev_1": bimodal_stdev_1,
            "mean_2": bimodal_mean_2,
            "stdev_2": bimodal_stdev_2,
            "lower_quantile": bimodal_lower_quantile,
            "upper_quantile": bimodal_upper_quantile,
        },
        "exponential": {
            "shape": exponential_shape,
            "lower_bound": exponential_lower_bound,
            "scale": exponential_scale,
            "lower_quantile": exponential_lower_quantile,
            "upper_quantile": exponential_upper_quantile,
        },
    }