def METHOD_NAME(
    labels: List[int],
    scores: List[float],
    p_target: float = 0.01,
    c_miss: int = 1,
    c_fa: int = 1,
):
    """Compute MinDCF.
    Computes the minimum of the detection cost function.  The comments refer to
    equations in Section 3 of the NIST 2016 Speaker Recognition Evaluation Plan.
    Args:
        scores (List[float]): List of hypotheses.
        labels (List[int]): List of references.
        p (float): The prior probability of positive class.
        c_miss (int): The cost of miss.
        c_fa (int): The cost of false alarm.
    Returns:
        min_dcf (float): The calculated min_dcf.
        min_c_det_threshold (float): The treshold to calculate min_dcf.
    """
    fpr, tpr, thresholds = roc_curve(labels, scores, pos_label=1)
    fnr = 1.0 - tpr
    min_c_det = float("inf")
    min_c_det_threshold = thresholds[0]
    for i in range(0, len(fnr)):
        c_det = c_miss * fnr[i] * p_target + c_fa * fpr[i] * (1 - p_target)
        if c_det < min_c_det:
            min_c_det = c_det
            min_c_det_threshold = thresholds[i]
    c_def = min(c_miss * p_target, c_fa * (1 - p_target))
    min_dcf = min_c_det / c_def
    return min_dcf, min_c_det_threshold