def METHOD_NAME(self, rdp_scale):
    """
    Args:
        rdp_scale: the ratio of the scale parameter and L1 sensitivity
        alpha: The order of the Renyi Divergence
    Return: Evaluation of the RDP's epsilon
    """
    alpha = 1.0 * self.alpha
    if np.isinf(alpha):
        return 1 / rdp_scale
    elif alpha == 1:
        # KL-divergence
        return 1 / rdp_scale + np.exp(-1 / rdp_scale) - 1
    elif alpha > 1:  # alpha > 1
        return stable_logsumexp_two((alpha - 1.0) / rdp_scale + np.log(alpha / (2.0 * alpha - 1)),
                                    -1.0 * alpha / rdp_scale + np.log((alpha - 1.0) / (2.0 * alpha - 1))) / (
                           alpha - 1)
    elif alpha == 0.5:
        return -2 * (-1.0 / (2 * rdp_scale) + np.log(
            1 + 1.0 / (2 * rdp_scale)))  # -2*np.log(np.exp(-1.0/(2*b))*(1+1.0/(2*b)))
    else:
        return np.log(
            alpha / (2.0 * alpha - 1) * np.exp((alpha - 1.0) / rdp_scale) + (alpha - 1.0) / (
                        2.0 * alpha - 1) * np.exp(
                -1.0 * alpha / rdp_scale)) / (alpha - 1)
        # Handling the case when alpha = 1/2?