def _lower_confidence_bound(self, n_class_samples: int, n_total_samples: int) -> float:
        """
        Uses Clopper-Pearson method to return a (1-alpha) lower confidence bound on bernoulli proportion

        :param n_class_samples: Number of samples of a specific class.
        :param n_total_samples: Number of samples for certification.
        :return: Lower bound on the binomial proportion w.p. (1-alpha) over samples.
        """
        from statsmodels.stats.proportion import proportion_confint

        return proportion_confint(n_class_samples, n_total_samples, alpha=2 * self.alpha, method="beta")[0]

        Uses Clopper-Pearson method to return a (1-alpha) lower confidence bound on bernoulli proportion

        :param n_class_samples: Number of samples of a specific class.
        :param n_total_samples: Number of samples for certification.
        :return: Lower bound on the binomial proportion w.p. (1-alpha) over samples.
        
art/estimators/certification/randomized_smoothing/randomized_smoothing.py