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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def confidence_interval(self, alpha=0.9):
 """Compute confidence interval on accumulated metric values Parameters alpha : float, optional Probability that the returned confidence interval contains the true metric value. Returns ------- (center, (lower, upper)) with center the mean of the conditional pdf of the metric value and (lower, upper) is a confidence interval centered on the median, containing the estimate to a probability alpha. See Also: --------- scipy.stats.bayes_mvs """

        m, _, _ = scipy.stats.bayes_mvs(
            [r[self.metric_name_] for _, r in self.results_], alpha=alpha)
        return m