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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 sample(self, means, covs, idxs, epsilons=None):
 """ Sample the epsilons and apply the corrections to the means. This method is called only if there are nonzero covs. :param means: array of E' loss ratios :param covs: array of E' floats :param idxs: array of E booleans with E >= E' :param epsilons: array of E floats (or None) :returns: array of E' loss ratios """

        if epsilons is None:
            return means
        self.set_distribution(epsilons)
        res = self.distribution.sample(means, covs, means * covs, idxs)
        return res