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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 prop_samples(self,prop,return_values=True,conf=0.683):
 """Returns samples of given property, based on MCMC sampling :param prop: Name of desired property. Must be column of ``self.samples``. :param return_values: (optional) If ``True`` (default), then also return (median, lo_err, hi_err) corresponding to desired credible interval. :param conf: (optional) Desired quantile for credible interval. Default = 0.683. :return: :class:`np.ndarray` of desired samples :return: Optionally also return summary statistics (median, lo_err, hi_err), if ``returns_values == True`` (this is default behavior) """

        samples = self.samples[prop].values
        
        if return_values:
            sorted = np.sort(samples)
            med = np.median(samples)
            n = len(samples)
            lo_ind = int(n*(0.5 - conf/2))
            hi_ind = int(n*(0.5 + conf/2))
            lo = med - sorted[lo_ind]
            hi = sorted[hi_ind] - med
            return samples, (med,lo,hi)
        else:
            return samples