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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 beta_diversity(self, metric="braycurtis", rank="auto"):
 """Calculate the diversity between two communities. Parameters metric : {'jaccard', 'braycurtis', 'cityblock'} The distance metric to calculate. rank : {'auto', 'kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species'}, optional Analysis will be restricted to abundances of taxa at the specified level. Returns ------- skbio.stats.distance.DistanceMatrix, a distance matrix. """

        if metric not in ("jaccard", "braycurtis", "cityblock"):
            raise OneCodexException(
                "For beta diversity, metric must be one of: jaccard, braycurtis, cityblock"
            )

        # needs read counts, not relative abundances
        if self._guess_normalized():
            raise OneCodexException("Beta diversity requires unnormalized read counts.")

        df = self.to_df(rank=rank, normalize=False)

        counts = []
        for c_id in df.index:
            counts.append(df.loc[c_id].tolist())

        return skbio.diversity.beta_diversity(metric, counts, df.index.tolist())