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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 cluster(self, n, embed_dim=None, algo=mds.CLASSICAL, method=methods.KMEANS):
 """ Cluster the embedded coordinates using multidimensional scaling Parameters n: int The number of clusters to return embed_dim int The dimensionality of the underlying coordinates Defaults to same value as n method: enum value (methods.KMEANS | methods.GMM) The clustering method to use Returns ------- Partition: Partition object describing the data partition """

        if n == 1:
            return Partition([1] * len(self.get_dm(False)))

        if embed_dim is None:
            embed_dim = n

        if algo == mds.CLASSICAL:
            self._coords = self.dm.embedding(embed_dim, 'cmds')
        elif algo == mds.METRIC:
            self._coords = self.dm.embedding(embed_dim, 'mmds')
        else:
            raise OptionError(algo, list(mds.reverse.values()))

        if method == methods.KMEANS:
            p = self.kmeans(n, self._coords.values)
        elif method == methods.GMM:
            p = self.gmm(n, self._coords.values)
        elif method == methods.WARD:
            linkmat = fastcluster.linkage(self._coords.values, 'ward')
            p = _hclust(linkmat, n)
        else:
            raise OptionError(method, list(methods.reverse.values()))
        #if self._verbosity > 0:
        #    print('Using clustering method: {}'.format(methods.reverse[method]))
        return p