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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, algorithm=None):
 """Cluster the codebook. The clusters of the data instances can be assigned based on the BMUs. The method populates the class variable Somoclu.clusters. If viewing methods are called after clustering, but without colors for best matching units, colors will be automatically assigned based on cluster membership. :param algorithm: Optional parameter to specify a scikit-learn clustering algorithm. The default is K-means with eight clusters. :type filename: sklearn.base.ClusterMixin. """


        import sklearn.base
        if algorithm is None:
            import sklearn.cluster
            algorithm = sklearn.cluster.KMeans()
        elif not isinstance(algorithm, sklearn.base.ClusterMixin):
            raise Exception("Cannot use algorithm of type " + type(algorithm))
        original_shape = self.codebook.shape
        self.codebook.shape = (self._n_columns * self._n_rows, self.n_dim)
        linear_clusters = algorithm.fit_predict(self.codebook)
        self.codebook.shape = original_shape
        self.clusters = np.zeros((self._n_rows, self._n_columns), dtype=int)
        for i, c in enumerate(linear_clusters):
            self.clusters[i // self._n_columns, i % self._n_columns] = c