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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 louvain(adjacency_matrix):
 """ Performs community embedding using the LOUVAIN method. Introduced in: Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. Inputs: - A in R^(nxn):
 Adjacency matrix of an undirected network represented as a SciPy Sparse COOrdinate matrix. Outputs: - X in R^(nxC_n):
 The latent space embedding represented as a SciPy Sparse COOrdinate matrix. """

    # Convert to networkx undirected graph.
    adjacency_matrix = nx.from_scipy_sparse_matrix(adjacency_matrix, create_using=nx.Graph())

    # Call LOUVAIN algorithm to calculate a hierarchy of communities.
    tree = community.generate_dendogram(adjacency_matrix, part_init=None)

    # Embed communities
    row = list()
    col = list()
    append_row = row.append
    append_col = col.append

    community_counter = 0
    for i in range(len(tree)):
        partition = community.partition_at_level(tree, i)
        for n, c in partition.items():
            append_row(n)
            append_col(community_counter + c)

        community_counter += max(partition.values()) + 1

    row = np.array(row)
    col = np.array(col)
    data = np.ones(row.size, dtype=np.float64)

    louvain_features = sparse.coo_matrix((data, (row, col)), shape=(len(partition.keys()), community_counter),
                                         dtype=np.float64)

    return louvain_features