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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 arcte_with_lazy_pagerank(adjacency_matrix, rho, epsilon, number_of_threads=None):
 """ Extracts local community features for all graph nodes based on the partitioning of node-centric similarity vectors. Inputs: - A in R^(nxn):
 Adjacency matrix of an undirected network represented as a SciPy Sparse COOrdinate matrix. - rho: Restart probability - epsilon: Approximation threshold Outputs: - X in R^(nxC_n):
 The latent space embedding represented as a SciPy Sparse COOrdinate matrix. """

    adjacency_matrix = sparse.csr_matrix(adjacency_matrix)
    number_of_nodes = adjacency_matrix.shape[0]

    if number_of_threads is None:
        number_of_threads = get_threads_number()
    if number_of_threads == 1:
        # Calculate natural random walk transition probability matrix.
        rw_transition, out_degree, in_degree = get_natural_random_walk_matrix(adjacency_matrix, make_shared=False)

        a = adjacency_matrix.copy()
        a.data = np.ones_like(a.data)
        edge_count_vector = np.squeeze(np.asarray(a.sum(axis=0), dtype=np.int64))

        iterate_nodes = np.where(edge_count_vector != 0)[0]
        argsort_indices = np.argsort(edge_count_vector[iterate_nodes])
        iterate_nodes = iterate_nodes[argsort_indices][::-1]
        iterate_nodes = iterate_nodes[np.where(edge_count_vector[iterate_nodes] > 1.0)[0]]

        # iterate_nodes = np.where(out_degree != 0)[0]
        # argsort_indices = np.argsort(out_degree[iterate_nodes])
        # iterate_nodes = iterate_nodes[argsort_indices][::-1]
        # iterate_nodes = iterate_nodes[np.where(out_degree[iterate_nodes] > 1.0)[0]]

        local_features = arcte_with_lazy_pagerank_worker(iterate_nodes,
                                                         rw_transition.indices,
                                                         rw_transition.indptr,
                                                         rw_transition.data,
                                                         out_degree,
                                                         in_degree,
                                                         rho,
                                                         epsilon)
    else:
        # Calculate natural random walk transition probability matrix.
        rw_transition, out_degree, in_degree = get_natural_random_walk_matrix(adjacency_matrix, make_shared=True)

        a = adjacency_matrix.copy()
        a.data = np.ones_like(a.data)
        edge_count_vector = np.squeeze(np.asarray(a.sum(axis=0), dtype=np.int64))

        iterate_nodes = np.where(edge_count_vector != 0)[0]
        argsort_indices = np.argsort(edge_count_vector[iterate_nodes])
        iterate_nodes = iterate_nodes[argsort_indices][::-1]
        iterate_nodes = iterate_nodes[np.where(edge_count_vector[iterate_nodes] > 1.0)[0]]

        # iterate_nodes = np.where(out_degree != 0)[0]
        # argsort_indices = np.argsort(out_degree[iterate_nodes])
        # iterate_nodes = iterate_nodes[argsort_indices][::-1]
        # iterate_nodes = iterate_nodes[np.where(out_degree[iterate_nodes] > 1.0)[0]]

        pool = mp.Pool(number_of_threads)
        node_chunks = list(parallel_chunks(iterate_nodes, number_of_threads))
        node_count = 0
        for chunk in node_chunks:
            node_count += len(list(chunk))
        results = list()
        for chunk_no in range(len(pool._pool)):
            pool.apply_async(arcte_with_lazy_pagerank_worker,
                             args=(node_chunks[chunk_no],
                                   rw_transition.indices,
                                   rw_transition.indptr,
                                   rw_transition.data,
                                   out_degree,
                                   in_degree,
                                   rho,
                                   epsilon),
                             callback=results.append)
        pool.close()
        pool.join()
        # local_features = sparse.hstack(results)
        local_features = results[0]
        for additive_features in results[1:]:
            local_features += additive_features
        local_features = sparse.csr_matrix(local_features)

    # Form base community feature matrix.
    identity_matrix = sparse.csr_matrix(sparse.eye(number_of_nodes, number_of_nodes, dtype=np.float64))
    adjacency_matrix_ones = adjacency_matrix
    adjacency_matrix_ones.data = np.ones_like(adjacency_matrix.data)
    base_community_features = identity_matrix + adjacency_matrix_ones

    # Stack horizontally matrices to form feature matrix.
    try:
        features = sparse.hstack([base_community_features, local_features]).tocsr()
    except ValueError as e:
        print("Failure with horizontal feature stacking.")
        features = base_community_features

    return features