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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 base_communities(adjacency_matrix):
 """ Forms the community indicator normalized feature matrix for any graph. 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. """

    number_of_nodes = adjacency_matrix.shape[0]

    # X = A + I
    adjacency_matrix = adjacency_matrix.tocsr()
    adjacency_matrix = adjacency_matrix.transpose()
    features = sparse.csr_matrix(sparse.eye(number_of_nodes, number_of_nodes)) + adjacency_matrix.tocsr()
    features = features.tocsr()
    features.data = np.ones_like(features.data)

    return features