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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 replicator_eigenmaps(adjacency_matrix, k):
 """ Performs spectral graph embedding on the centrality reweighted adjacency matrix Inputs: - A in R^(nxn):
 Adjacency matrix of an undirected network represented as a scipy.sparse.coo_matrix - k: The number of social dimensions/eigenvectors to extract - max_iter: The maximum number of iterations for the iterative eigensolution method Outputs: - S in R^(nxk):
 The social dimensions represented as a numpy.array matrix """

    number_of_nodes = adjacency_matrix.shape[0]

    max_eigenvalue = spla.eigsh(adjacency_matrix,
                                k=1,
                                which='LM',
                                return_eigenvectors=False)

    # Calculate Replicator matrix
    eye_matrix = sparse.eye(number_of_nodes, number_of_nodes, dtype=np.float64)
    eye_matrix = eye_matrix.tocsr()
    eye_matrix.data = eye_matrix.data*max_eigenvalue
    replicator = eye_matrix - adjacency_matrix

    # Calculate bottom k+1 eigenvalues and eigenvectors of normalised Laplacian
    try:
        eigenvalues, eigenvectors = spla.eigsh(replicator,
                                               k=k+1,
                                               which='SM',
                                               return_eigenvectors=True)
    except spla.ArpackNoConvergence as e:
        print("ARPACK has not converged.")
        eigenvalue = e.eigenvalues
        eigenvectors = e.eigenvectors

    eigenvectors = eigenvectors[:, 1:]

    return eigenvectors