code: def CLJP(S, color=False):
      
      if not isspmatrix_csr(S):
          raise TypeError('expected csr_matrix')
      S = remove_diagonal(S)
  
      colorid = 0
      if color:
          colorid = 1
  
      T = S.T.tocsr()  # transpose S for efficient column access
      splitting = np.empty(S.shape[0], dtype='intc')
  
      amg_core.cljp_naive_splitting(S.shape[0],
                                    S.indptr, S.indices,
                                    T.indptr, T.indices,
                                    splitting,
                                    colorid)
  
      return splitting
docs: Compute a C/F splitting using the parallel CLJP algorithm.
  
      Parameters
      ----------
      S : csr_matrix
          Strength of connection matrix indicating the strength between nodes i
          and j (S_ij)
      color : bool
          use the CLJP coloring approach
  
      Returns
      -------
      splitting : array
          Array of length of S of ones (coarse) and zeros (fine)
  
      Examples
      --------
      >>> from pyamg.gallery import poisson
      >>> from pyamg.classical.split import CLJP
      >>> S = poisson((7,), format='csr') # 1D mesh with 7 vertices
      >>> splitting = CLJP(S)
  
      See Also
      --------
      MIS, PMIS, CLJPc
  
      References
      ----------
      .. [8] David M. Alber and Luke N. Olson
         "Parallel coarse-grid selection"
         Numerical Linear Algebra with Applications 2007; 14:611-643.