code: def correct_transition_matrix(T, reversible=None):
      r
      row_sums = T.sum(axis=1).A1
      max_sum = np.max(row_sums)
      if max_sum == 0.0:
           max_sum = 1.0
      return (T + scipy.sparse.diags(-row_sums+max_sum, 0)) / max_sum
docs: r"""Normalize transition matrix
  
      Fixes a the row normalization of a transition matrix.
      To be used with the reversible estimators to fix an almost coverged
      transition matrix.
  
      Parameters
      ----------
      T : (M, M) ndarray
          matrix to correct
      reversible : boolean
          for future use
  
      Returns
      -------
      (M, M) ndarray
          corrected transition matrix