code: def aspreconditioner(self, cycle='V'):
          
          from scipy.sparse.linalg import LinearOperator
  
          shape = self.levels[0].A.shape
          dtype = self.levels[0].A.dtype
  
          def matvec(b):
              return self.solve(b, maxiter=1, cycle=cycle, tol=1e-12)
  
          return LinearOperator(shape, matvec, dtype=dtype)
docs: Create a preconditioner using this multigrid cycle.
  
          Parameters
          ----------
          cycle : {'V','W','F','AMLI'}
              Type of multigrid cycle to perform in each iteration.
  
          Returns
          -------
          precond : LinearOperator
              Preconditioner suitable for the iterative solvers in defined in
              the scipy.sparse.linalg module (e.g. cg, gmres) and any other
              solver that uses the LinearOperator interface.  Refer to the
              LinearOperator documentation in scipy.sparse.linalg
  
          See Also
          --------
          multilevel_solver.solve, scipy.sparse.linalg.LinearOperator
  
          Examples
          --------
          >>> from pyamg.aggregation import smoothed_aggregation_solver
          >>> from pyamg.gallery import poisson
          >>> from scipy.sparse.linalg import cg
          >>> import scipy as sp
          >>> A = poisson((100, 100), format='csr')          # matrix
          >>> b = sp.rand(A.shape[0])                        # random RHS
          >>> ml = smoothed_aggregation_solver(A)            # AMG solver
          >>> M = ml.aspreconditioner(cycle='V')             # preconditioner
          >>> x, info = cg(A, b, tol=1e-8, maxiter=30, M=M)  # solve with CG