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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 solve(self):
 r""" This method solves the sparse linear system, converts the solution vector from a PETSc.Vec instance to a numpy array, and finally destroys all the petsc objects to free memory. Parameters solver_type : string, optional Default is the iterative solver 'cg' based on the Conjugate Gradient method. preconditioner_type : string, optional Default is the 'jacobi' preconditioner, i.e., diagonal scaling preconditioning. The preconditioner is used with iterative solvers. When a direct solver is used, this parameter is ignored. factorization_type : string, optional The factorization type used with the direct solver. Default is 'lu'. This parameter is ignored when an iterative solver is used. Returns ------- Returns a numpy array corresponding to the solution of the linear sparse system Ax = b. Notes ----- Certain combinations of iterative solvers and precondioners or direct solvers and factorization types are not supported. The summary table of the different possibilities can be found here: https://www.mcs.anl.gov/petsc/documentation/linearsolvertable.html """

        self._initialize_A()
        self._create_solver()
        self._initialize_b_x()

        # PETSc
        self.ksp.setOperators(self.petsc_A)
        self.ksp.setFromOptions()

        self.ksp.solve(self.petsc_b, self.petsc_x)

        # Convert solution vector from PETSc.Vec instance
        # to a numpy array
        self.solution = PETSc.Vec.getArray(self.petsc_x)

        # Destroy petsc solver, coefficients matrix, rhs, and solution vectors
        PETSc.KSP.destroy(self.ksp)
        PETSc.Mat.destroy(self.petsc_A)
        PETSc.Vec.destroy(self.petsc_b)
        PETSc.Vec.destroy(self.petsc_x)

        return(self.solution)