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Complete this python program:
def factorized(A):
assistant
#Here is the Python code you requested.

    """Return a function for solving a sparse linear system, with A pre-factorized.
    Parameters
    A : (N, N) array_like
    Input.
    Returns
    solve : callable
    To solve the linear system of equations given in `A`, the `solve`
    callable should be passed an ndarray of shape (N,).
    Examples
    >>> from scipy.sparse.linalg import factorized
    >>> A = np.array([[ 3. ,  2. , -1. ],
    ...               [ 2. , -2. ,  4. ],
    ...               [-1. ,  0.5, -1. ]])
    >>> solve = factorized(A) # Makes LU decomposition.
    >>> rhs1 = np.array([1, -2, 0])
    >>> solve(rhs1) # Uses the LU factors.
    array([ 1., -2., -2.])
    """
    if useUmfpack:
        if noScikit:
            raise RuntimeError('Scikits.umfpack    not    installed.')
        if (not isspmatrix_csc(A)):
            A = csc_matrix(A)
            warn('splu    requires    CSC    matrix    format', SparseEfficiencyWarning)
        A = A.asfptype()
        if (A.dtype.char not in 'dD'):
            raise ValueError('convert    matrix    data    to    double,    please,    using    .astype(),    or    set    linsolve.useUmfpack    =    False')
        umf = umfpack.UmfpackContext(_get_umf_family(A))
        umf.numeric(A)
        def solve(b):
            return umf.solve(umfpack.UMFPACK_A, A, b, autoTranspose=True)
        return solve
    else:
        return splu(A).solve