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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 as_scipy_operator(op):
 """Wrap ``op`` as a ``scipy.sparse.linalg.LinearOperator``. This is intended to be used with the scipy sparse linear solvers. Parameters op : `Operator` A linear operator that should be wrapped Returns ------- ``scipy.sparse.linalg.LinearOperator`` : linear_op The wrapped operator, has attributes ``matvec`` which calls ``op``, and ``rmatvec`` which calls ``op.adjoint``. Examples -------- Wrap operator and solve simple problem (here toy problem ``Ix = b``) array([ 0., 1., 0.]) Notes ----- If the data representation of ``op``'s domain and range is of type `NumpyTensorSpace` this incurs no significant overhead. If the space type is ``CudaFn`` or some other nonlocal type, the overhead is significant. """

    # Lazy import to improve `import odl` time
    import scipy.sparse

    if not op.is_linear:
        raise ValueError('`op` needs to be linear')

    dtype = op.domain.dtype
    if op.range.dtype != dtype:
        raise ValueError('dtypes of ``op.domain`` and ``op.range`` needs to '
                         'match')

    shape = (native(op.range.size), native(op.domain.size))

    def matvec(v):
        return (op(v.reshape(op.domain.shape))).asarray().ravel()

    def rmatvec(v):
        return (op.adjoint(v.reshape(op.range.shape))).asarray().ravel()

    return scipy.sparse.linalg.LinearOperator(shape=shape,
                                              matvec=matvec,
                                              rmatvec=rmatvec,
                                              dtype=dtype)