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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_functional(func, return_gradient=False):
 """Wrap ``op`` as a function operating on linear arrays. This is intended to be used with the `scipy solvers <https://docs.scipy.org/doc/scipy/reference/optimize.html>`_. Parameters func : `Functional`. A functional that should be wrapped return_gradient : bool, optional ``True`` if the gradient of the functional should also be returned, ``False`` otherwise. Returns ------- function : ``callable`` The wrapped functional. gradient : ``callable``, optional The wrapped gradient. Only returned if ``return_gradient`` is true. Examples -------- Wrap functional and solve simple problem (here toy problem ``min_x ||x||^2``):
 True The gradient (jacobian) can also be provided: True Notes ----- If the data representation of ``op``'s domain is of type `NumpyTensorSpace`, this incurs no significant overhead. If the space type is ``CudaFn`` or some other nonlocal type, the overhead is significant. """

    def func_call(arr):
        return func(np.asarray(arr).reshape(func.domain.shape))

    if return_gradient:
        def func_gradient_call(arr):
            return np.asarray(
                func.gradient(np.asarray(arr).reshape(func.domain.shape)))

        return func_call, func_gradient_call
    else:
        return func_call