<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def weingarten_image_curvature(image, sigma=1.0, opt='mean'):
 """ Uses the weingarten map to estimate image mean or gaussian curvature ANTsR function: `weingartenImageCurvature` Arguments --------- image : ANTsImage image from which curvature is calculated sigma : scalar smoothing parameter opt : string mean by default, otherwise `gaussian` or `characterize` Returns ------- ANTsImage Example ------- """

    if image.dimension not in {2,3}:
        raise ValueError('image must be 2D or 3D')

    if image.dimension == 2:
        d = image.shape
        temp = np.zeros(list(d)+[10])
        for k in range(1,7):
            voxvals = image[:d[0],:d[1]]
            temp[:d[0],:d[1],k] = voxvals
        temp = core.from_numpy(temp)
        myspc = image.spacing
        myspc = list(myspc) + [min(myspc)]
        temp.set_spacing(myspc)
        temp = temp.clone('float')
    else:
        temp = image.clone('float')

    optnum = 0
    if opt == 'gaussian': 
        optnum = 6
    if opt == 'characterize':
        optnum = 5

    libfn = utils.get_lib_fn('weingartenImageCurvature')
    mykout = libfn(temp.pointer, sigma, optnum)
    mykout = iio.ANTsImage(pixeltype=image.pixeltype, dimension=3,
                            components=image.components, pointer=mykout)
    if image.dimension == 3:
        return mykout
    elif image.dimension == 2:
        subarr = core.from_numpy(mykout.numpy()[:,:,4])
        return core.copy_image_info(image, subarr)