How to use the nwbwidgets.utils.cmaps.linear_transfer_function function in nwbwidgets

import ipyvolume.pylab as p3

    nrois = len(plane_seg)

    dims = np.array([max(max(plane_seg['voxel_mask'][i][dim]) for i in range(nrois))
                     for dim in ['x', 'y', 'z']]).astype('int') + 1
    fig = p3.figure()
    for icolor, color in enumerate(color_wheel):
        vol = np.zeros(dims)
        sel = np.arange(icolor, nrois, len(color_wheel))
        for isel in sel:
            dat = plane_seg['voxel_mask'][isel]
            vol[tuple(dat['x'].astype('int')),
                tuple(dat['y'].astype('int')),
                tuple(dat['z'].astype('int'))] = 1
        p3.volshow(vol, tf=linear_transfer_function(color, max_opacity=.3))
    return fig

def show_image(index=0):
            p3.figure()
            p3.volshow(indexed_timeseries.data[index], tf=linear_transfer_function([0, 0, 0], max_opacity=.3))
            output.clear_output(wait=True)
            with output:
                p3.show()
    else:

def show_grayscale_volume(vol: GrayscaleVolume, neurodata_vis_spec: dict):
    import ipyvolume.pylab as p3

    fig = p3.figure()
    p3.volshow(vol.data, tf=linear_transfer_function([0, 0, 0], max_opacity=.1))
    return fig