How to use the gwcs.coordinate_frames function in gwcs

def tabular_wcs(xarray):

    coordinateframe = cf.CoordinateFrame(naxes=1, axes_type=('SPECTRAL',),
                                         axes_order=(0,))
    specframe = cf.SpectralFrame(unit=xarray.unit, axes_order=(0,))
    transform = Tabular1D(np.arange(len(xarray)), xarray.value)

    tabular_gwcs = gwcs.wcs.WCS([(coordinateframe, transform), (specframe, None)])

    return tabular_gwcs

def _get_frame_index(self, frame):
        """
        Return the index in the pipeline where this frame is locate.
        """
        if isinstance(frame, coordinate_frames.CoordinateFrame):
            frame = frame.name
        frame_names = [getattr(item[0], "name", item[0]) for item in self._pipeline]
        return frame_names.index(frame)

def gwcs_from_array(array):
    """
    Create a new WCS from provided tabular data. This defaults to being
    a GWCS object.
    """
    orig_array = u.Quantity(array)

    # TODO: Input arrays must be strictly ascending. This is not always the
    #  case for a spectral axis (e.g. when in frequency space). Thus, we
    #  convert to wavelength to create the wcs.
    if orig_array.unit.physical_type != 'length' and \
            orig_array.unit.is_equivalent(u.AA, equivalencies=u.spectral()):
        array = orig_array.to(u.AA, equivalencies=u.spectral())

    coord_frame = cf.CoordinateFrame(naxes=1,
                                     axes_type=('SPECTRAL',),
                                     axes_order=(0,))
    spec_frame = cf.SpectralFrame(unit=array.unit, axes_order=(0,))

    # In order for the world_to_pixel transformation to automatically convert
    # input units, the equivalencies in the look up table have to be extended
    # with spectral unit information.
    SpectralTabular1D = type("SpectralTabular1D", (Tabular1D,),
                             {'input_units_equivalencies': {'x0': u.spectral()}})

    forward_transform = SpectralTabular1D(np.arange(len(array)),
                                          lookup_table=array)
    forward_transform.inverse = SpectralTabular1D(
        array, lookup_table=np.arange(len(array)))

    class SpectralGWCS(GWCS):