def METHOD_NAME(self):
    # First get the toas for jump
    all_backends = list(set(self.toa_backends))
    dm_jump_value = self.model.jump_dm(self.toas)
    dm_jump_map = {dmj.key_value[0]: dmj for dmj in self.dm_jump_params}
    for be in all_backends:
        assert all(
            dm_jump_value[self.toa_backends == be] == -dm_jump_map[be].quantity
        )
    r = WidebandTOAResiduals(
        self.toas, self.model, dm_resid_args=dict(subtract_mean=False)
    )
    model2 = deepcopy(self.model)
    for i, be in enumerate(all_backends):
        dm_jump_map[be].value += i + 1
    r2 = WidebandTOAResiduals(
        self.toas, model2, dm_resid_args=dict(subtract_mean=False)
    )
    delta_dm = r2.dm.resids_value - r.dm.resids_value
    delta_dm_intended = np.zeros_like(delta_dm)
    for i, be in enumerate(all_backends):
        delta_dm_intended[self.toa_backends == be] = -(i + 1)
    assert np.allclose(delta_dm, delta_dm_intended)