def METHOD_NAME(self):
    double_f = lambda model: model.x_0_0 * 4
    triple_f = lambda model: model.x_0_0 * 15
    model = self.model.copy()
    model.x_0_1.tied = double_f
    model.x_0_2.tied = triple_f
    model.amplitude_0 = self.t0[0]
    # model.bounds = {}
    t0 = np.array(
        [
            self.amplitude_0,
            self.x_0_0,
            self.fwhm_0,
            self.amplitude_1,
            self.fwhm_1,
            self.amplitude_2,
            self.fwhm_2,
            self.whitenoise,
        ]
    )
    parest, res = fit_powerspectrum(self.ps, model, np.random.normal(t0, t0 / 10))
    true_pars = [
        self.amplitude_0,
        self.x_0_0,
        self.fwhm_0,
        self.amplitude_1,
        self.fwhm_1,
        self.amplitude_2,
        self.fwhm_2,
        self.whitenoise,
    ]
    assert np.all(np.isclose(true_pars, res.p_opt, rtol=0.5))