How to use the linearmodels.utility.InvalidTestStatistic function in linearmodels

def __init__(self, reason, *, name=None):
        self._reason = reason
        super(InvalidTestStatistic, self).__init__(NaN, NaN, df=1, df_denom=1, name=name)
        self.dist_name = 'None'

def _f_statistic(self, weps, y, x, root_w, df_resid):
        """Compute model F-statistic"""
        weps_const = y
        num_df = x.shape[1]
        name = 'Model F-statistic (homoskedastic)'
        if self.has_constant:
            if num_df == 1:
                return InvalidTestStatistic('Model contains only a constant',
                                            name=name)

            num_df -= 1
            weps_const = y - float((root_w.T @ y) / (root_w.T @ root_w))

        resid_ss = weps.T @ weps
        num = float(weps_const.T @ weps_const - resid_ss)
        denom = resid_ss
        denom_df = df_resid
        stat = float((num / num_df) / (denom / denom_df))
        return WaldTestStatistic(stat, null='All parameters ex. constant not zero',
                                 df=num_df, df_denom=denom_df, name=name)

def invalid_f():
            return InvalidTestStatistic('Model contains only a constant',
                                        name=name)

def __init__(self, reason, *, name=None):
        self._reason = reason
        super(InvalidTestStatistic, self).__init__(np.NaN, np.NaN, df=1, df_denom=1, name=name)
        self.dist_name = 'None'

cov = stats.cov
        k = cov.shape[0]
        sel = list(range(k))
        if stats.has_constant:
            sel.pop(stats.constant_loc)
        cov = cov[sel][:, sel]
        params = stats.params[sel]
        df = params.shape[0]
        nobs = stats.nobs
        null = 'All parameters ex. constant are zero'
        name = 'Equation F-statistic'
        try:
            stat = float(params.T @ inv(cov) @ params)

        except np.linalg.LinAlgError:
            return InvalidTestStatistic('Covariance is singular, possibly due '
                                        'to constraints.', name=name)

        if debiased:
            total_reg = np.sum(list(map(lambda s: s.shape[1], self._wx)))
            df_denom = len(self._wx) * nobs - total_reg
            wald = WaldTestStatistic(stat / df, null, df, df_denom=df_denom,
                                     name=name)
        else:
            return WaldTestStatistic(stat, null=null, df=df, name=name)

        return wald