def METHOD_NAME(self, Ts, E, entries, weights, Xs):
    # detect constant columns
    constant_col = (Xs.var(0) < 1e-8).idxmax()
    uni_model = GeneralizedGammaFitter()
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        if utils.CensoringType.is_right_censoring(self):
            uni_model.fit_right_censoring(Ts[0], event_observed=E, entry=entries, weights=weights)
        elif utils.CensoringType.is_interval_censoring(self):
            uni_model.fit_interval_censoring(Ts[0], Ts[1], entry=entries, weights=weights)
        elif utils.CensoringType.is_left_censoring(self):
            uni_model.fit_left_censoring(Ts[1], event_observed=E, entry=entries, weights=weights)
        # we may use these later in log_likelihood_test()
        self._ll_null_ = uni_model.log_likelihood_
        assert self._ll_null_dof == 3
        default_point = super(GeneralizedGammaRegressionFitter, self).METHOD_NAME(Ts, E, entries, weights, Xs)
        nested_point = {}
        nested_point["mu_"] = np.array([0.0] * (len(Xs["mu_"].columns)))
        if constant_col in Xs["mu_"].columns:
            nested_point["mu_"][Xs["mu_"].columns.index(constant_col)] = uni_model.mu_
        nested_point["sigma_"] = np.array([0.0] * (len(Xs["sigma_"].columns)))
        if constant_col in Xs["sigma_"].columns:
            nested_point["sigma_"][Xs["sigma_"].columns.index(constant_col)] = uni_model.ln_sigma_
        # this needs to be non-zero because we divide by it
        nested_point["lambda_"] = np.array([0.01] * (len(Xs["lambda_"].columns)))
        if constant_col in Xs["lambda_"].columns:
            nested_point["lambda_"][Xs["lambda_"].columns.index(constant_col)] = uni_model.lambda_
        return [nested_point, default_point]