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

self.rsquared, self.rsquared_within, self.rsquared_between,
                       self.rsquared_overall, self.f_statistic], 1)
        vals = [[i for i in v] for v in vals.T.values]
        vals[2] = [str(v) for v in vals[2]]
        for i in range(4, len(vals)):
            f = _str
            if i == 9:
                f = pval_format
            vals[i] = [f(v) for v in vals[i]]

        params = self.params
        precision = getattr(self, self._precision)
        params_fmt = []
        params_stub = []
        for i in range(len(params)):
            params_fmt.append([_str(v) for v in params.values[i]])
            precision_fmt = []
            for v in precision.values[i]:
                v_str = _str(v)
                v_str = '({0})'.format(v_str) if v_str.strip() else v_str
                precision_fmt.append(v_str)
            params_fmt.append(precision_fmt)
            params_stub.append(params.index[i])
            params_stub.append(' ')

        vals = table_concat((vals, params_fmt))
        stubs = stub_concat((stubs, params_stub))

        all_effects = []
        for key in self._results:
            res = self._results[key]
            effects = getattr(res, 'included_effects', [])

def _top_right(self):
        f_stat = _str(self.f_statistic.stat)
        if isnan(self.f_statistic.stat):
            f_stat = '      N/A'

        return [('R-squared:', _str(self.rsquared)),
                ('Adj. R-squared:', _str(self.rsquared_adj)),
                ('F-statistic:', f_stat),
                ('P-value (F-stat):', pval_format(self.f_statistic.pval)),
                ('Distribution:', str(self.f_statistic.dist_name)),
                ('R-squared (No Effects):', _str(round(self.absorbed_rsquared, 5))),
                ('Varaibles Absorbed:', _str(self.df_absorbed))
                ]

top_left = [('Dep. Variable:', mod.dependent.vars[0]),
                    ('Estimator:', self.name),
                    ('No. Observations:', self.nobs),
                    ('Date:', self._datetime.strftime('%a, %b %d %Y')),
                    ('Time:', self._datetime.strftime('%H:%M:%S')),
                    ('Cov. Estimator:', self._cov_type),
                    ('', ''),
                    ('Entities:', str(int(self.entity_info['total']))),
                    ('Avg Obs:', _str(self.entity_info['mean'])),
                    ('Min Obs:', _str(self.entity_info['min'])),
                    ('Max Obs:', _str(self.entity_info['max'])),
                    ('', ''),
                    ('Time periods:', str(int(self.time_info['total']))),
                    ('Avg Obs:', _str(self.time_info['mean'])),
                    ('Min Obs:', _str(self.time_info['min'])),
                    ('Max Obs:', _str(self.time_info['max'])),
                    ('', '')]

        is_invalid = np.isfinite(self.f_statistic.stat)
        f_stat = _str(self.f_statistic.stat) if is_invalid else '--'
        f_pval = pval_format(self.f_statistic.pval) if is_invalid else '--'
        f_dist = self.f_statistic.dist_name if is_invalid else '--'

        f_robust = _str(self.f_statistic_robust.stat) if is_invalid else '--'
        f_robust_pval = pval_format(self.f_statistic_robust.pval) if is_invalid else '--'
        f_robust_name = self.f_statistic_robust.dist_name if is_invalid else '--'

        top_right = [('R-squared:', _str(self.rsquared)),
                     ('R-squared (Between):', _str(self.rsquared_between)),
                     ('R-squared (Within):', _str(self.rsquared_within)),
                     ('R-squared (Overall):', _str(self.rsquared_overall)),

def summary(self):
        """:obj:`statsmodels.iolib.summary.Summary` : Summary table of model estimation results

        Supports export to csv, html and latex  using the methods ``summary.as_csv()``,
        ``summary.as_html()`` and ``summary.as_latex()``.
        """

        smry = super(PanelEffectsResults, self).summary

        is_invalid = np.isfinite(self.f_pooled.stat)
        f_pool = _str(self.f_pooled.stat) if is_invalid else '--'
        f_pool_pval = pval_format(self.f_pooled.pval) if is_invalid else '--'
        f_pool_name = self.f_pooled.dist_name if is_invalid else '--'

        extra_text = []
        if is_invalid:
            extra_text.append('F-test for Poolability: {0}'.format(f_pool))
            extra_text.append('P-value: {0}'.format(f_pool_pval))
            extra_text.append('Distribution: {0}'.format(f_pool_name))
            extra_text.append('')

        if self.included_effects:
            effects = ', '.join(self.included_effects)
            extra_text.append('Included effects: ' + effects)

        if self.other_info is not None:
            ncol = self.other_info.shape[1]

title = self.name + ' Estimation Summary'

        top_left = [('No. Test Portfolios:', len(self._portfolio_names)),
                    ('No. Factors:', len(self._factor_names)),
                    ('No. Observations:', self.nobs),
                    ('Date:', self._datetime.strftime('%a, %b %d %Y')),
                    ('Time:', self._datetime.strftime('%H:%M:%S')),
                    ('Cov. Estimator:', self._cov_type),
                    ('', '')]

        j_stat = _str(self.j_statistic.stat)
        j_pval = pval_format(self.j_statistic.pval)
        j_dist = self.j_statistic.dist_name

        top_right = [('R-squared:', _str(self.rsquared)),
                     ('J-statistic:', j_stat),
                     ('P-value', j_pval),
                     ('Distribution:', j_dist),
                     ('', ''),
                     ('', ''),
                     ('', '')]

        stubs = []
        vals = []
        for stub, val in top_left:
            stubs.append(stub)
            vals.append([val])
        table = SimpleTable(vals, txt_fmt=fmt_2cols, title=title, stubs=stubs)

        # create summary table instance
        smry = Summary()

('', '')]

        is_invalid = np.isfinite(self.f_statistic.stat)
        f_stat = _str(self.f_statistic.stat) if is_invalid else '--'
        f_pval = pval_format(self.f_statistic.pval) if is_invalid else '--'
        f_dist = self.f_statistic.dist_name if is_invalid else '--'

        f_robust = _str(self.f_statistic_robust.stat) if is_invalid else '--'
        f_robust_pval = pval_format(self.f_statistic_robust.pval) if is_invalid else '--'
        f_robust_name = self.f_statistic_robust.dist_name if is_invalid else '--'

        top_right = [('R-squared:', _str(self.rsquared)),
                     ('R-squared (Between):', _str(self.rsquared_between)),
                     ('R-squared (Within):', _str(self.rsquared_within)),
                     ('R-squared (Overall):', _str(self.rsquared_overall)),
                     ('Log-likelihood', _str(self._loglik)),
                     ('', ''),
                     ('F-statistic:', f_stat),
                     ('P-value', f_pval),
                     ('Distribution:', f_dist),
                     ('', ''),
                     ('F-statistic (robust):', f_robust),
                     ('P-value', f_robust_pval),
                     ('Distribution:', f_robust_name),
                     ('', ''),
                     ('', ''),
                     ('', ''),
                     ('', ''),
                     ]

        stubs = []
        vals = []

def _top_right(self):
        f_stat = _str(self.f_statistic.stat)
        if isnan(self.f_statistic.stat):
            f_stat = '      N/A'

        return [('R-squared:', _str(self.rsquared)),
                ('Adj. R-squared:', _str(self.rsquared_adj)),
                ('F-statistic:', f_stat),
                ('P-value (F-stat):', pval_format(self.f_statistic.pval)),
                ('Distribution:', str(self.f_statistic.dist_name)),
                ('R-squared (No Effects):', _str(round(self.absorbed_rsquared, 5))),
                ('Varaibles Absorbed:', _str(self.df_absorbed))
                ]

Supports export to csv, html and latex  using the methods ``summary.as_csv()``,
        ``summary.as_html()`` and ``summary.as_latex()``.
        """

        title = self.name + ' Estimation Summary'

        top_left = [('No. Test Portfolios:', len(self._portfolio_names)),
                    ('No. Factors:', len(self._factor_names)),
                    ('No. Observations:', self.nobs),
                    ('Date:', self._datetime.strftime('%a, %b %d %Y')),
                    ('Time:', self._datetime.strftime('%H:%M:%S')),
                    ('Cov. Estimator:', self._cov_type),
                    ('', '')]

        j_stat = _str(self.j_statistic.stat)
        j_pval = pval_format(self.j_statistic.pval)
        j_dist = self.j_statistic.dist_name

        top_right = [('R-squared:', _str(self.rsquared)),
                     ('J-statistic:', j_stat),
                     ('P-value', j_pval),
                     ('Distribution:', j_dist),
                     ('', ''),
                     ('', ''),
                     ('', '')]

        stubs = []
        vals = []
        for stub, val in top_left:
            stubs.append(stub)
            vals.append([val])

('Time:', self._datetime.strftime('%H:%M:%S')),
                    ('Cov. Estimator:', self._cov_type),
                    ('', ''),
                    ('Entities:', str(int(self.entity_info['total']))),
                    ('Avg Obs:', _str(self.entity_info['mean'])),
                    ('Min Obs:', _str(self.entity_info['min'])),
                    ('Max Obs:', _str(self.entity_info['max'])),
                    ('', ''),
                    ('Time periods:', str(int(self.time_info['total']))),
                    ('Avg Obs:', _str(self.time_info['mean'])),
                    ('Min Obs:', _str(self.time_info['min'])),
                    ('Max Obs:', _str(self.time_info['max'])),
                    ('', '')]

        is_invalid = np.isfinite(self.f_statistic.stat)
        f_stat = _str(self.f_statistic.stat) if is_invalid else '--'
        f_pval = pval_format(self.f_statistic.pval) if is_invalid else '--'
        f_dist = self.f_statistic.dist_name if is_invalid else '--'

        f_robust = _str(self.f_statistic_robust.stat) if is_invalid else '--'
        f_robust_pval = pval_format(self.f_statistic_robust.pval) if is_invalid else '--'
        f_robust_name = self.f_statistic_robust.dist_name if is_invalid else '--'

        top_right = [('R-squared:', _str(self.rsquared)),
                     ('R-squared (Between):', _str(self.rsquared_between)),
                     ('R-squared (Within):', _str(self.rsquared_within)),
                     ('R-squared (Overall):', _str(self.rsquared_overall)),
                     ('Log-likelihood', _str(self._loglik)),
                     ('', ''),
                     ('F-statistic:', f_stat),
                     ('P-value', f_pval),
                     ('Distribution:', f_dist),

extra_text.append('P-value: {0}'.format(f_pool_pval))
            extra_text.append('Distribution: {0}'.format(f_pool_name))
            extra_text.append('')

        if self.included_effects:
            effects = ', '.join(self.included_effects)
            extra_text.append('Included effects: ' + effects)

        if self.other_info is not None:
            ncol = self.other_info.shape[1]
            extra_text.append('Model includes {0} other effects'.format(ncol))
            for c in self.other_info.T:
                col = self.other_info.T[c]
                extra_text.append('Other Effect {0}:'.format(c))
                stats = 'Avg Obs: {0}, Min Obs: {1}, Max Obs: {2}, Groups: {3}'
                stats = stats.format(_str(col['mean']), _str(col['min']), _str(col['max']),
                                     int(col['total']))
                extra_text.append(stats)

        smry.add_extra_txt(extra_text)

        return smry