How to use the statsmodels.base.wrapper.populate_wrapper function in statsmodels

pr = np.r_[pr, 1 - pr.sum()]

            ax.plot(self.model.endog_values, pr, 'o-')

        ax.set_xlabel("Response value")
        ax.set_ylabel("Probability")
        ax.set_xticks(self.model.endog_values)
        ax.set_xticklabels(self.model.endog_values)
        ax.set_ylim(0, 1)

        return fig


class NominalGEEResultsWrapper(GEEResultsWrapper):
    pass
wrap.populate_wrapper(NominalGEEResultsWrapper, NominalGEEResults)  # noqa:E305


class _MultinomialLogit(Link):
    """
    The multinomial logit transform, only for use with GEE.

    Notes
    -----
    The data are assumed coded as binary indicators, where each
    observed multinomial value y is coded as I(y == S[0]), ..., I(y ==
    S[-1]), where S is the set of possible response labels, excluding
    the largest one.  Thererefore functions in this class should only
    be called using vector argument whose length is a multiple of |S|
    = ncut, which is an argument to be provided when initializing the
    class.

stubs=stubs)

            smry.tables.append(roots_table)
        return smry


class AutoRegResultsWrapper(wrap.ResultsWrapper):
    _attrs = {}
    _wrap_attrs = wrap.union_dicts(
        tsa_model.TimeSeriesResultsWrapper._wrap_attrs, _attrs)
    _methods = {}
    _wrap_methods = wrap.union_dicts(
        tsa_model.TimeSeriesResultsWrapper._wrap_methods, _methods)


wrap.populate_wrapper(AutoRegResultsWrapper, AutoRegResults)

doc = Docstring(AutoReg.__doc__)
_auto_reg_params = doc.extract_parameters(['trend', 'seasonal', 'exog',
                                           'hold_back', 'period', 'missing'],
                                          4)


@Substitution(auto_reg_params=_auto_reg_params)
def ar_select_order(endog, maxlag, ic='bic', glob=False, trend='c',
                    seasonal=False, exog=None, hold_back=None, period=None,
                    missing='none'):
    """
    Autoregressive AR-X(p) model order selection.

    Parameters
    ----------

    @cache_readonly
    def gcv(self):
        return self.scale / (1. - self.hat_matrix_trace / self.nobs)**2

    @cache_readonly
    def cv(self):
        cv_ = ((self.resid_pearson / (1. - self.hat_matrix_diag))**2).sum()
        cv_ /= self.nobs
        return cv_


class GLMGamResultsWrapper(GLMResultsWrapper):
    pass


wrap.populate_wrapper(GLMGamResultsWrapper, GLMGamResults)


class GLMGam(PenalizedMixin, GLM):
    """
    Generalized Additive Models (GAM)

    This inherits from `GLM`.

    Warning: Not all inherited methods might take correctly account of the
    penalization. Not all options including offset and exposure have been
    verified yet.

    Parameters
    ----------
    endog : array_like
        The response variable.

filter_results : HamiltonFilterResults or KimSmootherResults instance
        The underlying filter and, optionally, smoother output
    nobs : float
        The number of observations used to fit the model.
    params : array
        The parameters of the model.
    scale : float
        This is currently set to 1.0 and not used by the model or its results.
    """
    pass


class MarkovAutoregressionResultsWrapper(
        markov_regression.MarkovRegressionResultsWrapper):
    pass
wrap.populate_wrapper(MarkovAutoregressionResultsWrapper,  # noqa:E305
                      MarkovAutoregressionResults)

for i, r in enumerate(y):
                if r != 0:
                    grad[:, r - 1] += self.exog[ii[i], :]

            grad -= denomg / denom

        return grad.flatten()



class ConditionalResultsWrapper(lm.RegressionResultsWrapper):
    pass


wrap.populate_wrapper(ConditionalResultsWrapper, ConditionalResults)

param_header,
                                              params_stubs,
                                              txt_fmt=fmt_params)
            summary.tables.insert(-1, initial_state_table)

        return summary


class ExponentialSmoothingResultsWrapper(MLEResultsWrapper):
    _attrs = {}
    _wrap_attrs = wrap.union_dicts(MLEResultsWrapper._wrap_attrs,
                                   _attrs)
    _methods = {}
    _wrap_methods = wrap.union_dicts(MLEResultsWrapper._wrap_methods,
                                     _methods)
wrap.populate_wrapper(ExponentialSmoothingResultsWrapper,  # noqa:E305
                      ExponentialSmoothingResults)

ax.legend(loc='best')

        return fig


class ARMAResultsWrapper(wrap.ResultsWrapper):
    _attrs = {}
    _wrap_attrs = wrap.union_dicts(
        tsa_model.TimeSeriesResultsWrapper._wrap_attrs, _attrs)
    _methods = {}
    _wrap_methods = wrap.union_dicts(
        tsa_model.TimeSeriesResultsWrapper._wrap_methods, _methods)


wrap.populate_wrapper(ARMAResultsWrapper, ARMAResults)  # noqa:E305


class ARIMAResults(ARMAResults):
    @Appender(_arima_results_predict)
    def predict(self, start=None, end=None, exog=None, typ='linear',
                dynamic=False):
        return self.model.predict(self.params, start, end, exog, typ, dynamic)

    def _forecast_error(self, steps):
        sigma2 = self.sigma2
        ma_rep = arma2ma(np.r_[1, -self.arparams],
                         np.r_[1, self.maparams], lags=steps)

        fcerr = np.sqrt(np.cumsum(cumsum_n(ma_rep, self.k_diff) ** 2) * sigma2)
        return fcerr

dismalpy.ssm.mlemodel.MLEResults
    dismalpy.ssm.kalman_smoother.SmootherResults
    dismalpy.ssm.kalman_filter.FilterResults
    dismalpy.ssm.representation.FrozenRepresentation
    """
    pass


class VARMAXResultsWrapper(mlemodel.MLEResultsWrapper):
    _attrs = {}
    _wrap_attrs = wrap.union_dicts(
        mlemodel.MLEResultsWrapper._wrap_attrs, _attrs)
    _methods = {}
    _wrap_methods = wrap.union_dicts(
        mlemodel.MLEResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(VARMAXResultsWrapper, VARMAXResults)

dismalpy.ssm.mlemodel.MLEResults
    dismalpy.ssm.kalman_smoother.SmootherResults
    dismalpy.ssm.kalman_filter.FilterResults
    dismalpy.ssm.representation.FrozenRepresentation
    """
    pass


class DynamicFactorResultsWrapper(mlemodel.MLEResultsWrapper):
    _attrs = {}
    _wrap_attrs = wrap.union_dicts(
        mlemodel.MLEResultsWrapper._wrap_attrs, _attrs)
    _methods = {}
    _wrap_methods = wrap.union_dicts(
        mlemodel.MLEResultsWrapper._wrap_methods, _methods)
wrap.populate_wrapper(DynamicFactorResultsWrapper, DynamicFactorResults)