How to use the scikit-learn.sklearn.linear_model.sgd_fast.average_sgd function in scikit-learn

loss_function = self._get_loss_function(loss)
        penalty_type = self._get_penalty_type(self.penalty)
        learning_rate_type = self._get_learning_rate_type(learning_rate)

        if self.t_ is None:
            self.t_ = 1.0

        random_state = check_random_state(self.random_state)
        # numpy mtrand expects a C long which is a signed 32 bit integer under
        # Windows
        seed = random_state.randint(0, np.iinfo(np.int32).max)

        if self.average > 0:
            self.standard_coef_, self.standard_intercept_, \
                self.average_coef_, self.average_intercept_ =\
                average_sgd(self.standard_coef_,
                            self.standard_intercept_[0],
                            self.average_coef_,
                            self.average_intercept_[0],
                            loss_function,
                            penalty_type,
                            alpha, C,
                            self.l1_ratio,
                            dataset,
                            n_iter,
                            int(self.fit_intercept),
                            int(self.verbose),
                            int(self.shuffle),
                            seed,
                            1.0, 1.0,
                            learning_rate_type,
                            self.eta0, self.power_t, self.t_,

# numpy mtrand expects a C long which is a signed 32 bit integer under
    # Windows
    seed = random_state.randint(0, np.iinfo(np.int32).max)

    if not est.average:
        return plain_sgd(coef, intercept, est.loss_function,
                         penalty_type, alpha, C, est.l1_ratio,
                         dataset, n_iter, int(est.fit_intercept),
                         int(est.verbose), int(est.shuffle), seed,
                         pos_weight, neg_weight,
                         learning_rate_type, est.eta0,
                         est.power_t, est.t_, intercept_decay)

    else:
        standard_coef, standard_intercept, average_coef, \
            average_intercept = average_sgd(coef, intercept, average_coef,
                                            average_intercept,
                                            est.loss_function, penalty_type,
                                            alpha, C, est.l1_ratio, dataset,
                                            n_iter, int(est.fit_intercept),
                                            int(est.verbose), int(est.shuffle),
                                            seed, pos_weight, neg_weight,
                                            learning_rate_type, est.eta0,
                                            est.power_t, est.t_,
                                            intercept_decay,
                                            est.average)

        if len(est.classes_) == 2:
            est.average_intercept_[0] = average_intercept
        else:
            est.average_intercept_[i] = average_intercept