How to use the scikit-learn.sklearn.linear_model.stochastic_gradient.BaseSGD function in scikit-learn

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

        return standard_coef, standard_intercept


class BaseSGDClassifier(six.with_metaclass(ABCMeta, BaseSGD,
                                           LinearClassifierMixin)):

    loss_functions = {
        "hinge": (Hinge, 1.0),
        "squared_hinge": (SquaredHinge, 1.0),
        "perceptron": (Hinge, 0.0),
        "log": (Log, ),
        "modified_huber": (ModifiedHuber, ),
        "squared_loss": (SquaredLoss, ),
        "huber": (Huber, DEFAULT_EPSILON),
        "epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
        "squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
                                        DEFAULT_EPSILON),
    }

    @abstractmethod

def set_params(self, *args, **kwargs):
        super(BaseSGD, self).set_params(*args, **kwargs)
        self._validate_params()
        return self