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

Weights applied to individual samples.
            If not provided, uniform weights are assumed. These weights will
            be multiplied with class_weight (passed through the
            constructor) if class_weight is specified

        Returns
        -------
        self : returns an instance of self.
        """
        return self._fit(X, y, alpha=self.alpha, C=1.0,
                         loss=self.loss, learning_rate=self.learning_rate,
                         coef_init=coef_init, intercept_init=intercept_init,
                         sample_weight=sample_weight)


class SGDClassifier(BaseSGDClassifier, _LearntSelectorMixin):
    """Linear classifiers (SVM, logistic regression, a.o.) with SGD training.

    This estimator implements regularized linear models with stochastic
    gradient descent (SGD) learning: the gradient of the loss is estimated
    each sample at a time and the model is updated along the way with a
    decreasing strength schedule (aka learning rate). SGD allows minibatch
    (online/out-of-core) learning, see the partial_fit method.
    For best results using the default learning rate schedule, the data should
    have zero mean and unit variance.

    This implementation works with data represented as dense or sparse arrays
    of floating point values for the features. The model it fits can be
    controlled with the loss parameter; by default, it fits a linear support
    vector machine (SVM).

    The regularizer is a penalty added to the loss function that shrinks model

def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
                 fit_intercept=True, n_iter=5, shuffle=True, verbose=0,
                 epsilon=DEFAULT_EPSILON, n_jobs=1, random_state=None,
                 learning_rate="optimal", eta0=0.0, power_t=0.5,
                 class_weight=None, warm_start=False, average=False):

        super(BaseSGDClassifier, self).__init__(loss=loss, penalty=penalty,
                                                alpha=alpha, l1_ratio=l1_ratio,
                                                fit_intercept=fit_intercept,
                                                n_iter=n_iter, shuffle=shuffle,
                                                verbose=verbose,
                                                epsilon=epsilon,
                                                random_state=random_state,
                                                learning_rate=learning_rate,
                                                eta0=eta0, power_t=power_t,
                                                warm_start=warm_start,
                                                average=average)
        self.class_weight = class_weight
        self.classes_ = None
        self.n_jobs = int(n_jobs)