How to use the scikit-learn.sklearn.utils.compute_class_weight function in scikit-learn

def _partial_fit(self, X, y, alpha, C,
                     loss, learning_rate, n_iter,
                     classes, sample_weight,
                     coef_init, intercept_init):
        X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C")

        n_samples, n_features = X.shape

        self._validate_params()
        _check_partial_fit_first_call(self, classes)

        n_classes = self.classes_.shape[0]

        # Allocate datastructures from input arguments
        self._expanded_class_weight = compute_class_weight(self.class_weight,
                                                           self.classes_, y)
        sample_weight = self._validate_sample_weight(sample_weight, n_samples)

        if self.coef_ is None or coef_init is not None:
            self._allocate_parameter_mem(n_classes, n_features,
                                         coef_init, intercept_init)
        elif n_features != self.coef_.shape[-1]:
            raise ValueError("Number of features %d does not match previous "
                             "data %d." % (n_features, self.coef_.shape[-1]))

        self.loss_function = self._get_loss_function(loss)
        if self.t_ is None:
            self.t_ = 1.0

        # delegate to concrete training procedure
        if n_classes > 2: