How to use the susi.SOMUtils.modify_weight_matrix_online function in susi

desc="unsuper", **self.tqdm_params_):

                # select one input vector & calculate best matching unit (BMU)
                dp = np.random.randint(low=0, high=len(self.X_))
                bmu_pos = self.get_bmu(self.X_[dp], self.unsuper_som_)

                # calculate learning rate and neighborhood function
                learning_rate = self.calc_learning_rate(
                    curr_it=it, mode=self.learn_mode_unsupervised)
                nbh_func = self.calc_neighborhood_func(
                    curr_it=it, mode=self.neighborhood_mode_unsupervised)

                # calculate distance weight matrix and update weights
                dist_weight_matrix = self.get_nbh_distance_weight_matrix(
                    nbh_func, bmu_pos)
                self.unsuper_som_ = modify_weight_matrix_online(
                    self.unsuper_som_, dist_weight_matrix,
                    true_vector=self.X_[dp],
                    learningrate=learning_rate*self.sample_weights_[dp])

        elif self.train_mode_unsupervised == "batch":
            for it in tqdm(range(self.n_iter_unsupervised),
                           desc="unsuper", **self.tqdm_params_):

                # calculate BMUs
                bmus = self.get_bmus(self.X_)

                # calculate neighborhood function
                nbh_func = self.calc_neighborhood_func(
                    curr_it=it, mode=self.neighborhood_mode_unsupervised)

                # calculate distance weight matrix for all datapoints

dist_weight_matrix : np.array of float
            Current distance weight of the SOM for the specific node
        data : np.array, optional
            True vector(s)
        learningrate : float, optional
            Current learning rate of the SOM

        Returns
        -------
        modify_weight_matrix : np.array
            Weight vector of the SOM after the modification

        """
        modify_weight_matrix = None
        if self.train_mode_supervised == "online":
            modify_weight_matrix = modify_weight_matrix_online(
                self.super_som_, dist_weight_matrix, true_vector=true_vector,
                learningrate=learningrate)

        elif self.train_mode_supervised == "batch":
            modify_weight_matrix = self.modify_weight_matrix_batch(
                som_array=self.super_som_,
                dist_weight_matrix=dist_weight_matrix[self.labeled_indices_],
                data=self.y_[self.labeled_indices_])

        else:
            raise ValueError("Invalid train_mode_supervised: "+str(
                self.train_mode_supervised))

        return modify_weight_matrix