How to use the retentioneering.core.clustering.aggregate_cl function in retentioneering

if not (hasattr(self, '_tsne') and not refit):
            self._tsne = feature_extraction.learn_tsne(features, **kwargs)
        if plot_type == 'clusters':
            if kwargs.get('cmethod') is not None:
                kwargs['method'] = kwargs.pop('cmethod')
            old_targs = targets.copy()
            targets = self.get_clusters(plot_type=None, **kwargs)
        elif plot_type == 'targets':
            targets = self._tsne_targets
        else:
            return self._tsne
        if proj_type == '3d':
            plot.tsne_3d(
                self._obj,
                clustering.aggregate_cl(targets, 7) if kwargs.get('method') == 'dbscan' else targets,
                old_targs,
                **kwargs
            )
        else:
            plot.cluster_tsne(
                self._obj,
                clustering.aggregate_cl(targets, 7) if kwargs.get('method') == 'dbscan' else targets,
                targets,
                **kwargs
            )
        return self._tsne

self.clusters, self._metrics = clusterer(features, **kwargs)
            self._create_cluster_mapping(features.index.values)

        if hasattr(self, 'datatype') and self.datatype == 'features':
            target = kwargs.pop('target')
        else:
            target = self.get_positive_users(**kwargs)
        target = features.index.isin(target)
        self._metrics['homogen'] = clustering.homogeneity_score(target, self.clusters)
        if hasattr(self, '_tsne'):
            features.retention._tsne = self._tsne
        if plot_type:
            func = getattr(plot, plot_type)
            res = func(
                features,
                clustering.aggregate_cl(self.clusters, 7) if method == 'dbscan' else self.clusters,
                target,
                metrics=self._metrics,
                **kwargs
            )
            if res is not None:
                self._tsne = res
        return self.clusters