How to use the retentioneering.core.feature_extraction.learn_tsne function in retentioneering

if targets is None:
                if regression_targets is not None:
                    targets = self.make_regression_targets(features, regression_targets)
                else:
                    targets = features.index.isin(self.get_positive_users(**kwargs))
                    targets = np.where(targets, self.retention_config['positive_target_event'],
                                       self.retention_config['negative_target_event'])
            self._tsne_targets = targets

        if sample_frac is not None:
            features = features.sample(frac=sample_frac, random_state=0)
        elif sample_size is not None:
            features = features.sample(n=sample_size, random_state=0)

        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
            )