How to use the phylib.io.array._unique function in phylib

def _assign_update_info(spike_ids, old_spike_clusters, new_spike_clusters):
    old_clusters = _unique(old_spike_clusters)
    new_clusters = _unique(new_spike_clusters)
    largest_old_cluster = np.bincount(old_spike_clusters).argmax()
    descendants = list(set(zip(old_spike_clusters, new_spike_clusters)))
    update_info = UpdateInfo(
        description='assign',
        spike_ids=list(spike_ids),
        spike_clusters=list(new_spike_clusters),
        added=list(new_clusters),
        deleted=list(old_clusters),
        descendants=descendants,
        largest_old_cluster=int(largest_old_cluster),
    )
    return update_info

spike_ids = _as_array(spike_ids)
        if len(new_spike_clusters) == 1 and len(spike_ids) > 1:
            new_spike_clusters = np.ones(len(spike_ids), dtype=np.int64) * new_spike_clusters[0]
        old_spike_clusters = self._spike_clusters[spike_ids]

        assert len(spike_ids) == len(old_spike_clusters)
        assert len(new_spike_clusters) == len(spike_ids)

        # Update the spikes per cluster structure.
        old_clusters = _unique(old_spike_clusters)

        # NOTE: shortcut to a merge if this assignment is effectively a merge
        # i.e. if all spikes are assigned to a single cluster.
        # The fact that spike selection has been previously extended to
        # whole clusters is critical here.
        new_clusters = _unique(new_spike_clusters)
        if len(new_clusters) == 1:
            return self._do_merge(spike_ids, old_clusters, new_clusters[0])

        # We return the UpdateInfo structure.
        up = _assign_update_info(spike_ids, old_spike_clusters, new_spike_clusters)

        # We update the new cluster id (strictly increasing during a session).
        self._new_cluster_id = max(self._new_cluster_id, max(up.added) + 1)

        # We make the assignments.
        self._spike_clusters[spike_ids] = new_spike_clusters
        # OPTIM: we update spikes_per_cluster manually.
        new_spc = _spikes_per_cluster(new_spike_clusters, spike_ids)
        self._update_cluster_ids(to_remove=old_clusters, to_add=new_spc)
        return up

def _extend_spikes(spike_ids, spike_clusters):
    """Return all spikes belonging to the clusters containing the specified
    spikes."""
    # We find the spikes belonging to modified clusters.
    # What are the old clusters that are modified by the assignment?
    old_spike_clusters = spike_clusters[spike_ids]
    unique_clusters = _unique(old_spike_clusters)
    # Now we take all spikes from these clusters.
    changed_spike_ids = _spikes_in_clusters(spike_clusters, unique_clusters)
    # These are the new spikes that need to be reassigned.
    extended_spike_ids = np.setdiff1d(changed_spike_ids, spike_ids, assume_unique=True)
    return extended_spike_ids

def make_depths(self):
        """Make spikes.depths.npy and clusters.depths.npy."""
        p = self.dir_path

        channel_positions = self.model.channel_positions
        assert channel_positions.ndim == 2

        cluster_channels = np.load(p / 'clusters.peakChannel.npy')
        assert cluster_channels.ndim == 1
        n_clusters = cluster_channels.shape[0]

        spike_clusters = self.model.spike_clusters
        assert spike_clusters.ndim == 1
        n_spikes = spike_clusters.shape[0]
        self.cluster_ids = _unique(self.model.spike_clusters)

        clusters_depths = channel_positions[cluster_channels, 1]
        assert clusters_depths.shape == (n_clusters,)

        spike_clusters_rel = _index_of(spike_clusters, self.cluster_ids)
        assert spike_clusters_rel.max() < clusters_depths.shape[0]
        spikes_depths = clusters_depths[spike_clusters_rel]
        assert spikes_depths.shape == (n_spikes,)

        np.save(p / 'spikes.depths.npy', spikes_depths)
        np.save(p / 'clusters.depths.npy', clusters_depths)

def _assign_update_info(spike_ids, old_spike_clusters, new_spike_clusters):
    old_clusters = _unique(old_spike_clusters)
    new_clusters = _unique(new_spike_clusters)
    largest_old_cluster = np.bincount(old_spike_clusters).argmax()
    descendants = list(set(zip(old_spike_clusters, new_spike_clusters)))
    update_info = UpdateInfo(
        description='assign',
        spike_ids=list(spike_ids),
        spike_clusters=list(new_spike_clusters),
        added=list(new_clusters),
        deleted=list(old_clusters),
        descendants=descendants,
        largest_old_cluster=int(largest_old_cluster),
    )
    return update_info