How to use the kmapper.visuals._format_cluster_statistics function in kmapper

def test_cluster_stats_with_no_names(self):
        # This would be the default.

        X = np.ones((1000, 3))
        ids = np.random.choice(20, 1000)
        X[ids, 0] = 0

        cluster_data = visuals._format_cluster_statistics(ids, X, [])

        assert len(cluster_data["below"]) >= 1
        assert cluster_data["below"][0]["feature"] == "f_0"
        assert cluster_data["below"][0]["mean"] == 0

def test_cluster_stats_above(self):
        X = np.ones((1000, 3))
        ids = np.random.choice(20, 1000)
        X[ids, 0] = 10

        cluster_data = visuals._format_cluster_statistics(ids, X, ["a", "b", "c"])

        assert len(cluster_data["above"]) >= 1
        assert cluster_data["above"][0]["feature"] == "a"
        assert cluster_data["above"][0]["mean"] == 10

def test_cluster_stats_below(self):
        X = np.ones((1000, 3))
        ids = np.random.choice(20, 1000)
        X[ids, 0] = 0

        cluster_data = visuals._format_cluster_statistics(ids, X, ["a", "b", "c"])

        assert len(cluster_data["below"]) >= 1
        assert cluster_data["below"][0]["feature"] == "a"
        assert cluster_data["below"][0]["mean"] == 0

def test_cluster_stats(self):
        X = np.random.random((1000, 3))
        ids = np.random.choice(20, 1000)

        cluster_data = visuals._format_cluster_statistics(ids, X, ["a", "b", "c"])

        assert isinstance(cluster_data, dict)
        assert cluster_data["size"] == len(ids)

def _hover_format(member_ids, custom_tooltips, X, X_names, lens, lens_names):
    cluster_data = _format_cluster_statistics(member_ids, X, X_names)
    tooltip = ""
    custom_tooltips = (
        custom_tooltips[member_ids] if custom_tooltips is not None else member_ids
    )
    val_size = cluster_data["size"]
    tooltip += "{val_size}".format(**locals())
    return tooltip