How to use the ivis.data.knn.build_annoy_index function in ivis

def test_dense_annoy_index(annoy_index_file):
    data = np.random.choice([0, 1], size=(10, 5))
    index = build_annoy_index(data, annoy_index_file)
    assert os.path.exists(annoy_index_file)

    loaded_index = AnnoyIndex(5, metric='angular')
    loaded_index.load(annoy_index_file)

    assert index.f == loaded_index.f == 5
    assert index.get_n_items() == loaded_index.get_n_items() == 10
    assert index.get_nns_by_item(0, 5) == loaded_index.get_nns_by_item(0, 5)

    index.unload()
    loaded_index.unload()

def test_build_sparse_annoy_index(annoy_index_file):
    data = np.random.choice([0, 1], size=(10, 5))
    sparse_data = csr_matrix(data)

    index = build_annoy_index(sparse_data, annoy_index_file)
    assert os.path.exists(annoy_index_file)

    loaded_index = AnnoyIndex(5, metric='angular')
    loaded_index.load(annoy_index_file)

    assert index.f == loaded_index.f == 5
    assert index.get_n_items() == loaded_index.get_n_items() == 10
    assert index.get_nns_by_item(0, 5) == loaded_index.get_nns_by_item(0, 5)

    index.unload()
    loaded_index.unload()

def _fit(self, X, Y=None, shuffle_mode=True):

        if self.annoy_index_path is None:
            self.annoy_index_path = 'annoy.index'
            if self.verbose > 0:
                print('Building KNN index')
            build_annoy_index(X, self.annoy_index_path,
                              ntrees=self.ntrees,
                              build_index_on_disk=self.build_index_on_disk,
                              verbose=self.verbose)

        datagen = generator_from_index(X, Y,
                                       index_path=self.annoy_index_path,
                                       k=self.k,
                                       batch_size=self.batch_size,
                                       search_k=self.search_k,
                                       precompute=self.precompute,
                                       verbose=self.verbose)

        loss_monitor = 'loss'
        try:
            triplet_loss_func = triplet_loss(distance=self.distance,
                                             margin=self.margin)