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

def test_knn_retrieval():
    annoy_index_filepath = 'tests/data/.test-annoy-index.index'
    expected_neighbour_list = np.load('tests/data/test_knn_k4.npy')

    iris = datasets.load_iris()
    X = iris.data

    k = 4
    search_k = -1
    neighbour_list = extract_knn(X, annoy_index_filepath,
                                 k=k, search_k=search_k)

    assert np.all(expected_neighbour_list == neighbour_list)

neighbour_matrix = extract_knn(X, index_path, k=k,
                                           search_k=search_k, verbose=verbose)
            return KnnTripletGenerator(X, neighbour_matrix,
                                       batch_size=batch_size)
        else:
            index = AnnoyIndex(X.shape[1], metric='angular')
            index.load(index_path)
            return AnnoyTripletGenerator(X, index, k=k,
                                         batch_size=batch_size,
                                         search_k=search_k)
    else:
        if precompute:
            if verbose > 0:
                print('Extracting KNN from index')

            neighbour_matrix = extract_knn(X, index_path, k=k,
                                           search_k=search_k, verbose=verbose)
            return LabeledKnnTripletGenerator(X, Y, neighbour_matrix,
                                              batch_size=batch_size)
        else:
            index = AnnoyIndex(X.shape[1], metric='angular')
            index.load(index_path)
            return LabeledAnnoyTripletGenerator(X, Y, index,
                                                k=k, batch_size=batch_size,
                                                search_k=search_k)

precompute=True, verbose=1):
    if k >= X.shape[0] - 1:
        raise Exception('''k value greater than or equal to (num_rows - 1)
                        (k={}, rows={}). Lower k to a smaller
                        value.'''.format(k, X.shape[0]))
    if batch_size > X.shape[0]:
        raise Exception('''batch_size value larger than num_rows in dataset
                        (batch_size={}, rows={}). Lower batch_size to a
                        smaller value.'''.format(batch_size, X.shape[0]))

    if Y is None:
        if precompute:
            if verbose > 0:
                print('Extracting KNN from index')

            neighbour_matrix = extract_knn(X, index_path, k=k,
                                           search_k=search_k, verbose=verbose)
            return KnnTripletGenerator(X, neighbour_matrix,
                                       batch_size=batch_size)
        else:
            index = AnnoyIndex(X.shape[1], metric='angular')
            index.load(index_path)
            return AnnoyTripletGenerator(X, index, k=k,
                                         batch_size=batch_size,
                                         search_k=search_k)
    else:
        if precompute:
            if verbose > 0:
                print('Extracting KNN from index')

            neighbour_matrix = extract_knn(X, index_path, k=k,
                                           search_k=search_k, verbose=verbose)