code: def test_angular_nn_descent_neighbor_accuracy(
      nn_data,
  ):  # pragma: no cover
      knn_indices, knn_dists, _ = nearest_neighbors(
          nn_data, 10, "cosine", {}, True, np.random
      )
      angular_data = normalize(nn_data, norm="l2")
      percent_correct = knn(knn_indices, angular_data)
      assert (
          percent_correct >= 0.85
      ), "NN-descent did not get 89% accuracy on nearest neighbors"
  
file_path: umap/umap/tests/test_umap_nn.py
imports: import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal
  from sklearn.neighbors import KDTree
  from sklearn.preprocessing import normalize
  from umap import distances
  from umap.umap_ import nearest_neighbors, smooth_knn_dist
label_desc: Use image node_id 7 for calling a global function with example usage: test_angular_nn_descent_neighbor_accuracy(nn_data) without return types