code: def test_nn_descent_neighbor_accuracy_low_memory(
      nn_data,
  ):  # pragma: no cover
      knn_indices, knn_dists, _ = nearest_neighbors(
          nn_data,
          10,
          "euclidean",
          {},
          False,
          np.random,
          low_memory=True,
      )
      percent_correct = knn(knn_indices, nn_data)
      assert (
          percent_correct >= 0.89
      ), "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 6 for calling a global function with example usage: test_nn_descent_neighbor_accuracy_low_memory(nn_data) without return types