code: def test_haversine_fails_high_dimensions():
      data = np.array([[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]])
  
      cunn = cuKNN(metric="haversine", n_neighbors=2, algorithm="brute")
  
      cunn.fit(data).kneighbors(data)
  
imports: import gc
  from cuml.common import has_scipy
  import cuml
  import sklearn
  from cuml.internals.safe_imports import cpu_only_import_from
  from numpy.testing import assert_array_equal, assert_allclose
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  import math
  from cuml.testing.utils import array_equal, unit_param, quality_param, stress_param
  from cuml.neighbors import NearestNeighbors
  from sklearn.neighbors import NearestNeighbors
  from cuml.datasets import make_blobs
  from sklearn.metrics import pairwise_distances
  from cuml.metrics import pairwise_distances
  from cuml.internals import logger
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 19 for calling a global function with example usage: test_haversine_fails_high_dimensions() without return types