code: def test_jensen_shannon():
      test_data = np.random.random(size=(10, 50))
      test_data = normalize(test_data, norm="l1")
      for i in range(test_data.shape[0]):
          for j in range(i + 1, test_data.shape[0]):
              m = (test_data[i] + test_data[j]) / 2.0
              p = test_data[i]
              q = test_data[j]
              d1 = (
                  -np.sum(m * np.log(m))
                  + (np.sum(p * np.log(p)) + np.sum(q * np.log(q)))
                  / 2.0
              )
              d2 = dist.jensen_shannon_divergence(p, q)
              assert np.isclose(d1, d2, rtol=1e-4)
  
imports: import pytest
  import numpy
  from numpy.testing import assert_array_equal, assert_array_almost_equal
  import pynndescent.distances
  import pynndescent.sparse
  from scipy import stats
  from scipy.sparse import csr_matrix
  from scipy.version import full_version
  from sklearn.metrics import pairwise_distances
  from sklearn.neighbors import BallTree
  from sklearn.preprocessing import normalize
label_desc: Use image node_id 11 for calling a global function with example usage: test_jensen_shannon() without return types