code: def test_alternative_distances():
      for distname in dist.fast_distance_alternatives:
          true_dist = dist.named_distances[distname]
          alt_dist = dist.fast_distance_alternatives[distname]["dist"]
          correction = dist.fast_distance_alternatives[distname][
              "correction"
          ]
  
          for i in range(100):
              x = np.random.random(30).astype(np.float32)
              y = np.random.random(30).astype(np.float32)
              x[x < 0.25] = 0.0
              y[y < 0.25] = 0.0
  
              true_distance = true_dist(x, y)
              corrected_alt_distance = correction(alt_dist(x, y))
  
              assert np.isclose(true_distance, corrected_alt_distance)
  
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 10 for calling a global function with example usage: test_alternative_distances() without return types