code: def testRandomSelf(self):
      data1 = np.asarray(
          [
              [1, 0, 0],
              [0, 1, 0],
              [0, 0, 1],
              [1, 0, 1],
              [1, 1, 1],
          ]
      )
  
      for space, expected_distances in [
          ("l2", [[0.0, 1.0, 2.0, 2.0, 2.0]]),
          ("ip", [[-2.0, -1.0, 0.0, 0.0, 0.0]]),
          ("cosine", [[0, 1.835e-01, 4.23e-01, 4.23e-01, 4.23e-01]]),
      ]:
          for rightdim in range(1, 128, 3):
              for leftdim in range(1, 32, 5):
                  data2 = np.concatenate(
                      [
                          np.zeros([data1.shape[0], leftdim]),
                          data1,
                          np.zeros([data1.shape[0], rightdim]),
                      ],
                      axis=1,
                  )
                  dim = data2.shape[1]
                  p = hnswlib.Index(space=space, dim=dim)
                  p.init_index(
                      max_elements=5, ef_construction=100, M=16
                  )
  
                  p.set_ef(10)
  
                  p.add_items(data2)
  
                  # Query the elements for themselves and measure recall:
                  labels, distances = p.knn_query(
                      np.asarray(data2[-1:]), k=5
                  )
  
                  diff = np.mean(np.abs(distances - expected_distances))
                  self.assertAlmostEqual(diff, 0, delta=1e-3)
  
imports: import unittest
  import numpy
  import hnswlib
label_desc: Use image node_id 1 for calling the RandomSelfTestCase obj's underlying member method code with example usage: obj.testRandomSelf() without return types