code: def testRandomSelf(self):
      dim = 16
      num_elements = 10000
  
      # Generating sample data
      data = np.float32(np.random.random((num_elements, dim)))
  
      # Declaring index
      p = hnswlib.Index(
          space="l2", dim=dim
      )  # possible options are l2, cosine or ip
  
      # Initiating index
      # max_elements - the maximum number of elements, should be known beforehand
      #     (probably will be made optional in the future)
      #
      # ef_construction - controls index search speed/build speed tradeoff
      # M - is tightly connected with internal dimensionality of the data
      #     strongly affects the memory consumption
  
      p.init_index(max_elements=num_elements, ef_construction=100, M=16)
  
      # Controlling the recall by setting ef:
      # higher ef leads to better accuracy, but slower search
      p.set_ef(10)
  
      p.set_num_threads(4)  # by default using all available cores
  
      # We split the data in two batches:
      data1 = data[: num_elements // 2]
      data2 = data[num_elements // 2 :]
  
      print("Adding first batch of %d elements" % (len(data1)))
      p.add_items(data1)
  
      # Query the elements for themselves and measure recall:
      labels, distances = p.knn_query(data1, k=1)
      self.assertAlmostEqual(
          np.mean(labels.reshape(-1) == np.arange(len(data1))), 1.0, 3
      )
  
      # Serializing and deleting the index:
      index_path = "first_half.bin"
      print("Saving index to '%s'" % index_path)
      p.save_index(index_path)
      del p
  
      # Re-initiating, loading the index
      p = hnswlib.Index(space="l2", dim=dim)  # you can change the sa
  
      print("\nLoading index from '%s'\n" % index_path)
      p.load_index(index_path)
  
      print("Adding the second batch of %d elements" % (len(data2)))
      p.add_items(data2)
  
      # Query the elements for themselves and measure recall:
      labels, distances = p.knn_query(data, k=1)
  
      self.assertAlmostEqual(
          np.mean(labels.reshape(-1) == np.arange(len(data))), 1.0, 3
      )
  
      os.remove(index_path)
  
imports: import os
  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