code: def testMetadata(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
  
      # Initing 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
      #     stronlgy 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(100)
  
      p.set_num_threads(4)  # by default using all available cores
  
      print("Adding all elements (%d)" % (len(data)))
      p.add_items(data)
  
      # test methods
      self.assertEqual(p.get_max_elements(), num_elements)
      self.assertEqual(p.get_current_count(), num_elements)
  
      # test properties
      self.assertEqual(p.space, "l2")
      self.assertEqual(p.dim, dim)
      self.assertEqual(p.M, 16)
      self.assertEqual(p.ef_construction, 100)
      self.assertEqual(p.max_elements, num_elements)
      self.assertEqual(p.element_count, num_elements)
  
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.testMetadata() without return types