code: def test_reconstruct(self):
      d = 32
      index = faiss.IndexFlatL2(d)
  
      xb = torch.rand(100, d, dtype=torch.float32)
      index.add(xb)
  
      # Test reconstruct with torch cpu (native return)
      y = index.reconstruct(7)
      self.assertTrue(torch.equal(xb[7], y))
  
      # Test reconstruct with numpy output provided
      y = np.empty(d, dtype=np.float32)
      index.reconstruct(11, y)
      self.assertTrue(np.array_equal(xb.numpy()[11], y))
  
      # Test reconstruct with torch cpu output providesd
      y = torch.empty(d, dtype=torch.float32)
      index.reconstruct(12, y)
      self.assertTrue(torch.equal(xb[12], y))
  
      # Test reconstruct_n with torch cpu (native return)
      y = index.reconstruct_n(10, 10)
      self.assertTrue(torch.equal(xb[10:20], y))
  
      # Test reconstruct with numpy output provided
      y = np.empty((10, d), dtype=np.float32)
      index.reconstruct_n(20, 10, y)
      self.assertTrue(np.array_equal(xb.cpu().numpy()[20:30], y))
  
      # Test reconstruct_n with torch cpu output provided
      y = torch.empty(10, d, dtype=torch.float32)
      index.reconstruct_n(40, 10, y)
      self.assertTrue(torch.equal(xb[40:50].cpu(), y))
  
file_path: faiss/tests/torch_test_contrib.py
imports: import faiss
  import torch
  import unittest
  import numpy
  import faiss.contrib.torch_utils
label_desc: Use image node_id 3 for calling the TestTorchUtilsCPU obj's underlying member method code with example usage: obj.test_reconstruct() without return types