code: def test_normalized_cut():
      row = torch.tensor([0, 1, 1, 1, 2, 2, 3, 3, 4, 4])
      col = torch.tensor([1, 0, 2, 3, 1, 4, 1, 4, 2, 3])
      edge_attr = torch.tensor(
          [3.0, 3.0, 6.0, 3.0, 6.0, 1.0, 3.0, 2.0, 1.0, 2.0]
      )
      expected = torch.tensor(
          [4.0, 4.0, 5.0, 2.5, 5.0, 1.0, 2.5, 2.0, 1.0, 2.0]
      )
  
      out = normalized_cut(torch.stack([row, col], dim=0), edge_attr)
      assert torch.allclose(out, expected)
  
      if is_full_test():
          jit = torch.jit.script(normalized_cut)
          out = jit(torch.stack([row, col], dim=0), edge_attr)
          assert torch.allclose(out, expected)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_normalized_cut() without return types