code: def test_get_laplacian():
      edge_index = torch.tensor(
          [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long
      )
      edge_weight = torch.tensor([1, 2, 2, 4], dtype=torch.float)
  
      lap = get_laplacian(edge_index, edge_weight)
      assert lap[0].tolist() == [
          [0, 1, 1, 2, 0, 1, 2],
          [1, 0, 2, 1, 0, 1, 2],
      ]
      assert lap[1].tolist() == [-1, -2, -2, -4, 1, 4, 4]
  
      if is_full_test():
          jit = torch.jit.script(get_laplacian)
          lap = jit(edge_index, edge_weight)
          assert lap[0].tolist() == [
              [0, 1, 1, 2, 0, 1, 2],
              [1, 0, 2, 1, 0, 1, 2],
          ]
          assert lap[1].tolist() == [-1, -2, -2, -4, 1, 4, 4]
  
      lap_sym = get_laplacian(
          edge_index, edge_weight, normalization="sym"
      )
      assert lap_sym[0].tolist() == lap[0].tolist()
      assert lap_sym[1].tolist() == [-0.5, -1, -0.5, -1, 1, 1, 1]
  
      lap_rw = get_laplacian(
          edge_index, edge_weight, normalization="rw"
      )
      assert lap_rw[0].tolist() == lap[0].tolist()
      assert lap_rw[1].tolist() == [-1, -0.5, -0.5, -1, 1, 1, 1]
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_get_laplacian() without return types