code: def test_laplacian_lambda_max():
      out = str(LaplacianLambdaMax())
      assert out == "LaplacianLambdaMax(normalization=None)"
  
      edge_index = torch.tensor(
          [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long
      )
      edge_attr = torch.tensor([1, 1, 2, 2], dtype=torch.float)
  
      data = Data(
          edge_index=edge_index, edge_attr=edge_attr, num_nodes=3
      )
      out = LaplacianLambdaMax(normalization=None, is_undirected=True)(
          data
      )
      assert len(out) == 4
      assert torch.allclose(
          torch.tensor(out.lambda_max), torch.tensor(4.732049)
      )
  
      data = Data(
          edge_index=edge_index, edge_attr=edge_attr, num_nodes=3
      )
      out = LaplacianLambdaMax(normalization="sym", is_undirected=True)(
          data
      )
      assert len(out) == 4
      assert torch.allclose(
          torch.tensor(out.lambda_max), torch.tensor(2.0)
      )
  
      data = Data(
          edge_index=edge_index, edge_attr=edge_attr, num_nodes=3
      )
      out = LaplacianLambdaMax(normalization="rw", is_undirected=True)(
          data
      )
      assert len(out) == 4
      assert torch.allclose(
          torch.tensor(out.lambda_max), torch.tensor(2.0)
      )
  
      data = Data(
          edge_index=edge_index,
          edge_attr=torch.randn(4, 2),
          num_nodes=3,
      )
      out = LaplacianLambdaMax(normalization=None)(data)
      assert len(out) == 4
      assert torch.allclose(
          torch.tensor(out.lambda_max), torch.tensor(3.0)
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_laplacian_lambda_max() without return types