code: def test_pmlp():
      x = torch.randn(4, 16)
      edge_index = torch.tensor([[0, 1, 1, 2], [1, 0, 2, 1]])
  
      pmlp = PMLP(
          in_channels=16,
          hidden_channels=32,
          out_channels=2,
          num_layers=4,
      )
      assert str(pmlp) == "PMLP(16, 2, num_layers=4)"
  
      pmlp.training = True
      assert pmlp(x).size() == (4, 2)
  
      pmlp.training = False
      assert pmlp(x, edge_index).size() == (4, 2)
  
      with pytest.raises(
          ValueError, match="'edge_index' needs to be present"
      ):
          pmlp.training = False
          pmlp(x)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_pmlp() without return types