code: def test_pbe():
      input_size = 3
      hidden_size = 3
      n_layers = 3
      modeltype = 1
      nnmodel = _construct_nn_model(
          input_size, hidden_size, n_layers, modeltype
      ).to(torch.double)
      e_type = "ae"
      true_val = 0.237898
      systems = [
          {
              "moldesc": "Be 0 0 0; H -2.5065 0 0; H 2.5065 0 0",
              "basis": "6-311++G(3df,3pd)",
          },
          {
              "moldesc": "H 0 0 0",
              "basis": "6-311++G(3df,3pd)",
              "spin": "1",
              "number": "2",
          },
          {"moldesc": "Be 0 0 0", "basis": "6-311++G(3df,3pd)"},
      ]
  
      entry = DFTEntry.create(e_type, true_val, systems)
      hybridxc = HybridXC("gga_x_pbe", nnmodel, aweight0=0.0)
      evl = XCNNSCF(hybridxc, entry)
      qcs = []
      for system in entry.get_systems():
          qcs.append(evl.run(system))
      output = qcs[0].energy()
      expected_output = torch.tensor(-15.7262, dtype=torch.float64)
      torch.testing.assert_close(
          output, expected_output, atol=1e-4, rtol=0
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_pbe() without return types