code: def test_densHF():
      inputs = "deepchem/models/tests/assets/test_HFdp.yaml"
      data = DFTYamlLoader()
      dataset = data.create_dataset(inputs)
      labels = torch.as_tensor(dataset.y)
      nnmodel = (
          torch.nn.Sequential(
              torch.nn.Linear(2, 10),
              torch.nn.Softplus(),
              torch.nn.Linear(10, 1, bias=False),
          )
      ).to(torch.double)
      hybridxc = HybridXC("lda_x", nnmodel, aweight0=0.0)
      entry = dataset.X[0]
      grid = (dataset.X[0]).get_integration_grid()
      volume = grid.get_dvolume()
      evl = XCNNSCF(hybridxc, entry)
      qcs = []
      for system in entry.get_systems():
          qcs.append(evl.run(system))
      val = entry.get_val(qcs)
      output = torch.as_tensor(val)
      loss = (
          (
              (DensityProfileLoss()._create_pytorch_loss(volume))(
                  output, labels
              )
          )
          .detach()
          .numpy()
      )
      expected = np.array(0.0068712)
      assert np.allclose(loss, expected)
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_densHF() without return types