code: def test_profiler(capfd, get_dataset, device):
      x = torch.randn(10, 16, device=device)
      edge_index = torch.tensor(
          [
              [0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9],
              [1, 0, 2, 1, 3, 2, 4, 3, 5, 4, 6, 5, 7, 6, 8, 7, 9, 8],
          ],
          device=device,
      )
  
      model = GraphSAGE(16, hidden_channels=32, num_layers=2).to(device)
  
      with Profiler(
          model, profile_memory=True, use_cuda=x.is_cuda
      ) as prof:
          model(x, edge_index)
  
      _, err = capfd.readouterr()
      assert "Completed Stage" in err
  
      (
          _,
          heading_list,
          raw_results,
          layer_names,
          layer_stats,
      ) = prof.get_trace()
      assert "Self CPU total" in heading_list
      assert "aten::relu" in raw_results
      assert "-act--aten::relu" in layer_names
  
imports: import torch
  from torch_geometric.nn import GraphSAGE
  from torch_geometric.profile.profiler import Profiler
  from torch_geometric.testing import withCUDA, withPackage
label_desc: Use image node_id 1 for calling a global function with example usage: test_profiler(capfd, get_dataset, device) without return types