code: def test_sequential_tracable():
      model = Sequential(
          "x, edge_index",
          [
              (GCNConv(16, 64), "x, edge_index -> x1"),
              ReLU(inplace=True),
              (GCNConv(64, 64), "x1, edge_index -> x2"),
              ReLU(inplace=True),
              (lambda x1, x2: x1 + x2, "x1, x2 -> x"),
              Linear(64, 7),
          ],
      )
      symbolic_trace(model)
  
file_path: pytorch_geometric/test/nn/test_sequential.py
imports: from collections import OrderedDict
  import torch
  import torch.fx
  from torch.nn import Dropout, Linear, ReLU
  import torch_geometric.typing
  from torch_geometric.nn import GCNConv, JumpingKnowledge, MessagePassing, SAGEConv, Sequential, global_mean_pool, to_hetero
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 4 for calling a global function with example usage: test_sequential_tracable() without return types