code: def test_sequential_with_ordered_dict():
      x = torch.randn(4, 16)
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
  
      model = Sequential(
          "x, edge_index",
          modules=OrderedDict(
              [
                  ("conv1", (GCNConv(16, 32), "x, edge_index -> x")),
                  ("conv2", (GCNConv(32, 64), "x, edge_index -> x")),
              ]
          ),
      )
  
      assert isinstance(model.conv1, GCNConv)
      assert isinstance(model.conv2, GCNConv)
  
      x = model(x, edge_index)
      assert x.size() == (4, 64)
  
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 6 for calling a global function with example usage: test_sequential_with_ordered_dict() without return types