code: def test_sequential_with_multiple_return_values():
      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",
          [
              (GCNConv(16, 32), "x, edge_index -> x1"),
              (GCNConv(32, 64), "x1, edge_index -> x2"),
              (lambda x1, x2: (x1, x2), "x1, x2 -> x1, x2"),
          ],
      )
  
      x1, x2 = model(x, edge_index)
      assert x1.size() == (4, 32)
      assert x2.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 5 for calling a global function with example usage: test_sequential_with_multiple_return_values() without return types