code: def test_summary_with_jit_model():
      model = nn.Sequential(
          nn.Linear(32, 16), nn.ReLU(), nn.Linear(16, 8)
      )
      model = torch.jit.script(model)
      x = torch.randn(100, 32)
  
      expected = """
  +----------------------------+---------------+----------------+----------+
  | Layer                      | Input Shape   | Output Shape   | #Param   |
  |----------------------------+---------------+----------------+----------|
  | RecursiveScriptModule      | --            | --             | 664      |
  | ├─(0)RecursiveScriptModule | --            | --             | 528      |
  | ├─(1)RecursiveScriptModule | --            | --             | --       |
  | ├─(2)RecursiveScriptModule | --            | --             | 136      |
  +----------------------------+---------------+----------------+----------+
  """
      assert summary(model, x) == expected[1:-1]
  
imports: from typing import Optional
  import pytest
  import torch
  from torch import Tensor, nn
  import torch_geometric.typing
  from torch_geometric.nn import Linear, SAGEConv, summary, to_hetero
  from torch_geometric.nn.models import GCN
  from torch_geometric.testing import withPackage
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 10 for calling a global function with example usage: test_summary_with_jit_model() without return types