code: def test_summary_with_reusing_layers():
      act = nn.ReLU(inplace=True)
      model1 = nn.Sequential(
          act, nn.Identity(), act, nn.Identity(), act
      )
      model2 = nn.Sequential(
          nn.ReLU(inplace=True),
          nn.Identity(),
          nn.ReLU(inplace=True),
          nn.Identity(),
          nn.ReLU(inplace=True),
      )
      x = torch.randn(10)
  
      assert summary(model1, x) == summary(model2, x)
  
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 7 for calling a global function with example usage: test_summary_with_reusing_layers() without return types