code: def gcn():
      torch.manual_seed(1)
      model = GCN(32, 16, num_layers=2, out_channels=32)
      x = torch.randn(100, 32)
      edge_index = torch.randint(100, size=(2, 20))
      adj_t: Optional[SparseTensor] = None
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj_t = SparseTensor.from_edge_index(
              edge_index,
              sparse_sizes=(100, 100),
          ).t()
      return dict(model=model, x=x, edge_index=edge_index, adj_t=adj_t)
  
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 1 for calling a global function with example usage: gcn() and returns: dict