code: def test_res_gated_graph_conv(edge_dim):
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 32)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      edge_attr = (
          torch.randn(edge_index.size(1), edge_dim)
          if edge_dim
          else None
      )
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      conv = ResGatedGraphConv(8, 32, edge_dim=edge_dim)
      assert str(conv) == "ResGatedGraphConv(8, 32)"
  
      out = conv(x1, edge_index, edge_attr)
      assert out.size() == (4, 32)
      assert torch.allclose(
          conv(x1, adj1.t(), edge_attr), out, atol=1e-6
      )
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, edge_attr, (4, 4)
          )
          assert torch.allclose(conv(x1, adj2.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit(x1, edge_index, edge_attr), out, atol=1e-6
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj2.t()), out, atol=1e-6)
  
      # Test bipartite message passing:
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 2))
  
      conv = ResGatedGraphConv((8, 32), 32, edge_dim=edge_dim)
      assert str(conv) == "ResGatedGraphConv((8, 32), 32)"
  
      out = conv((x1, x2), edge_index, edge_attr)
      assert out.size() == (2, 32)
      assert torch.allclose(
          conv((x1, x2), adj1.t(), edge_attr), out, atol=1e-6
      )
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, edge_attr, (4, 2)
          )
          assert torch.allclose(
              conv((x1, x2), adj2.t()), out, atol=1e-6
          )
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit((x1, x2), edge_index, edge_attr), out, atol=1e-6
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, x2), adj2.t()), out, atol=1e-6
              )
  
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import ResGatedGraphConv
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_torch_csc_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_res_gated_graph_conv(edge_dim) without return types