code: def test_general_conv(kwargs):
      x = torch.randn(4, 8)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      edge_attr = torch.randn(edge_index.size(1), 16)
  
      conv = GeneralConv(8, 32, in_edge_channels=16, **kwargs)
      assert str(conv) == "GeneralConv(8, 32)"
      out = conv(x, edge_index, edge_attr)
      assert out.size() == (4, 32)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, edge_attr, (4, 4)
          )
          assert torch.allclose(conv(x, adj.t()), out, atol=1e-6)
  
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import GeneralConv
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_general_conv(kwargs) without return types