code: def test_cg_conv_with_edge_features():
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      value = torch.rand(edge_index.size(1), 3)
  
      conv = CGConv(8, dim=3)
      assert str(conv) == "CGConv(8, dim=3)"
      out = conv(x1, edge_index, value)
      assert out.size() == (4, 8)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(edge_index, value, (4, 4))
          assert torch.allclose(conv(x1, adj.t()), out)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x1, edge_index, value), out)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj.t()), out)
  
      # Test bipartite message passing:
      conv = CGConv((8, 16), dim=3)
      assert str(conv) == "CGConv((8, 16), dim=3)"
      out = conv((x1, x2), edge_index, value)
      assert out.size() == (2, 16)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(edge_index, value, (4, 2))
          assert torch.allclose(conv((x1, x2), adj.t()), out)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit((x1, x2), edge_index, value), out)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit((x1, x2), adj.t()), out)
  
file_path: pytorch_geometric/test/nn/conv/test_cg_conv.py
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import CGConv
  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 2 for calling a global function with example usage: test_cg_conv_with_edge_features() without return types