code: def test_cg_conv(batch_norm):
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      conv = CGConv(8, batch_norm=batch_norm)
      assert str(conv) == "CGConv(8, dim=0)"
      out = conv(x1, edge_index)
      assert out.size() == (4, 8)
      assert torch.allclose(conv(x1, adj1.t()), out)
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv(x1, adj2.t()), out)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x1, edge_index), 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 = CGConv((8, 16))
      assert str(conv) == "CGConv((8, 16), dim=0)"
      out = conv((x1, x2), edge_index)
      assert out.size() == (2, 16)
      assert torch.allclose(conv((x1, x2), adj1.t()), out, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(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), out, atol=1e-6
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, x2), adj2.t()), out, atol=1e-6
              )
  
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 1 for calling a global function with example usage: test_cg_conv(batch_norm) without return types