code: def test_wl_conv():
      edge_index = torch.tensor(
          [[0, 1, 1, 2], [1, 0, 2, 1]], dtype=torch.long
      )
      x = torch.tensor([[-1], [0], [1]], dtype=torch.float)
  
      conv = WLConvContinuous()
      assert str(conv) == "WLConvContinuous()"
  
      out = conv(x, edge_index)
      assert out.tolist() == [[-0.5], [0.0], [0.5]]
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(3, 3)
          )
          assert torch.allclose(conv(x, adj.t()), out)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x, edge_index), out)
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x, adj.t()), out, atol=1e-6)
  
      # Test bipartite message passing:
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 8)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      edge_weight = torch.randn(edge_index.size(1))
  
      out1 = conv((x1, None), edge_index, edge_weight, size=(4, 2))
      assert out1.size() == (2, 8)
  
      out2 = conv((x1, x2), edge_index, edge_weight)
      assert out2.size() == (2, 8)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, edge_weight, (4, 2)
          )
          assert torch.allclose(conv((x1, None), adj.t()), out1)
          assert torch.allclose(conv((x1, x2), adj.t()), out2)
  
      if is_full_test():
          assert torch.allclose(
              jit((x1, None), edge_index, edge_weight, size=(4, 2)),
              out1,
          )
          assert torch.allclose(
              jit((x1, x2), edge_index, edge_weight), out2
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, None), adj.t()), out1, atol=1e-6
              )
              assert torch.allclose(
                  jit((x1, x2), adj.t()), out2, atol=1e-6
              )
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import WLConvContinuous
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_wl_conv() without return types