code: def test_wl_conv():
      x1 = torch.tensor([1, 0, 0, 1])
      x2 = one_hot(x1)
      edge_index = torch.tensor(
          [[0, 1, 1, 2, 2, 3], [1, 0, 2, 1, 3, 2]]
      )
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      conv = WLConv()
      assert str(conv) == "WLConv()"
  
      out = conv(x1, edge_index)
      assert out.tolist() == [0, 1, 1, 0]
      assert torch.equal(conv(x2, edge_index), out)
      assert torch.equal(conv(x1, adj1.t()), out)
      assert torch.equal(conv(x2, adj1.t()), out)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.equal(conv(x1, adj2.t()), out)
          assert torch.equal(conv(x2, adj2.t()), out)
  
      assert conv.histogram(out).tolist() == [[2, 2]]
      assert torch.allclose(
          conv.histogram(out, norm=True),
          torch.tensor([[0.7071, 0.7071]]),
      )
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import WLConv
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import one_hot, to_torch_csc_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_wl_conv() without return types