code: def test_mf_conv():
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
  
      conv = MFConv(8, 32)
      assert str(conv) == "MFConv(8, 32)"
      out = conv(x1, edge_index)
      assert out.size() == (4, 32)
      assert torch.allclose(conv(x1, edge_index, size=(4, 4)), out)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(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), out)
          assert torch.allclose(jit(x1, edge_index, size=(4, 4)), out)
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj.t()), out)
  
      # Test bipartite message passing:
      conv = MFConv((8, 16), 32)
      assert str(conv) == "MFConv((8, 16), 32)"
  
      out1 = conv((x1, x2), edge_index)
      assert out1.size() == (2, 32)
      assert torch.allclose(conv((x1, x2), edge_index, (4, 2)), out1)
  
      out2 = conv((x1, None), edge_index, (4, 2))
      assert out2.size() == (2, 32)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 2)
          )
          assert torch.allclose(conv((x1, x2), adj.t()), out1)
          assert torch.allclose(conv((x1, None), adj.t()), out2)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit((x1, x2), edge_index), out1)
          assert torch.allclose(
              jit((x1, x2), edge_index, size=(4, 2)), out1
          )
          assert torch.allclose(
              jit((x1, None), edge_index, size=(4, 2)), out2
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit((x1, x2), adj.t()), out1)
              assert torch.allclose(jit((x1, None), adj.t()), out2)
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import MFConv
  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_mf_conv() without return types