code: def test_signed_conv():
      x = torch.randn(4, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      conv1 = SignedConv(16, 32, first_aggr=True)
      assert str(conv1) == "SignedConv(16, 32, first_aggr=True)"
  
      conv2 = SignedConv(32, 48, first_aggr=False)
      assert str(conv2) == "SignedConv(32, 48, first_aggr=False)"
  
      out1 = conv1(x, edge_index, edge_index)
      assert out1.size() == (4, 64)
      assert torch.allclose(conv1(x, adj1.t(), adj1.t()), out1)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv1(x, adj2.t(), adj2.t()), out1)
  
      out2 = conv2(out1, edge_index, edge_index)
      assert out2.size() == (4, 96)
      assert torch.allclose(conv2(out1, adj1.t(), adj1.t()), out2)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(conv2(out1, adj2.t(), adj2.t()), out2)
  
      if is_full_test() and torch_geometric.typing.WITH_PT112:
          jit1 = torch.jit.script(conv1)
          jit2 = torch.jit.script(conv2)
          assert torch.allclose(jit1(x, edge_index, edge_index), out1)
          assert torch.allclose(
              jit2(out1, edge_index, edge_index), out2
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit1(x, adj2.t(), adj2.t()), out1)
              assert torch.allclose(
                  jit2(out1, adj2.t(), adj2.t()), out2
              )
  
      # Test bipartite message passing:
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 2))
  
      assert torch.allclose(
          conv1((x, x[:2]), edge_index, edge_index), out1[:2], atol=1e-6
      )
      assert torch.allclose(
          conv1((x, x[:2]), adj1.t(), adj1.t()), out1[:2], atol=1e-6
      )
      assert torch.allclose(
          conv2((out1, out1[:2]), edge_index, edge_index),
          out2[:2],
          atol=1e-6,
      )
      assert torch.allclose(
          conv2((out1, out1[:2]), adj1.t(), adj1.t()),
          out2[:2],
          atol=1e-6,
      )
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 2)
          )
          assert torch.allclose(
              conv1((x, x[:2]), adj2.t(), adj2.t()), out1[:2], atol=1e-6
          )
          assert torch.allclose(
              conv2((out1, out1[:2]), adj2.t(), adj2.t()),
              out2[:2],
              atol=1e-6,
          )
  
      if is_full_test() and torch_geometric.typing.WITH_PT112:
          assert torch.allclose(
              jit1((x, x[:2]), edge_index, edge_index),
              out1[:2],
              atol=1e-6,
          )
          assert torch.allclose(
              jit2((out1, out1[:2]), edge_index, edge_index),
              out2[:2],
              atol=1e-6,
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit1((x, x[:2]), adj2.t(), adj2.t()),
                  out1[:2],
                  atol=1e-6,
              )
              assert torch.allclose(
                  jit2((out1, out1[:2]), adj2.t(), adj2.t()),
                  out2[:2],
                  atol=1e-6,
              )
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import SignedConv
  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_signed_conv() without return types