code: def test_antisymmetric_conv():
      x = torch.randn(4, 8)
      edge_index = torch.tensor(
          [[0, 0, 0, 1, 2, 3], [1, 2, 3, 0, 0, 0]]
      )
      value = torch.rand(edge_index.size(1))
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
      adj2 = to_torch_csc_tensor(edge_index, value, size=(4, 4))
  
      conv = AntiSymmetricConv(8)
      assert str(conv) == (
          "AntiSymmetricConv(8, phi=GCNConv(8, 8), "
          "num_iters=1, epsilon=0.1, gamma=0.1)"
      )
  
      out1 = conv(x, edge_index)
      assert out1.size() == (4, 8)
      assert torch.allclose(conv(x, adj1.t()), out1, atol=1e-6)
  
      out2 = conv(x, edge_index, value)
      assert out2.size() == (4, 8)
      assert torch.allclose(conv(x, adj2.t()), out2, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj3 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          adj4 = SparseTensor.from_edge_index(edge_index, value, (4, 4))
          assert torch.allclose(conv(x, adj3.t()), out1, atol=1e-6)
          assert torch.allclose(conv(x, adj4.t()), out2, atol=1e-6)
  
imports: import torch
  import torch_geometric.typing
  from torch_geometric.nn import AntiSymmetricConv
  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_antisymmetric_conv() without return types