code: def test_gen_conv(aggr):
      x1 = torch.randn(4, 16)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      value = torch.randn(edge_index.size(1), 16)
      adj1 = to_torch_coo_tensor(edge_index, size=(4, 4))
      adj2 = to_torch_coo_tensor(edge_index, value, size=(4, 4))
  
      conv = GENConv(16, 32, aggr, edge_dim=16, msg_norm=True)
      assert str(conv) == f"GENConv(16, 32, aggr={aggr})"
      out1 = conv(x1, edge_index)
      assert out1.size() == (4, 32)
      assert torch.allclose(conv(x1, edge_index, size=(4, 4)), out1)
      assert torch.allclose(conv(x1, adj1.t().coalesce()), out1)
  
      out2 = conv(x1, edge_index, value)
      assert out2.size() == (4, 32)
      assert torch.allclose(conv(x1, edge_index, value, (4, 4)), out2)
      # t() expects a tensor with <= 2 sparse and 0 dense dimensions
      assert torch.allclose(
          conv(x1, adj2.transpose(1, 0).coalesce()), out2
      )
  
      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(x1, adj3.t()), out1, atol=1e-4)
          assert torch.allclose(conv(x1, adj4.t()), out2, atol=1e-4)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x1, edge_index), out1, atol=1e-4)
          assert torch.allclose(
              jit(x1, edge_index, size=(4, 4)), out1, atol=1e-4
          )
          assert torch.allclose(
              jit(x1, edge_index, value), out2, atol=1e-4
          )
          assert torch.allclose(
              jit(x1, edge_index, value, size=(4, 4)), out2, atol=1e-4
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj3.t()), out1, atol=1e-4)
              assert torch.allclose(jit(x1, adj4.t()), out2, atol=1e-4)
  
      # Test bipartite message passing:
      adj1 = to_torch_coo_tensor(edge_index, size=(4, 2))
      adj2 = to_torch_coo_tensor(edge_index, value, size=(4, 2))
  
      out1 = conv((x1, x2), edge_index)
      assert out1.size() == (2, 32)
      assert torch.allclose(
          conv((x1, x2), edge_index, size=(4, 2)), out1
      )
      assert torch.allclose(conv((x1, x2), adj1.t().coalesce()), out1)
  
      out2 = conv((x1, x2), edge_index, value)
      assert out2.size() == (2, 32)
      assert torch.allclose(
          conv((x1, x2), edge_index, value, (4, 2)), out2
      )
      assert torch.allclose(
          conv((x1, x2), adj2.transpose(1, 0).coalesce()), out2
      )
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj3 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 2)
          )
          adj4 = SparseTensor.from_edge_index(edge_index, value, (4, 2))
          assert torch.allclose(
              conv((x1, x2), adj3.t()), out1, atol=1e-4
          )
          assert torch.allclose(
              conv((x1, x2), adj4.t()), out2, atol=1e-4
          )
  
      if is_full_test():
          assert torch.allclose(
              jit((x1, x2), edge_index), out1, atol=1e-4
          )
          assert torch.allclose(
              jit((x1, x2), edge_index, size=(4, 2)), out1, atol=1e-4
          )
          assert torch.allclose(
              jit((x1, x2), edge_index, value), out2, atol=1e-4
          )
          assert torch.allclose(
              jit((x1, x2), edge_index, value, (4, 2)), out2, atol=1e-4
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, x2), adj3.t()), out1, atol=1e-4
              )
              assert torch.allclose(
                  jit((x1, x2), adj4.t()), out2, atol=1e-4
              )
  
      # Test bipartite message passing with unequal feature dimensions:
      conv.reset_parameters()
      assert float(conv.msg_norm.scale) == 1
  
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 16)
  
      conv = GENConv((8, 16), 32, aggr)
      assert str(conv) == f"GENConv((8, 16), 32, aggr={aggr})"
  
      out1 = conv((x1, x2), edge_index)
      assert out1.size() == (2, 32)
      assert torch.allclose(
          conv((x1, x2), edge_index, size=(4, 2)), out1
      )
      assert torch.allclose(conv((x1, x2), adj1.t().coalesce()), out1)
  
      out2 = conv((x1, None), edge_index, size=(4, 2))
      assert out2.size() == (2, 32)
      assert torch.allclose(conv((x1, None), adj1.t().coalesce()), out2)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(
              conv((x1, x2), adj3.t()), out1, atol=1e-4
          )
          assert torch.allclose(
              conv((x1, None), adj3.t()), out2, atol=1e-4
          )
  
      # Test lazy initialization:
      conv = GENConv((-1, -1), 32, aggr, edge_dim=-1)
      assert str(conv) == f"GENConv((-1, -1), 32, aggr={aggr})"
      out1 = conv((x1, x2), edge_index, value)
      assert out1.size() == (2, 32)
      assert torch.allclose(
          conv((x1, x2), edge_index, value, size=(4, 2)), out1
      )
      assert torch.allclose(
          conv((x1, x2), adj2.transpose(1, 0).coalesce()), out1
      )
  
      out2 = conv((x1, None), edge_index, value, size=(4, 2))
      assert out2.size() == (2, 32)
      assert torch.allclose(
          conv((x1, None), adj2.transpose(1, 0).coalesce()), out2
      )
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          assert torch.allclose(conv((x1, x2), adj4.t()), out1)
          assert torch.allclose(conv((x1, None), adj4.t()), out2)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit((x1, x2), edge_index, value), out1, atol=1e-4
          )
          assert torch.allclose(
              jit((x1, x2), edge_index, value, size=(4, 2)),
              out1,
              atol=1e-4,
          )
          assert torch.allclose(
              jit((x1, None), edge_index, value, size=(4, 2)),
              out2,
              atol=1e-4,
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, x2), adj4.t()), out1, atol=1e-4
              )
              assert torch.allclose(
                  jit((x1, None), adj4.t()), out2, atol=1e-4
              )
  
imports: import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import GENConv
  from torch_geometric.testing import is_full_test
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_torch_coo_tensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_gen_conv(aggr) without return types