code: def test_gin_conv():
      x1 = torch.randn(4, 16)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 4))
  
      nn = Seq(Lin(16, 32), ReLU(), Lin(32, 32))
      conv = GINConv(nn, train_eps=True)
      assert str(conv) == (
          "GINConv(nn=Sequential(\n"
          "  (0): Linear(in_features=16, out_features=32, bias=True)\n"
          "  (1): ReLU()\n"
          "  (2): Linear(in_features=32, out_features=32, bias=True)\n"
          "))"
      )
      out = conv(x1, edge_index)
      assert out.size() == (4, 32)
      assert torch.allclose(
          conv(x1, edge_index, size=(4, 4)), out, atol=1e-6
      )
      assert torch.allclose(conv(x1, adj1.t()), out, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 4)
          )
          assert torch.allclose(conv(x1, adj2.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit(x1, edge_index), out, atol=1e-6)
          assert torch.allclose(
              jit(x1, edge_index, size=(4, 4)), out, atol=1e-6
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj2.t()), out, atol=1e-6)
  
      # Test bipartite message passing:
      adj1 = to_torch_csc_tensor(edge_index, size=(4, 2))
  
      out1 = conv((x1, x2), edge_index)
      assert out1.size() == (2, 32)
      assert torch.allclose(
          conv((x1, x2), edge_index, (4, 2)), out1, atol=1e-6
      )
      assert torch.allclose(conv((x1, x2), adj1.t()), out1, atol=1e-6)
  
      out2 = conv((x1, None), edge_index, (4, 2))
      assert out2.size() == (2, 32)
      assert torch.allclose(conv((x1, None), adj1.t()), out2, atol=1e-6)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj2 = SparseTensor.from_edge_index(
              edge_index, sparse_sizes=(4, 2)
          )
          assert torch.allclose(
              conv((x1, x2), adj2.t()), out1, atol=1e-6
          )
          assert torch.allclose(
              conv((x1, None), adj2.t()), out2, atol=1e-6
          )
  
      if is_full_test():
          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), adj2.t()), out1)
              assert torch.allclose(jit((x1, None), adj2.t()), out2)
  
file_path: pytorch_geometric/test/nn/conv/test_gin_conv.py
imports: import torch
  from torch.nn import Linear
  from torch.nn import ReLU
  from torch.nn import Sequential
  import torch_geometric.typing
  from torch_geometric.nn import GINConv, GINEConv
  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_gin_conv() without return types