code: def test_spline_conv():
      warnings.filterwarnings("ignore", ".*non-optimized CPU version.*")
  
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      value = torch.rand(edge_index.size(1), 3)
  
      conv = SplineConv(8, 32, dim=3, kernel_size=5)
      assert str(conv) == "SplineConv(8, 32, dim=3)"
      out = conv(x1, edge_index, value)
      assert out.size() == (4, 32)
      assert torch.allclose(
          conv(x1, edge_index, value, size=(4, 4)), out
      )
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(edge_index, value, (4, 4))
          assert torch.allclose(conv(x1, adj.t()), out, atol=1e-6)
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(
              jit(x1, edge_index, value), out, atol=1e-6
          )
          assert torch.allclose(
              jit(x1, edge_index, value, size=(4, 4)), out
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(jit(x1, adj.t()), out, atol=1e-6)
  
      # Test bipartite message passing:
      conv = SplineConv((8, 16), 32, dim=3, kernel_size=5)
      assert str(conv) == "SplineConv((8, 16), 32, dim=3)"
  
      out1 = conv((x1, x2), edge_index, value)
      assert out1.size() == (2, 32)
      assert torch.allclose(
          conv((x1, x2), edge_index, value, (4, 2)), out1
      )
  
      out2 = conv((x1, None), edge_index, value, (4, 2))
      assert out2.size() == (2, 32)
  
      if torch_geometric.typing.WITH_TORCH_SPARSE:
          adj = SparseTensor.from_edge_index(edge_index, value, (4, 2))
          assert torch.allclose(
              conv((x1, x2), adj.t()), out1, atol=1e-6
          )
          assert torch.allclose(
              conv((x1, None), adj.t()), out2, atol=1e-6
          )
  
      if is_full_test():
          jit = torch.jit.script(conv)
          assert torch.allclose(jit((x1, x2), edge_index, value), out1)
          assert torch.allclose(
              jit((x1, x2), edge_index, value, size=(4, 2)),
              out1,
              atol=1e-6,
          )
          assert torch.allclose(
              jit((x1, None), edge_index, value, size=(4, 2)),
              out2,
              atol=1e-6,
          )
  
          if torch_geometric.typing.WITH_TORCH_SPARSE:
              assert torch.allclose(
                  jit((x1, x2), adj.t()), out1, atol=1e-6
              )
              assert torch.allclose(
                  jit((x1, None), adj.t()), out2, atol=1e-6
              )
  
imports: import warnings
  import torch
  import torch_geometric.typing
  from torch_geometric.nn import SplineConv
  from torch_geometric.testing import is_full_test, withPackage
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_spline_conv() without return types