code: def test_lazy_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(-1, 32, dim=3, kernel_size=5)
      assert str(conv) == "SplineConv(-1, 32, dim=3)"
      out = conv(x1, edge_index, value)
      assert out.size() == (4, 32)
  
      conv = SplineConv((-1, -1), 32, dim=3, kernel_size=5)
      assert str(conv) == "SplineConv((-1, -1), 32, dim=3)"
      out = conv((x1, x2), edge_index, value)
      assert out.size() == (2, 32)
  
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 2 for calling a global function with example usage: test_lazy_spline_conv() without return types