text: import torch
  from torch_sparse import SparseTensor
  
  from torch_geometric.nn import SplineConv
  from torch_geometric.testing import is_full_test
  
  
  def test_spline_conv():
      x1 = torch.randn(4, 8)
      x2 = torch.randn(2, 16)
      edge_index = torch.tensor([[0, 1, 2, 3], [0, 0, 1, 1]])
      row, col = edge_index
      value = torch.rand(row.size(0), 3)
      adj = SparseTensor(row=row, col=col, value=value, sparse_sizes=(4, 4))
  
      conv = SplineConv(8, 32, dim=3, kernel_size=5)
      assert conv.__repr__() == '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)
      assert torch.allclose(conv(x1, adj.t()), out)
  
      if is_full_test():
          t = '(Tensor, Tensor, OptTensor, Size) -> Tensor'
          jit = torch.jit.script(conv.jittable(t))
          assert torch.allclose(jit(x1, edge_index, value), out)
          assert torch.allclose(jit(x1, edge_index, value, size=(4, 4)), out)
  
          t = '(Tensor, SparseTensor, OptTensor, Size) -> Tensor'
          jit = torch.jit.script(conv.jittable(t))
          assert torch.allclose(jit(x1, adj.t()), out)
  
      adj = adj.sparse_resize((4, 2))
      conv = SplineConv((8, 16), 32, dim=3, kernel_size=5)
      assert conv.__repr__() == 'SplineConv((8, 16), 32, dim=3)'
      out1 = conv((x1, x2), edge_index, value)
      out2 = conv((x1, None), edge_index, value, (4, 2))
      assert out1.size() == (2, 32)
      assert out2.size() == (2, 32)
      assert torch.allclose(conv((x1, x2), edge_index, value, (4, 2)), out1)
      assert torch.allclose(conv((x1, x2), adj.t()), out1)
      assert torch.allclose(conv((x1, None), adj.t()), out2)
  
      if is_full_test():
          t = '(OptPairTensor, Tensor, OptTensor, Size) -> Tensor'
          jit = torch.jit.script(conv.jittable(t))
          assert torch.allclose(jit((x1, x2), edge_index, value), out1)
          assert torch.allclose(jit((x1, x2), edge_index, value, size=(4, 2)),
                                out1)
          assert torch.allclose(jit((x1, None), edge_index, value, size=(4, 2)),
                                out2)
  
          t = '(OptPairTensor, SparseTensor, OptTensor, Size) -> Tensor'
          jit = torch.jit.script(conv.jittable(t))
          assert torch.allclose(jit((x1, x2), adj.t()), out1)
          assert torch.allclose(jit((x1, None), adj.t()), out2)
  
  
  def test_lazy_spline_conv():
      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 conv.__repr__() == '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 conv.__repr__() == 'SplineConv((-1, -1), 32, dim=3)'
      out = conv((x1, x2), edge_index, value)
      assert out.size() == (2, 32)
  