code: def test_logsumexp():
      inputs = torch.tensor(
          [
              0.5,
              0.5,
              0.0,
              -2.1,
              3.2,
              7.0,
              -1.0,
              -100.0,
          ]
      )
      inputs.requires_grad_()
      index = torch.tensor([0, 0, 1, 1, 1, 2, 4, 4])
      splits = [2, 3, 1, 0, 2]
  
      outputs = scatter_logsumexp(inputs, index)
  
      for src, out in zip(inputs.split(splits), outputs.unbind()):
          if src.numel() > 0:
              assert (
                  out.tolist() == torch.logsumexp(src, dim=0).tolist()
              )
          else:
              assert out.item() == 0.0
  
      outputs.backward(torch.randn_like(outputs))
  
      jit = torch.jit.script(scatter_logsumexp)
      assert jit(inputs, index).tolist() == outputs.tolist()
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_logsumexp() without return types