code: def test_std():
      src = torch.tensor(
          [[2, 0, 1, 4, 3], [0, 2, 1, 3, 4]], dtype=torch.float
      )
      src.requires_grad_()
      index = torch.tensor(
          [[0, 0, 0, 0, 0], [1, 1, 1, 1, 1]], dtype=torch.long
      )
  
      out = scatter_std(src, index, dim=-1, unbiased=True)
      std = src.std(dim=-1, unbiased=True)[0]
      expected = torch.tensor([[std, 0], [0, std]])
      assert torch.allclose(out, expected)
  
      out.backward(torch.randn_like(out))
  
      jit = torch.jit.script(scatter_std)
      assert (
          jit(src, index, dim=-1, unbiased=True).tolist()
          == out.tolist()
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_std() without return types