code: def test_to_symmetric(device):
      row = torch.tensor([0, 0, 0, 1, 1], device=device)
      col = torch.tensor([0, 1, 2, 0, 2], device=device)
      value = torch.arange(1, 6, device=device)
      mat = SparseTensor(row=row, col=col, value=value)
      assert not mat.is_symmetric()
  
      mat = mat.to_symmetric()
  
      assert mat.is_symmetric()
      assert mat.to_dense().tolist() == [
          [2, 6, 3],
          [6, 0, 5],
          [3, 5, 0],
      ]
  
imports: from itertools import product
  import pytest
  import torch
  from torch_sparse import SparseTensor
  from torch_sparse.testing import devices, grad_dtypes
label_desc: Use image node_id 2 for calling a global function with example usage: test_to_symmetric(device) without return types