code: def test_transpose(dtype, device):
      row = torch.tensor([1, 0, 1, 0, 2, 1], device=device)
      col = torch.tensor([0, 1, 1, 1, 0, 0], device=device)
      index = torch.stack([row, col], dim=0)
      value = tensor(
          [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6], [6, 7]],
          dtype,
          device,
      )
  
      index, value = transpose(index, value, m=3, n=2)
      assert index.tolist() == [[0, 0, 1, 1], [1, 2, 0, 1]]
      assert value.tolist() == [[7, 9], [5, 6], [6, 8], [3, 4]]
  
imports: from itertools import product
  import pytest
  import torch
  from torch_sparse import transpose
  from torch_sparse.testing import devices, dtypes, tensor
label_desc: Use image node_id 2 for calling a global function with example usage: test_transpose(dtype, device) without return types