code: def test_trimesh_vice_versa():
      pos = torch.tensor(
          [[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0]],
          dtype=torch.float,
      )
      face = torch.tensor([[0, 1, 2], [1, 2, 3]]).t()
  
      data = Data(pos=pos, face=face)
      mesh = to_trimesh(data)
      data = from_trimesh(mesh)
  
      assert pos.tolist() == data.pos.tolist()
      assert face.tolist() == data.face.tolist()
  
imports: import pytest
  import scipy.sparse
  import torch
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.testing import get_random_edge_index, withPackage
  from torch_geometric.utils import from_cugraph, from_dgl, from_networkit, from_networkx, from_scipy_sparse_matrix, from_trimesh, sort_edge_index, subgraph, to_cugraph, to_dgl, to_networkit, to_networkx, to_scipy_sparse_matrix, to_trimesh
label_desc: Use image node_id 21 for calling a global function with example usage: test_trimesh_vice_versa() without return types