code: def test_to_networkit_vice_versa():
      edge_index = torch.tensor([[0, 1], [1, 0]])
  
      g = to_networkit(edge_index, directed=False)
      assert not g.isDirected()
      assert not g.isWeighted()
  
      edge_index, edge_weight = from_networkit(g)
      assert edge_index.tolist() == [[0, 1], [1, 0]]
      assert edge_weight is None
  
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 18 for calling a global function with example usage: test_to_networkit_vice_versa() without return types