code: def test_networkx_vice_versa_convert():
      import networkx as nx
  
      G = nx.complete_graph(5)
      assert G.is_directed() is False
      data = from_networkx(G)
      assert data.is_directed() is False
      G = to_networkx(data)
      assert G.is_directed() is True
      G = nx.to_undirected(G)
      assert G.is_directed() is False
  
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 10 for calling a global function with example usage: test_networkx_vice_versa_convert() without return types