code: def test_from_networkx_subgraph_convert():
      import networkx as nx
  
      G = nx.complete_graph(5)
  
      edge_index = from_networkx(G).edge_index
      sub_edge_index_1, _ = subgraph(
          [0, 1, 3, 4], edge_index, relabel_nodes=True
      )
  
      sub_edge_index_2 = from_networkx(
          G.subgraph([0, 1, 3, 4])
      ).edge_index
  
      assert sub_edge_index_1.tolist() == sub_edge_index_2.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 16 for calling a global function with example usage: test_from_networkx_subgraph_convert() without return types