code: def test_to_heterogeneous_empty_edge_index():
      data = Data(
          x=torch.randn(5, 10),
          edge_index=torch.empty(2, 0, dtype=torch.long),
      )
      hetero_data = data.to_heterogeneous()
      assert hetero_data.node_types == ["0"]
      assert hetero_data.edge_types == []
      assert len(hetero_data) == 1
      assert torch.equal(hetero_data["0"].x, data.x)
  
      hetero_data = data.to_heterogeneous(
          node_type_names=["0"],
          edge_type_names=[("0", "to", "0")],
      )
      assert hetero_data.node_types == ["0"]
      assert hetero_data.edge_types == [("0", "to", "0")]
      assert len(hetero_data) == 2
      assert torch.equal(hetero_data["0"].x, data.x)
      assert torch.equal(
          hetero_data["0", "to", "0"].edge_index, data.edge_index
      )
  
imports: import copy
  import pytest
  import torch
  import torch.multiprocessing
  import torch_geometric
  from torch_geometric.data import Data
  from torch_geometric.data.storage import AttrType
  from torch_geometric.testing import get_random_tensor_frame, withPackage
label_desc: Use image node_id 3 for calling a global function with example usage: test_to_heterogeneous_empty_edge_index() without return types