code: def test_hetero_data_update():
      data = HeteroData()
      data["paper"].x = torch.arange(0, 5)
      data["paper"].y = torch.arange(5, 10)
      data["author"].x = torch.arange(10, 15)
  
      other = HeteroData()
      other["paper"].x = torch.arange(15, 20)
      other["author"].y = torch.arange(20, 25)
      other["paper", "paper"].edge_index = torch.randint(5, (2, 20))
  
      data.update(other)
      assert len(data) == 3
      assert torch.equal(data["paper"].x, torch.arange(15, 20))
      assert torch.equal(data["paper"].y, torch.arange(5, 10))
      assert torch.equal(data["author"].x, torch.arange(10, 15))
      assert torch.equal(data["author"].y, torch.arange(20, 25))
      assert torch.equal(
          data["paper", "paper"].edge_index,
          other["paper", "paper"].edge_index,
      )
  
imports: import copy
  import pytest
  import torch
  from torch_geometric.data import HeteroData
  from torch_geometric.data.storage import EdgeStorage
  from torch_geometric.testing import get_random_edge_index, get_random_tensor_frame, withPackage
  from torch_geometric.typing import TensorFrame
label_desc: Use image node_id 14 for calling a global function with example usage: test_hetero_data_update() without return types