code: def test_pickling(tmp_path):
      data = Data(x=torch.randn(5, 16))
      batch = Batch.from_data_list([data, data, data, data])
      assert id(batch._store._parent()) == id(batch)
      assert batch.num_nodes == 20
  
      # filename = f'{random.randrange(sys.maxsize)}.pt'
      path = osp.join(tmp_path, "batch.pt")
      torch.save(batch, path)
      assert id(batch._store._parent()) == id(batch)
      assert batch.num_nodes == 20
  
      batch = torch.load(path)
      assert id(batch._store._parent()) == id(batch)
      assert batch.num_nodes == 20
  
      assert batch.__class__.__name__ == "DataBatch"
      assert batch.num_graphs == len(batch) == 4
  
imports: import os.path
  import numpy
  import pytest
  import torch
  import torch_geometric
  from torch_geometric import EdgeIndex
  from torch_geometric.data import Batch, Data, HeteroData
  from torch_geometric.testing import get_random_edge_index, withPackage
  from torch_geometric.typing import SparseTensor
  from torch_geometric.utils import to_edge_index, to_torch_sparse_tensor
label_desc: Use image node_id 6 for calling a global function with example usage: test_pickling(tmp_path) without return types