code: def test_batching_of_batches():
      data = Data(x=torch.randn(2, 16))
      batch = Batch.from_data_list([data, data])
  
      batch = Batch.from_data_list([batch, batch])
      assert batch.num_graphs == len(batch) == 2
      assert batch.x[0:2].tolist() == data.x.tolist()
      assert batch.x[2:4].tolist() == data.x.tolist()
      assert batch.x[4:6].tolist() == data.x.tolist()
      assert batch.x[6:8].tolist() == data.x.tolist()
      assert batch.batch.tolist() == [0, 0, 1, 1, 2, 2, 3, 3]
  
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 8 for calling a global function with example usage: test_batching_of_batches() without return types