code: def test_lists_of_sparse_tensors():
      e1 = torch.tensor([[4, 1, 3, 2, 2, 3], [1, 3, 2, 3, 3, 2]])
      e2 = torch.tensor([[0, 1, 4, 7, 2, 9], [7, 2, 2, 1, 4, 7]])
      e3 = torch.tensor([[3, 5, 1, 2, 3, 3], [5, 0, 2, 1, 3, 7]])
      e4 = torch.tensor([[0, 1, 9, 2, 0, 3], [1, 1, 2, 1, 3, 2]])
      adj1 = SparseTensor.from_edge_index(e1, sparse_sizes=(11, 11))
      adj2 = SparseTensor.from_edge_index(e2, sparse_sizes=(22, 22))
      adj3 = SparseTensor.from_edge_index(e3, sparse_sizes=(12, 12))
      adj4 = SparseTensor.from_edge_index(e4, sparse_sizes=(15, 15))
  
      d1 = Data(adj_test=[adj1, adj2])
      d2 = Data(adj_test=[adj3, adj4])
  
      data_list = [d1, d2]
      dataset = MyTestDataset(data_list)
      assert len(dataset) == 2
      assert dataset[0].adj_test[0].sparse_sizes() == (11, 11)
      assert dataset[0].adj_test[1].sparse_sizes() == (22, 22)
      assert dataset[1].adj_test[0].sparse_sizes() == (12, 12)
      assert dataset[1].adj_test[1].sparse_sizes() == (15, 15)
  
imports: import copy
  import pytest
  import torch
  from torch_geometric import EdgeIndex
  from torch_geometric.data import Data, HeteroData, InMemoryDataset
  from torch_geometric.datasets import KarateClub
  from torch_geometric.testing import withPackage
  from torch_geometric.transforms import BaseTransform
  from torch_geometric.typing import SparseTensor
label_desc: Use image node_id 13 for calling a global function with example usage: test_lists_of_sparse_tensors() without return types