code: def test_citeseer_with_random_split(get_dataset):
      dataset = get_dataset(
          name="CiteSeer",
          split="random",
          num_train_per_class=11,
          num_val=29,
          num_test=41,
      )
      data = dataset[0]
      # from torch_geometric import EdgeIndex
      # assert isinstance(data.edge_index, EdgeIndex)
      # assert data.edge_index.sparse_size() == (data.num_nodes, data.num_nodes)
      # assert data.edge_index.is_undirected
      # assert data.edge_index.is_sorted_by_col
  
      assert data.train_mask.sum() == dataset.num_classes * 11
      assert data.val_mask.sum() == 29
      assert data.test_mask.sum() == 41
      assert (
          data.train_mask & data.val_mask & data.test_mask
      ).sum() == 0
  
imports: from torch_geometric.loader import DataLoader
  from torch_geometric.testing import onlyOnline
label_desc: Use image node_id 3 for calling a global function with example usage: test_citeseer_with_random_split(get_dataset) without return types