code: def test_enzymes(get_dataset):
      dataset = get_dataset(name="ENZYMES")
      assert len(dataset) == 600
      assert dataset.num_features == 3
      assert dataset.num_classes == 6
      assert str(dataset) == "ENZYMES(600)"
  
      assert len(dataset[0]) == 3
      assert len(dataset.shuffle()) == 600
      assert len(dataset.shuffle(return_perm=True)) == 2
      assert len(dataset[:100]) == 100
      assert len(dataset[0.1:0.2]) == 60
      assert len(dataset[torch.arange(100, dtype=torch.long)]) == 100
      mask = torch.zeros(600, dtype=torch.bool)
      mask[:100] = 1
      assert len(dataset[mask]) == 100
  
      loader = DataLoader(dataset, batch_size=len(dataset))
      for batch in loader:
          assert batch.num_graphs == len(batch) == 600
  
          avg_num_nodes = batch.num_nodes / batch.num_graphs
          assert pytest.approx(avg_num_nodes, abs=1e-2) == 32.63
  
          avg_num_edges = batch.num_edges / (2 * batch.num_graphs)
          assert pytest.approx(avg_num_edges, abs=1e-2) == 62.14
  
          assert list(batch.x.size()) == [batch.num_nodes, 3]
          assert list(batch.y.size()) == [batch.num_graphs]
          assert batch.y.max() + 1 == 6
          assert list(batch.batch.size()) == [batch.num_nodes]
          assert batch.ptr.numel() == batch.num_graphs + 1
  
          assert batch.has_isolated_nodes()
          assert not batch.has_self_loops()
          assert batch.is_undirected()
  
      loader = DataListLoader(dataset, batch_size=len(dataset))
      for data_list in loader:
          assert len(data_list) == 600
  
      dataset.transform = ToDense(num_nodes=126)
      loader = DenseDataLoader(dataset, batch_size=len(dataset))
      for data in loader:
          assert list(data.x.size()) == [600, 126, 3]
          assert list(data.adj.size()) == [600, 126, 126]
          assert list(data.mask.size()) == [600, 126]
          assert list(data.y.size()) == [600, 1]
  
imports: import pytest
  import torch
  from torch_geometric.loader import DataListLoader, DataLoader, DenseDataLoader
  from torch_geometric.testing import onlyOnline
  from torch_geometric.transforms import ToDense
label_desc: Use image node_id 1 for calling a global function with example usage: test_enzymes(get_dataset) without return types