code: def testBatchGroverGraph():
      import torch
      from deepchem.utils.grover import BatchGroverGraph
  
      grover_featurizer = dc.feat.GroverFeaturizer(
          features_generator=dc.feat.CircularFingerprint()
      )
  
      smiles = ["CC", "CCC"]
      mol_graphs = grover_featurizer.featurize(smiles)
      mol_graph = mol_graphs[0]
  
      batched_graph = BatchGroverGraph(mol_graphs)
      components = batched_graph.get_components()
      (
          f_atoms,
          f_bonds,
          a2b,
          b2a,
          b2revb,
          a2a,
          a_scope,
          b_scope,
          fg_labels,
      ) = components
  
      assert batched_graph.additional_features.shape == (
          2,
          mol_graph.additional_features.shape[0],
      )
      # 6 atoms: CC -> 2, CCC -> 3
      assert f_atoms.shape == (5, mol_graph.node_features.shape[1])
      # 7 bonds: CC -> 2, CCC -> 4 (bonds are considered as undirected
      # and a single bond contributes to 2 bonds)
      assert f_bonds.shape == (6, mol_graph.edge_features.shape[1])
      assert fg_labels.shape == (2, mol_graph.fg_labels.shape[0])
      assert (a_scope == torch.Tensor([[0, 2], [2, 3]])).all()
      assert (b_scope == torch.Tensor([[0, 2], [2, 4]])).all()
  
      assert (
          a2b == torch.Tensor([[0, 0], [1, 0], [0, 0], [3, 0], [5, 0]])
      ).all()
      assert (b2a == torch.Tensor([0, 1, 2, 4, 3, 4])).all()
  
      assert (b2revb == torch.Tensor([1, 0, 3, 2, 5, 4])).all()
      assert (
          a2a == torch.Tensor([[0, 0], [1, 0], [0, 0], [4, 0], [4, 0]])
      ).all()
  
label_desc: Use image node_id 1 for calling a global function with example usage: testBatchGroverGraph() without return types