code: def testGroverPretrain(grover_batched_graph):
      from deepchem.models.torch_models.grover import GroverPretrain
      from deepchem.models.torch_models.grover_layers import (
          GroverEmbedding,
          GroverAtomVocabPredictor,
          GroverBondVocabPredictor,
          GroverFunctionalGroupPredictor,
      )
  
      (
          f_atoms,
          f_bonds,
          a2b,
          b2a,
          b2revb,
          a2a,
          a_scope,
          b_scope,
          _,
      ) = grover_batched_graph.get_components()
      components = {}
      components["embedding"] = GroverEmbedding(
          node_fdim=f_atoms.shape[1], edge_fdim=f_bonds.shape[1]
      )
      components["atom_vocab_task_atom"] = GroverAtomVocabPredictor(
          vocab_size=10, in_features=128
      )
      components["atom_vocab_task_bond"] = GroverAtomVocabPredictor(
          vocab_size=10, in_features=128
      )
      components["bond_vocab_task_atom"] = GroverBondVocabPredictor(
          vocab_size=10, in_features=128
      )
      components["bond_vocab_task_bond"] = GroverBondVocabPredictor(
          vocab_size=10, in_features=128
      )
      components[
          "functional_group_predictor"
      ] = GroverFunctionalGroupPredictor(functional_group_size=10)
      model = GroverPretrain(**components)
  
      inputs = f_atoms, f_bonds, a2b, b2a, b2revb, a_scope, b_scope, a2a
      output = model(inputs)
      assert len(output) == 8
      # 9: number of atoms
      assert output[0].shape == (9, 10)
      assert output[1].shape == (9, 10)
      # 6: number of bonds
      assert output[2].shape == (6, 10)
      assert output[3].shape == (6, 10)
      # 3: number of molecules
      assert output[4].shape == (3, 10)
      assert output[5].shape == (3, 10)
      assert output[6].shape == (3, 10)
      assert output[7].shape == (3, 10)
  
file_path: deepchem/deepchem/models/torch_models/tests/test_grover.py
imports: import os
  import pytest
  import numpy
  import deepchem
label_desc: Use image node_id 3 for calling a global function with example usage: testGroverPretrain(grover_batched_graph) without return types