code: def test_infograph_pretrain_overfit():
      """This tests the intended use of InfoGraph and InfoGraphStar together, with InfoGraph serving as a pretraining step for InfoGraphStar."""
      from deepchem.models.torch_models.infograph import (
          InfoGraphModel,
          InfoGraphStarModel,
      )
      import torch
  
      torch.manual_seed(123)
      np.random.seed(123)
  
      dataset, _ = get_regression_dataset()
      num_feat = 30
      edge_dim = 11
      dim = 32
  
      infograph = InfoGraphModel(num_feat, edge_dim)
      infographstar = InfoGraphStarModel(
          num_feat,
          edge_dim,
          dim,
          num_gc_layers=2,
          task="semisupervised",
      )
  
      loss1 = infographstar.fit(dataset, nb_epoch=10)
      infograph.fit(dataset, nb_epoch=20)
      infographstar.load_from_pretrained(infograph, ["unsup_encoder"])
      loss2 = infographstar.fit(dataset, nb_epoch=10)
      infographstar.fit(dataset, nb_epoch=200)
      prediction = infographstar.predict_on_batch(dataset.X).reshape(
          -1, 1
      )
      assert np.allclose(np.round(dataset.y), np.round(prediction))
      assert loss1 > loss2
  
imports: import os
  import pytest
  import numpy
  import deepchem
  from deepchem.feat.molecule_featurizers import MolGraphConvFeaturizer
label_desc: Use image node_id 14 for calling a global function with example usage: test_infograph_pretrain_overfit() without return types