code: def test_infograph_finetune_classification():
      from deepchem.models.torch_models.infograph import InfoGraphModel
      import torch
  
      torch.manual_seed(123)
      dataset, metric = get_classification_dataset()
      num_feat = 30
      edge_dim = 11
      model = InfoGraphModel(
          num_feat,
          edge_dim,
          num_gc_layers=3,
          task="classification",
          device=torch.device("cpu"),
          n_classes=2,
          n_tasks=1,
      )
  
      model.fit(dataset, nb_epoch=100)
      scores = model.evaluate(dataset, [metric])
      assert scores["mean-roc_auc_score"] >= 0.9
  
imports: import os
  import pytest
  import numpy
  import deepchem
  from deepchem.feat.molecule_featurizers import MolGraphConvFeaturizer
label_desc: Use image node_id 9 for calling a global function with example usage: test_infograph_finetune_classification() without return types