code: def test_chemception_classification():
      n_tasks = 5
      dataset, metric = get_dataset(
          mode="classification",
          featurizer="smiles2img",
          n_tasks=n_tasks,
      )
      model = ChemCeption(
          n_tasks=n_tasks,
          img_spec="engd",
          model_dir=None,
          mode="classification",
      )
      model.fit(dataset, nb_epoch=300)
      scores = model.evaluate(dataset, [metric], [])
      assert scores["mean-roc_auc_score"] >= 0.9
  
file_path: deepchem/deepchem/models/tests/test_chemnet_models.py
imports: import os
  import numpy
  import tempfile
  import pytest
  from flaky import flaky
  import deepchem
  from deepchem.feat import create_char_to_idx, SmilesToSeq, SmilesToImage
  from deepchem.molnet.load_function.chembl25_datasets import CHEMBL25_TASKS
label_desc: Use image node_id 3 for calling a global function with example usage: test_chemception_classification() without return types