code: def test_smiles_to_vec_classification():
      n_tasks = 5
      max_seq_len = 20
      (
          dataset,
          metric,
          char_to_idx,
      ) = get_dataset(
          mode="classification",
          featurizer="smiles2seq",
          n_tasks=n_tasks,
          max_seq_len=max_seq_len,
      )
      model = Smiles2Vec(
          char_to_idx=char_to_idx,
          max_seq_len=max_seq_len,
          use_conv=True,
          n_tasks=n_tasks,
          model_dir=None,
          mode="classification",
      )
      model.fit(dataset, nb_epoch=500)
      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 5 for calling a global function with example usage: test_smiles_to_vec_classification() without return types