code: def test_smiles_to_vec_regression():
      n_tasks = 5
      max_seq_len = 20
      dataset, metric, char_to_idx = get_dataset(
          mode="regression",
          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="regression",
      )
      model.fit(dataset, nb_epoch=500)
      scores = model.evaluate(dataset, [metric], [])
      assert scores["mean_absolute_error"] < 0.1
  
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 4 for calling a global function with example usage: test_smiles_to_vec_regression() without return types