code: def test_dag_regression_model():
      np.random.seed(1234)
      tf.random.set_seed(1234)
      tasks, dataset, transformers, metric = get_dataset(
          "regression", "GraphConv"
      )
  
      max_atoms = max([mol.get_num_atoms() for mol in dataset.X])
      transformer = DAGTransformer(max_atoms=max_atoms)
      dataset = transformer.transform(dataset)
  
      model = DAGModel(
          len(tasks),
          max_atoms=max_atoms,
          mode="regression",
          learning_rate=0.003,
      )
  
      model.fit(dataset, nb_epoch=100)
      scores = model.evaluate(dataset, [metric], transformers)
      assert scores["mean_absolute_error"] < 0.15
  
imports: import os
  import numpy
  import pytest
  from scipy import io
  from deepchem.data import NumpyDataset, CSVLoader
  from deepchem.trans import DAGTransformer
  from deepchem.molnet import load_bace_classification, load_delaney
  from deepchem.feat import ConvMolFeaturizer
  from deepchem.metrics import Metric, roc_auc_score, mean_absolute_error
  from deepchem.utils.data_utils import download_url, get_data_dir
  from flaky import flaky
label_desc: Use image node_id 9 for calling a global function with example usage: test_dag_regression_model() without return types