code: def test_dtnn_multitask_regression_overfit():
      current_dir = os.path.dirname(os.path.abspath(__file__))
      dataset_file = os.path.join(current_dir, "assets/qm9_mini.sdf")
      TASKS = ["alpha", "u0_atom"]
      loader = dc.data.SDFLoader(
          tasks=TASKS,
          featurizer=dc.feat.CoulombMatrix(29),
          sanitize=True,
      )
      data = loader.create_dataset(dataset_file, shard_size=100)
      dataset = data.select(range(10))
  
      regression_metric = dc.metrics.Metric(
          dc.metrics.pearson_r2_score, task_averager=np.mean
      )
      model = dc.models.torch_models.DTNNModel(dataset.y.shape[1])
      model.fit(dataset, nb_epoch=100)
      scores = model.evaluate(dataset, [regression_metric])
      assert scores[regression_metric.name] > 0.7
  
imports: import os
  import numpy
  import pytest
  from flaky import flaky
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  import deepchem
  from deepchem.models.optimizers import Adam
label_desc: Use image node_id 31 for calling a global function with example usage: test_dtnn_multitask_regression_overfit() without return types