code: def test_dtnn_regression_model():
      current_dir = os.path.dirname(os.path.abspath(__file__))
      input_file = os.path.join(current_dir, "assets/example_DTNN.mat")
      dataset = scipy_io.loadmat(input_file)
      X = dataset["X"]
      y = dataset["T"]
      w = np.ones_like(y)
      dataset = NumpyDataset(X, y, w, ids=None)
      n_tasks = y.shape[1]
  
      model = DTNNModel(
          n_tasks,
          n_embedding=20,
          n_distance=100,
          learning_rate=1.0,
          mode="regression",
      )
  
      # Fit trained model
      model.fit(dataset, nb_epoch=250)
  
      # Eval model on train
      pred = model.predict(dataset)
      mean_rel_error = np.mean(np.abs(1 - pred / y))
      assert mean_rel_error < 0.1
  
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 14 for calling a global function with example usage: test_dtnn_regression_model() without return types