code: def test_DTNN_regression_reload():
      """Test DTNN can reload datasets."""
      np.random.seed(123)
      tf.random.set_seed(123)
      n_tasks = 1
  
      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 = dc.data.NumpyDataset(X, y, w, ids=None)
      n_tasks = y.shape[1]
  
      model_dir = tempfile.mkdtemp()
      model = dc.models.DTNNModel(
          n_tasks,
          n_embedding=20,
          n_distance=100,
          learning_rate=1.0,
          model_dir=model_dir,
          mode="regression",
      )
  
      # Fit trained model
      model.fit(dataset, nb_epoch=250)
  
      reloaded_model = dc.models.DTNNModel(
          n_tasks,
          n_embedding=20,
          n_distance=100,
          learning_rate=1.0,
          model_dir=model_dir,
          mode="regression",
      )
      reloaded_model.restore()
  
      # Check predictions match on random sample
      origpred = model.predict(dataset)
      reloadpred = reloaded_model.predict(dataset)
      assert np.all(origpred == reloadpred)
  
imports: import os
  import pytest
  import tempfile
  import numpy
  import deepchem
  import scipy.io
  from flaky import flaky
  from sklearn.ensemble import RandomForestClassifier
  from deepchem.molnet.load_function.chembl25_datasets import CHEMBL25_TASKS
  from deepchem.feat import create_char_to_idx
label_desc: Use image node_id 20 for calling a global function with example usage: test_DTNN_regression_reload() without return types