code: def test_mpnn_reload():
      # load datasets
      featurizer = MolGraphConvFeaturizer(use_edges=True)
      tasks, dataset, transformers, metric = get_dataset(
          "classification", featurizer=featurizer
      )
  
      # initialize models
      n_tasks = len(tasks)
      model_dir = tempfile.mkdtemp()
      model = MPNNModel(
          mode="classification",
          n_tasks=n_tasks,
          model_dir=model_dir,
          batch_size=10,
          learning_rate=0.001,
      )
  
      model.fit(dataset, nb_epoch=200)
      scores = model.evaluate(dataset, [metric], transformers)
      assert scores["mean-roc_auc_score"] >= 0.80
  
      reloaded_model = MPNNModel(
          mode="classification",
          n_tasks=n_tasks,
          model_dir=model_dir,
          batch_size=10,
          learning_rate=0.001,
      )
      reloaded_model.restore()
  
      pred_mols = ["CCCC", "CCCCCO", "CCCCC"]
      X_pred = featurizer(pred_mols)
      random_dataset = dc.data.NumpyDataset(X_pred)
      original_pred = model.predict(random_dataset)
      reload_pred = reloaded_model.predict(random_dataset)
      assert np.all(original_pred == reload_pred)
  
file_path: deepchem/deepchem/models/tests/test_mpnn.py
imports: import pytest
  import tempfile
  import numpy
  import deepchem
  from deepchem.feat import MolGraphConvFeaturizer
  from deepchem.models.tests.test_graph_models import get_dataset
label_desc: Use image node_id 3 for calling a global function with example usage: test_mpnn_reload() without return types