code: def test_mpnn_model():
      tasks, dataset, transformers, metric = get_dataset(
          "classification", "Weave"
      )
  
      model = MPNNModel(
          len(tasks),
          mode="classification",
          n_hidden=75,
          n_atom_feat=75,
          n_pair_feat=14,
          T=1,
          M=1,
          learning_rate=0.0005,
      )
  
      model.fit(dataset, nb_epoch=150)
      scores = model.evaluate(dataset, [metric], transformers)
      assert scores["mean-roc_auc_score"] >= 0.9
  
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 11 for calling a global function with example usage: test_mpnn_model() without return types