code: def test_attentivefp_classification():
      # load datasets
      featurizer = MolGraphConvFeaturizer(use_edges=True)
      tasks, dataset, transformers, metric = get_dataset(
          "classification", featurizer=featurizer
      )
  
      # initialize models
      n_tasks = len(tasks)
      model = AttentiveFPModel(
          mode="classification",
          n_tasks=n_tasks,
          batch_size=10,
          learning_rate=0.001,
      )
  
      # overfit test
      model.fit(dataset, nb_epoch=100)
      scores = model.evaluate(dataset, [metric], transformers)
      assert scores["mean-roc_auc_score"] >= 0.85
  
      # test on a small MoleculeNet dataset
      from deepchem.molnet import load_bace_classification
  
      tasks, all_dataset, transformers = load_bace_classification(
          featurizer=featurizer
      )
      train_set, _, _ = all_dataset
      model = AttentiveFPModel(
          mode="classification",
          n_tasks=len(tasks),
          num_layers=1,
          num_timesteps=1,
          graph_feat_size=2,
      )
      model.fit(train_set, nb_epoch=1)
  
file_path: deepchem/deepchem/models/tests/test_attentivefp.py
imports: import tempfile
  import pytest
  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 2 for calling a global function with example usage: test_attentivefp_classification() without return types