def METHOD_NAME():
    # load datasets
    featurizer = MolGraphConvFeaturizer()
    tasks, dataset, transformers, metric = get_dataset('regression',
                                                       featurizer=featurizer)
    # initialize models
    n_tasks = len(tasks)
    model = GATModel(mode='regression',
                     n_tasks=n_tasks,
                     number_atom_features=30,
                     batch_size=10,
                     learning_rate=0.001)
    # overfit test
    model.fit(dataset, nb_epoch=500)
    scores = model.evaluate(dataset, [metric], transformers)
    assert scores['mean_absolute_error'] < 0.5
    # test on a small MoleculeNet dataset
    from deepchem.molnet import load_delaney
    tasks, all_dataset, transformers = load_delaney(featurizer=featurizer)
    train_set, _, _ = all_dataset
    model = dc.models.GATModel(mode='regression',
                               n_tasks=len(tasks),
                               graph_attention_layers=[2],
                               n_attention_heads=1,
                               residual=False,
                               predictor_hidden_feats=2)
    model.fit(train_set, nb_epoch=1)