def METHOD_NAME():
    """A simple test for running an atomic convolution on featurized data."""
    dir_path = os.path.dirname(os.path.realpath(__file__))
    ligand_file = os.path.join(dir_path,
                               "../../feat/tests/data/3zso_ligand_hyd.pdb")
    protein_file = os.path.join(dir_path,
                                "../../feat/tests/data/3zso_protein_noH.pdb")
    # Pulled from PDB files. For larger datasets with more PDBs, would use
    # max num atoms instead of exact.
    frag1_num_atoms = 44  # for ligand atoms
    frag2_num_atoms = 2334  # for protein atoms
    complex_num_atoms = 2378  # in total
    max_num_neighbors = 4
    # Cutoff in angstroms
    neighbor_cutoff = 4
    complex_featurizer = AtomicConvFeaturizer(frag1_num_atoms, frag2_num_atoms,
                                              complex_num_atoms,
                                              max_num_neighbors,
                                              neighbor_cutoff)
    # arbitrary label
    labels = np.array([0])
    features = complex_featurizer.featurize([(ligand_file, protein_file)])
    dataset = NumpyDataset(features, labels)
    batch_size = 1
    print("Constructing Atomic Conv model")
    atomic_convnet = atomic_conv.AtomicConvModel(
        n_tasks=1,
        batch_size=batch_size,
        layer_sizes=[10],
        frag1_num_atoms=frag1_num_atoms,
        frag2_num_atoms=frag2_num_atoms,
        complex_num_atoms=complex_num_atoms)
    print("About to call fit")
    # Run a fitting operation
    atomic_convnet.fit(dataset)
    preds = atomic_convnet.predict(dataset)
    assert preds.shape == (1, 1, 1)
    assert np.count_nonzero(preds) > 0