def METHOD_NAME():
    """A simple test that initializes and fits an AtomicConvModel on variable input size."""
    frag1_num_atoms = 1000
    frag2_num_atoms = 1200
    complex_num_atoms = frag1_num_atoms + frag2_num_atoms
    batch_size = 1
    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)
    # Creates a set of dummy features that contain the coordinate and
    # neighbor-list features required by the AtomicConvModel.
    features = []
    frag1_coords = np.random.rand(frag1_num_atoms, 3)
    frag1_nbr_list = {i: [] for i in range(frag1_num_atoms)}
    frag1_z = np.random.randint(10, size=(frag1_num_atoms))
    frag2_coords = np.random.rand(frag2_num_atoms, 3)
    frag2_nbr_list = {i: [] for i in range(frag2_num_atoms)}
    frag2_z = np.random.randint(10, size=(frag2_num_atoms))
    system_coords = np.random.rand(complex_num_atoms, 3)
    system_nbr_list = {i: [] for i in range(complex_num_atoms)}
    system_z = np.random.randint(10, size=(complex_num_atoms))
    features.append(
        (frag1_coords, frag1_nbr_list, frag1_z, frag2_coords, frag2_nbr_list,
         frag2_z, system_coords, system_nbr_list, system_z))
    features = np.asarray(features)
    labels = np.zeros(batch_size)
    train = NumpyDataset(features, labels)
    atomic_convnet.fit(train, nb_epoch=1)
    preds = atomic_convnet.predict(train)
    assert preds.shape == (1, 1, 1)
    assert np.count_nonzero(preds) > 0