def METHOD_NAME(df_iris):
    ds = df_iris
    features = ['sepal_width', 'petal_length', 'sepal_length', 'petal_width']
    # Vanilla lightgbm
    X = np.array(ds[features])
    dtrain = lgb.Dataset(X, label=ds.class_.to_numpy())
    lgb_bst = lgb.train(params, dtrain, 3)
    lgb_pred = lgb_bst.predict(X)
    # Vaex.ml.lightgbm
    booster = ds.ml.lightgbm_model(target=ds.class_, num_boost_round=3, features=features, params=params, transform=False)
    vaex_pred = booster.predict(ds)
    # Comparing the the predictions of lightgbm vs vaex.ml
    np.testing.assert_equal(vaex_pred, lgb_pred, verbose=True, err_msg='The predictions of vaex.ml do not match those of lightgbm')