def METHOD_NAME(pd_df, tmp):
    data, users, items = pd_df
    # Test if wide and deep columns have correct features
    wide_columns, deep_columns = build_feature_columns(
        users, items, model_type="wide_deep"
    )
    assert len(wide_columns) == 3
    assert len(deep_columns) == 2
    # Check model type
    model = build_model(
        os.path.join(tmp, "wide_deep_" + MODEL_DIR),
        wide_columns=wide_columns,
        deep_columns=deep_columns,
    )
    assert isinstance(model, tf.compat.v1.estimator.DNNLinearCombinedRegressor)
    # Test if model train works
    model.train(
        input_fn=pandas_input_fn(
            df=data,
            y_col=DEFAULT_RATING_COL,
            batch_size=1,
            num_epochs=None,
            shuffle=True,
        ),
        steps=1,
    )
    # Close the event file so that the model folder can be cleaned up.
    summary_writer = tf.compat.v1.summary.FileWriterCache.get(model.model_dir)
    summary_writer.close()