def METHOD_NAME():
    input_features = [
        sequence_feature(
            encoder={
                "vocab_size": TEST_VOCAB_SIZE,
                "embedding_size": TEST_EMBEDDING_SIZE,
                "state_size": TEST_STATE_SIZE,
                "hidden_size": TEST_HIDDEN_SIZE,
                "num_filters": TEST_NUM_FILTERS,
                "min_len": 5,
                "max_len": 10,
                "type": "rnn",
                "cell_type": "lstm",
            }
        )
    ]
    output_features = [
        sequence_feature(
            decoder={"type": "generator", "min_len": 5, "max_len": 10, "cell_type": "lstm", "attention": "bahdanau"}
        )
    ]
    # generate synthetic data set testing
    dataset = build_synthetic_dataset(150, copy.deepcopy(input_features) + copy.deepcopy(output_features))
    raw_data = "\n".join([r[0] + "," + r[1] for r in dataset])
    df = pd.read_csv(StringIO(raw_data))
    return df, input_features, output_features