How to use the squad.squad_evaluators.BoundedSquadSpanEvaluator function in squad

predictor=BoundsPredictor(
            ChainBiMapper(
                first_layer=recurrent_layer,
                second_layer=recurrent_layer
            ),
        )
    )
    with open(__file__, "r") as f:
        notes = f.read()

    corpus = SquadCorpus()
    train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3), True, False)
    eval_batching = ClusteredBatcher(45, ContextLenKey(), False, False)
    data = DocumentQaTrainingData(corpus, None, train_batching, eval_batching)

    eval = [LossEvaluator(), BoundedSquadSpanEvaluator(bound=[17])]
    trainer.start_training(data, model, train_params, eval, model_dir.ModelDir(out), notes)

save_period=900, eval_samples=dict(train=6000, dev=6000))

    model = ContextOnly(
        DocumentAndQuestionEncoder(SingleSpanAnswerEncoder()),
        FixedWordEmbedder(vec_name="glove.6B.100d", word_vec_init_scale=0, learn_unk=False),
        None,
        FullyConnected(50),
        BoundsPredictor(NullBiMapper())
    )

    corpus = SquadCorpus()
    train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3), True, False)
    eval_batching = ClusteredBatcher(45, ContextLenKey(), False, False)
    data = DocumentQaTrainingData(corpus, None, train_batching, eval_batching)

    eval = [LossEvaluator(), BoundedSquadSpanEvaluator(bound=[17])]
    trainer.start_training(data, model, train_params, eval, model_dir.ModelDir(out), "")

predictor=BoundsPredictor(
            ChainBiMapper(
                first_layer=recurrent_layer,
                second_layer=recurrent_layer
            ),
        )
    )

    with open(__file__, "r") as f:
        notes = f.read()

    train_batching = ClusteredBatcher(45, ContextLenBucketedKey(3), True, False)
    eval_batching = ClusteredBatcher(45, ContextLenKey(), False, False)
    data = DocumentQaTrainingData(SquadCorpus(), None, train_batching, eval_batching)

    eval = [LossEvaluator(), SpanProbability(), BoundedSquadSpanEvaluator(bound=[17])]
    trainer.start_training(data, model, train_params, eval, trainer.ModelDir(out), notes)