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

ChainBiMapper(
                first_layer=BiRecurrentMapper(GruCellSpec(80)),
                second_layer=BiRecurrentMapper(GruCellSpec(80))
            ),
            aggregate="sum"
        )
    )
    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, trainer.ModelDir(out), notes, False)

aggregate="sum"
        )
    )
    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 = PreprocessedData(SquadCorpus(),
                            TagTextAnswers(),
                            ParagraphAndQuestionDatasetBuilder(train_batching, eval_batching),
                            # sample=20, sample_dev=20,
                            eval_on_verified=False)
    data.preprocess()

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

first_layer=recurrent_layer,
                second_layer=recurrent_layer
            ),
            span_predictor=BoundedSpanPredictor(20)
        )
    )

    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)

WithProjectedProduct(include_tiled=True),
            ChainBiMapper(
                first_layer=recurrent_layer,
                second_layer=recurrent_layer
            ),
            IndependentBoundsJointLoss()
        )
    )
    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, model_dir.ModelDir(out), notes, False)