          if scorer_cfg is None:
              scorer_cfg = {}
          if scorer is None:
              kwargs = dict(scorer_cfg)
              kwargs.setdefault("verbose", verbose)
              kwargs.setdefault("nlp", self)
              scorer = Scorer(**kwargs)
          texts = [eg.reference.text for eg in examples]
          docs = [eg.predicted for eg in examples]
          start_time = timer()
 
</s> remove             if verbose:
                print(doc)
 </s> add             util.logger.debug(doc) </s> remove                 scores=scores,
                default_score_weights=default_score_weights,
 </s> add  </s> remove         scores: Iterable[str] = tuple(),
        default_score_weights: Dict[str, float] = SimpleFrozenDict(),
 </s> add  </s> remove     docs: Union[Iterable[Doc], Doc],
 </s> add     docs: Union[Iterable[Union[Doc, Span]], Doc, Span], </s> remove     scores = nlp.evaluate(dev_dataset, verbose=False)
 </s> add     scores = nlp.evaluate(dev_dataset)
          if scorer_cfg is None:
              scorer_cfg = {}
          if scorer is None:
              kwargs = dict(scorer_cfg)

              kwargs.setdefault("nlp", self)
              scorer = Scorer(**kwargs)
          texts = [eg.reference.text for eg in examples]
          docs = [eg.predicted for eg in examples]
          start_time = timer()
keep keep keep keep replace keep keep keep keep keep
 Update docs, types and API consistency 
spacy/language.py