  from typing import Optional, Iterable, Dict, Any, Callable, Tuple, TYPE_CHECKING
  import numpy as np
  
  from .gold import Example
  from .tokens import Token, Doc
  from .errors import Errors
  from .util import get_lang_class
  from .morphology import Morphology
  
  if TYPE_CHECKING:
 
</s> remove         scores: Iterable[str] = tuple(),
        default_score_weights: Dict[str, float] = SimpleFrozenDict(),
 </s> add  </s> remove         scores (Iterable[str]): All scores set by the component if it's trainable,
            e.g. ["ents_f", "ents_r", "ents_p"].
        default_score_weights (Dict[str, float]): The scores to report during
            training, and their default weight towards the final score used to
            select the best model. Weights should sum to 1.0 per component and
            will be combined and normalized for the whole pipeline.
 </s> add  </s> remove             kwargs.setdefault("verbose", verbose)
 </s> add  </s> remove             if verbose:
                print(doc)
 </s> add             util.logger.debug(doc) </s> remove                 scores=scores,
                default_score_weights=default_score_weights,
 </s> add 
from .tokens import Token, Doc, Span
  from typing import Optional, Iterable, Dict, Any, Callable, Tuple, TYPE_CHECKING
  import numpy as np
  
  from .gold import Example
from .tokens import Token, Doc, Span
  from .errors import Errors
  from .util import get_lang_class
  from .morphology import Morphology
  
  if TYPE_CHECKING:
keep keep keep keep replace keep keep keep keep keep
 Update docs, types and API consistency 
spacy/scorer.py