      DOCS: https://spacy.io/api/entitylinker
      """
      NIL = "NIL"  # string used to refer to a non-existing link
  
      @classmethod
      def Model(cls, **cfg):
          embed_width = cfg.get("embed_width", 300)
          hidden_width = cfg.get("hidden_width", 128)
          type_to_int = cfg.get("type_to_int", dict())
  
          model = build_nel_encoder(embed_width=embed_width, hidden_width=hidden_width, ner_types=len(type_to_int), **cfg)
          return model
  
      def __init__(self, vocab, **cfg):
          self.vocab = vocab
          self.model = True
          self.kb = None
          self.cfg = dict(cfg)
          self.distance = CosineDistance(normalize=False)
 
</s> remove         self.model = True
 </s> add         self.model = model </s> remove     def __init__(self, vocab, model=True, **cfg):
 </s> add     def __init__(self, vocab, model, **cfg): </s> remove     def __init__(self, vocab, model=True, **cfg):
 </s> add     def __init__(self, vocab, model, **cfg): </s> remove     @classmethod
    def Model(cls, vocab, tok2vec, **cfg):
        output_size = vocab.vectors.data.shape[1]
        output_layer = chain(
            Maxout(nO=output_size, nI=tok2vec.get_dim("nO"), nP=3, normalize=True, dropout=0.0),
            Linear(nO=output_size, nI=output_size, init_W=zero_init)
        )
        model = chain(tok2vec, output_layer)
        model = masked_language_model(vocab, model)
        return model

    def __init__(self, vocab, model=True, **cfg):
 </s> add     def __init__(self, vocab, model, **cfg): </s> remove     @classmethod
    def Model(cls, **cfg):
        if cfg.get('pretrained_dims') and not cfg.get('pretrained_vectors'):
            raise ValueError(TempErrors.T008)
        class_map = Morphology.create_class_map()
        return build_morphologizer_model(class_map.field_sizes, **cfg)

    def __init__(self, vocab, model=True, **cfg):
 </s> add     def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
      DOCS: https://spacy.io/api/entitylinker
      """
      NIL = "NIL"  # string used to refer to a non-existing link
  
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
    def __init__(self, vocab, model, **cfg):
          self.vocab = vocab
          self.model = True
          self.kb = None
          self.cfg = dict(cfg)
          self.distance = CosineDistance(normalize=False)
keep keep keep keep replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Default settings to configurations (#4995)

* fix grad_clip naming

* cleaning up pretrained_vectors out of cfg

* further refactoring Model init's

* move Model building out of pipes

* further refactor to require a model config when creating a pipe

* small fixes

* making cfg in nn_parser more consistent

* fixing nr_class for parser

* fixing nn_parser's nO

* fix printing of loss

* architectures in own file per type, consistent naming

* convenience methods default_tagger_config and default_tok2vec_config

* let create_pipe access default config if available for that component

* default_parser_config

* move defaults to separate folder

* allow reading nlp from package or dir with argument 'name'

* architecture spacy.VocabVectors.v1 to read static vectors from file

* cleanup

* default configs for nel, textcat, morphologizer, tensorizer

* fix imports

* fixing unit tests

* fixes and clean up

* fixing defaults, nO, fix unit tests

* restore parser IO

* fix IO

* 'fix' serialization test

* add *.cfg to manifest

* fix example configs with additional arguments

* replace Morpohologizer with Tagger

* add IO bit when testing overfitting of tagger (currently failing)

* fix IO - don't initialize when reading from disk

* expand overfitting tests to also check IO goes OK

* remove dropout from HashEmbed to fix Tagger performance

* add defaults for sentrec

* update thinc

* always pass a Model instance to a Pipe

* fix piped_added statement

* remove obsolete W029

* remove obsolete errors

* restore byte checking tests (work again)

* clean up test

* further test cleanup

* convert from config to Model in create_pipe

* bring back error when component is not initialized

* cleanup

* remove calls for nlp2.begin_training

* use thinc.api in imports

* allow setting charembed's nM and nC

* fix for hardcoded nM/nC + unit test

* formatting fixes

* trigger build 
spacy/pipeline/pipes.pyx