      """Decorator for pipeline components. Can decorate both function components
      and class components and will automatically register components in the
      Language.factories. If the component is a class and needs access to the
      nlp object or config parameters, it can expose a from_nlp classmethod
      that takes the nlp object and **cfg arguments and returns the initialized
      component.
      """
  
      # NB: This decorator needs to live here, because it needs to write to
      # Language.factories. All other solutions would cause circular import.
  
 
</s> remove         moves (TransitionSystem): Defines how the parse-state is created,
            updated and evaluated. The value is set to the .moves attribute
            unless True (default), in which case a new instance is created with
            `Parser.Moves()`.
        model (object): Defines how the parse-state is created, updated and
            evaluated. The value is set to the .model attribute. If set to True
            (default), a new instance will be created with `Parser.Model()`
            in parser.begin_training(), parser.from_disk() or parser.from_bytes().
        **cfg: Arbitrary configuration parameters. Set to the `.cfg` attribute
 </s> add         **cfg: Configuration parameters. Set to the `.cfg` attribute.
             If it doesn't include a value for 'moves',  a new instance is
             created with `self.TransitionSystem()`. This defines how the
             parse-state is created, updated and evaluated. </s> remove     model = masked_language_model(nlp.vocab, model)
 </s> add     model = build_masked_language_model(nlp.vocab, model) </s> remove     nlp = Language()
 </s> add     nlp = English()
    that takes the nlp & model objects and **cfg arguments, and returns the
    initialized component.
      """Decorator for pipeline components. Can decorate both function components
      and class components and will automatically register components in the
      Language.factories. If the component is a class and needs access to the
      nlp object or config parameters, it can expose a from_nlp classmethod
    that takes the nlp & model objects and **cfg arguments, and returns the
    initialized component.
    that takes the nlp & model objects and **cfg arguments, and returns the
    initialized component.
      """
  
      # NB: This decorator needs to live here, because it needs to write to
      # Language.factories. All other solutions would cause circular import.
  
keep keep keep keep 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/language.py