  
  
  @component("tok2vec", assigns=["doc.tensor"])
  class Tok2Vec(Pipe):
      @classmethod
      def from_nlp(cls, nlp, **cfg):
          return cls(nlp.vocab, **cfg)
  
      @classmethod
      def Model(cls, architecture, **cfg):
          """Create a new statistical model for the class.
  
 
</s> remove     def Model(cls, architecture, **cfg):
        """Create a new statistical model for the class.

        architecture (str): The registered model architecture to use.
        **cfg: Config parameters.
        RETURNS (Model): A `thinc.model.Model` or similar instance.
        """
        model = registry.architectures.get(architecture)
        return model(**cfg)
 </s> add     def from_nlp(cls, nlp, model, **cfg):
        return cls(nlp.vocab, model, **cfg) </s> remove     def Model(cls, *shape, **kwargs):
        """Initialize a model for the pipe."""
        raise NotImplementedError

    @classmethod
    def from_nlp(cls, nlp, **cfg):
        return cls(nlp.vocab, **cfg)
 </s> add     def from_nlp(cls, nlp, model, **cfg):
        return cls(nlp.vocab, model, **cfg) </s> remove     def from_nlp(cls, nlp, **cfg):
        return cls(nlp.vocab, **cfg)
 </s> add     def from_nlp(cls, nlp, model, **cfg):
        return cls(nlp.vocab, model, **cfg) </s> remove     @classmethod
    def Model(cls, output_size=300, **cfg):
        """Create a new statistical model for the class.

        width (int): Output size of the model.
        embed_size (int): Number of vectors in the embedding table.
        **cfg: Config parameters.
        RETURNS (Model): A `thinc.model.Model` or similar instance.
        """
        input_size = util.env_opt("token_vector_width", cfg.get("input_size", 96))
        return Linear(output_size, input_size, init_W=zero_init)

    def __init__(self, vocab, model=True, **cfg):
 </s> add     def __init__(self, vocab, model, **cfg): </s> remove     def from_nlp(cls, nlp, **cfg):
 </s> add     def from_nlp(cls, nlp, model=None, **cfg):
  
  
  @component("tok2vec", assigns=["doc.tensor"])
  class Tok2Vec(Pipe):



  
      @classmethod
      def Model(cls, architecture, **cfg):
          """Create a new statistical model for the class.
  
keep keep keep keep 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/tok2vec.py