              sgd = self.create_optimizer()
          return sgd
  
      def predict(self, docs):
          self.require_model()
          tokvecs = self.model.tok2vec(docs)
          vectors = self.model.output_layer(tokvecs)
          return tokvecs, vectors
  
      def get_loss(self, examples, vectors, prediction):
          # The simplest way to implement this would be to vstack the
          # token.vector values, but that's a bit inefficient, especially on GPU.
 
</s> remove         self.require_model()
        tokvecs = self.model.tok2vec(docs)
        scores = self.model.softmax(tokvecs)
 </s> add         tokvecs = self.model.get_ref("tok2vec")(docs)
        scores = self.model.get_ref("softmax")(tokvecs) </s> remove     @classmethod
    def Model(cls, n_tags, tok2vec=None, **cfg):
        token_vector_width = util.env_opt("token_vector_width", 96)
        model = chain(
            tok2vec,
            Maxout(nO=token_vector_width*2, nI=token_vector_width, nP=3, dropout=0.0),
            LayerNorm(token_vector_width*2),
            Softmax(nO=n_tags, nI=token_vector_width*2)
        )
        return model

 </s> add  </s> remove         self.require_model()
 </s> add 
        tokvecs = self.model.get_ref("tok2vec")(docs)
        vectors = self.model.get_ref("output_layer")(tokvecs)
              sgd = self.create_optimizer()
          return sgd
  
      def predict(self, docs):
        tokvecs = self.model.get_ref("tok2vec")(docs)
        vectors = self.model.get_ref("output_layer")(tokvecs)
        tokvecs = self.model.get_ref("tok2vec")(docs)
        vectors = self.model.get_ref("output_layer")(tokvecs)
        tokvecs = self.model.get_ref("tok2vec")(docs)
        vectors = self.model.get_ref("output_layer")(tokvecs)
          return tokvecs, vectors
  
      def get_loss(self, examples, vectors, prediction):
          # The simplest way to implement this would be to vstack the
          # token.vector values, but that's a bit inefficient, especially on GPU.
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/pipes.pyx