              ops = get_current_ops()
              if self.vocab.vectors.data.shape[1] >= 1:
                  self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
          link_vectors_to_models(self.vocab)
          if self.vocab.vectors.data.shape[1]:
              cfg["pretrained_vectors"] = self.vocab.vectors
          if sgd is None:
              sgd = create_default_optimizer()
          self._optimizer = sgd
          for name, proc in self.pipeline:
              if hasattr(proc, "_rehearsal_model"):
 
</s> remove         if self.vocab.vectors.data.shape[1]:
            cfg["pretrained_vectors"] = self.vocab.vectors.name
            cfg["pretrained_dims"] = self.vocab.vectors.data.shape[1]
 </s> add  </s> remove                 if getattr(model, "tok2vec", None):
                    self.input_models.append(model.tok2vec)
        if self.model is True:
            self.model = self.Model(**self.cfg)
 </s> add                 if model.has_ref("tok2vec"):
                    self.input_models.append(model.get_ref("tok2vec")) </s> remove         self.model.initialize()
 </s> add  </s> remove         if self.model is True:
            token_vector_width = util.env_opt("token_vector_width")
            self.model = self.Model(len(self.labels), tok2vec=tok2vec)
        link_vectors_to_models(self.vocab)
 </s> add 
              ops = get_current_ops()
              if self.vocab.vectors.data.shape[1] >= 1:
                  self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
          link_vectors_to_models(self.vocab)


          if sgd is None:
              sgd = create_default_optimizer()
          self._optimizer = sgd
          for name, proc in self.pipeline:
              if hasattr(proc, "_rehearsal_model"):
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