          exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
          util.from_disk(path, deserializers, exclude)
          if 'model' not in exclude:
              path = util.ensure_path(path)
              if self.model is True:
                  self.model, cfg = self.Model(**self.cfg)
              else:
                  cfg = {}
              with (path / 'model').open('rb') as file_:
                  bytes_data = file_.read()
              try:
                  self.model.from_bytes(bytes_data)
              except AttributeError:
 
</s> remove             # TODO: Remove this once we don't have to handle previous models
            if self.cfg.get('pretrained_dims') and 'pretrained_vectors' not in self.cfg:
                self.cfg['pretrained_vectors'] = self.vocab.vectors
            if self.model is True:
                self.model, cfg = self.Model(**self.cfg)
            else:
                cfg = {}
 </s> add  </s> remove             'model': lambda p: None
 </s> add             'model': lambda p: None, </s> remove         deserialize["cfg"] = lambda p: self.cfg.update(_load_cfg(p))
 </s> add  </s> remove             if self.model is True:
                self.model = self.Model(len(self.labels), **self.cfg)
 </s> add 
          exclude = util.get_serialization_exclude(deserializers, exclude, kwargs)
          util.from_disk(path, deserializers, exclude)
          if 'model' not in exclude:
              path = util.ensure_path(path)




              with (path / 'model').open('rb') as file_:
                  bytes_data = file_.read()
              try:
                  self.model.from_bytes(bytes_data)
              except AttributeError:
keep keep keep keep 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/syntax/nn_parser.pyx