          user_factories = util.registry.factories.get_all()
          self.factories.update(user_factories)
          self._meta = dict(meta)
          self._path = None
          if vocab is True:
              factory = self.Defaults.create_vocab
              vocab = factory(self, **meta.get("vocab", {}))
              if vocab.vectors.name is None:
 
</s> remove         return factory(self, **config)
 </s> add         default_config = self.defaults.get(name, None)

        # transform the model's config to an actual Model
        model_cfg = None
        if "model" in config:
            model_cfg = config["model"]
            if not isinstance(model_cfg, dict):
                user_warning(Warnings.W099.format(type=type(model_cfg), pipe=name))
                model_cfg = None
            del config["model"]
        if model_cfg is None and default_config is not None:
            user_warning(Warnings.W098)
            model_cfg = default_config["model"]
        model = None
        if model_cfg is not None:
            self.config[name] = {"model":  model_cfg}
            model = registry.make_from_config({"model": model_cfg}, validate=True)["model"]
        return factory(self, model, **config) </s> remove         if self.model is True:
            for hp in ["token_vector_width", "conv_depth"]:
                if hp in kwargs:
                    self.cfg[hp] = kwargs[hp]
            self.model = self.Model(len(self.labels), **self.cfg)
 </s> add         self.set_output(len(self.labels))
        self.model.initialize() </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  </s> remove         if moves is True:
            self.moves = self.TransitionSystem(self.vocab.strings)
        else:
            self.moves = moves
        if 'beam_width' not in cfg:
            cfg['beam_width'] = util.env_opt('beam_width', 1)
        if 'beam_density' not in cfg:
            cfg['beam_density'] = util.env_opt('beam_density', 0.0)
        if 'beam_update_prob' not in cfg:
            cfg['beam_update_prob'] = util.env_opt('beam_update_prob', 1.0)
        cfg.setdefault('cnn_maxout_pieces', 3)
        cfg.setdefault("nr_feature_tokens", self.nr_feature)
        self.cfg = cfg
 </s> add         moves = cfg.get("moves", None)
        if moves is None:
            # defined by EntityRecognizer as a BiluoPushDown
            moves = self.TransitionSystem(self.vocab.strings)
        self.moves = moves
        cfg.setdefault('min_action_freq', 30)
        cfg.setdefault('learn_tokens', False)
        cfg.setdefault('beam_width', 1)
        cfg.setdefault('beam_update_prob', 1.0)  # or 0.5 (both defaults were previously used) </s> remove         if "exclusive_classes" not in cfg:
            self.cfg["exclusive_classes"] = True
 </s> add 
        self._config = config
        if not self._config:
            self._config = Config()
          user_factories = util.registry.factories.get_all()
          self.factories.update(user_factories)
          self._meta = dict(meta)
        self._config = config
        if not self._config:
            self._config = Config()
          self._path = None
          if vocab is True:
              factory = self.Defaults.create_vocab
              vocab = factory(self, **meta.get("vocab", {}))
              if vocab.vectors.name is None:
keep keep add 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