          #     best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
          # msg.good("Created best model", best_model_path)
  
  
  def create_nlp_from_config(lang, vectors, pipeline):
      lang_class = spacy.util.get_lang_class(lang)
      nlp = lang_class()
      if vectors is not None:
          spacy.cli.train._load_vectors(nlp, vectors)
      for name, component_cfg in pipeline.items():
          factory = component_cfg.pop("factory")
          component = nlp.create_pipe(factory, config=component_cfg)
          nlp.add_pipe(component, name=name)
      return nlp
  
  
  def create_train_batches(nlp, corpus, cfg):
      while True:
          train_examples = corpus.train_dataset(
              nlp,
              noise_level=0.0,
 
</s> remove         if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
            component.tok2vec.from_bytes(weights_data)
 </s> add         if hasattr(component, "model") and component.model.has_ref("tok2vec"):
            component.get_ref("tok2vec").from_bytes(weights_data) </s> remove         self.require_model()
        tokvecs = self.model.tok2vec(docs)
        vectors = self.model.output_layer(tokvecs)
 </s> add         tokvecs = self.model.get_ref("tok2vec")(docs)
        vectors = self.model.get_ref("output_layer")(tokvecs) </s> remove     nlp = Language()
 </s> add     nlp = English()
          #     best_model_path = _collate_best_model(meta, output_path, nlp.pipe_names)
          # msg.good("Created best model", best_model_path)
  
  












  def create_train_batches(nlp, corpus, cfg):
      while True:
          train_examples = corpus.train_dataset(
              nlp,
              noise_level=0.0,
keep keep keep keep replace replace replace replace replace replace replace replace 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/cli/train_from_config.py