code: def _load_pretrained_tok2vec(nlp, loc):
      
      with loc.open("rb") as file_:
          weights_data = file_.read()
      loaded = []
      for name, component in nlp.pipeline:
          if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
              component.tok2vec.from_bytes(weights_data)
              loaded.append(name)
      return loaded
docs: Load pre-trained weights for the 'token-to-vector' part of the component
      models, which is typically a CNN. See 'spacy pretrain'. Experimental.