  
      model (Model[List[Doc], Floats2d]): A model that learns document vector
          representations. Given a batch of Doc objects, it should return a single
          array, with one row per item in the batch.
      kb (KnowledgeBase): The knowledge-base to link entities to.
      labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
      incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
      incl_context (bool): Whether or not to include the local context in the model.
      """
      return EntityLinker(
 
</s> remove         kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
 </s> add 
  
      model (Model[List[Doc], Floats2d]): A model that learns document vector
          representations. Given a batch of Doc objects, it should return a single
          array, with one row per item in the batch.

      labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
      incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
      incl_context (bool): Whether or not to include the local context in the model.
      """
      return EntityLinker(
keep keep keep keep replace keep keep keep keep keep
 move kb_loader to initialize for NEL instead of constructor 
spacy/pipeline/entity_linker.py