          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(
          nlp.vocab,
          model,
          name,
          labels_discard=labels_discard,
 
</s> remove     kb (KnowledgeBase): The knowledge-base to link entities to.
 </s> add  </s> remove         kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
 </s> add  </s> remove         kb_loader=kb_loader,
 </s> add 
    entity_vector_length (int): Size of encoding vectors in the KB.
    get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
        produces a list of candidates, given a certain knowledge base and a textual mention.
          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.
    entity_vector_length (int): Size of encoding vectors in the KB.
    get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
        produces a list of candidates, given a certain knowledge base and a textual mention.
      """
      return EntityLinker(
          nlp.vocab,
          model,
          name,
          labels_discard=labels_discard,
keep keep keep add keep keep keep keep keep keep
 move kb_loader to initialize for NEL instead of constructor 
spacy/pipeline/entity_linker.py