              losses during training.
          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.
  
          DOCS: https://nightly.spacy.io/api/entitylinker#init
          """
          self.vocab = vocab
 
</s> remove         kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
 </s> add  </s> remove     kb (KnowledgeBase): The knowledge-base to link entities to.
 </s> add  </s> remove         self._require_kb()
 </s> add         self.validate_kb()
        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.
              losses during training.
          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.
  
          DOCS: https://nightly.spacy.io/api/entitylinker#init
          """
          self.vocab = vocab
keep keep keep add keep keep keep keep
 move kb_loader to initialize for NEL instead of constructor 
spacy/pipeline/entity_linker.py