text: from typing import Dict, List
  
  from jina.executors.rankers import Match2DocRanker
  from jina.executors.decorators import batching_multi_input
  
  
  class DummyRanker(Match2DocRanker):
      """
      :class:`LevenshteinRanker` Computes the negative Levenshtein distance
          between a query and its matches. The distance is negative, in order to
          achieve a bigger=better sorting in the respective driver.
      """
  
      def __init__(self, *args, **kwargs):
          super().__init__(*args, **kwargs)
          self.match_required_keys = ['tags__dummy_score']
  
      @batching_multi_input(slice_nargs=3)
      def score(
          self,
          old_match_scores: List[Dict],
          queries_metas: List[Dict],
          matches_metas: List[List[Dict]],
      ) -> List[List[float]]:
          return [
              [m['tags__dummy_score'] for m in match_meta] for match_meta in matches_metas
          ]
  