code: def jaccard_similarity(
      correct_duplicates: List, retrieved_duplicates: List
  ) -> float:
      """
      Get jaccard similarity for a single query given correct and retrieved file names.
  
      Args:
          correct_duplicates: List of correct duplicates i.e., ground truth)
          retrieved_duplicates: List of retrieved duplicates for one single query
  
      Returns:
          Jaccard similarity for this query.
      """
      if (
          len(retrieved_duplicates) == 0
          and len(correct_duplicates) == 0
      ):
          return 1.0
  
      if not len(retrieved_duplicates) or not len(correct_duplicates):
          return 0.0
  
      set_correct_duplicates = set(correct_duplicates)
      set_retrieved_duplicates = set(retrieved_duplicates)
  
      intersection_dups = set_retrieved_duplicates.intersection(
          set_correct_duplicates
      )
      union_dups = set_retrieved_duplicates.union(
          set_correct_duplicates
      )
  
      jacc_sim = len(intersection_dups) / len(union_dups)
      return jacc_sim
  
imports: from typing import List, Dict
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: jaccard_similarity(correct_duplicates, retrieved_duplicates) and returns: jacc_sim, int, int