code: def make_query_strategy(
      utility_measure: Callable, selector: Callable
  ) -> Callable:
      """
      Takes the given utility measure and selector functions and makes a query strategy by combining them.
  
      Args:
          utility_measure: Utility measure, for instance :func:`~modAL.disagreement.vote_entropy`, but it can be a custom
              function as well. Should take a classifier and the unlabelled data and should return an array containing the
              utility scores.
          selector: Function selecting instances for query. Should take an array of utility scores and should return an
              array containing the queried items.
  
      Returns:
          A function which returns queried instances given a classifier and an unlabelled pool.
      """
  
      def query_strategy(
          classifier: BaseEstimator, X: modALinput
      ) -> Tuple:
          utility = utility_measure(classifier, X)
          return selector(utility)
  
      return query_strategy
  
imports: from typing import Callable, Optional, Sequence, Tuple
  import numpy
  from modAL.utils.data import modALinput
  from sklearn.base import BaseEstimator
label_desc: Use image node_id 3 for calling a global function with example usage: make_query_strategy(utility_measure, selector) and returns: query_strategy, selector