code: def monge_elkan(
      seq1: Sequence[str],
      seq2: Sequence[str],
      sim_func: Callable[[str, str], float] = edits.levenshtein,
  ) -> float:
      """
      Measure the similarity between two sequences of strings using the (symmetric)
      Monge-Elkan method, which takes the average of the maximum pairwise similarity
      between the tokens in each sequence as compared to those in the other sequence.
  
      Args:
          seq1
          seq2
          sim_func: Callable that computes a string-to-string similarity metric;
              by default, Levenshtein edit distance.
  
      Returns:
          Similarity between ``seq1`` and ``seq2`` in the interval [0.0, 1.0],
          where larger values correspond to more similar strings.
  
      See Also:
          :func:`textacy.similarity.edits.levenshtein()`
      """
      if not seq1 or not seq2:
          return 0.0
  
      sum_maxsim1 = sum(
          max(sim_func(tok1, tok2) for tok2 in seq2) for tok1 in seq1
      )
      sum_maxsim2 = sum(
          max(sim_func(tok2, tok1) for tok1 in seq1) for tok2 in seq2
      )
      return ((sum_maxsim1 / len(seq1)) + (sum_maxsim2 / len(seq2))) / 2
  
imports: from __future__ import annotations
  from typing import Callable, Sequence
  from .None import edits
  from ..None import constants
label_desc: Use image node_id 3 for calling a global function with example usage: monge_elkan(seq1, seq2, sim_func) and returns: unknown, int