code: def damerau_levenshtein_distance(s1, s2):
      _check_type(s1)
      _check_type(s2)
  
      len1 = len(s1)
      len2 = len(s2)
      infinite = len1 + len2
  
      # character array
      da = defaultdict(int)
  
      # distance matrix
      score = [[0] * (len2 + 2) for x in range(len1 + 2)]
  
      score[0][0] = infinite
      for i in range(0, len1 + 1):
          score[i + 1][0] = infinite
          score[i + 1][1] = i
      for i in range(0, len2 + 1):
          score[0][i + 1] = infinite
          score[1][i + 1] = i
  
      for i in range(1, len1 + 1):
          db = 0
          for j in range(1, len2 + 1):
              i1 = da[s2[j - 1]]
              j1 = db
              cost = 1
              if s1[i - 1] == s2[j - 1]:
                  cost = 0
                  db = j
  
              score[i + 1][j + 1] = min(
                  score[i][j] + cost,
                  score[i + 1][j] + 1,
                  score[i][j + 1] + 1,
                  score[i1][j1] + (i - i1 - 1) + 1 + (j - j1 - 1),
              )
          da[s1[i - 1]] = i
  
      return score[len1 + 1][len2 + 1]
  
file_path: jellyfish/python/jellyfish/_jellyfish.py
imports: import unicodedata
  from collections import defaultdict
  from itertools import zip_longest
  import warnings
label_desc: Use image node_id 7 for calling a global function with example usage: damerau_levenshtein_distance(s1, s2) and returns: score