class_docstr: Класс для поиска близких слов
  в соответствии с расстоянием Левенштейна
class_docstr_tok: ["\u041a\u043b\u0430\u0441\u0441","\u0434\u043b\u044f","\u043f\u043e\u0438\u0441\u043a\u0430","\u0431\u043b\u0438\u0437\u043a\u0438\u0445","\u0441\u043b\u043e\u0432","\u0432","\u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0438\u0438","\u0441","\u0440\u0430\u0441\u0441\u0442\u043e\u044f\u043d\u0438\u0435\u043c","\u041b\u0435\u0432\u0435\u043d\u0448\u0442\u0435\u0439\u043d\u0430"]
code: def _minimal_replacement_cost(self, first, second):
      first_symbols, second_symbols = set(), set()
      removal_cost, insertion_cost = 0, 0
      for a, b in itertools.zip_longest(first, second, fillvalue=None):
          if a is not None:
              first_symbols.add(a)
          if b is not None:
              second_symbols.add(b)
          removal_cost = max(
              removal_cost, len(first_symbols - second_symbols)
          )
          insertion_cost = max(
              insertion_cost, len(second_symbols - first_symbols)
          )
      return min(removal_cost, insertion_cost)
  
imports: import copy
  import itertools
  import numpy
  from sortedcontainers import SortedListWithKey
  from .tabled_trie import Trie, make_trie
label_desc: Use image node_id 8 for calling the LevenshteinSearcher obj's underlying member method code with example usage: obj._minimal_replacement_cost(first, second) and returns: min