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 __contains__(self, word):
      return word in self.dictionary
  
imports: import copy
  import itertools
  import numpy
  from sortedcontainers import SortedListWithKey
  from .tabled_trie import Trie, make_trie
label_desc: Use image node_id 2 for calling the LevenshteinSearcher obj's underlying member method code with example usage: obj.__contains__(word) and returns: unknown