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"]
docstr: Finds all dictionary words in d-window from word
docstr_tok: ["Finds","all","dictionary","words","in","d-window","from","word"]
code: def search(self, word, d, allow_spaces=True, return_cost=True):
      """
      Finds all dictionary words in d-window from word
      """
      if not all(
          (c in self.alphabet or (c == " " and self.allow_spaces))
          for c in word
      ):
          return []
          # raise ValueError("{0} contains an incorrect symbol".format(word))
      return self._trie_search(
          word, d, allow_spaces=allow_spaces, return_cost=return_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 3 for calling the LevenshteinSearcher obj's underlying member method code with example usage: obj.search(word, d, allow_spaces, return_cost) and returns: self, list