class_docstr: Component that finds replacement candidates for tokens at a set Damerau-Levenshtein distance
  
  Args:
      words: list of every correct word
      max_distance: maximum allowed Damerau-Levenshtein distance between source words and candidates
      error_probability: assigned probability for every edit
      vocab_penalty: assigned probability of an out of vocabulary token being the correct one without changes
  
  Attributes:
      max_distance: maximum allowed Damerau-Levenshtein distance between source words and candidates
      error_probability: assigned logarithmic probability for every edit
      vocab_penalty: assigned logarithmic probability of an out of vocabulary token being the correct one without
       changes
class_docstr_tok: ["Component","that","finds","replacement","candidates","for","tokens","at","a","set","Damerau-Levenshtein","distance","Args",":","words",":","list","of","every","correct","word","max_distance",":","maximum","allowed","Damerau-Levenshtein","distance","between","source","words","and","candidates","error_probability",":","assigned","probability","for","every","edit","vocab_penalty",":","assigned","probability","of","an","out","of","vocabulary","token","being","the","correct","one","without","changes","Attributes",":","max_distance",":","maximum","allowed","Damerau-Levenshtein","distance","between","source","words","and","candidates","error_probability",":","assigned","logarithmic","probability","for","every","edit","vocab_penalty",":","assigned","logarithmic","probability","of","an","out","of","vocabulary","token","being","the","correct","one","without","changes"]
docstr: Propose candidates for tokens in sentences
  
  Args:
      batch: batch of tokenized sentences
  
  Returns:
      batch of lists of probabilities and candidates for every token
docstr_tok: ["Propose","candidates","for","tokens","in","sentences","Args",":","batch",":","batch","of","tokenized","sentences","Returns",":","batch","of","lists","of","probabilities","and","candidates","for","every","token"]
code: def __call__(
      self, batch: Iterable[Iterable[str]], *args, **kwargs
  ) -> List[List[List[Tuple[float, str]]]]:
      """Propose candidates for tokens in sentences
  
      Args:
          batch: batch of tokenized sentences
  
      Returns:
          batch of lists of probabilities and candidates for every token
      """
      return [self._infer_instance(tokens) for tokens in batch]
  
file_path: DeepPavlov/deeppavlov/models/spelling_correction/levenshtein/searcher_component.py
imports: import string
  from logging import getLogger
  from math import log10
  from typing import Iterable, List, Tuple, Optional
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.component import Component
  from .levenshtein_searcher import LevenshteinSearcher
label_desc: Use image node_id 3 for calling the LevenshteinSearcherComponent obj's underlying member method code with example usage: obj.__call__(batch) and returns: unknown