class_docstr: Component that chooses a candidate with the highest product of base and language model probabilities
  
  Args:
       load_path: path to the kenlm model file
       beam_size: beam size for highest probability search
  
  Attributes:
      lm: kenlm object
      beam_size: beam size for highest probability search
docstr: Choose the best candidate for every token
  
  Args:
      batch: batch of probabilities and string values of candidates for every token in a sentence
  
  Returns:
      batch of corrected tokenized sentences
code: def __call__(
      self, batch: List[List[List[Tuple[float, str]]]]
  ) -> List[List[str]]:
      """Choose the best candidate for every token
  
      Args:
          batch: batch of probabilities and string values of candidates for every token in a sentence
  
      Returns:
          batch of corrected tokenized sentences
      """
      return [self._infer_instance(candidates) for candidates in batch]
  
file_path: DeepPavlov/deeppavlov/models/spelling_correction/electors/kenlm_elector.py
imports: from logging import getLogger
  from pathlib import Path
  from typing import List, Tuple
  import kenlm
  from deeppavlov.core.commands.utils import expand_path
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.component import Component
label_desc: Use image node_id 2 for calling the KenlmElector obj's underlying member method code with example usage: obj.__call__(batch) and returns: unknown