class_docstr: Select best answer using squad model logits. Make several batches for a single batch, send each batch
  to the squad model separately and get a single best answer for each batch.
  
  Args:
     squad_model: a loaded squad model
     batch_size: batch size to use with squad model
     sort_noans: whether to downgrade noans tokens in the most possible answers
     top_n: number of answers to return
  
  Attributes:
     squad_model: a loaded squad model
     batch_size: batch size to use with squad model
     top_n: number of answers to return
class_docstr_tok: ["Select","best","answer","using","squad","model","logits",".","Make","several","batches","for","a","single","batch",",","send","each","batch","to","the","squad","model","separately","and","get","a","single","best","answer","for","each","batch",".","Args",":","squad_model",":","a","loaded","squad","model","batch_size",":","batch","size","to","use","with","squad","model","sort_noans",":","whether","to","downgrade","noans","tokens","in","the","most","possible","answers","top_n",":","number","of","answers","to","return","Attributes",":","squad_model",":","a","loaded","squad","model","batch_size",":","batch","size","to","use","with","squad","model","top_n",":","number","of","answers","to","return"]
code: def __init__(
      self,
      squad_model: Union[Chainer, Component],
      batch_size: int = 50,
      sort_noans: bool = False,
      top_n: int = 1,
      return_answer_sentence: bool = False,
      **kwargs,
  ):
      self.squad_model = squad_model
      self.batch_size = batch_size
      self.sort_noans = sort_noans
      self.top_n = top_n
      self.return_answer_sentence = return_answer_sentence
  
file_path: DeepPavlov/deeppavlov/models/doc_retrieval/logit_ranker.py
imports: from logging import getLogger
  from operator import itemgetter
  from typing import List, Union, Tuple, Optional
  from deeppavlov.core.common.chainer import Chainer
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.estimator import Component
  from deeppavlov.models.doc_retrieval.utils import find_answer_sentence
label_desc: Use image node_id 1 to create a new LogitRanker object from inherited base classes: Component with example: obj = LogitRanker(squad_model, batch_size, sort_noans, top_n, return_answer_sentence)