class_docstr: Bert-based on PyTorch model for SQuAD-like problem setting:
  It predicts start and end position of answer for given question and context.
  
  [CLS] token is used as no_answer. If model selects [CLS] token as most probable
  answer, it means that there is no answer in given context.
  
  Start and end position of answer are predicted by linear transformation
  of Bert outputs.
  
  Args:
      pretrained_bert: pretrained Bert checkpoint path or key title (e.g. "bert-base-uncased")
      attention_probs_keep_prob: keep_prob for Bert self-attention layers
      hidden_keep_prob: keep_prob for Bert hidden layers
      bert_config_file: path to Bert configuration file, or None, if `pretrained_bert` is a string name
      psg_cls: whether to use a separate linear layer to define if a passage contains the answer to the question
      batch_size: batch size for inference of squad model
code: def accepted_keys(self) -> Tuple[str]:
      if self.is_data_parallel:
          accepted_keys = self.model.module.forward.__code__.co_varnames
      else:
          accepted_keys = self.model.forward.__code__.co_varnames
      return accepted_keys
  
imports: from collections import namedtuple
  from logging import getLogger
  from pathlib import Path
  from typing import List, Tuple, Optional, Dict
  import numpy
  import torch
  from transformers import AutoModelForQuestionAnswering, AutoConfig, AutoModel
  from transformers.data.processors.utils import InputFeatures
  from deeppavlov.core.commands.utils import expand_path
  from deeppavlov.core.common.errors import ConfigError
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.torch_model import TorchModel
label_desc: Use image node_id 3 for calling the TorchTransformersSquad obj's underlying member method code with example usage: obj.accepted_keys() and returns: accepted_keys