code: def __call__(
      self,
      questions_batch: List[str],
      rels_batch: List[List[List[str]]],
  ):
      lengths, proc_rels_batch = [], []
      for question, rels_list in zip(questions_batch, rels_batch):
          proc_rels_list = []
          for rels in rels_list:
              if isinstance(rels, str):
                  rels = [rels]
              rels_str = ""
              if len(rels) == 1:
                  if self.additional_special_tokens:
                      rels_str = f"<one_rel> {rels[0]} </one_rel>"
                  else:
                      rels_str = rels[0]
              elif len(rels) == 2:
                  if rels[0] == rels[1]:
                      rels_str = f"<double> {rels[0]} </double>"
                  else:
                      rels_str = f"<first_rel> {rels[0]} <mid> {rels[1]} </second_rel>"
              encoding = self.tokenizer.encode_plus(
                  text=question,
                  text_pair=rels_str,
                  return_attention_mask=True,
                  add_special_tokens=True,
                  truncation=True,
              )
              lengths.append(len(encoding["input_ids"]))
              proc_rels_list.append(rels_str)
          proc_rels_batch.append(proc_rels_list)
  
      max_len = min(max(lengths), self.max_seq_length)
      input_ids_batch, attention_mask_batch, token_type_ids_batch = (
          [],
          [],
          [],
      )
      for question, rels_list in zip(questions_batch, proc_rels_batch):
          input_ids_list, attention_mask_list, token_type_ids_list = (
              [],
              [],
              [],
          )
          for rels_str in rels_list:
              encoding = self.tokenizer.encode_plus(
                  text=question,
                  text_pair=rels_str,
                  truncation=True,
                  max_length=max_len,
                  add_special_tokens=True,
                  pad_to_max_length=True,
                  return_attention_mask=True,
              )
              input_ids_list.append(encoding["input_ids"])
              attention_mask_list.append(encoding["attention_mask"])
              if "token_type_ids" in encoding:
                  token_type_ids_list.append(encoding["token_type_ids"])
              else:
                  token_type_ids_list.append([0])
          input_ids_batch.append(input_ids_list)
          attention_mask_batch.append(attention_mask_list)
          token_type_ids_batch.append(token_type_ids_list)
      input_features = {
          "input_ids": input_ids_batch,
          "attention_mask": attention_mask_batch,
          "token_type_ids": token_type_ids_batch,
      }
      return input_features
  
imports: import math
  import random
  import re
  from collections import defaultdict
  from dataclasses import dataclass
  from logging import getLogger
  from pathlib import Path
  from typing import Tuple, List, Optional, Union, Dict, Set, Any
  import nltk
  import numpy
  import torch
  from transformers import AutoTokenizer
  from transformers.data.processors.utils import InputFeatures
  from deeppavlov.core.commands.utils import expand_path
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.data.utils import zero_pad
  from deeppavlov.core.models.component import Component
  from deeppavlov.models.preprocessors.mask import Mask
label_desc: Use image node_id 2 for calling the PathRankingPreprocessor obj's underlying member method code with example usage: obj.__call__(questions_batch, rels_batch) and returns: input_features