class_docstr: Combines flat classification examples into nested examples. When called returns nested examples
  that weren't previously returned during current iteration over examples.
  
  Args:
      is_binary: signifies whether the classifier uses binary classification head
  Attributes:
      record_example_accumulator: underling accumulator that transforms flat examples
      total_examples: overall number of flat examples that must be processed during current iteration
class_docstr_tok: ["Combines","flat","classification","examples","into","nested","examples",".","When","called","returns","nested","examples","that","were","n't","previously","returned","during","current","iteration","over","examples",".","Args",":","is_binary",":","signifies","whether","the","classifier","uses","binary","classification","head","Attributes",":","record_example_accumulator",":","underling","accumulator","that","transforms","flat","examples","total_examples",":","overall","number","of","flat","examples","that","must","be","processed","during","current","iteration"]
docstr: Reinitialize the underlying accumulator from scratch
          
docstr_tok: ["Reinitialize","the","underlying","accumulator","from","scratch"]
code: def reset_accumulator(self):
      """Reinitialize the underlying accumulator from scratch"""
      self.record_example_accumulator = RecordExampleAccumulator()
  
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 3 for calling the TorchRecordPostprocessor obj's underlying member method code with example usage: obj.reset_accumulator() without return types