docstr: Truncates a sequence pair in place to the maximum length.
  
  This will always truncate the longer sequence one token at a time.
  This makes more sense than truncating an equal percent
  of tokens from each, since if one sequence is very short then each token
  that's truncated likely contains more information than a longer sequence.
docstr_tok: ["Truncates","a","sequence","pair","in","place","to","the","maximum","length",".","This","will","always","truncate","the","longer","sequence","one","token","at","a","time",".","This","makes","more","sense","than","truncating","an","equal","percent","of","tokens","from","each",",","since","if","one","sequence","is","very","short","then","each","token","that","'s","truncated","likely","contains","more","information","than","a","longer","sequence","."]
code: def _truncate_seq_pair(
      self, tokens_a: str, tokens_b: str, max_length: int
  ):
      """Truncates a sequence pair in place to the maximum length.
  
      This will always truncate the longer sequence one token at a time.
      This makes more sense than truncating an equal percent
      of tokens from each, since if one sequence is very short then each token
      that's truncated likely contains more information than a longer sequence.
      """
      while True:
          total_length = len(tokens_a) + len(tokens_b)
          if total_length <= max_length:
              break
          if len(tokens_a) > len(tokens_b):
              tokens_a.pop()
          else:
              tokens_b.pop()
  
imports: import os
  import pickle
  from typing import Dict, List, Optional, Union
  import numpy
  import torch
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.glue_benchmark.data_processors import ColaProcessor, MnliMismatchedProcessor, MnliProcessor, MrpcProcessor, QnliProcessor, QqpProcessor, RteProcessor, Sst2Processor, StsbProcessor, WnliProcessor, XNLIProcessor
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType, RegressionValuesType
  from nemo.utils import logging
label_desc: Use image node_id 6 for calling the GLUEDataset obj's underlying member method code with example usage: obj._truncate_seq_pair(tokens_a, tokens_b, max_length) without return types