docstr: truncate the inputs from the rear
          
docstr_tok: ["truncate","the","inputs","from","the","rear"]
code: def truncate_from_tail(
      input_dict: Dict, num_tokens_to_truncate: int = 0
  ) -> Dict:
      r"""truncate the inputs from the rear"""
      truncated_example = defaultdict(list)
      shortenable_ids = input_dict["shortenable_ids"]
      for key in input_dict:
          parts = input_dict[key]
          to_trunc = num_tokens_to_truncate
          for i, part in enumerate(parts[::-1]):
              if (
                  len(part) == 0
              ):  # to prevent some part are empty after tokenization
                  continue
              if (
                  shortenable_ids[-1 - i][0] == 0
              ):  # ==0 means the part is not shortenable
                  continue
              parts[-1 - i] = (
                  part[:-to_trunc] if to_trunc < len(part) else []
              )
              to_trunc -= len(part)
              if to_trunc <= 0:
                  break
          truncated_example[key] = parts
      return truncated_example
  
imports: import itertools
  import warnings
  import torch
  import torch.nn
  from torch.utils.data import Dataset
  from typing import Union, List, Tuple, Dict, Optional
  from transformers.data.processors.utils import InputExample, InputFeatures
  from transformers.tokenization_utils import PreTrainedTokenizer
  from collections import defaultdict
  from openprompt.utils import round_list
  import numpy
label_desc: Use image node_id 7 for calling the TokenizerWrapper obj's underlying member method code with example usage: obj.truncate_from_tail(input_dict, num_tokens_to_truncate) and returns: truncated_example