code: def truncate(self, encoder_inputs):
      total_tokens = sum(
          [len(part) for part in encoder_inputs["input_ids"]]
      )
      num_specials = self.num_special_tokens_to_add
      num_tokens_to_truncate = (
          total_tokens - self.max_seq_length + num_specials
      )
      self.total_passed_sentences += 1
      if num_tokens_to_truncate > 0:
          self.num_truncated_sentences += 1
          encoder_inputs = self.truncate_fct(
              input_dict=encoder_inputs,
              num_tokens_to_truncate=num_tokens_to_truncate,
          )
      return encoder_inputs
  
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 12 for calling the TokenizerWrapper obj's underlying member method code with example usage: obj.truncate(encoder_inputs) and returns: encoder_inputs