code: def padding(
      input_dict: Dict,
      max_len: int,
      pad_id_for_inputs: int = 0,
      pad_id_for_others: int = 0,
  ) -> None:
      for key, value in input_dict.items():
          if len(input_dict[key]) > max_len:
              raise ValueError(
                  f"""Truncated seq length of '{key}' still greater than max length {max_len}."\
                      "One possible reason is that no enough shortenable parts in template. Try adding {{"shortenable": "True"}} property.
                  """
              )
          if "input" in key:
              input_dict[key].extend(
                  [pad_id_for_inputs] * (max_len - len(value))
              )
          else:
              input_dict[key].extend(
                  [pad_id_for_others] * (max_len - len(value))
              )
      return input_dict
  
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 10 for calling the TokenizerWrapper obj's underlying member method code with example usage: obj.padding(input_dict, max_len, pad_id_for_inputs, pad_id_for_others) and returns: input_dict