class_docstr: This is the implementation of knowledeagble verbalizer, which uses external knowledge to expand the set of label words.
  This class inherit the ``ManualVerbalizer`` class.
  
  Args:
      tokenizer (:obj:`PreTrainedTokenizer`): The tokenizer of the current pre-trained model to point out the vocabulary.
      classes (:obj:`classes`): The classes (or labels) of the current task.
      prefix (:obj:`str`, optional): The prefix string of the verbalizer.
      multi_token_handler (:obj:`str`, optional): The handling strategy for multiple tokens produced by the tokenizer.
      max_token_split (:obj:`int`, optional):
      verbalizer_lr (:obj:`float`, optional): The learning rate of the verbalizer optimization.
      candidate_frac (:obj:`float`, optional):
code: def delete_common_words(self, d):
      word_count = {}
      for d_perclass in d:
          for w in d_perclass:
              if w not in word_count:
                  word_count[w] = 1
              else:
                  word_count[w] += 1
      for w in word_count:
          if word_count[w] >= 2:
              for d_perclass in d:
                  if w in d_perclass[1:]:
                      findidx = d_perclass[1:].index(w)
                      d_perclass.pop(findidx + 1)
      return d
  
imports: import os
  from openprompt.prompts.manual_template import ManualTemplate
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.utils.dummy_pt_objects import PreTrainedModel
  from openprompt.data_utils import InputFeatures
  import re
  from openprompt.prompts.manual_verbalizer import ManualVerbalizer
  from typing import *
  import torch
  import torch.nn
  import torch.nn.functional
  from openprompt.utils.logging import logger
label_desc: Use image node_id 3 for calling the KnowledgeableVerbalizer obj's underlying member method code with example usage: obj.delete_common_words(d) and returns: d