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):
docstr: The return value if the normalized (sum to 1) probs of label words.
          
docstr_tok: ["The","return","value","if","the","normalized","(","sum","to","1",")","probs","of","label","words","."]
code: def project(
      self,
      logits: torch.Tensor,
      **kwargs,
  ) -> torch.Tensor:
      r"""The return value if the normalized (sum to 1) probs of label words."""
      label_words_logits = logits[:, self.label_words_ids]
      label_words_logits = self.handle_multi_token(
          label_words_logits, self.words_ids_mask
      )
      label_words_logits -= 10000 * (1 - self.label_words_mask)
      return label_words_logits
  
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 7 for calling the KnowledgeableVerbalizer obj's underlying member method code with example usage: obj.project(logits) and returns: label_words_logits