class_docstr: The basic manually defined verbalizer class, this class is inherited from the :obj:`Verbalizer` class.
  
  Args:
      tokenizer (:obj:`PreTrainedTokenizer`): The tokenizer of the current pre-trained model to point out the vocabulary.
      classes (:obj:`List[Any]`): The classes (or labels) of the current task.
      label_words (:obj:`Union[List[str], List[List[str]], Dict[List[str]]]`, optional): The label words that are projected by the labels.
      prefix (:obj:`str`, optional): The prefix string of the verbalizer (used in PLMs like RoBERTa, which is sensitive to prefix space)
      multi_token_handler (:obj:`str`, optional): The handling strategy for multiple tokens produced by the tokenizer.
      post_log_softmax (:obj:`bool`, optional): Whether to apply log softmax post processing on label_logits. Default to True.
code: def on_label_words_set(self):
      super().on_label_words_set()
      self.label_words = self.add_prefix(self.label_words, self.prefix)
  
      # TODO should Verbalizer base class has label_words property and setter?
      # it don't have label_words init argument or label words from_file option at all
  
      self.generate_parameters()
  
imports: import json
  from transformers.tokenization_utils import PreTrainedTokenizer
  from yacs.config import CfgNode
  from openprompt.data_utils import InputFeatures
  import re
  from openprompt import Verbalizer
  from typing import *
  import torch
  import torch.nn
  import torch.nn.functional
  from openprompt.utils.logging import logger
label_desc: Use image node_id 2 for calling the ManualVerbalizer obj's underlying member method code with example usage: obj.on_label_words_set() without return types