class_docstr: Base class for all the verbalizers.
  
  Args:
      tokenizer (:obj:`PreTrainedTokenizer`): A tokenizer to appoint the vocabulary and the tokenization strategy.
      classes (:obj:`Sequence[str]`): A sequence of classes that need to be projected.
class_docstr_tok: ["Base","class","for","all","the","verbalizers",".","Args",":","tokenizer","(",":","obj",":","`","PreTrainedTokenizer","`",")",":","A","tokenizer","to","appoint","the","vocabulary","and","the","tokenization","strategy",".","classes","(",":","obj",":","`","Sequence","[","str","]","`",")",":","A","sequence","of","classes","that","need","to","be","projected","."]
docstr: A hook to do something when textual label words were set.
          
docstr_tok: ["A","hook","to","do","something","when","textual","label","words","were","set","."]
code: def on_label_words_set(
      self,
  ):
      r"""A hook to do something when textual label words were set."""
      pass
  
imports: import paddle
  from abc import abstractmethod
  from typing import *
  import numpy
  import paddle.nn.functional
label_desc: Use image node_id 6 for calling the Verbalizer obj's underlying member method code with example usage: obj.on_label_words_set() without return types