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","."]
code: def __init__(
      self,
      tokenizer=None,
      classes=None,
      num_classes: Optional[int] = None,
  ):
      super().__init__()
      self.tokenizer = tokenizer
      self.classes = classes
      if classes is not None and num_classes is not None:
          assert (
              len(classes) == num_classes
          ), "len(classes) != num_classes, Check you config."
          self.num_classes = num_classes
      elif num_classes is not None:
          self.num_classes = num_classes
      elif classes is not None:
          self.num_classes = len(classes)
      else:
          self.num_classes = None
          # raise AttributeError("No able to configure num_classes")
      self._in_on_label_words_set = False
  
imports: import paddle
  from abc import abstractmethod
  from typing import *
  import numpy
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new Verbalizer object from inherited base classes: paddle.nn with example: obj = Verbalizer(tokenizer, classes, num_classes)