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: This function aims to register logits that need to be calibrated, and detach the original logits from the current graph.
docstr_tok: ["This","function","aims","to","register","logits","that","need","to","be","calibrated",",","and","detach","the","original","logits","from","the","current","graph","."]
code: def register_calibrate_logits(self, logits):
      r"""
      This function aims to register logits that need to be calibrated, and detach the original logits from the current graph.
      """
      if logits.requires_grad:
          logits = logits.detach()
      self._calibrate_logits = logits
  
imports: import paddle
  from abc import abstractmethod
  from typing import *
  import numpy
  import paddle.nn.functional
label_desc: Use image node_id 10 for calling the Verbalizer obj's underlying member method code with example usage: obj.register_calibrate_logits(logits) without return types