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 handle_multi_token(self, label_words_logits, mask):
      if self.multi_token_handler == "first":
          label_words_logits = label_words_logits.select(
              dim=-1, index=0
          )
      elif self.multi_token_handler == "max":
          label_words_logits = label_words_logits - 1000 * (
              1 - mask.unsqueeze(0)
          )
          label_words_logits = label_words_logits.max(dim=-1).values
      elif self.multi_token_handler == "mean":
          label_words_logits = (
              label_words_logits * mask.unsqueeze(0)
          ).sum(dim=-1) / (mask.unsqueeze(0).sum(dim=-1) + 1e-15)
      else:
          raise ValueError(
              "multi_token_handler {} not configured".format(
                  self.multi_token_handler
              )
          )
      return label_words_logits
  
imports: import paddle
  from abc import abstractmethod
  from typing import *
  import numpy
  import paddle.nn.functional
label_desc: Use image node_id 12 for calling the Verbalizer obj's underlying member method code with example usage: obj.handle_multi_token(label_words_logits, mask) and returns: label_words_logits