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: To aggregate logits on multiple label words into the label's logits
  Basic aggregator: mean of each label words' logits to a label's logits
  Can be re-implemented in advanced verbaliezer.
  
  Args:
      label_words_logits (:obj:`torch.Tensor`): The logits of the label words only.
  
  Return:
      :obj:`torch.Tensor`: The final logits calculated by the label words.
code: def aggregate(label_words_logits: torch.Tensor) -> torch.Tensor:
      r"""To aggregate logits on multiple label words into the label's logits
      Basic aggregator: mean of each label words' logits to a label's logits
      Can be re-implemented in advanced verbaliezer.
  
      Args:
          label_words_logits (:obj:`torch.Tensor`): The logits of the label words only.
  
      Return:
          :obj:`torch.Tensor`: The final logits calculated by the label words.
      """
      if label_words_logits.dim() > 2:
          return label_words_logits.mean(dim=-1)
      else:
          return label_words_logits
  
imports: from abc import abstractmethod
  import json
  from transformers.file_utils import ModelOutput
  from openprompt.config import convert_cfg_to_dict
  from transformers.utils.dummy_pt_objects import PreTrainedModel
  from openprompt.utils.utils import signature
  from yacs.config import CfgNode
  from openprompt.data_utils import InputFeatures, InputExample
  import torch
  import torch.nn
  from typing import *
  from transformers.tokenization_utils import PreTrainedTokenizer
  from openprompt.utils.logging import logger
  import numpy
  import torch.nn.functional
label_desc: Use image node_id 13 for calling the Verbalizer obj's underlying member method code with example usage: obj.aggregate(label_words_logits) and returns: label_words_logits, label_words_logits