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: By default, the verbalizer will process the logits of the PLM's
  output.
  
  Args:
      logits (:obj:`torch.Tensor`): The current logits generated by pre-trained language models.
      batch (:obj:`Union[Dict, InputFeatures]`): The input features of the data.
code: def process_outputs(
      self,
      outputs: torch.Tensor,
      batch: Union[Dict, InputFeatures],
      **kwargs,
  ):
      r"""By default, the verbalizer will process the logits of the PLM's
      output.
  
      Args:
          logits (:obj:`torch.Tensor`): The current logits generated by pre-trained language models.
          batch (:obj:`Union[Dict, InputFeatures]`): The input features of the data.
      """
  
      return self.process_logits(outputs, batch=batch, **kwargs)
  
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 11 for calling the Verbalizer obj's underlying member method code with example usage: obj.process_outputs(outputs, batch) and returns: self