class_docstr: The implementation of the prototypical verbalizer in `Prototypical Verbalizer for Prompt-based Few-shot Tuning <https://arxiv.org/pdf/2104.08691v1.pdf>`_ This class is inherited from the :obj:`Verbalizer` class.
  
  Args:
      tokenizer (:obj:`PreTrainedTokenizer`): The tokenizer of the current pre-trained model to point out the vocabulary.
      classes (:obj:`List[Any]`): The classes (or labels) of the current task.
      label_words (:obj:`Union[List[str], List[List[str]], Dict[List[str]]]`, optional): The label words that are projected by the labels.
      prefix (:obj:`str`, optional): The prefix string of the verbalizer (used in PLMs like RoBERTa, which is sensitive to prefix space)
      multi_token_handler (:obj:`str`, optional): The handling strategy for multiple tokens produced by the tokenizer.
      post_log_softmax (:obj:`bool`, optional): Whether to apply log softmax post processing on label_logits. Default to True.
      lr: (:obj:`float`, optional): The learning rate for prototypes.
      mid_dim: (:obj:`int`, optional): The dimension of prototype embeddings.
      epochs: (:obj:`int`, optional): The training epochs of prototypes.
      multi_verb (:obj:`str`, optional): `multi` to ensemble with manual verbalizers, `proto` to use only ProtoVerb.
docstr: Given logits regarding the entire vocabulary, return the probs over the label words set.
  
  Args:
      logits (:obj:`Tensor`): The logits over the entire vocabulary.
  
  Returns:
      :obj:`Tensor`: The logits over the label words set.
docstr_tok: ["Given","logits","regarding","the","entire","vocabulary",",","return","the","probs","over","the","label","words","set",".","Args",":","logits","(",":","obj",":","`","Tensor","`",")",":","The","logits","over","the","entire","vocabulary",".","Returns",":",":","obj",":","`","Tensor","`",":","The","logits","over","the","label","words","set","."]
code: def normalize(self, logits: torch.Tensor) -> torch.Tensor:
      """
      Given logits regarding the entire vocabulary, return the probs over the label words set.
  
      Args:
          logits (:obj:`Tensor`): The logits over the entire vocabulary.
  
      Returns:
          :obj:`Tensor`: The logits over the label words set.
  
      """
      batch_size = logits.shape[0]
      return F.softmax(logits.reshape(batch_size, -1), dim=-1).reshape(
          *logits.shape
      )
  
imports: from inspect import Parameter
  import json
  from os import stat
  from transformers.file_utils import ModelOutput
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.utils.dummy_pt_objects import PreTrainedModel
  from yacs.config import CfgNode
  from openprompt.data_utils import InputFeatures
  import re
  from openprompt import Verbalizer
  from typing import *
  import torch
  import torch.nn
  import torch.nn.functional
  from openprompt.utils.logging import logger
  import copy
  from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, MaskedLMOutput
  from transformers.models.t5 import T5ForConditionalGeneration
label_desc: Use image node_id 9 for calling the ProtoVerbalizer obj's underlying member method code with example usage: obj.normalize(logits) and returns: F