class_docstr: The basic manually defined verbalizer class, this class is inherited from the :obj:`Verbalizer` class.
  This class restrict the use of label words to one words per label. For a verbalzer with less constraints,
  please use Basic ManualVerbalizer.
  
  Args:
      tokenizer (:obj:`PreTrainedTokenizer`): The tokenizer of the current pre-trained model to point out the vocabulary.
      classes (:obj:`classes`): The classes (or labels) of the current task.
      num_classes (:obj:`int`): Optional. The number of classes of the verbalizer. Only one of `classes` and `num_classes` should be used.
      label_words (:obj:`Union[Sequence[str], Mapping[str, 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.
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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.
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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: import json
  from transformers.tokenization_utils import PreTrainedTokenizer
  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
label_desc: Use image node_id 7 for calling the One2oneVerbalizer obj's underlying member method code with example usage: obj.normalize(logits) and returns: F