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.
code: def sim(x, y):
      norm_x = F.normalize(x, dim=-1)
      norm_y = F.normalize(y, dim=-1)
      return torch.matmul(norm_x, norm_y.transpose(1, 0))
  
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 16 for calling the ProtoVerbalizer obj's underlying member method code with example usage: obj.sim(x, y) and returns: torch