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.
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docstr: The verbalizer can be seen as an extra layer on top of the original
  pre-trained models. In manual verbalizer, it is a fixed one-hot vector of dimension
  ``vocab_size``, with the position of the label word being 1 and 0 everywhere else.
  In other situation, the parameters may be a continuous vector over the
  vocab, with each dimension representing a weight of that token.
  Moreover, the parameters may be set to trainable to allow label words selection.
  
  Therefore, this function serves as an abstract methods for generating the parameters
  of the verbalizer, and must be instantiated in any derived class.
  
  Note that the parameters need to be registered as a part of pytorch's module to
  It can be achieved by wrapping a tensor using ``nn.Parameter()``.
code: def generate_parameters(self, **kwargs) -> List:
      r"""
      The verbalizer can be seen as an extra layer on top of the original
      pre-trained models. In manual verbalizer, it is a fixed one-hot vector of dimension
      ``vocab_size``, with the position of the label word being 1 and 0 everywhere else.
      In other situation, the parameters may be a continuous vector over the
      vocab, with each dimension representing a weight of that token.
      Moreover, the parameters may be set to trainable to allow label words selection.
  
      Therefore, this function serves as an abstract methods for generating the parameters
      of the verbalizer, and must be instantiated in any derived class.
  
      Note that the parameters need to be registered as a part of pytorch's module to
      It can be achieved by wrapping a tensor using ``nn.Parameter()``.
      """
      raise NotImplementedError
  
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 9 for calling the Verbalizer obj's underlying member method code with example usage: obj.generate_parameters() without return types