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: load a verbalizer from verbalizer's configuration node.
  
  Args:
      config (:obj:`CfgNode`): the sub-configuration of verbalizer, i.e. ``config[config.verbalizer]``
                  if config is a global config node.
      kwargs: Other kwargs that might be used in initialize the verbalizer.
              The actual value should match the arguments of ``__init__`` functions.
code: def from_config(cls, config: CfgNode, **kwargs):
      r"""load a verbalizer from verbalizer's configuration node.
  
      Args:
          config (:obj:`CfgNode`): the sub-configuration of verbalizer, i.e. ``config[config.verbalizer]``
                      if config is a global config node.
          kwargs: Other kwargs that might be used in initialize the verbalizer.
                  The actual value should match the arguments of ``__init__`` functions.
      """
  
      init_args = signature(cls.__init__).args
      _init_dict = (
          {**convert_cfg_to_dict(config), **kwargs}
          if config is not None
          else kwargs
      )
      init_dict = {
          key: _init_dict[key] for key in _init_dict if key in init_args
      }
      verbalizer = cls(**init_dict)
      if hasattr(verbalizer, "from_file"):
          if not hasattr(config, "file_path"):
              pass
          else:
              if (
                  not hasattr(config, "label_words")
                  or config.label_words is None
              ) and config.file_path is not None:
                  if config.choice is None:
                      config.choice = 0
                  verbalizer.from_file(config.file_path, config.choice)
              elif (
                  hasattr(config, "label_words")
                  and config.label_words is not None
              ) and config.file_path is not None:
                  raise RuntimeError(
                      "The text can't be both set from `text` and `file_path`."
                  )
      return verbalizer
  
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 17 for calling the Verbalizer obj's underlying member method code with example usage: obj.from_config(cls, config) and returns: verbalizer