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: retrieve useful output for the verbalizer from the whole model output
  By default, it will only retrieve the logits
  
  Args:
      outputs (:obj:`ModelOutput`) The output from the pretrained language model.
  
  Return:
      :obj:`torch.Tensor` The gathered output, should be of shape (``batch_size``,
      ``seq_len``, ``any``)
code: def gather_outputs(self, outputs: ModelOutput):
      r"""retrieve useful output for the verbalizer from the whole model output
      By default, it will only retrieve the logits
  
      Args:
          outputs (:obj:`ModelOutput`) The output from the pretrained language model.
  
      Return:
          :obj:`torch.Tensor` The gathered output, should be of shape (``batch_size``,
          ``seq_len``, ``any``)
      """
      return outputs.logits
  
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 12 for calling the Verbalizer obj's underlying member method code with example usage: obj.gather_outputs(outputs) and returns: outputs