code: def calibrate(
      prompt_model: PromptForClassification,
      dataloader: PromptDataLoader,
  ) -> torch.Tensor:
      r"""Calibrate. See `Paper <https://arxiv.org/abs/2108.02035>`_
  
      Args:
          prompt_model (:obj:`PromptForClassification`): the PromptForClassification model.
          dataloader (:obj:`List`): the dataloader to conduct the calibrate, could be a virtual one, i.e. contain an only-template example.
  
      Return:
          (:obj:`torch.Tensor`) A tensor of shape  (vocabsize) or (mask_num, vocabsize), the logits calculated for each word in the vocabulary
      """
      all_logits = []
      prompt_model.eval()
      for batch in tqdm(dataloader, desc="ContextCali"):
          batch = batch.to(prompt_model.device)
          logits = prompt_model.forward_without_verbalize(batch)
          all_logits.append(logits.detach())
      all_logits = torch.cat(all_logits, dim=0)
      return all_logits.mean(dim=0)
  
file_path: OpenPrompt/openprompt/utils/calibrate.py
imports: from yacs.config import CfgNode
  from openprompt.data_utils import FewShotSampler
  from torch.utils.data.dataset import Dataset
  from transformers.data.processors.utils import InputExample
  from openprompt.pipeline_base import PromptDataLoader, PromptModel, PromptForClassification
  from typing import *
  import torch
  from tqdm import tqdm
label_desc: Use image node_id 1 for calling a global function with example usage: calibrate(prompt_model, dataloader) and returns: all_logits