code: def evaluate(prompt_model, dataloader, desc):
      prompt_model.eval()
      allpreds = []
      alllabels = []
  
      for step, inputs in enumerate(dataloader):
          if use_cuda:
              inputs = inputs.cuda()
          logits = prompt_model(inputs)
          labels = inputs["label"]
          alllabels.extend(labels.cpu().tolist())
          allpreds.extend(torch.argmax(logits, dim=-1).cpu().tolist())
      acc = sum(
          [int(i == j) for i, j in zip(allpreds, alllabels)]
      ) / len(allpreds)
      return acc
  
imports: from tqdm import tqdm
  from openprompt.data_utils import PROCESSORS
  import torch
  from openprompt.data_utils.utils import InputExample
  import argparse
  import numpy
  from openprompt import PromptDataLoader
  from openprompt.prompts import ManualVerbalizer
  from openprompt.prompts import SoftTemplate
  from openprompt import PromptForClassification
  import time
  import os
  import random
  from openprompt.utils.reproduciblity import set_seed
  from openprompt.plms.seq2seq import T5TokenizerWrapper, T5LMTokenizerWrapper
  from transformers import T5Config, T5Tokenizer, T5ForConditionalGeneration
  from openprompt.data_utils.data_sampler import FewShotSampler
  from openprompt.plms import load_plm
  from transformers import AdamW, get_linear_schedule_with_warmup, get_constant_schedule_with_warmup
  from transformers.optimization import Adafactor, AdafactorSchedule
  import os
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(prompt_model, dataloader, desc) and returns: acc