code: def evaluate(model, val_dataloader):
      model.eval()
      allpreds = []
      alllabels = []
      with torch.no_grad():
          for step, inputs in enumerate(val_dataloader):
              if cuda:
                  inputs = inputs.cuda()
              logits = 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 openprompt.data_utils.text_classification_dataset import SST2Processor
  from openprompt.plms import load_plm
  from openprompt.prompts import ManualVerbalizer, ManualTemplate
  from openprompt.prompts.prompt_generator import LMBFFTemplateGenerationTemplate
  from openprompt.plms import load_plm
  from openprompt.prompts.prompt_generator import T5TemplateGenerator
  from openprompt.pipeline_base import PromptDataLoader, PromptForClassification
  from openprompt.prompts import ManualTemplate
  from openprompt.trainer import ClassificationRunner
  import copy
  import torch
  from transformers import AdamW, get_linear_schedule_with_warmup
  import numpy
  from tqdm import tqdm
  from openprompt.prompts.prompt_generator import RobertaVerbalizerGenerator
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(model, val_dataloader) and returns: acc