code: def evaluate(args, model, val_dataloader, accelerator):
      model.eval()
      val_loss = MeanMetric().to(model.device)
  
      with torch.no_grad():
          for i, batch in enumerate(
              tqdm(val_dataloader),
          ):
              loss = model(batch["input_ids"])
  
              loss_values = accelerator.gather_for_metrics(
                  {"loss": loss.detach()}
              )
  
              val_loss.update(loss_values["loss"])
  
      return val_loss
  
file_path: OpenPrompt/tutorial/9_UltraChat.py
imports: import argparse
  import torch
  from openprompt import plms
  from openprompt.plms import *
  from transformers import GPTJConfig, GPTJModel, GPTJForCausalLM, GPT2Tokenizer
  from openprompt.plms import load_plm
  from openprompt.prompts import MixedTemplate
  from openprompt.data_utils.conditional_generation_dataset import UltraChatProcessor
  from transformers import AdamW
  from openprompt import PromptDataLoader
  from openprompt import PromptForGeneration
  from transformers.optimization import get_linear_schedule_with_warmup
  from accelerate import Accelerator
  from torchmetrics import MeanMetric
  from sklearn.model_selection import train_test_split
  from tqdm import tqdm
  from accelerate.utils import set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(args, model, val_dataloader, accelerator) and returns: val_loss