code: def evaluate(args, model, eval_dataloader, accelerator, eval_dataset):
      model.eval()
      losses = []
      for step, batch in enumerate(eval_dataloader):
          with torch.no_grad():
              outputs = model(**batch)
  
          loss = outputs.loss
          losses.append(
              accelerator.gather_for_metrics(
                  loss.repeat(args.per_device_eval_batch_size)
              )
          )
  
      losses = torch.cat(losses)
      try:
          eval_loss = torch.mean(losses)
          perplexity = math.exp(eval_loss)
      except OverflowError:
          perplexity = float("inf")
      return perplexity, eval_loss
  
imports: import argparse
  import json
  import logging
  import math
  import os
  import random
  from itertools import chain
  from pathlib import Path
  import datasets
  import torch
  import transformers
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, SchedulerType, default_data_collator, get_scheduler
  from transformers.utils import get_full_repo_name
  from transformers.utils.versions import require_version
  from accelerate import Accelerator, DistributedType
  from accelerate.logging import get_logger
  from accelerate.utils import DummyOptim, DummyScheduler, set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(args, model, eval_dataloader, accelerator, eval_dataset) and returns: perplexity,  eval_loss