code: def evaluate(args):
      model.eval()
      losses = []
      for step, batch in enumerate(eval_dataloader):
          with torch.no_grad():
              outputs = model(batch, labels=batch)
          loss = outputs.loss.repeat(args.valid_batch_size)
          losses.append(accelerator.gather(loss))
          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
              break
      losses = torch.cat(losses)
      loss = losses[: eval_dataloader.dataset.current_size].mean()
      try:
          perplexity = torch.exp(loss)
      except OverflowError:
          perplexity = float("inf")
      return loss.item(), perplexity.item()
  
imports: import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  from accelerate.utils import ProjectConfiguration
  from arguments import TrainingArguments
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate(args) and returns: loss,  perplexity