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 datasets import load_dataset
  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 accelerate import Accelerator, DistributedType
  from arguments import TrainingArguments
  from huggingface_hub import Repository
  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