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.batch_size)
          losses.append(accelerator.gather(loss))
  
          if args.max_eval_steps > 0 and step >= args.max_eval_steps:
              break
      loss = torch.mean(torch.cat(losses))
      try:
          perplexity = torch.exp(loss)
      except OverflowError:
          perplexity = float("inf")
      return loss.item(), perplexity.item()
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/examples/research_projects/codeparrot/scripts/validation_loss.py
imports: import logging
  import torch
  from datasets import load_dataset
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from accelerate import Accelerator
  from arguments import EvaluationArguments
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(args) and returns: loss,  perplexity