code: def eval_ppl_epoch(args, eval_data, eval_examples, model, tokenizer):
      eval_sampler = SequentialSampler(eval_data)
      eval_dataloader = DataLoader(
          eval_data,
          sampler=eval_sampler,
          batch_size=args.eval_batch_size,
          num_workers=4,
          pin_memory=True,
      )
      # Start evaluating model
      logger.info("  " + "***** Running ppl evaluation *****")
      logger.info("  Num examples = %d", len(eval_examples))
      logger.info("  Batch size = %d", args.eval_batch_size)
  
      model.eval()
      eval_loss, batch_num = 0, 0
      for batch in tqdm(
          eval_dataloader, total=len(eval_dataloader), desc="Eval ppl"
      ):
          batch = tuple(t.to(args.device) for t in batch)
          source_ids, target_ids = batch
          source_mask = source_ids.ne(tokenizer.pad_token_id)
          target_mask = target_ids.ne(tokenizer.pad_token_id)
  
          with torch.no_grad():
              if args.model_type == "roberta":
                  loss, _, _ = model(
                      source_ids=source_ids,
                      source_mask=source_mask,
                      target_ids=target_ids,
                      target_mask=target_mask,
                  )
              else:
                  outputs = model(
                      input_ids=source_ids,
                      attention_mask=source_mask,
                      labels=target_ids,
                      decoder_attention_mask=target_mask,
                  )
                  loss = outputs.loss
  
          eval_loss += loss.item()
          batch_num += 1
      eval_loss = eval_loss / batch_num
      eval_ppl = round(np.exp(eval_loss), 5)
      return eval_ppl
  
imports: import os
  import logging
  import argparse
  import math
  import numpy
  from tqdm import tqdm
  import multiprocessing
  import time
  import torch
  from torch.utils.tensorboard import SummaryWriter
  from torch.utils.data import DataLoader, SequentialSampler, RandomSampler
  from torch.utils.data.distributed import DistributedSampler
  from transformers import AdamW, get_linear_schedule_with_warmup
  from models import build_or_load_gen_model
  from evaluator import smooth_bleu
  from evaluator.CodeBLEU import calc_code_bleu
  from evaluator.bleu import _bleu
  from utils import get_filenames, get_elapse_time, load_and_cache_gen_data
  from configs import add_args, set_seed, set_dist
label_desc: Use image node_id 1 for calling a global function with example usage: eval_ppl_epoch(args, eval_data, eval_examples, model, tokenizer) and returns: eval_ppl