code: def evaluate(args):
      tokenizer = BertTokenizer.from_pretrained(
          "bert-base-uncased", do_lower_case=True
      )
      model = BertAbs.from_pretrained(
          "remi/bertabs-finetuned-extractive-abstractive-summarization"
      )
      model.to(args.device)
      model.eval()
  
      symbols = {
          "BOS": tokenizer.vocab["[unused0]"],
          "EOS": tokenizer.vocab["[unused1]"],
          "PAD": tokenizer.vocab["[PAD]"],
      }
  
      if args.compute_rouge:
          reference_summaries = []
          generated_summaries = []
  
          import nltk
  
          import rouge
  
          nltk.download("punkt")
          rouge_evaluator = rouge.Rouge(
              metrics=["rouge-n", "rouge-l"],
              max_n=2,
              limit_length=True,
              length_limit=args.beam_size,
              length_limit_type="words",
              apply_avg=True,
              apply_best=False,
              alpha=0.5,  # Default F1_score
              weight_factor=1.2,
              stemming=True,
          )
  
      # these (unused) arguments are defined to keep the compatibility
      # with the legacy code and will be deleted in a next iteration.
      args.result_path = ""
      args.temp_dir = ""
  
      data_iterator = build_data_iterator(args, tokenizer)
      predictor = build_predictor(args, tokenizer, symbols, model)
  
      logger.info("***** Running evaluation *****")
      logger.info("  Number examples = %d", len(data_iterator.dataset))
      logger.info("  Batch size = %d", args.batch_size)
      logger.info("")
      logger.info("***** Beam Search parameters *****")
      logger.info("  Beam size = %d", args.beam_size)
      logger.info("  Minimum length = %d", args.min_length)
      logger.info("  Maximum length = %d", args.max_length)
      logger.info("  Alpha (length penalty) = %.2f", args.alpha)
      logger.info(
          "  Trigrams %s be blocked",
          ("will" if args.block_trigram else "will NOT"),
      )
  
      for batch in tqdm(data_iterator):
          batch_data = predictor.translate_batch(batch)
          translations = predictor.from_batch(batch_data)
          summaries = [format_summary(t) for t in translations]
          save_summaries(
              summaries, args.summaries_output_dir, batch.document_names
          )
  
          if args.compute_rouge:
              reference_summaries += batch.tgt_str
              generated_summaries += summaries
  
      if args.compute_rouge:
          scores = rouge_evaluator.get_scores(
              generated_summaries, reference_summaries
          )
          str_scores = format_rouge_scores(scores)
          save_rouge_scores(str_scores)
          print(str_scores)
  
imports: import argparse
  import logging
  import os
  import sys
  from collections import namedtuple
  import torch
  from torch.utils.data import DataLoader, SequentialSampler
  from tqdm import tqdm
  from modeling_bertabs import BertAbs, build_predictor
  from transformers import BertTokenizer
  from .utils_summarization import CNNDMDataset, build_mask, compute_token_type_ids, encode_for_summarization, truncate_or_pad
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(args) without return types