code: def evaluate(
      args,
      model,
      metric,
      tokenizer,
      eval_dataloader,
      accelerator,
      max_length,
  ):
      accelerator.print("starting evaluation")
      count_printed = 0
  
      def postprocess_text(preds, labels):
          preds = [pred.strip() for pred in preds]
          labels = [[label.strip()] for label in labels]
  
          return preds, labels
  
      model.eval()
      if args.val_max_target_length is None:
          args.val_max_target_length = args.max_target_length
  
      gen_kwargs = {
          "max_length": args.val_max_target_length
          if args is not None
          else max_length,
          "num_beams": args.num_beams,
          "min_length": args.val_min_target_length,
          "length_penalty": False,
          "no_repeat_ngram_size": 3,
          "encoder_no_repeat_ngram_size": 3,
          "repetition_penalty": 1.2,
      }
      samples_seen = 0
      for step, batch in enumerate(eval_dataloader):
          with torch.no_grad():
              generated_tokens = accelerator.unwrap_model(
                  model
              ).generate(
                  batch["input_ids"],
                  attention_mask=batch["attention_mask"],
                  **gen_kwargs,
              )
  
              generated_tokens = accelerator.pad_across_processes(
                  generated_tokens,
                  dim=1,
                  pad_index=tokenizer.pad_token_id,
              )
              labels = batch["labels"]
              if not args.pad_to_max_length:
                  # If we did not pad to max length, we need to pad the labels too
                  labels = accelerator.pad_across_processes(
                      batch["labels"],
                      dim=1,
                      pad_index=tokenizer.pad_token_id,
                  )
  
              generated_tokens, labels = accelerator.gather(
                  (generated_tokens, labels)
              )
              generated_tokens = generated_tokens.cpu().numpy()
              labels = labels.cpu().numpy()
  
              if args.ignore_pad_token_for_loss:
                  # Replace -100 in the labels as we can't decode them.
                  labels = np.where(
                      labels != -100, labels, tokenizer.pad_token_id
                  )
  
              decoded_preds = tokenizer.batch_decode(
                  generated_tokens, skip_special_tokens=True
              )
              decoded_labels = tokenizer.batch_decode(
                  labels, skip_special_tokens=True
              )
  
              if count_printed < args.n_val_batch_generations:
                  logger.info(
                      "printing few sample generations and corresponding labels from eval set"
                  )
                  logger.info("prompt | generated | label")
                  decoded_prompts = tokenizer.batch_decode(
                      batch["input_ids"], skip_special_tokens=False
                  )
                  for prompt, generated_response, response in zip(
                      decoded_prompts, decoded_preds, decoded_labels
                  ):
                      cleaned_prompt = prompt.replace(
                          "<pad>", ""
                      ).strip()
                      logger.info(
                          f"{cleaned_prompt} | {generated_response} | {response}"
                      )
                  count_printed += 1
  
              decoded_preds, decoded_labels = postprocess_text(
                  decoded_preds, decoded_labels
              )
              # If we are in a multiprocess environment, the last batch has duplicates
              if accelerator.num_processes > 1:
                  if step == len(eval_dataloader) - 1:
                      decoded_preds = decoded_preds[
                          : len(eval_dataloader.dataset) - samples_seen
                      ]
                      decoded_labels = decoded_labels[
                          : len(eval_dataloader.dataset) - samples_seen
                      ]
                  else:
                      samples_seen += len(decoded_labels)
  
              metric.add_batch(
                  predictions=decoded_preds,
                  references=decoded_labels,
              )
      result = metric.compute()
      logger.info({"bleu": result["score"]})
      accelerator.print("evaluation completed")
      return result["score"]
  
imports: import argparse
  import json
  import logging
  import math
  import os
  import random
  from pathlib import Path
  from time import time
  import datasets
  import nltk
  import numpy
  import torch
  from datasets import load_dataset, load_metric
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import DummyOptim, DummyScheduler, set_seed
  from filelock import FileLock
  from huggingface_hub import Repository
  from transformers import CONFIG_MAPPING, MODEL_MAPPING, AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer, DataCollatorForSeq2Seq, SchedulerType, get_scheduler
  from transformers.utils import get_full_repo_name, is_offline_mode
  from transformers.utils.versions import require_version
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(args, model, metric, tokenizer, eval_dataloader, accelerator, max_length) and returns: result, preds,  labels