code: def evaluate(
      args,
      accelerator,
      dataloader,
      eval_set,
      model,
      checkpoint,
      has_labels=True,
      write_to_file=True,
  ):
      """Evaluate a model checkpoint on the given evaluation data."""
  
      num_examples = args.num_examples[eval_set]
      eval_metric = None
      completed_steps = 0
      eval_loss = 0.0
      all_predictions = None
      all_references = None
      all_probabilities = None
  
      if has_labels:
          # Get the metric function
          eval_metric = load_metric(args.eval_metric)
  
      eval_results = {}
      model.eval()
      for _, batch in enumerate(dataloader):
          with torch.no_grad():
              outputs = model(**batch)
  
          eval_loss += outputs.loss.item()
          logits = outputs.logits
          predictions = (
              logits.argmax(dim=-1)
              if not args.is_regression
              else logits.squeeze()
          )
          predictions = accelerator.gather(predictions)
  
          if all_predictions is None:
              all_predictions = predictions.detach().cpu().numpy()
          else:
              all_predictions = np.append(
                  all_predictions,
                  predictions.detach().cpu().numpy(),
                  axis=0,
              )
  
          if not args.is_regression:
              probabilities = logits.softmax(dim=-1).max(dim=-1).values
              probabilities = accelerator.gather(probabilities)
              if all_probabilities is None:
                  all_probabilities = (
                      probabilities.detach().cpu().numpy()
                  )
              else:
                  all_probabilities = np.append(
                      all_probabilities,
                      probabilities.detach().cpu().numpy(),
                      axis=0,
                  )
  
          if has_labels:
              references = batch["labels"]
              references = accelerator.gather(references)
              if all_references is None:
                  all_references = references.detach().cpu().numpy()
              else:
                  all_references = np.append(
                      all_references,
                      references.detach().cpu().numpy(),
                      axis=0,
                  )
  
              eval_metric.add_batch(
                  predictions=predictions,
                  references=references,
              )
          completed_steps += 1
  
      if has_labels:
          eval_results.update(eval_metric.compute())
          eval_results["completed_steps"] = completed_steps
          eval_results["avg_eval_loss"] = eval_loss / completed_steps
  
          if write_to_file:
              accelerator.wait_for_everyone()
              if accelerator.is_main_process:
                  results_file = os.path.join(
                      args.output_dir,
                      f"{eval_set}_results_{checkpoint}.json",
                  )
                  with open(results_file, "w") as f:
                      json.dump(
                          eval_results, f, indent=4, sort_keys=True
                      )
  
      if write_to_file:
          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
              output_file = os.path.join(
                  args.output_dir, f"{eval_set}_output_{checkpoint}.csv"
              )
              if not args.is_regression:
                  assert len(all_predictions) == len(all_probabilities)
                  df = pd.DataFrame(
                      list(zip(all_predictions, all_probabilities)),
                      columns=["prediction", "probability"],
                  )
              else:
                  df = pd.DataFrame(
                      all_predictions, columns=["prediction"]
                  )
              df = df.head(num_examples)
              df.to_csv(output_file, header=True, index=False)
      return eval_results
  
imports: import argparse
  import dataclasses
  import json
  import logging
  import math
  import os
  import random
  import shutil
  from typing import List, Optional
  import datasets
  import numpy
  import pandas
  import torch
  from datasets import load_dataset, load_metric
  from torch.utils.data import DataLoader
  from tqdm.auto import tqdm
  from transformers import AdamW, AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, default_data_collator, get_scheduler, set_seed
  from transformers.file_utils import ExplicitEnum
  from transformers.trainer_utils import IntervalStrategy
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(args, accelerator, dataloader, eval_set, model, checkpoint, has_labels, write_to_file) and returns: eval_results