code: def evaluation_loop(accelerator, model, eval_dataloader, metric):
      model.eval()
      samples_seen = 0
      for step, batch in enumerate(eval_dataloader):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device)
          with torch.no_grad():
              outputs = model(**batch)
          predictions = outputs.logits.argmax(dim=-1)
          # It is slightly faster to call this once, than multiple times
          predictions, references = accelerator.gather(
              (predictions, batch["labels"])
          )  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
              if step == len(eval_dataloader) - 1:
                  predictions = predictions[
                      : len(eval_dataloader.dataset) - samples_seen
                  ]
                  references = references[
                      : len(eval_dataloader.dataset) - samples_seen
                  ]
              else:
                  samples_seen += references.shape[0]
          metric.add_batch(
              predictions=predictions,
              references=references,
          )
  
      eval_metric = metric.compute()
      return eval_metric["accuracy"]
  
file_path: accelerate/src/accelerate/test_utils/scripts/external_deps/test_checkpointing.py
imports: import argparse
  import json
  import os
  import evaluate
  import torch
  from datasets import load_dataset
  from torch.optim import AdamW
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
  from accelerate import Accelerator, DistributedType
  from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
label_desc: Use image node_id 2 for calling a global function with example usage: evaluation_loop(accelerator, model, eval_dataloader, metric) and returns: eval_metric