code: def test_loop(dataloader, model, mode="Test"):
      assert mode in ["Valid", "Test"]
      size = len(dataloader.dataset)
      correct = 0
  
      model.eval()
      with torch.no_grad():
          for X, y in dataloader:
              X, y = X.to(device), y.to(device)
              pred = model(X)
              correct += (
                  (pred.argmax(1) == y).type(torch.float).sum().item()
              )
  
      # correct /= size
      # correct *= WORLD_SIZE
      correct = correct / (size / WORLD_SIZE)
      print(f"{mode} Accuracy: {(100*correct):>0.1f}%\n")
      return correct
  
imports: import torch
  import argparse
  import os
  import logging
  import time
  from torch import nn
  from contextlib import nullcontext
  from datasets import load_dataset
  from torch.utils.data import DataLoader
  from torch.utils.data.distributed import DistributedSampler
  import torch.distributed
  from transformers import BertTokenizer, BertModel, AdamW
label_desc: Use image node_id 5 for calling a global function with example usage: test_loop(dataloader, model, mode) and returns: correct