code: def evaluate(args, model, test_loader):
      criterion = nn.CrossEntropyLoss()
      losses = []
      accuracies = []
      device = torch.device(args.device)
      model = model.eval().to(device)
  
      with torch.no_grad():
          for data, label in tqdm(test_loader):
              data = data.to(device)
              label = label.to(device)
  
              predictions = model(data).squeeze(1)
  
              loss = criterion(predictions, label)
              acc = binary_accuracy(predictions, label)
  
              losses.append(loss.item())
              accuracies.append(acc.item())
  
      mean_accuracy = np.mean(accuracies)
      print(
          "\nTest set: Average loss: {:.4f}, Accuracy: {:.2f}%\n".format(
              np.mean(losses), mean_accuracy * 100
          )
      )
      return mean_accuracy
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from datasets import load_dataset
  from opacus import PrivacyEngine
  from torch.nn.utils.rnn import pad_sequence
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from transformers import BertTokenizerFast
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate(args, model, test_loader) and returns: mean_accuracy