code: def test(model, device, test_loader):
      """Evaluate the performance on the test dataset."""
      model.eval()
      test_loss = 0
      correct = 0
      for data, target in test_loader:
          data, target = data.to(device), target.to(device)
          output = model(data)
          test_loss += F.cross_entropy(
              output, target, reduction="sum"
          ).item()
          pred = output.argmax(dim=1, keepdim=True)
          correct += pred.eq(target.view_as(pred)).sum().item()
  
      test_loss /= len(test_loader.dataset)
  
      print(
          "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)\n".format(
              test_loss,
              correct,
              len(test_loader.dataset),
              100.0 * correct / len(test_loader.dataset),
          )
      )
  
imports: import argparse
  import typing
  import torch
  from torch import nn
  from torch.utils.data import DataLoader
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  import higher
label_desc: Use image node_id 3 for calling a global function with example usage: test(model, device, test_loader) without return types