code: def test_accuracy(net, device="cpu"):
      trainset, testset = load_data()
  
      testloader = torch.utils.data.DataLoader(
          testset, batch_size=4, shuffle=False, num_workers=2
      )
  
      correct = 0
      total = 0
      with torch.no_grad():
          for data in testloader:
              images, labels = data
              images, labels = images.to(device), labels.to(device)
              outputs = net(images)
              _, predicted = torch.max(outputs.data, 1)
              total += labels.size(0)
              correct += (predicted == labels).sum().item()
  
      return correct / total
  
imports: from functools import partial
  import os
  from tempfile import TemporaryDirectory
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.utils.data import random_split
  import torchvision
  import torchvision.transforms
  from ray import train, tune
  from ray.train import Checkpoint
  from ray.tune.schedulers import ASHAScheduler
label_desc: Use image node_id 3 for calling a global function with example usage: test_accuracy(net, device) and returns: unknown