code: def test_func(model, data_loader, device=None):
      device = device or torch.device("cpu")
      model.eval()
      correct = 0
      total = 0
      with torch.no_grad():
          for batch_idx, (data, target) in enumerate(data_loader):
              if batch_idx * len(data) > TEST_SIZE:
                  break
              data, target = data.to(device), target.to(device)
              outputs = model(data)
              _, predicted = torch.max(outputs.data, 1)
              total += target.size(0)
              correct += (predicted == target).sum().item()
  
      return correct / total
  
imports: import os
  import argparse
  from filelock import FileLock
  import tempfile
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torchvision import datasets, transforms
  import ray
  from ray import train, tune
  from ray.train import Checkpoint
  from ray.tune.schedulers import AsyncHyperBandScheduler
label_desc: Use image node_id 2 for calling a global function with example usage: test_func(model, data_loader, device) and returns: unknown