code: def test_best_model(
      config: Dict, checkpoint: "Checkpoint", smoke_test=False
  ):
      best_trained_model = Net(config["l1"], config["l2"])
      device = "cuda:0" if torch.cuda.is_available() else "cpu"
      best_trained_model.to(device)
  
      with checkpoint.as_directory() as checkpoint_dir:
          checkpoint_path = os.path.join(
              checkpoint_dir, "checkpoint.pt"
          )
          model_state, optimizer_state = torch.load(checkpoint_path)
          best_trained_model.load_state_dict(model_state)
  
      if smoke_test:
          _, testset = load_test_data()
      else:
          _, testset = load_data(DATA_DIR)
  
      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 = best_trained_model(images)
              _, predicted = torch.max(outputs.data, 1)
              total += labels.size(0)
              correct += (predicted == labels).sum().item()
  
      print("Best trial test set accuracy: {}".format(correct / total))
  
imports: import numpy
  import os
  import tempfile
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from filelock import FileLock
  from torch.utils.data import random_split
  import torchvision
  import torchvision.transforms
  from typing import Dict
  import ray
  from ray import train, tune
  from ray.train import Checkpoint
  from ray.tune.schedulers import ASHAScheduler
label_desc: Use image node_id 4 for calling a global function with example usage: test_best_model(config, checkpoint, smoke_test) without return types