code: def main(
      num_samples=10,
      max_num_epochs=10,
      gpus_per_trial=2,
      smoke_test=False,
  ):
      config = {
          "l1": tune.sample_from(
              lambda _: 2 ** np.random.randint(2, 9)
          ),
          "l2": tune.sample_from(
              lambda _: 2 ** np.random.randint(2, 9)
          ),
          "lr": tune.loguniform(1e-4, 1e-1),
          "batch_size": tune.choice([2, 4, 8, 16]),
          "smoke_test": smoke_test,
      }
      scheduler = ASHAScheduler(
          max_t=max_num_epochs, grace_period=1, reduction_factor=2
      )
  
      tuner = tune.Tuner(
          tune.with_resources(
              tune.with_parameters(train_cifar),
              resources={"cpu": 2, "gpu": gpus_per_trial},
          ),
          tune_config=tune.TuneConfig(
              metric="loss",
              mode="min",
              num_samples=num_samples,
              scheduler=scheduler,
          ),
          param_space=config,
      )
      results = tuner.fit()
      best_result = results.get_best_result("loss", "min")
      print("Best trial config: {}".format(best_result.config))
      print(
          "Best trial final validation loss: {}".format(
              best_result.metrics["loss"]
          )
      )
      print(
          "Best trial final validation accuracy: {}".format(
              best_result.metrics["accuracy"]
          )
      )
  
      test_best_model(
          best_result.config,
          best_result.checkpoint,
          smoke_test=smoke_test,
      )
  
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 5 for calling a global function with example usage: main(num_samples, max_num_epochs, gpus_per_trial, smoke_test) without return types