code: def easy_objective(config):
      # Hyperparameters
      width, height = config["width"], config["height"]
  
      for step in range(config["steps"]):
          # Iterative training function - can be any arbitrary training procedure
          intermediate_score = evaluation_fn(step, width, height)
          # Feed the score back back to Tune.
          train.report(
              {"iterations": step, "mean_loss": intermediate_score}
          )
          time.sleep(0.1)
  
imports: import time
  from ray import train, tune
  from ray.tune.schedulers import AsyncHyperBandScheduler
  from ray.tune.search import ConcurrencyLimiter
  from ray.tune.search.bayesopt import BayesOptSearch
label_desc: Use image node_id 2 for calling a global function with example usage: easy_objective(config) without return types