code: def easy_objective(config_in):
      # Hyperparameters
      config = f_unpack_dict(config_in)
      width, height, mult = (
          config["width"],
          config["height"],
          config.get("mult", 1),
      )
      print(config)
  
      for step in range(config["steps"]):
          # Iterative training function - can be any arbitrary training procedure
          intermediate_score = evaluation_fn(step, width, height, mult)
          # Feed the score back back to Tune.
          train.report(
              {"iterations": step, "mean_loss": intermediate_score}
          )
          time.sleep(0.1)
  
file_path: ray/python/ray/tune/examples/hyperopt_conditional_search_space_example.py
imports: import time
  import ray
  from ray import train, tune
  from ray.tune.search import ConcurrencyLimiter
  from ray.tune.schedulers import AsyncHyperBandScheduler
  from ray.tune.search.hyperopt import HyperOptSearch
  from hyperopt import hp
label_desc: Use image node_id 3 for calling a global function with example usage: easy_objective(config_in) without return types