code: def trainable(
      config,
  ):  # Pass a "config" dictionary into your trainable.
      for x in range(
          20
      ):  # "Train" for 20 iterations and compute intermediate scores.
          score = objective(x, config["a"], config["b"])
  
          train.report({"score": score})
  
file_path: ray/doc/source/tune/doc_code/key_concepts.py
imports: from ray import train
  from ray import tune
  from ray.tune.search.bayesopt import BayesOptSearch
  from ray import train
  from ray.tune.schedulers import HyperBandScheduler
label_desc: Use image node_id 2 for calling a global function with example usage: trainable(config) without return types