code: def setup(self, config):
      # config (dict): A dict of hyperparameters
      self.x = 0
      self.a = config["a"]
      self.b = config["b"]
  
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 1 for calling the Trainable obj's underlying member method code with example usage: obj.setup(config) without return types