original_docstring: Train the model with a hypyer-parameter tuner (ray).
  
      Args:
          config (dict): All the parameters for the model.
      
docstring: Train the model with a hypyer-parameter tuner (ray).
docstring_tokens: Train the model with a hypyer - parameter tuner ( ray ) .
code: def tune_train(config):
      """Train the model with a hypyer-parameter tuner (ray).
  
      Args:
          config (dict): All the parameters for the model.
      """
      train_engine = LightGCN_train(DictToObject(config))
      best_performance = train_engine.train()
      tune.track.log(valid_metric=best_performance)
      train_engine.test()
      while train_engine.eval_engine.n_worker > 0:
          time.sleep(20)
code_tokens: def tune_train ( config ) : train_engine = LightGCN_train ( DictToObject ( config ) ) best_performance = train_engine . train ( ) tune . track . log ( valid_metric = best_performance ) train_engine . test ( ) while train_engine . eval_engine . n_worker > 0 : time . sleep ( 20 )
short_docstring: Train the model with a hypyer-parameter tuner (ray).
short_docstring_tokens: Train the model with a hypyer - parameter tuner ( ray ) .
comment: """Train the model with a hypyer-parameter tuner (ray).
  
      Args:
          config (dict): All the parameters for the model.
      """