code: def train_model(config):
      # Load dataset
      data, labels = datasets.load_breast_cancer(return_X_y=True)
      # Split into train and test set
      train_x, test_x, train_y, test_y = train_test_split(
          data, labels, test_size=0.25
      )
  
      train_set = RayDMatrix(train_x, train_y)
      test_set = RayDMatrix(test_x, test_y)
  
      evals_result = {}
      bst = train(
          params=config,
          dtrain=train_set,
          evals=[(test_set, "eval")],
          evals_result=evals_result,
          verbose_eval=False,
          ray_params=RayParams(
              num_actors=num_actors, cpus_per_actor=num_cpus_per_actor
          ),
      )
      bst.booster_.save_model("model.lgbm")
  
imports: from sklearn import datasets
  from sklearn.model_selection import train_test_split
  from lightgbm_ray import RayDMatrix, RayParams, train
label_desc: Use image node_id 1 for calling a global function with example usage: train_model(config) without return types