code: def train_random_forest(ntrees):
      with mlflow.start_run():
          rf = H2ORandomForestEstimator(ntrees=ntrees)
          train_cols = [n for n in wine.col_names if n != "quality"]
          rf.train(
              train_cols,
              "quality",
              training_frame=train,
              validation_frame=test,
          )
  
          mlflow.log_param("ntrees", ntrees)
  
          mlflow.log_metric("rmse", rf.rmse())
          mlflow.log_metric("r2", rf.r2())
          mlflow.log_metric("mae", rf.mae())
  
          mlflow.h2o.log_model(rf, "model")
  
imports: import h2o
  from h2o.estimators.random_forest import H2ORandomForestEstimator
  import mlflow
  import mlflow.h2o
label_desc: Use image node_id 1 for calling a global function with example usage: train_random_forest(ntrees) without return types