code: def uplift_train_predict(
      uplift_metric,
      x_names,
      treatment_column,
      response_column,
      train_h2o,
      seed,
  ):
      print("train_predict: uplift_metric {0}".format(uplift_metric))
      uplift_model = H2OUpliftRandomForestEstimator(
          ntrees=10,
          max_depth=5,
          treatment_column=treatment_column,
          uplift_metric=uplift_metric,
          distribution="bernoulli",
          min_rows=10,
          nbins=1000,
          seed=seed,
          sample_rate=0.99,
          auuc_type="gain",
      )
      uplift_model.train(
          y=response_column, x=x_names, training_frame=train_h2o
      )
      prediction = uplift_model.predict(train_h2o)
      print(prediction)
      return prediction["uplift_predict"].as_data_frame(
          use_pandas=True
      )["uplift_predict"]
  
imports: import sys, os
  import h2o
  from tests import pyunit_utils
  from h2o.estimators import H2OUpliftRandomForestEstimator
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: uplift_train_predict(uplift_metric, x_names, treatment_column, response_column, train_h2o, seed) and returns: prediction