code: def xgboost_milsongs_gaussian_medium():
      assert H2OXGBoostEstimator.available()
  
      # Import big dataset to ensure run across multiple nodes
      training_frame = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/milsongs/milsongs-train.csv.gz"
          )
      )
      test_frame = h2o.import_file(
          pyunit_utils.locate(
              "bigdata/laptop/milsongs/milsongs-test.csv.gz"
          )
      )
      x = list(range(1, training_frame.ncol))
      y = 0
  
      # Model with maximum of 2 trees
      model_2_trees = H2OXGBoostEstimator(
          training_frame=training_frame,
          learn_rate=0.3,
          booster="gbtree",
          seed=1,
          ntrees=2,
          distribution="gaussian",
      )
      model_2_trees.train(x=x, y=y, training_frame=training_frame)
      prediction_2_trees = model_2_trees.predict(test_frame)
  
      assert prediction_2_trees.nrows == test_frame.nrows
  
      # Model with 10 trees
      model_10_trees = H2OXGBoostEstimator(
          training_frame=training_frame,
          learn_rate=0.3,
          booster="gbtree",
          seed=1,
          ntrees=10,
          distribution="gaussian",
      )
      model_10_trees.train(x=x, y=y, training_frame=training_frame)
      prediction_10_trees = model_10_trees.predict(test_frame)
  
      assert prediction_10_trees.nrows == test_frame.nrows
  
      ## Mean square error on model with lower number of decision trees should be higher
      assert model_2_trees.mse() > model_10_trees.mse()
  
label_desc: Use image node_id 1 for calling a global function with example usage: xgboost_milsongs_gaussian_medium() without return types