code: def scale_pos_weight_test():
      assert H2OXGBoostEstimator.available() is True
  
      train = pyunit_utils.genTrainFrame(
          1000, 0, enumCols=10, enumFactors=2, miscfrac=0.1, randseed=17
      )
  
      xgboost = H2OXGBoostEstimator(
          ntrees=1, seed=1, scale_pos_weight=1.2
      )
      xgboost.train(y="response", training_frame=train)
  
      native_params = xgboost._model_json["output"][
          "native_parameters"
      ].as_data_frame()
      assert (
          min(
              native_params[
                  native_params["name"] == "scale_pos_weight"
              ]["value"]
          )
          == 1.2
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: scale_pos_weight_test() without return types