code: def test_XGBRegressor(self):
      from sys import platform
  
      if platform in ("darwin", "win32"):
          return
  
      if self.sc.version.startswith(
          "3.0"
      ) or self.sc.version.startswith("2.4"):
          data = self.sc.parallelize(
              [
                  (
                      1.0,
                      2.0,
                      3.0,
                      4.0,
                      5.0,
                      1.0,
                      2.0,
                      4.0,
                      8.0,
                      3.0,
                      116.3668,
                  ),
                  (
                      1.0,
                      3.0,
                      8.0,
                      6.0,
                      5.0,
                      9.0,
                      5.0,
                      6.0,
                      7.0,
                      4.0,
                      116.367,
                  ),
                  (
                      2.0,
                      1.0,
                      5.0,
                      7.0,
                      6.0,
                      7.0,
                      4.0,
                      1.0,
                      2.0,
                      3.0,
                      116.367,
                  ),
                  (
                      2.0,
                      1.0,
                      4.0,
                      3.0,
                      6.0,
                      1.0,
                      3.0,
                      2.0,
                      1.0,
                      3.0,
                      116.3668,
                  ),
              ]
          )
          columns = [
              "f1",
              "f2",
              "f3",
              "f4",
              "f5",
              "f6",
              "f7",
              "f8",
              "f9",
              "f10",
              "label",
          ]
          df = data.toDF(columns)
          from pyspark.ml.feature import VectorAssembler
  
          vecasembler = VectorAssembler(
              inputCols=columns, outputCol="features"
          )
          assembledf = (
              vecasembler.transform(df)
              .select("features", "label")
              .cache()
          )
          assembledf.printSchema()
          testdf = (
              vecasembler.transform(df)
              .select("features", "label")
              .cache()
          )
          xgbRf0 = XGBRegressor()
          xgbRf0.setNthread(1)
          xgbRf0.setNumRound(10)
          xgbmodel = XGBRegressorModel(xgbRf0.fit(assembledf))
          xgbmodel.save("/tmp/modelfile/")
          xgbmodel.setFeaturesCol("features")
          assembledf.show()
          yxgb = xgbmodel.transform(assembledf)
          model = xgbmodel.load("/tmp/modelfile/")
          yxgb.show()
          model.setFeaturesCol("features")
          y0 = model.transform(assembledf)
          y0.show()
          assert y0.subtract(yxgb).count() == 0
  
imports: import errno
  import shutil
  import pytest
  from bigdl.nn.criterion import *
  from bigdl.nn.layer import *
  from bigdl.optim.optimizer import *
  from numpy.testing import assert_allclose
  from pyspark.ml import Pipeline, PipelineModel
  from pyspark.ml.feature import MinMaxScaler
  from pyspark.ml.tuning import ParamGridBuilder
  from pyspark.sql.types import *
  from zoo.common.nncontext import *
  from zoo.feature.common import *
  from zoo.feature.image import *
  from zoo.pipeline.api.keras import layers
  from zoo.pipeline.api.keras.models import Model
  from zoo.pipeline.api.keras.optimizers import Adam
  from zoo.pipeline.nnframes import *
  from zoo.util.tf import *
label_desc: Use image node_id 40 for calling the TestNNClassifer obj's underlying member method code with example usage: obj.test_XGBRegressor() without return types