code: def test_LGBMRegressor_train_transform(self):
      if float(self.sc.version[:3]) < 3.1:
          return
      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()
      )
      regressor = LightGBMRegressor()
      model = regressor.fit(assembledf)
      predicts = model.transform(assembledf)
      predicts.show()
      assert predicts.count() == 4
  
file_path: BigDL/python/dllib/test/bigdl/nnframes/test_treemodel.py
imports: import pytest
  from bigdl.dllib.nncontext import *
  from bigdl.dllib.nnframes.tree_model import *
  from bigdl.dllib.utils.tf import *
  from pyspark.ml.linalg import DenseVector, VectorUDT
  from pyspark.sql.functions import udf, array
label_desc: Use image node_id 11 for calling the TestTreeModel obj's underlying member method code with example usage: obj.test_LGBMRegressor_train_transform() without return types