code: def test_XGBClassifierModel_predict(self):
      from sys import platform
  
      if platform in ("darwin", "win32"):
          return
  
      resource_path = os.path.join(
          os.path.split(__file__)[0], "../resources"
      )
      path = os.path.join(resource_path, "xgbclassifier/")
      modelPath = path + "XGBClassifer.bin"
      filePath = path + "test.csv"
      model = XGBClassifierModel.loadModel(modelPath, 2)
      df = self.sqlContext.read.csv(
          filePath, sep=",", inferSchema=True, header=True
      )
      df = df.select(
          array("age", "gender", "jointime", "star").alias("features")
      ).withColumn(
          "features",
          udf(lambda x: DenseVector(x), VectorUDT())("features"),
      )
  
      model.setFeaturesCol("features")
      predict = model.transform(df)
      assert predict.count() == 14
  
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 3 for calling the TestTreeModel obj's underlying member method code with example usage: obj.test_XGBClassifierModel_predict() without return types