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)
  
      from pyspark.sql import SparkSession
  
      spark = SparkSession.builder.getOrCreate()
      df = spark.read.csv(
          filePath, sep=",", inferSchema=True, header=True
      )
      model.setFeaturesCol(["age", "gender", "jointime", "star"])
      predict = model.transform(df)
      predict.count()
  
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 39 for calling the TestNNClassifer obj's underlying member method code with example usage: obj.test_XGBClassifierModel_predict() without return types