code: def process(filepath, demo):
      sparkConf = init_spark_conf().setAppName("testXGBClassifier")
      sparkConf = sparkConf.set("xgboost.spark.ignoreSsl", True)
      sc = init_nncontext(sparkConf)
      sqlContext = SQLContext(sc)
      if demo:
          data = sc.parallelize(
              [
                  (1.0, 2.0, 3.0, 4.0, 7.0, 1),
                  (1.0, 3.0, 8.0, 2.0, 5.0, 0),
                  (2.0, 3.4, 5.0, 2.0, 4.0, 1),
              ]
          )
          N = 6
          train_data = data
          test_data = data
      else:
          dataset = np.loadtxt(filepath, delimiter=",")
          print(type(dataset))
          M, N = dataset.shape
          train_X = dataset[: (int)(0.8 * M), :]
          test_X = dataset[(int)(0.8 * M) :, :]
          train_data = sc.parallelize(train_X.tolist())
          test_data = sc.parallelize(test_X.tolist())
      columns = ["f" + str(i) for i in range(1, N)]
      features = ["f" + str(i) for i in range(1, N)]
  
      columns.append("label")
      df1 = train_data.toDF(columns)
      vecasembler = VectorAssembler(
          inputCols=features, outputCol="features"
      )
      traindf = (
          vecasembler.transform(df1).select("features", "label").cache()
      )
      traindf.show()
  
      df2 = test_data.toDF(columns)
      testdf = (
          vecasembler.transform(df2).select("features", "label").cache()
      )
      testdf.show()
      xgbCf0 = XGBClassifier()
      xgbCf0.setNthread(1)
      xgbCf0.setNumRound(10)
      xgbCf0.setMissing(0)
      xgbmodel = xgbCf0.fit(traindf)
      xgbmodel.setFeaturesCol("features")
      y0 = xgbmodel.transform(testdf)
      print(y0)
      y0.show()
  
imports: from optparse import OptionParser
  import numpy
  from bigdl.dllib.nn.criterion import *
  from bigdl.dllib.nn.layer import *
  from bigdl.dllib.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 pyspark.ml.feature import VectorAssembler
  from bigdl.dllib.nncontext import *
  from bigdl.dllib.feature.common import *
  from bigdl.dllib.feature.image import *
  from bigdl.dllib.keras import layers
  from bigdl.dllib.keras.models import Model
  from bigdl.dllib.keras.optimizers import Adam
  from bigdl.dllib.nnframes import *
  from bigdl.dllib.utils.tf import *
  import csv
  import errno
label_desc: Use image node_id 1 for calling a global function with example usage: process(filepath, demo) without return types