code: def Processdata(filepath, demo):
      """
      preProcess the data read from filepath
      :param filepath:
      :return: assembledf:
      """
      sparkConf = init_spark_conf().setAppName("testNNClassifer")
      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,
                      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,
                  ),
              ]
          )
          N = 11
          train_data = data
          test_data = data
  
      else:
          dataset = np.loadtxt(filepath, delimiter=",", skiprows=1)
          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 = ["c" + str(i) for i in range(1, N)]
      columns.append("label")
      df1 = train_data.toDF(columns)
      vecasembler = VectorAssembler(
          inputCols=columns, outputCol="features"
      )
      traindf = (
          vecasembler.transform(df1).select("features", "label").cache()
      )
      df2 = test_data.toDF(columns)
      testdf = (
          vecasembler.transform(df2).select("features", "label").cache()
      )
      xgbRf0 = XGBRegressor()
      xgbRf0.setNthread(1)
      xgbRf0.setNumRound(10)
      xgbmodel = XGBRegressorModel(xgbRf0.fit(traindf))
      y0 = xgbmodel.transform(testdf)
      y0.show()
      sc.stop()
  
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: Processdata(filepath, demo) without return types