code: def mock_process(
      client_id, data_train, data_test, target="localhost:8980"
  ):
      # FLContext is a singleton in JVM, so another process initialization is needed
      # prepare_env()
      init_fl_context(client_id, target)
  
      df_train = pd.read_csv(os.path.join(resource_path, data_train))
  
      fgboost_regression = FGBoostRegression()
      if "SalePrice" in df_train:
          df_x = df_train.drop("SalePrice", 1)
          df_y = df_train.filter(items=["SalePrice"])
          fgboost_regression.fit(
              df_x,
              df_y,
              feature_columns=df_x.columns,
              label_columns=["SalePrice"],
              num_round=15,
          )
      else:
          fgboost_regression.fit(
              df_train, feature_columns=df_train.columns, num_round=15
          )
  
      df_test = pd.read_csv(os.path.join(resource_path, data_test))
      result = fgboost_regression.predict(
          df_test, feature_columns=df_test.columns
      )
  
imports: from multiprocessing import Process
  import unittest
  from uuid import uuid4
  import numpy
  import pandas
  import os
  import math
  from bigdl.ppml.fl import *
  from bigdl.ppml.fl.algorithms.fgboost_regression import FGBoostRegression
  from bigdl.ppml.fl.fl_server import FLServer
  from bigdl.ppml.fl.utils import init_fl_context
  from bigdl.ppml.fl.utils import FLTest
label_desc: Use image node_id 1 for calling a global function with example usage: mock_process(client_id, data_train, data_test, target) without return types