code: def test_save_load(self):
      self.fl_server.set_client_num(1)
      self.fl_server.build()
      self.fl_server.start()
      df_train = pd.read_csv(
          os.path.join(
              resource_path, "house-prices-train-preprocessed.csv"
          )
      )
      server_model_path = "/tmp/fgboost_server_model"
      fgboost_regression = FGBoostRegression(
          server_model_path=server_model_path
      )
  
      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=1,
      )
      tmp_file_name = f"/tmp/{str(uuid4())}"
  
      fgboost_regression.save_model(tmp_file_name)
      model_loaded = FGBoostRegression.load_model(tmp_file_name)
  
      df_test = pd.read_csv(
          os.path.join(
              resource_path, "house-prices-test-preprocessed.csv"
          )
      )
      result = model_loaded.predict(
          df_test, feature_columns=df_x.columns
      )
      result = list(map(lambda x: math.exp(x), result))
      os.remove(server_model_path)
      os.remove(tmp_file_name)
      result
  
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 5 for calling the TestFGBoostRegression obj's underlying member method code with example usage: obj.test_save_load() without return types