code: def get_spark_df():
      def get_df(size):
          import pandas as pd
  
          values = np.random.randn(size, 4)
          df = pd.DataFrame(values, columns=["f1", "f2", "f3", "t"])
          spark_df = spark.createDataFrame(df)
          return spark_df
  
      from bigdl.orca import OrcaContext
      from pyspark.sql import SparkSession
  
      sc = OrcaContext.get_spark_context()
      spark = SparkSession(sc)
  
      feature_cols = ["f1", "f2", "f3"]
      label_cols = ["t"]
      train_df = get_df(size=100)
      val_df = get_df(size=30)
      return train_df, val_df, feature_cols, label_cols
  
file_path: BigDL/python/orca/test/bigdl/orca/automl/xgboost/test_auto_xgb.py
imports: import pytest
  from bigdl.orca.automl.xgboost import AutoXGBRegressor
  import numpy
  from unittest import TestCase
  from bigdl.orca.automl import hp
label_desc: Use image node_id 3 for calling a global function with example usage: get_spark_df() and returns: train_df,  val_df,  feature_cols,  label_cols, spark_df