code: def get_multi_inputs_outputs_data():
      def get_df(size):
          rdd = sc.parallelize(range(size))
          df = rdd.map(
              lambda x: (
                  [float(x)] * 32,
                  [float(x)] * 64,
                  [float(x)] * 32,
                  [int(np.random.randint(0, 2, size=()))],
              )
          ).toDF(["f1", "f2", "middle", "label"])
          return df
  
      from pyspark.sql import SparkSession
      from bigdl.orca import OrcaContext
  
      sc = OrcaContext.get_spark_context()
      spark = SparkSession(sc)
      feature_cols = ["f1", "f2"]
      label_cols = ["middle", "label"]
      train_df = get_df(size=100)
      val_df = get_df(size=30)
      return train_df, val_df, feature_cols, label_cols
  
imports: import tensorflow
  import numpy
  from unittest import TestCase
  from bigdl.orca.automl.auto_estimator import AutoEstimator
  import pytest
label_desc: Use image node_id 4 for calling a global function with example usage: get_multi_inputs_outputs_data() and returns: train_df,  val_df,  feature_cols,  label_cols, df