code: def test_convert_predict_rdd_to_dataframe_multi_output(self):
      rdd = self.sc.range(0, 100)
      df = rdd.map(
          lambda x: (
              [float(x)] * 50,
              [int(np.random.randint(0, 2, size=()))],
          )
      ).toDF(["feature", "label"])
      pred_rdd = rdd.map(
          lambda x: [
              np.array([float(x)] * 25),
              np.array([float(x)] * 25),
          ]
      )
      result_df = convert_predict_rdd_to_dataframe(df, pred_rdd)
      expr = "sum(cast(feature <> flatten(prediction) as int)) as error"
      assert result_df.selectExpr(expr).first()["error"] == 0
  
imports: from unittest import TestCase
  import os
  import pytest
  import numpy
  from pyspark.sql.types import ArrayType, DoubleType
  from zoo.orca import init_orca_context, stop_orca_context
  from zoo.orca.data import SparkXShards
  from zoo.orca.data.image.utils import chunks
  from zoo.orca.learn.utils import convert_predict_rdd_to_dataframe, _dataframe_to_xshards, convert_predict_xshards_to_dataframe, convert_predict_rdd_to_xshard, update_predict_xshards
label_desc: Use image node_id 4 for calling the TestUtil obj's underlying member method code with example usage: obj.test_convert_predict_rdd_to_dataframe_multi_output() without return types