code: def test_openvino_multi_output(self):
      from pyspark.sql import SparkSession
      from bigdl.orca.learn.utils import dataframe_to_xshards
  
      self.load_multi_output_model()
      sc = init_nncontext()
      spark = SparkSession(sc)
      data = np.random.rand(3, 550, 550)
      rdd = sc.range(0, 2, numSlices=1)
      df = rdd.map(lambda x: [data.tolist()]).toDF(["input"])
      result_df = self.est.predict(df, feature_cols=["input"])
      df_c = result_df.rdd.map(
          lambda row: [row[1], row[2], row[3], row[4]]
      ).collect()
      df_c = [
          np.concatenate((np.array(df_c[0][i]), np.array(df_c[1][i])))
          for i in range(4)
      ]
      shards, _ = dataframe_to_xshards(
          df,
          validation_data=None,
          feature_cols=["input"],
          label_cols=None,
          mode="predict",
      )
      result_shard = self.est.predict(shards, batch_size=4)
      shard_c = result_shard.collect()[0]
      nd_input = np.squeeze(np.array(df.select("input").collect()))
      result_np = self.est.predict(nd_input)
      assert np.all(
          [np.allclose(r1, r2) for r1, r2 in zip(df_c, result_np)]
      )
      assert np.all(
          [np.allclose(r1, r2) for r1, r2 in zip(shard_c, result_np)]
      )
  
imports: import os
  import tarfile
  import tempfile
  import shutil
  from unittest import TestCase
  import numpy
  from bigdl.dllib.feature.dataset.base import maybe_download
  from bigdl.dllib.nncontext import init_nncontext
  from bigdl.orca.data import SparkXShards
  from bigdl.orca.learn.openvino import Estimator
label_desc: Use image node_id 11 for calling the TestEstimatorForOpenVINO obj's underlying member method code with example usage: obj.test_openvino_multi_output() without return types