code: def test_openvino_predict_spark_df(self):
      from pyspark.sql import SparkSession
  
      self.load_resnet()
      sc = init_nncontext()
      spark = SparkSession(sc)
      input_list = self.input.tolist()
      rdd = sc.range(0, 18, numSlices=5)
      input_df = rdd.map(lambda x: [input_list]).toDF(["feature"])
      result_df = self.est.predict(input_df, feature_cols=["feature"])
      result = list(
          map(
              lambda row: np.array(
                  row["resnet_v1_50/predictions/Softmax"]
              ),
              result_df.select(
                  "resnet_v1_50/predictions/Softmax"
              ).collect(),
          )
      )
      assert np.array(
          result_df.select("resnet_v1_50/predictions/Softmax").first()
      ).shape == (1, 1, 1000)
      assert result_df.count() == 18
      assert self.check_result(result, 18)
  
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 10 for calling the TestEstimatorForOpenVINO obj's underlying member method code with example usage: obj.test_openvino_predict_spark_df() without return types