code: def load_resnet(self):
      input_file_path = os.path.join(
          resource_path, "orca/learn/resnet_input"
      )
      output_file_path = os.path.join(
          resource_path, "orca/learn/resnet_output"
      )
      self.input = read_file_and_cast(input_file_path)
      self.output = read_file_and_cast(output_file_path)
      self.input = np.array(self.input).reshape([3, 224, 224])
      self.output = np.array(self.output).reshape([4, 1000])[:1]
  
      os.makedirs(local_path, exist_ok=True)
      model_url = (
          data_url + "/analytics-zoo-data/openvino2020_resnet50.tar"
      )
      model_path = maybe_download(
          "openvino2020_resnet50.tar", local_path, model_url
      )
      tar = tarfile.open(model_path)
      tar.extractall(path=local_path)
      tar.close()
      model_path = os.path.join(
          local_path, "openvino2020_resnet50/resnet_v1_50.xml"
      )
      self.est = Estimator.from_openvino(model_path=model_path)
  
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 4 for calling the TestEstimatorForOpenVINO obj's underlying member method code with example usage: obj.load_resnet() without return types