code: def predict(model_path, img_path):
      model = InferenceModel()
      model.load_openvino(
          model_path,
          weight_path=model_path[: model_path.rindex(".")] + ".bin",
          batch_size=BATCH_SIZE,
      )
      sc = init_nncontext(
          "OpenVINO Python resnet_v1_50 Inference Example"
      )
      # pre-processing
      infer_transformer = ChainedPreprocessing(
          [
              ImageBytesToMat(),
              ImageResize(256, 256),
              ImageCenterCrop(224, 224),
              ImageMatToTensor(format="NHWC", to_RGB=True),
          ]
      )
      image_set = (
          ImageSet.read(img_path, sc)
          .transform(infer_transformer)
          .get_image()
          .collect()
      )
      image_set = np.expand_dims(image_set, axis=1)
  
      for i in range(len(image_set) // BATCH_SIZE + 1):
          index = i * BATCH_SIZE
          # check whether out of index
          if index >= len(image_set):
              break
          batch = image_set[index]
          # put 4 images in one batch
          for j in range(
              index + 1, min(index + BATCH_SIZE, len(image_set))
          ):
              batch = np.vstack((batch, image_set[j]))
          batch = np.expand_dims(batch, axis=0)
          # predict batch
          predictions = model.predict(batch)
          result = predictions[0]
  
          # post-processing for Top-1
          print("batch_" + str(i))
          for r in result:
              output = {}
              max_index = np.argmax(r)
              output["Top-1"] = str(max_index)
              print("* Predict result " + str(output))
      print("finished...")
      sc.stop()
  
imports: from optparse import OptionParser
  from bigdl.orca.inference import InferenceModel
  from bigdl.dllib.nncontext import init_nncontext
  from bigdl.dllib.feature.image import *
  from bigdl.dllib.nnframes import *
label_desc: Use image node_id 1 for calling a global function with example usage: predict(model_path, img_path) without return types