code: def predict(model_path, img_path, output_path, partition_num):
      model = ObjectDetector.load_model(model_path)
      image_set = ImageSet.read(
          img_path, sc, image_codec=1, min_partitions=partition_num
      )
      output = model.predict_image_set(image_set)
  
      config = model.get_config()
      visualizer = Visualizer(config.label_map(), encoding="jpg")
      visualized = visualizer(output).get_image(to_chw=False).collect()
      for img_id in range(len(visualized)):
          cv2.imwrite(
              output_path + "/" + str(img_id) + ".jpg",
              visualized[img_id],
          )
  
imports: import argparse
  import cv2
  from zoo.common.nncontext import init_nncontext
  from zoo.models.image.objectdetection import *
label_desc: Use image node_id 1 for calling a global function with example usage: predict(model_path, img_path, output_path, partition_num) without return types