code: def test_mmdet_object_detection_save_and_load(checkpoint_name):
      data_dir = download_sample_dataset()
  
      test_path = os.path.join(
          data_dir, "Annotations", "test_cocoformat.json"
      )
      # Init predictor
      predictor = MultiModalPredictor(
          hyperparameters={
              "model.mmdet_image.checkpoint_name": checkpoint_name,
              "env.num_gpus": -1,
          },
          problem_type="object_detection",
      )
  
      pred = predictor.predict(test_path)
  
      model_save_subdir = predictor._learner._model.save()
  
      new_predictor = MultiModalPredictor(
          hyperparameters={
              "model.mmdet_image.checkpoint_name": model_save_subdir,
              "env.num_gpus": -1,
          },
          problem_type="object_detection",
      )
      new_pred = new_predictor.predict(test_path)
  
      assert (
          abs(
              pred["bboxes"][0][0]["score"]
              - new_pred["bboxes"][0][0]["score"]
          )
          < 1e-4
      )
  
file_path: autogluon/multimodal/tests/unittests/others/test_object_detection.py
imports: import json
  import os
  import pytest
  import requests
  from PIL import Image
  from autogluon.core.utils.loaders import load_zip
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.utils import from_coco_or_voc
label_desc: Use image node_id 6 for calling a global function with example usage: test_mmdet_object_detection_save_and_load(checkpoint_name) without return types