code: def test_mmdet_object_detection_save_and_load():
      zip_file = "https://automl-mm-bench.s3.amazonaws.com/object_detection_dataset/tiny_motorbike_coco.zip"
      download_dir = "./tiny_motorbike_coco"
  
      load_zip.unzip(zip_file, unzip_dir=download_dir)
      data_dir = os.path.join(download_dir, "tiny_motorbike")
  
      test_path = os.path.join(
          data_dir, "Annotations", "test_cocoformat.json"
      )
      # Init predictor
      predictor = MultiModalPredictor(
          hyperparameters={
              "model.mmdet_image.checkpoint_name": "yolov3_mobilenetv2_8xb24-320-300e_coco",
              "env.num_gpus": -1,
          },
          problem_type="object_detection",
      )
  
      pred = predictor.predict(test_path)
  
      model_save_dir = predictor.dump_model()
      detection_model_save_subdir = os.path.join(
          model_save_dir, predictor._learner._model.prefix
      )
  
      new_predictor = MultiModalPredictor(
          hyperparameters={
              "model.mmdet_image.checkpoint_name": detection_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_dump_model.py
imports: import os
  import pytest
  import timm
  import transformers
  from autogluon.core.utils.loaders import load_zip
  from autogluon.multimodal import MultiModalPredictor
  from autogluon.multimodal.utils.misc import shopee_dataset
  from ..utils.unittest_datasets import AEDataset, PetFinderDataset
label_desc: Use image node_id 4 for calling a global function with example usage: test_mmdet_object_detection_save_and_load() without return types