code: def test_mmdet_object_detection_fit_with_freeze_backbone(
      checkpoint_name,
  ):
      data_dir = download_sample_dataset()
  
      train_path = os.path.join(
          data_dir, "Annotations", "trainval_cocoformat.json"
      )
      test_path = os.path.join(
          data_dir, "Annotations", "test_cocoformat.json"
      )
      # Init predictor
      train_df = from_coco_or_voc(train_path)
      predictor = MultiModalPredictor(
          hyperparameters={
              "model.mmdet_image.checkpoint_name": checkpoint_name,
              "model.mmdet_image.frozen_layers": ["backbone"],
              "env.num_gpus": -1,
          },
          problem_type="object_detection",
          sample_data_path=train_df,
      )
  
      predictor.fit(
          train_df,
          hyperparameters={
              "optimization.learning_rate": 2e-4,
              "env.per_gpu_batch_size": 2,
          },
          time_limit=30,
      )
  
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 8 for calling a global function with example usage: test_mmdet_object_detection_fit_with_freeze_backbone(checkpoint_name) without return types