code: def test_detector_hyperparameters_consistency():
      data_dir = download_sample_dataset()
  
      train_path = os.path.join(
          data_dir, "Annotations", "trainval_cocoformat.json"
      )
      train_df = from_coco_or_voc(train_path)
  
      hyperparameters = {
          "model.mmdet_image.checkpoint_name": "yolov3_mobilenetv2_8xb24-320-300e_coco",
          "env.num_gpus": -1,
      }
  
      # pass hyperparameters to init()
      predictor = MultiModalPredictor(
          problem_type="object_detection",
          sample_data_path=train_df,
          hyperparameters=hyperparameters,
      )
      predictor.fit(train_df, time_limit=10)
  
      # pass hyperparameters to fit()
      predictor_2 = MultiModalPredictor(
          problem_type="object_detection",
          sample_data_path=train_df,
      )
      predictor_2.fit(
          train_df,
          hyperparameters=hyperparameters,
          time_limit=10,
      )
      assert predictor._learner._config == predictor_2._learner._config
  
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 9 for calling a global function with example usage: test_detector_hyperparameters_consistency() without return types