code: def object_detection(presets: str = DEFAULT):
      """
      Register the presets for object_detection.
  
      Parameters
      ----------
      presets
          The preset name.
  
      Returns
      -------
      hyperparameters
          The hyperparameters for a given preset.
      hyperparameter_tune_kwargs
          The hyperparameter tuning kwargs.
      """
      hyperparameters = {
          "model.names": ["mmdet_image"],
          "model.mmdet_image.checkpoint_name": "yolox_s",
          "env.eval_batch_size_ratio": 1,
          "env.precision": 32,
          "env.strategy": "ddp",
          "env.auto_select_gpus": True,  # Turn on for detection to return devices in a list, TODO: fix the extra GPU usage bug
          "env.num_gpus": -1,
          "env.per_gpu_batch_size": 8,  # Works on 8G GPU
          "env.num_workers": 2,
          "optimization.learning_rate": 1e-4,
          "optimization.lr_decay": 0.9,
          "optimization.lr_mult": 100,
          "optimization.lr_choice": "two_stages",
          "optimization.top_k": 1,
          "optimization.top_k_average_method": "best",
          "optimization.warmup_steps": 0.0,
          "optimization.patience": 10,
          "optimization.val_check_interval": 0.5,
          "optimization.check_val_every_n_epoch": 1,
      }
      hyperparameter_tune_kwargs = {}
  
      presets, use_hpo = parse_presets_str(presets)
      if use_hpo:
          (
              default_tunable_hyperparameters,
              default_hyperparameter_tune_kwargs,
          ) = get_default_hpo_setup()
          hyperparameters.update(default_tunable_hyperparameters)
          hyperparameter_tune_kwargs.update(
              default_hyperparameter_tune_kwargs
          )
  
      if presets == MEDIUM_QUALITY:
          hyperparameters.update(
              {
                  "model.mmdet_image.checkpoint_name": "yolox_l",
                  "env.per_gpu_batch_size": 2,  # Works on 8G GPU
                  "optimization.learning_rate": 5e-5,
                  "optimization.patience": 3,
                  "optimization.max_epochs": 50,
                  "optimization.val_check_interval": 1.0,
                  "optimization.check_val_every_n_epoch": 3,
              }
          )
      elif presets in [DEFAULT, HIGH_QUALITY]:
          hyperparameters.update(
              {
                  "model.mmdet_image.checkpoint_name": "dino-4scale_r50_8xb2-12e_coco",
                  "model.mmdet_image.frozen_layers": [
                      "backbone",
                      "model.level_embed",
                  ],
                  "env.per_gpu_batch_size": 1,  # Works on 16G GPU
                  "optimization.learning_rate": 1e-4,
                  "optimization.patience": 20,
                  "optimization.max_epochs": 50,
                  "optimization.val_check_interval": 1.0,
                  "optimization.check_val_every_n_epoch": 1,
              }
          )
      elif presets == BEST_QUALITY:
          hyperparameters.update(
              {
                  "model.mmdet_image.checkpoint_name": "dino-5scale_swin-l_8xb2-36e_coco",
                  "model.mmdet_image.frozen_layers": [
                      "backbone",
                      "model.level_embed",
                  ],
                  "env.per_gpu_batch_size": 1,  # Works on 24G GPU
                  "optimization.learning_rate": 1e-4,
                  "optimization.patience": 20,
                  "optimization.max_epochs": 50,
                  "optimization.val_check_interval": 1.0,
                  "optimization.check_val_every_n_epoch": 1,
              }
          )
      else:
          raise ValueError(f"Unknown preset type: {presets}")
  
      return hyperparameters, hyperparameter_tune_kwargs
  
imports: from typing import List, Optional
  from autogluon.common.utils.try_import import try_import_ray
  from .constants import BEST_QUALITY, BINARY, DATA, DEFAULT, ENVIRONMENT, HIGH_QUALITY, MEDIUM_QUALITY, MODEL, MULTICLASS, OPTIMIZATION, REGRESSION
  from .registry import Registry
label_desc: Use image node_id 7 for calling a global function with example usage: object_detection(presets) and returns: hyperparameters,  hyperparameter_tune_kwargs