code: def semantic_segmentation(presets: str = DEFAULT):
      """
      Register the presets for semantic_segmentation.
  
      Parameters
      ----------
      presets
          The preset name.
  
      Returns
      -------
      hyperparameters
          The hyperparameters for a given preset.
      hyperparameter_tune_kwargs
          The hyperparameter tuning kwargs.
      """
      hyperparameters = {
          "model.names": ["sam"],
          "model.sam.checkpoint_name": "facebook/sam-vit-huge",
          "env.batch_size": 4,
          "env.per_gpu_batch_size": 1,
          "env.eval_batch_size_ratio": 1,
          "env.strategy": "ddp_find_unused_parameters_true",
          "env.auto_select_gpus": False,
          "env.num_gpus": -1,
          "env.num_workers": 4,
          "env.precision": "16-mixed",
          "optimization.learning_rate": 1e-4,
          "optimization.loss_function": "structure_loss",
          "optimization.lr_decay": 0,
          "optimization.lr_mult": 1,
          "optimization.lr_choice": "single_stage",
          "optimization.lr_schedule": "polynomial_decay",
          "optimization.max_epochs": 30,
          "optimization.top_k": 3,
          "optimization.top_k_average_method": "best",
          "optimization.warmup_steps": 0.0,
          "optimization.weight_decay": 0.0001,
          "optimization.patience": 10,
          "optimization.val_check_interval": 1.0,
          "optimization.check_val_every_n_epoch": 1,
          "optimization.efficient_finetune": "lora",
          "optimization.lora.module_filter": [".*vision_encoder.*attn"],
          "optimization.lora.filter": ["q", "v"],
          "optimization.extra_trainable_params": [".*mask_decoder"],
          "optimization.lora.r": 3,
          "optimization.lora.alpha": 32,
      }
      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
          )
  
      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 8 for calling a global function with example usage: semantic_segmentation(presets) and returns: hyperparameters,  hyperparameter_tune_kwargs