code: def update_hyperparameters(
      problem_type,
      presets,
      provided_hyperparameters,
      provided_hyperparameter_tune_kwargs,
  ):
      """
      Update preset hyperparameters hyperparameter_tune_kwargs by the provided.
      Currently, this is mainly used for HPO presets, which define some searchable hyperparameters.
      We need to combine these searchable hyperparameters with ones provided by users.
  
      Parameters
      ----------
      problem_type
          Problem type.
      presets
          A preset string regarding modality quality or hpo.
      provided_hyperparameters
          The hyperparameters provided by users.
      provided_hyperparameter_tune_kwargs
          The hyperparameter_tune_kwargs provided by users.
  
      Returns
      -------
      The updated hyperparameters and hyperparameter_tune_kwargs.
      """
      hyperparameters, hyperparameter_tune_kwargs = get_automm_presets(
          problem_type=problem_type, presets=presets
      )
  
      if (
          hyperparameter_tune_kwargs
          and provided_hyperparameter_tune_kwargs
      ):
          hyperparameter_tune_kwargs.update(
              provided_hyperparameter_tune_kwargs
          )
      elif provided_hyperparameter_tune_kwargs:
          hyperparameter_tune_kwargs = (
              provided_hyperparameter_tune_kwargs
          )
  
      if hyperparameter_tune_kwargs:
          if provided_hyperparameters:
              hyperparameters.update(provided_hyperparameters)
      else:  # use the provided hyperparameters if no hpo. The preset hyperparameters will be also used later in get_config.
          hyperparameters = provided_hyperparameters
  
      if hyperparameter_tune_kwargs:
          assert isinstance(
              hyperparameters, dict
          ), "Please provide hyperparameters as a dictionary if you want to do HPO"
  
      return hyperparameters, hyperparameter_tune_kwargs
  
imports: import copy
  import logging
  import os
  import re
  import warnings
  from typing import Callable, Dict, List, Optional, Tuple, Union
  from omegaconf import DictConfig, OmegaConf
  from packaging import version
  from torch import nn
  from ..constants import AUTOMM, DATA, FT_TRANSFORMER, FUSION_TRANSFORMER, HF_MODELS, MODEL, REGRESSION, VALID_CONFIG_KEYS
  from ..presets import get_automm_presets, get_basic_automm_config
  from .data import get_detected_data_types
label_desc: Use image node_id 16 for calling a global function with example usage: update_hyperparameters(problem_type, presets, provided_hyperparameters, provided_hyperparameter_tune_kwargs) and returns: hyperparameters,  hyperparameter_tune_kwargs