code: def split_hyperparameters(hyperparameters: Dict):
      """
      Split out some advanced hyperparameters whose values are complex objects instead of strings or numbers.
  
      Parameters
      ----------
      hyperparameters
          The user provided hyperparameters.
  
      Returns
      -------
      Hyperparameters and advanced hyperparameters.
      """
      if not isinstance(
          hyperparameters, dict
      ):  # only support complex objects in dict.
          return hyperparameters, dict()
  
      if not hyperparameters:
          return hyperparameters, dict()
  
      advanced_hyperparameters = dict()
      for k, v in hyperparameters.items():
          if re.search("^model.*train_transforms$", k) or re.search(
              "^model.*val_transforms$", k
          ):
              if all(
                  [
                      isinstance(trans, str)
                      for trans in hyperparameters[k]
                  ]
              ):
                  pass
              elif all(
                  [
                      isinstance(trans, Callable)
                      for trans in hyperparameters[k]
                  ]
              ):
                  advanced_hyperparameters[k] = copy.deepcopy(v)
                  hyperparameters[k] = str(
                      v
                  )  # get the objects' class strings
              else:
                  raise ValueError(
                      f"transform_types {v} contain neither all strings nor all callable objects."
                  )
  
      return hyperparameters, advanced_hyperparameters
  
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 18 for calling a global function with example usage: split_hyperparameters(hyperparameters) and returns: hyperparameters,  advanced_hyperparameters, hyperparameters,  dict, hyperparameters,  dict