code: def _fill_config(
      config: Dict,
      hyperparam_bounds: Dict[str, Union[dict, list, tuple]],
  ) -> Dict:
      """Fills missing hyperparameters in config by sampling uniformly from the
      specified `hyperparam_bounds`.
      Recursively fills the config if `hyperparam_bounds` is a nested dict.
  
      This is a helper used to set initial hyperparameter values if the user doesn't
      specify them in the Tuner `param_space`.
  
      Returns the dict of filled hyperparameters.
      """
      filled_hyperparams = {}
      for param_name, bounds in hyperparam_bounds.items():
          if isinstance(bounds, dict):
              if param_name not in config:
                  config[param_name] = {}
              filled_hyperparams[param_name] = _fill_config(
                  config[param_name], bounds
              )
          elif (
              isinstance(bounds, (list, tuple))
              and param_name not in config
          ):
              if log_once(param_name + "-missing"):
                  logger.debug(
                      f"Cannot find {param_name} in config. Initializing by "
                      "sampling uniformly from the provided `hyperparam_bounds`."
                  )
              assert len(bounds) == 2
              low, high = bounds
              config[param_name] = filled_hyperparams[
                  param_name
              ] = np.random.uniform(low, high)
      return filled_hyperparams
  
imports: from typing import Callable, Dict, Optional, Tuple, Union, TYPE_CHECKING
  from copy import deepcopy
  import logging
  import numpy
  import pandas
  from ray.tune import TuneError
  from ray.tune.experiment import Trial
  from ray.tune.schedulers import PopulationBasedTraining
  from ray.tune.schedulers.pbt import _PBTTrialState
  from ray.tune.utils.util import flatten_dict, unflatten_dict
  from ray.util.debug import log_once
label_desc: Use image node_id 2 for calling a global function with example usage: _fill_config(config, hyperparam_bounds) and returns: filled_hyperparams