code: def generate_possible_configs(config_options: Dict[str, Any]):
      """Generate exhaustive configs from config_options.
  
      This function does not take a cross product of all the options for all the config parameters. It selects parameter
      values independently from each other.
  
      Args:
          config_options: dictionary mapping from ludwig config parameter to all values to be explored.
              Here's an example of what it could look like:
  
                  trainer.batch_size: ["auto", 2, 43]
                  trainer.learning_rate: ["auto", 0.1, 0.00002, 0.32424]
                  ...
      """
      # The number of configs to generate is the max length of the lists of samples over all parameters.
      num_configs = 1
      for parameter_name in config_options:
          if isinstance(config_options[parameter_name], list):
              num_configs = max(
                  num_configs, len(config_options[parameter_name])
              )
              config_options[parameter_name] = deque(
                  config_options[parameter_name]
              )
  
      for _ in range(num_configs):
          config = {}
          for parameter_name in config_options:
              # if parameter is regular parameter with explored values.
              if config_options[parameter_name] and not isinstance(
                  config_options[parameter_name], str
              ):
                  config[parameter_name] = config_options[
                      parameter_name
                  ].popleft()
              # case for parameters where we don't have choices such as `encoder.type: parallel_cnn` that
              # cause the downstream parameters to change.
              elif isinstance(config_options[parameter_name], str):
                  config[parameter_name] = config_options[
                      parameter_name
                  ]
          yield create_nested_dict(config)
  
imports: import copy
  import random
  from collections import deque, namedtuple
  from typing import Any, Deque, Dict, List, Tuple, Union
  import pandas
  from ludwig.config_sampling.parameter_sampling import handle_property_type, ParameterBaseTypes
  from ludwig.constants import SEQUENCE, TEXT, TIMESERIES
  from ludwig.data.dataset_synthesizer import build_synthetic_dataset_df
  from ludwig.schema.model_types.base import ModelConfig
  from ludwig.types import ModelConfigDict
  from ludwig.utils.misc_utils import merge_dict
label_desc: Use image node_id 6 for calling a global function with example usage: generate_possible_configs(config_options) without return types