code: def fill_in_config_options(
      config_template_path: PathOrStr,
      values_to_fill_dict: Dict[str, Any],
      sub_prefix: str,
      good_probability_option: bool = False,
  ) -> Path:
      """
      Fill in configuration options in the given template file.
  
      Parameters
      ----------
      config_template_path : :class:`skll.types.PathOrStr`
          Path to the template configuration file.
      values_to_fill_dict : Dict[str, Any]
          Dictionary containing the options to fille the keys and the
          corresponding values.
      sub_prefix : str
          The sub-prefix to add to the name when creating
          the filled configuration file on disk.
      good_probability_option : bool, default=False
          Whether to add the "probability" option in the correct
          section or an incorrect section.
  
      Returns
      -------
      Path
          The path to the filled configuration file.
      """
      # convert path to Path object if it's a string
      config_template_path = Path(config_template_path)
  
      config = _setup_config_parser(
          config_template_path, validate=False
      )
  
      # The dictionary that says which option to fill in where
      # Note: (a) `bad_option` and `duplicate_option` are needed
      # for test_config_parsing_invalid_option and
      # test_config_parsing_duplicate_option() respectively.
      # (b) `probability` is deliberately specified in the wrong
      # section for test_config_parsing_option_in_wrong_section().
      to_fill_in = {
          "General": ["experiment_name", "task"],
          "Input": [
              "train_directory",
              "train_file",
              "test_directory",
              "test_file",
              "featuresets",
              "featureset_names",
              "custom_learner_path",
              "feature_hasher",
              "hasher_features",
              "learners",
              "sampler",
              "shuffle",
              "feature_scaling",
              "learning_curve_cv_folds_list",
              "folds_file",
              "learning_curve_train_sizes",
              "fixed_parameters",
              "num_cv_folds",
              "cv_seed",
              "bad_option",
              "duplicate_option",
              "suffix",
          ],
          "Tuning": [
              "grid_search",
              "objective",
              "min_feature_count",
              "use_folds_file_for_grid_search",
              "grid_search_folds",
              "pos_label",
              "param_grids",
              "objectives",
              "duplicate_option",
          ],
          "Output": [
              "results",
              "log",
              "logs",
              "models",
              "metrics",
              "predictions",
              "pipeline",
              "save_cv_folds",
              "save_cv_models",
              "save_votes",
              "wandb_credentials",
          ],
      }
  
      if good_probability_option:
          to_fill_in["Output"].append("probability")
      else:
          to_fill_in["Tuning"].append("probability")
  
      for section in to_fill_in:
          for param_name in to_fill_in[section]:
              if param_name in values_to_fill_dict:
                  value = str(values_to_fill_dict[param_name])
                  config.set(section, param_name, value)
  
      config_prefix_match = re.search(
          r"^(.*)\.template\.cfg", str(config_template_path)
      )
      assert config_prefix_match is not None
      config_prefix = config_prefix_match.groups()[0]
      new_config_path = Path(f"{config_prefix}_{sub_prefix}.cfg")
  
      with open(new_config_path, "w") as new_config_file:
          config.write(new_config_file)
  
      return new_config_path
  
imports: import os
  import re
  from collections import OrderedDict
  from math import floor, log10
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple
  import numpy
  from numpy.random import RandomState
  from sklearn.datasets import fetch_california_housing, load_digits, make_classification, make_regression
  from sklearn.feature_extraction import FeatureHasher
  from sklearn.model_selection import KFold, ShuffleSplit, StratifiedKFold
  from sklearn.utils import shuffle
  from skll.config import _setup_config_parser, fix_json
  from skll.data import FeatureSet, NDJWriter
  from skll.types import PathOrStr
label_desc: Use image node_id 4 for calling a global function with example usage: fill_in_config_options(config_template_path, values_to_fill_dict, sub_prefix, good_probability_option) and returns: new_config_path