code: def process_config(
      ludwig_config: dict, experiment_dict: dict
  ) -> dict:
      """Modify a Ludwig config.
  
      :param ludwig_config: a Ludwig config.
      :param experiment_dict: a benchmarking config experiment dictionary.
  
      returns: a modified Ludwig config.
      """
  
      # Only keep input_features and output_features for the ames_housing dataset.
      if experiment_dict["dataset_name"] == "ames_housing":
          main_config_keys = list(ludwig_config.keys())
          for key in main_config_keys:
              if key not in ["input_features", "output_features"]:
                  del ludwig_config[key]
  
      # Set the early_stop criteria to stop training after 7 epochs of no score improvement.
      ludwig_config["trainer"] = {"early_stop": 7}
  
      # use sparse encoder for categorical features to mimic logreg
      ludwig_config["combiner"] = {"type": "concat"}
      for i, feature in enumerate(ludwig_config["input_features"]):
          if feature["type"] == "category":
              ludwig_config["input_features"][i]["encoder"] = "sparse"
      for i, feature in enumerate(ludwig_config["output_features"]):
          if feature["type"] == "category":
              ludwig_config["output_features"][i]["encoder"] = "sparse"
  
      return ludwig_config
  
label_desc: Use image node_id 1 for calling a global function with example usage: process_config(ludwig_config, experiment_dict) and returns: ludwig_config