code: def process_config(
      ludwig_config: dict, experiment_dict: dict
  ) -> dict:
      """Modify a Ludwig config by programmatically adding elements to the config dictionary.
  
      The purpose is to apply changes for all datasets that are the same or are based on the
       attributes of `experiment_dict` (e.g. dataset_name) removing the need to manually apply
       small changes to configs on many datasets.
  
      :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
      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]
  
      temp = {
          "preprocessing": {"split": {"type": "fixed"}},
          "trainer": {
              "epochs": 1024,
              "early_stop": 7,
              "eval_batch_size": 16384,
              "evaluate_training_set": False,
          },
          "hyperopt": {
              "goal": "maximize",
              "output_feature": None,
              "metric": None,
              "split": "validation",
              "parameters": {
                  "defaults.number.preprocessing.normalization": {
                      "space": "choice",
                      "categories": ["zscore", None],
                  },
                  "defaults.number.preprocessing.missing_value_strategy": {
                      "space": "choice",
                      "categories": [
                          "fill_with_const",
                          "fill_with_mean",
                      ],
                  },
                  "combiner.type": {
                      "space": "choice",
                      "categories": ["tabnet", "concat"],
                  },
                  "trainer.learning_rate_scheduler.decay": {
                      "space": "choice",
                      "categories": [True, False],
                  },
                  "trainer.learning_rate": {
                      "space": "loguniform",
                      "lower": 0.0001,
                      "upper": 0.1,
                  },
                  "trainer.learning_rate_scheduler.decay_rate": {
                      "space": "uniform",
                      "lower": 0.4,
                      "upper": 0.96,
                  },
                  "trainer.batch_size": {
                      "space": "randint",
                      "lower": 32,
                      "upper": 2048,
                  },
              },
              "search_alg": {"type": "hyperopt"},
              "executor": {"type": "ray", "num_samples": 1000},
              "scheduler": {"type": "bohb", "reduction_factor": 2},
          },
      }
  
      # add config parameters from temp
      for key, value in temp.items():
          ludwig_config[key] = value
  
      dataset_name_to_metric = {
          "ames_housing": "r2",
          "mercedes_benz_greener": "r2",
          "mushroom_edibility": "accuracy",
          "amazon_employee_access_challenge": "roc_auc",
          "naval": "r2",
          "sarcos": "r2",
          "protein": "r2",
          "adult_census_income": "accuracy",
          "otto_group_product": "accuracy",
          "santander_customer_satisfaction": "accuracy",
          "amazon_employee_access": "roc_auc",
          "numerai28pt6": "accuracy",
          "bnp_claims_management": "accuracy",
          "allstate_claims_severity": "r2",
          "santander_customer_transaction": "accuracy",
          "connect4": "accuracy",
          "forest_cover": "accuracy",
          "ieee_fraud": "accuracy",
          "porto_seguro_safe_driver": "accuracy",
          "walmart_recruiting": "accuracy",
          "poker_hand": "accuracy",
          "higgs": "accuracy",
      }
  
      # add hyperopt output feature and metric.
      dataset_name = experiment_dict["dataset_name"]
      ludwig_config["hyperopt"]["metric"] = dataset_name_to_metric[
          dataset_name
      ]
      ludwig_config["hyperopt"]["output_feature"] = ludwig_config[
          "output_features"
      ][0]["name"]
  
      # use sparse encoder for categorical features to mimic logistic regression.
      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"
  
      # make sure to return the ludwig_config
      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