code: def get_scored_params(
      experiment_description_path, target_metric, get_description=False
  ):
      """Retrieve the hyperparameters of a completed Experiment, along with its performance evaluation
  
      Parameters
      ----------
      experiment_description_path: String
          The path to an Experiment's description .json file
      target_metric: Tuple
          A path denoting the metric to be used. If tuple, the first value should be one of ['oof',
          'holdout', 'in_fold'], and the second value should be the name of a metric supplied in
          :attr:`environment.Environment.metrics_params`
      get_description: Boolean, default=False
          If True, return a tuple of: ((`all_hyperparameters`, `evaluation`), `description`), in which
          `description` is the original description dict for the experiment. Else, return a tuple of:
          (`all_hyperparameters`, `evaluation`)
  
      Returns
      -------
      all_hyperparameters: Dict
          A dict of the hyperparameters used by the Experiment
      evaluation: Float
          Value of the Experiment's `target_metric`"""
      description = read_json(file_path=experiment_description_path)
      evaluation = get_path(
          description["final_evaluations"], target_metric
      )
      all_hyperparameters = description["hyperparameters"]
  
      if description["module_name"].lower() == "keras":
          all_hyperparameters["model_init_params"][
              "layers"
          ] = consolidate_layers(
              all_hyperparameters["model_init_params"]["layers"],
              class_name_key=False,
          )
  
      if get_description:
          return ((all_hyperparameters, evaluation), description)
      return (all_hyperparameters, evaluation)
  
file_path: hyperparameter_hunter/hyperparameter_hunter/utils/optimization_utils.py
imports: from hyperparameter_hunter.i_o.exceptions import ContinueRemap
  from hyperparameter_hunter.keys.hashing import make_hash_sha256
  from hyperparameter_hunter.space.dimensions import Real, Integer, Categorical, RejectedOptional
  from hyperparameter_hunter.utils.boltons_utils import get_path, remap
  from hyperparameter_hunter.utils.file_utils import read_json
  from hyperparameter_hunter.utils.general_utils import extra_enter_attrs
  from contextlib import suppress
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: get_scored_params(experiment_description_path, target_metric, get_description) and returns: all_hyperparameters,  evaluation, all_hyperparameters,  evaluation,  description