code: def load(self):
      if os.path.exists(self.tuning_fname):
          params = json.loads(open(self.tuning_fname).read())
          for k, v in params.items():
              self.tuning[k] = v
  
imports: import json
  import os
  import warnings
  import joblib
  import matplotlib
  import optuna
  from matplotlib import pyplot
  from supervised.exceptions import AutoMLException
  from supervised.preprocessing.preprocessing_utils import PreprocessingUtils
  from supervised.tuner.optuna.catboost import CatBoostObjective
  from supervised.tuner.optuna.extra_trees import ExtraTreesObjective
  from supervised.tuner.optuna.knn import KNNObjective
  from supervised.tuner.optuna.lightgbm import LightgbmObjective
  from supervised.tuner.optuna.nn import NeuralNetworkObjective
  from supervised.tuner.optuna.random_forest import RandomForestObjective
  from supervised.tuner.optuna.xgboost import XgboostObjective
  from supervised.utils.metric import Metric
  from supervised.utils.jsonencoder import MLJSONEncoder
label_desc: Use image node_id 6 for calling the OptunaTuner obj's underlying member method code with example usage: obj.load() without return types