code: def is_optimizable(algorithm_name):
      return algorithm_name in [
          "Extra Trees",
          "Random Forest",
          "CatBoost",
          "Xgboost",
          "LightGBM",
          "Nearest Neighbors",
          "Neural Network",
      ]
  
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 2 for calling the OptunaTuner obj's underlying member method code with example usage: obj.is_optimizable(algorithm_name) and returns: unknown