code: def train(self, config: Configuration, seed: int = 0) -> float:
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore", category=ConvergenceWarning)
  
          classifier = MLPClassifier(
              hidden_layer_sizes=[config["n_neurons"]]
              * config["n_layer"],
              solver=config["optimizer"],
              batch_size=config["batch_size"],
              activation=config["activation"],
              learning_rate_init=config["learning_rate_init"],
              random_state=seed,
              max_iter=5,
          )
  
          # Returns the 5-fold cross validation accuracy
          cv = StratifiedKFold(
              n_splits=5, random_state=seed, shuffle=True
          )  # to make CV splits consistent
          score = cross_val_score(
              classifier,
              digits.data,
              digits.target,
              cv=cv,
              error_score="raise",
          )
  
      return 1 - np.mean(score)
  
file_path: SMAC3/examples/1_basics/6_priors.py
imports: import warnings
  import numpy
  from ConfigSpace import BetaIntegerHyperparameter, CategoricalHyperparameter, Configuration, ConfigurationSpace, NormalFloatHyperparameter, UniformIntegerHyperparameter
  from sklearn.datasets import load_digits
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.model_selection import StratifiedKFold, cross_val_score
  from sklearn.neural_network import MLPClassifier
  from smac import HyperparameterOptimizationFacade, Scenario
  from smac.acquisition.function import PriorAcquisitionFunction
label_desc: Use image node_id 2 for calling the MLP obj's underlying member method code with example usage: obj.train(config, seed) and returns: unknown