code: def train(
      self, config: Configuration, seed: int = 0, budget: int = 25
  ) -> float:
      assert self.dataset is not None
      # For deactivated parameters (by virtue of the conditions),
      # the configuration stores None-values.
      # This is not accepted by the MLP, so we replace them with placeholder values.
      lr = (
          config["learning_rate"]
          if config["learning_rate"]
          else "constant"
      )
      lr_init = (
          config["learning_rate_init"]
          if config["learning_rate_init"]
          else 0.001
      )
      batch_size = config["batch_size"] if config["batch_size"] else 200
  
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore")
  
          classifier = MLPClassifier(
              hidden_layer_sizes=[config["n_neurons"]]
              * config["n_layer"],
              solver=config["solver"],
              batch_size=batch_size,
              activation=config["activation"],
              learning_rate=lr,
              learning_rate_init=lr_init,
              max_iter=int(np.ceil(budget)),
              random_state=seed,
          )
  
          # 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,
              self.dataset.get_X(),
              self.dataset.get_Y(),
              cv=cv,
              error_score="raise",
          )
  
      return 1 - np.mean(score)
  
file_path: SMAC3/benchmark/src/models/mlp.py
imports: from __future__ import annotations
  import warnings
  import numpy
  from ConfigSpace import Categorical, Configuration, ConfigurationSpace, EqualsCondition, Float, Integer
  from ConfigSpace.conditions import InCondition
  from sklearn.model_selection import StratifiedKFold, cross_val_score
  from sklearn.neural_network import MLPClassifier
  from src.models.model import Model
label_desc: Use image node_id 2 for calling the MLPModel obj's underlying member method code with example usage: obj.train(config, seed, budget) and returns: unknown