code: def configspace(self) -> ConfigurationSpace:
      cs = ConfigurationSpace()
  
      n_layer = Integer("n_layer", (1, 5), default=1)
      n_neurons = Integer("n_neurons", (8, 256), log=True, default=10)
      activation = Categorical(
          "activation", ["logistic", "tanh", "relu"], default="tanh"
      )
      solver = Categorical(
          "solver", ["lbfgs", "sgd", "adam"], default="adam"
      )
      batch_size = Integer("batch_size", (30, 300), default=200)
      learning_rate = Categorical(
          "learning_rate",
          ["constant", "invscaling", "adaptive"],
          default="constant",
      )
      learning_rate_init = Float(
          "learning_rate_init", (0.0001, 1.0), default=0.001, log=True
      )
  
      cs.add_hyperparameters(
          [
              n_layer,
              n_neurons,
              activation,
              solver,
              batch_size,
              learning_rate,
              learning_rate_init,
          ]
      )
  
      use_lr = EqualsCondition(
          child=learning_rate, parent=solver, value="sgd"
      )
      use_lr_init = InCondition(
          child=learning_rate_init,
          parent=solver,
          values=["sgd", "adam"],
      )
      use_batch_size = InCondition(
          child=batch_size, parent=solver, values=["sgd", "adam"]
      )
  
      # We can also add multiple conditions on hyperparameters at once:
      cs.add_conditions([use_lr, use_batch_size, use_lr_init])
  
      return cs
  
file_path: SMAC3/examples/3_multi_objective/2_parego.py
imports: from __future__ import annotations
  import time
  import warnings
  import matplotlib.pyplot
  import numpy
  from ConfigSpace import Categorical, Configuration, ConfigurationSpace, EqualsCondition, Float, InCondition, Integer
  from sklearn.datasets import load_digits
  from sklearn.model_selection import StratifiedKFold, cross_val_score
  from sklearn.neural_network import MLPClassifier
  from smac import HyperparameterOptimizationFacade
  from smac import Scenario
  from smac.facade.abstract_facade import AbstractFacade
  from smac.multi_objective.parego import ParEGO
label_desc: Use image node_id 1 for calling the MLP obj's underlying member method code with example usage: obj.configspace() and returns: cs