code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      cs = ConfigurationSpace()
      hidden_layer_depth = UniformIntegerHyperparameter(
          name="hidden_layer_depth", lower=1, upper=3, default_value=1
      )
      num_nodes_per_layer = UniformIntegerHyperparameter(
          name="num_nodes_per_layer",
          lower=16,
          upper=216,
          default_value=32,
      )
      activation = CategoricalHyperparameter(
          name="activation",
          choices=["identity", "logistic", "tanh", "relu"],
          default_value="relu",
      )
      alpha = UniformFloatHyperparameter(
          name="alpha", lower=0.0001, upper=1.0, default_value=0.0001
      )
      solver = CategoricalHyperparameter(
          name="solver",
          choices=["lbfgs", "sgd", "adam"],
          default_value="adam",
      )
      cs.add_hyperparameters(
          [
              hidden_layer_depth,
              num_nodes_per_layer,
              activation,
              alpha,
              solver,
          ]
      )
      return cs
  
file_path: auto-sklearn/examples/80_extending/example_extending_classification.py
imports: from typing import Optional
  from pprint import pprint
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter, UniformFloatHyperparameter
  import sklearn.metrics
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.classification
  import autosklearn.pipeline.components.classification
  from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 6 for calling the MLPClassifier obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs