code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None,
  ) -> ConfigurationSpace:
      cs = ConfigurationSpace()
      # TODO parametrize like the Random Forest as n_quantiles = n_features^param
      n_quantiles = UniformIntegerHyperparameter(
          "n_quantiles", lower=10, upper=2000, default_value=1000
      )
      output_distribution = CategoricalHyperparameter(
          "output_distribution", ["normal", "uniform"]
      )
      cs.add_hyperparameters((n_quantiles, output_distribution))
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import Rescaling
  from autosklearn.pipeline.constants import DENSE, INPUT, SIGNED_DATA, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 3 for calling the QuantileTransformerComponent obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs