class_docstr: This class implements a pipeline for data preprocessing of numerical features.
  It assumes that the data to be transformed is made only of numerical features.
  The steps of this pipeline are:
      1 - Imputation: Substitution of missing values (NaN)
      2 - VarianceThreshold: Removes low-variance features
      3 - Rescaling: rescale features according to a certain rule (e.g. normalization,
          standartization or min-max)
  Parameters
  ----------
  config : ConfigSpace.configuration_space.Configuration
      The configuration to evaluate.
  random_state : Optional[int | RandomState]
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance
      used by `np.random`.
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docstr: Create the hyperparameter configuration space.
  Parameters
  ----------
  Returns
  -------
  cs : ConfigSpace.configuration_space.Configuration
      The configuration space describing the SimpleRegressionClassifier.
docstr_tok: ["Create","the","hyperparameter","configuration","space",".","Parameters","--","--","--","--","--","Returns","--","--","--","-","cs",":","ConfigSpace.configuration_space.Configuration","The","configuration","space","describing","the","SimpleRegressionClassifier","."]
code: def _get_hyperparameter_search_space(
      self,
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      include: Optional[Dict[str, str]] = None,
      exclude: Optional[Dict[str, str]] = None,
      dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None,
  ) -> ConfigurationSpace:
      """Create the hyperparameter configuration space.
      Parameters
      ----------
      Returns
      -------
      cs : ConfigSpace.configuration_space.Configuration
          The configuration space describing the SimpleRegressionClassifier.
      """
      cs = ConfigurationSpace()
  
      if dataset_properties is None or not isinstance(
          dataset_properties, dict
      ):
          dataset_properties = dict()
  
      cs = self._get_base_search_space(
          cs=cs,
          dataset_properties=dataset_properties,
          exclude=exclude,
          include=include,
          pipeline=self.steps,
          feat_type=feat_type,
      )
  
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/feature_type_numerical.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
  from sklearn.base import BaseEstimator
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline
  from autosklearn.pipeline.components.data_preprocessing import rescaling
  from autosklearn.pipeline.components.data_preprocessing.imputation.numerical_imputation import NumericalImputation
  from autosklearn.pipeline.components.data_preprocessing.variance_threshold.variance_threshold import VarianceThreshold
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 3 for calling the NumericalPreprocessingPipeline obj's underlying member method code with example usage: obj._get_hyperparameter_search_space(feat_type, include, exclude, dataset_properties) and returns: cs