class_docstr: Base class for all pipeline objects.
  
  Notes
  -----
  This class should not be instantiated, only subclassed.
docstr: Return the configuration space for the CASH problem.
  
  This method should be called by the method
  get_hyperparameter_search_space of a subclass. After the subclass
  assembles a list of available estimators and preprocessor components,
  _get_hyperparameter_search_space can be called to do the work of
  creating the actual
  ConfigSpace.configuration_space.ConfigurationSpace object.
  
  Parameters
  ----------
  feat_type: dict
      python dictionary which maps the columns of the dataset to the data types
  
  estimator_name : str
      Name of the estimator hyperparameter which will be used in the
      configuration space. For a classification task, this would be
      'classifier'.
  
  estimator_components : dict {name: component}
      Dictionary with all estimator components to be included in the
      configuration space.
  
  preprocessor_components : dict {name: component}
      Dictionary with all preprocessor components to be included in the
      configuration space. .
  
  always_active : list of str
      A list of components which will always be active in the pipeline.
      This is useful for components like imputation which have
      hyperparameters to be configured, but which do not have any parent.
  
  default_estimator : str
      Default value for the estimator hyperparameter.
  
  Returns
  -------
  cs : ConfigSpace.configuration_space.Configuration
      The configuration space describing the AutoSklearnClassifier.
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code: def _get_hyperparameter_search_space(
      self,
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      include=None,
      exclude=None,
      dataset_properties=None,
  ):
      """Return the configuration space for the CASH problem.
  
      This method should be called by the method
      get_hyperparameter_search_space of a subclass. After the subclass
      assembles a list of available estimators and preprocessor components,
      _get_hyperparameter_search_space can be called to do the work of
      creating the actual
      ConfigSpace.configuration_space.ConfigurationSpace object.
  
      Parameters
      ----------
      feat_type: dict
          python dictionary which maps the columns of the dataset to the data types
  
      estimator_name : str
          Name of the estimator hyperparameter which will be used in the
          configuration space. For a classification task, this would be
          'classifier'.
  
      estimator_components : dict {name: component}
          Dictionary with all estimator components to be included in the
          configuration space.
  
      preprocessor_components : dict {name: component}
          Dictionary with all preprocessor components to be included in the
          configuration space. .
  
      always_active : list of str
          A list of components which will always be active in the pipeline.
          This is useful for components like imputation which have
          hyperparameters to be configured, but which do not have any parent.
  
      default_estimator : str
          Default value for the estimator hyperparameter.
  
      Returns
      -------
      cs : ConfigSpace.configuration_space.Configuration
          The configuration space describing the AutoSklearnClassifier.
      """
      raise NotImplementedError()
  
imports: from abc import ABCMeta
  from typing import Dict, Optional, Union
  import numpy
  import scipy.sparse
  from ConfigSpace import Configuration
  from sklearn.pipeline import Pipeline
  import autosklearn.pipeline.create_searchspace_util
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from .components.base import AutoSklearnChoice, AutoSklearnComponent
label_desc: Use image node_id 13 for calling the BasePipeline obj's underlying member method code with example usage: obj._get_hyperparameter_search_space(feat_type, include, exclude, dataset_properties) without return types