docstr: Returns the Configuration Space object, from which Auto-Sklearn
  will sample configurations and build pipelines.
  
  Parameters
  ----------
  X : array-like or sparse matrix of shape = [n_samples, n_features]
      Array with the training features, used to get characteristics like
      data sparsity
  y : array-like, shape = [n_samples] or [n_samples, n_outputs]
      Array with the problem labels
  X_test : array-like or sparse matrix of shape = [n_samples, n_features]
      Array with features used for performance estimation
  y_test : array-like, shape = [n_samples] or [n_samples, n_outputs]
      Array with the problem labels for the testing split
  dataset_name: Optional[str]
      A string to tag the Auto-Sklearn run
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code: def get_configuration_space(
      self,
      X: SUPPORTED_FEAT_TYPES,
      y: Union[SUPPORTED_TARGET_TYPES, spmatrix],
      X_test: Optional[SUPPORTED_FEAT_TYPES] = None,
      y_test: Optional[Union[SUPPORTED_TARGET_TYPES, spmatrix]] = None,
      dataset_name: Optional[str] = None,
      feat_type: Optional[List[str]] = None,
  ) -> ConfigurationSpace:
      """
      Returns the Configuration Space object, from which Auto-Sklearn
      will sample configurations and build pipelines.
  
      Parameters
      ----------
      X : array-like or sparse matrix of shape = [n_samples, n_features]
          Array with the training features, used to get characteristics like
          data sparsity
      y : array-like, shape = [n_samples] or [n_samples, n_outputs]
          Array with the problem labels
      X_test : array-like or sparse matrix of shape = [n_samples, n_features]
          Array with features used for performance estimation
      y_test : array-like, shape = [n_samples] or [n_samples, n_outputs]
          Array with the problem labels for the testing split
      dataset_name: Optional[str]
          A string to tag the Auto-Sklearn run
      """
      if self.automl_ is None:
          self.automl_ = self.build_automl()
  
      return (
          self.automl_.fit(
              X,
              y,
              X_test=X_test,
              y_test=y_test,
              dataset_name=dataset_name,
              feat_type=feat_type,
              only_return_configuration_space=True,
          )
          if self.automl_.configuration_space is None
          else self.automl_.configuration_space
      )
  
imports: from __future__ import annotations
  from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence, Tuple, Type, Union
  import warnings
  import dask.distributed
  import joblib
  import numpy
  import pandas
  from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
  from scipy.sparse import spmatrix
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
  from sklearn.ensemble import VotingClassifier, VotingRegressor
  from sklearn.utils.multiclass import type_of_target
  from smac.runhistory.runhistory import RunInfo, RunValue
  from typing_extensions import Literal
  from autosklearn.automl import AutoML, AutoMLClassifier, AutoMLRegressor
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES, convert_if_sparse
  from autosklearn.ensembles.abstract_ensemble import AbstractEnsemble
  from autosklearn.ensembles.ensemble_selection import EnsembleSelection
  from autosklearn.ensembles.multiobjective_dummy_ensemble import MultiObjectiveDummyEnsemble
  from autosklearn.metrics import Scorer
  from autosklearn.pipeline.base import BasePipeline
  from autosklearn.util.smac_wrap import SMACCallback
label_desc: Use image node_id 22 for calling the AutoSklearnEstimator obj's underlying member method code with example usage: obj.get_configuration_space(X, y, X_test, y_test, dataset_name, feat_type) and returns: unknown