class_docstr: This class implements the classification task.
  
  It implements a pipeline, which includes one preprocessing step and one
  classification algorithm. It can render a search space including all known
  classification and preprocessing algorithms.
  
  Contrary to the sklearn API it is not possible to enumerate the
  possible parameters in the __init__ function because we only know the
  available classifiers at runtime. For this reason the user must
  specifiy the parameters by passing an instance of
  ConfigSpace.configuration_space.Configuration.
  
  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`.
  
  Attributes
  ----------
  _estimator : The underlying scikit-learn classification model. This
      variable is assigned after a call to the
      :meth:`autosklearn.pipeline.classification.SimpleClassificationPipeline
      .fit` method.
  
  _preprocessor : The underlying scikit-learn preprocessing algorithm. This
      variable is only assigned if a preprocessor is specified and
      after a call to the
      :meth:`autosklearn.pipeline.classification.SimpleClassificationPipeline
      .fit` method.
  
  See also
  --------
  
  References
  ----------
  
  Examples
  --------
code: def _get_estimator_hyperparameter_name(self):
      return "classifier"
  
imports: from typing import Optional, Union
  import copy
  from itertools import product
  import numpy
  from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
  from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause
  from sklearn.base import ClassifierMixin
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import BasePipeline
  from autosklearn.pipeline.components.classification import ClassifierChoice
  from autosklearn.pipeline.components.data_preprocessing import DataPreprocessorChoice
  from autosklearn.pipeline.components.data_preprocessing.balancing.balancing import Balancing
  from autosklearn.pipeline.components.feature_preprocessing import FeaturePreprocessorChoice
  from autosklearn.pipeline.constants import SPARSE
label_desc: Use image node_id 6 for calling the SimpleClassificationPipeline obj's underlying member method code with example usage: obj._get_estimator_hyperparameter_name() and returns: str