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 fit_transformer(self, X, y, fit_params=None):
      if fit_params is None:
          fit_params = {}
  
      if self.config["balancing:strategy"] == "weighting":
          balancing = Balancing(strategy="weighting")
          _init_params, _fit_params = balancing.get_weights(
              y,
              self.config["classifier:__choice__"],
              self.config["feature_preprocessor:__choice__"],
              {},
              {},
          )
          _init_params.update(self.init_params)
          self.set_hyperparameters(
              feat_type=self.feat_type,
              configuration=self.config,
              init_params=_init_params,
          )
  
          if _fit_params is not None:
              fit_params.update(_fit_params)
  
      X, fit_params = super().fit_transformer(
          X, y, fit_params=fit_params
      )
  
      return X, fit_params
  
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 2 for calling the SimpleClassificationPipeline obj's underlying member method code with example usage: obj.fit_transformer(X, y, fit_params) and returns: X,  fit_params