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 __init__(
      self,
      config: Optional[Configuration] = None,
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      steps=None,
      dataset_properties=None,
      include=None,
      exclude=None,
      random_state: Optional[Union[int, np.random.RandomState]] = None,
      init_params=None,
  ):
      self._output_dtype = np.int32
      if dataset_properties is None:
          dataset_properties = dict()
      if "target_type" not in dataset_properties:
          dataset_properties["target_type"] = "classification"
      super().__init__(
          feat_type=feat_type,
          config=config,
          steps=steps,
          dataset_properties=dataset_properties,
          include=include,
          exclude=exclude,
          random_state=random_state,
          init_params=init_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 1 to create a new SimpleClassificationPipeline object from inherited base classes: BasePipeline, ClassifierMixin with example: obj = SimpleClassificationPipeline(config, feat_type, steps, dataset_properties, include, exclude, random_state, init_params)