class_docstr: This class implements the regression task.
  
  It implements a pipeline, which includes one preprocessing step and one
  regression algorithm. It can render a search space including all known
  regression 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 regressors 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 regression model. This
      variable is assigned after a call to the
      :meth:`autosklearn.pipeline.regression.SimpleRegressionPipeline.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.regression.SimpleRegressionPipeline.fit`
      method.
  
  See also
  --------
  
  References
  ----------
  
  Examples
  --------
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docstr: Predict the classes using the selected model.
  
  Predicted values are capped to approximately the maximum and minimum labels
  seen during training.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_features)
  
  batch_size: int or None, defaults to None
      batch_size controls whether the pipeline will be
      called on small chunks of the data. Useful when calling the
      predict method on the whole array X results in a MemoryError.
  
  Returns
  -------
  array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
      Returns the predicted values
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code: def predict(self, X, batch_size=None):
      """Predict the classes using the selected model.
  
      Predicted values are capped to approximately the maximum and minimum labels
      seen during training.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_features)
  
      batch_size: int or None, defaults to None
          batch_size controls whether the pipeline will be
          called on small chunks of the data. Useful when calling the
          predict method on the whole array X results in a MemoryError.
  
      Returns
      -------
      array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)
          Returns the predicted values"""
      y = super().predict(X, batch_size=batch_size)
  
      if self.y_max_ > 0:
          y[y > (2 * self.y_max_)] = 2 * self.y_max_
      elif self.y_max_ < 0:
          y[y > (0.5 * self.y_max_)] = 0.5 * self.y_max_
      if self.y_min_ < 0:
          y[y < (2 * self.y_min_)] = 2 * self.y_min_
      elif self.y_min_ > 0:
          y[y < (0.5 * self.y_min_)] = 0.5 * self.y_min_
  
      return y
  
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 RegressorMixin
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import BasePipeline
  from autosklearn.pipeline.components import feature_preprocessing
  from autosklearn.pipeline.components import regression
  from autosklearn.pipeline.components.data_preprocessing import DataPreprocessorChoice
  from autosklearn.pipeline.constants import SPARSE
label_desc: Use image node_id 4 for calling the SimpleRegressionPipeline obj's underlying member method code with example usage: obj.predict(X, batch_size) and returns: y