code: def __init__(
      self, alpha, kernel, gamma, degree, coef0, random_state=None
  ):
      self.alpha = alpha
      self.kernel = kernel
      self.gamma = gamma
      self.degree = degree
      self.coef0 = coef0
      self.random_state = random_state
      self.estimator = None
  
file_path: auto-sklearn/examples/80_extending/example_extending_regression.py
imports: from typing import Optional
  from pprint import pprint
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, UniformIntegerHyperparameter, CategoricalHyperparameter
  from ConfigSpace.conditions import EqualsCondition
  import sklearn.metrics
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.regression
  import autosklearn.pipeline.components.regression
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS
  from sklearn.datasets import load_diabetes
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 1 to create a new KernelRidgeRegression object from inherited base classes: AutoSklearnRegressionAlgorithm with example: obj = KernelRidgeRegression(alpha, kernel, gamma, degree, coef0, random_state)