code: def fit(self, X, y):
      self.alpha = float(self.alpha)
      self.gamma = float(self.gamma)
      self.degree = int(self.degree)
      self.coef0 = float(self.coef0)
  
      import sklearn.kernel_ridge
  
      self.estimator = sklearn.kernel_ridge.KernelRidge(
          alpha=self.alpha,
          kernel=self.kernel,
          gamma=self.gamma,
          degree=self.degree,
          coef0=self.coef0,
      )
      self.estimator.fit(X, y)
      return self
  
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 2 for calling the KernelRidgeRegression obj's underlying member method code with example usage: obj.fit(X, y) and returns: self