code: def fit(self, X, y):
      import sklearn.gaussian_process
  
      self.alpha = float(self.alpha)
      self.thetaL = float(self.thetaL)
      self.thetaU = float(self.thetaU)
  
      n_features = X.shape[1]
      kernel = sklearn.gaussian_process.kernels.RBF(
          length_scale=[1.0] * n_features,
          length_scale_bounds=[(self.thetaL, self.thetaU)] * n_features,
      )
  
      # Instanciate a Gaussian Process model
      self.estimator = (
          sklearn.gaussian_process.GaussianProcessRegressor(
              kernel=kernel,
              n_restarts_optimizer=10,
              optimizer="fmin_l_bfgs_b",
              alpha=self.alpha,
              copy_X_train=True,
              random_state=self.random_state,
              normalize_y=True,
          )
      )
  
      if y.ndim == 2 and y.shape[1] == 1:
          y = y.flatten()
  
      self.estimator.fit(X, y)
  
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/regression/gaussian_process.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA
label_desc: Use image node_id 2 for calling the GaussianProcess obj's underlying member method code with example usage: obj.fit(X, y) and returns: self