code: def fit(self, X, y):
      from sklearn.linear_model import ARDRegression
  
      self.n_iter = int(self.n_iter)
      self.tol = float(self.tol)
      self.alpha_1 = float(self.alpha_1)
      self.alpha_2 = float(self.alpha_2)
      self.lambda_1 = float(self.lambda_1)
      self.lambda_2 = float(self.lambda_2)
      self.threshold_lambda = float(self.threshold_lambda)
      self.fit_intercept = check_for_bool(self.fit_intercept)
  
      self.estimator = ARDRegression(
          n_iter=self.n_iter,
          tol=self.tol,
          alpha_1=self.alpha_1,
          alpha_2=self.alpha_2,
          lambda_1=self.lambda_1,
          lambda_2=self.lambda_2,
          compute_score=False,
          threshold_lambda=self.threshold_lambda,
          fit_intercept=True,
          normalize=False,
          copy_X=False,
          verbose=False,
      )
  
      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/ard_regression.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 2 for calling the ARDRegression obj's underlying member method code with example usage: obj.fit(X, y) and returns: self