code: def fit(self, X, y):
      import sklearn.ensemble
      import sklearn.tree
  
      self.n_estimators = int(self.n_estimators)
      self.learning_rate = float(self.learning_rate)
      self.max_depth = int(self.max_depth)
      base_estimator = sklearn.tree.DecisionTreeRegressor(
          max_depth=self.max_depth
      )
  
      self.estimator = sklearn.ensemble.AdaBoostRegressor(
          base_estimator=base_estimator,
          n_estimators=self.n_estimators,
          learning_rate=self.learning_rate,
          loss=self.loss,
          random_state=self.random_state,
      )
  
      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/adaboost.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 2 for calling the AdaboostRegressor obj's underlying member method code with example usage: obj.fit(X, y) and returns: self