code: def fit(self, X, Y, sample_weight=None):
      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.DecisionTreeClassifier(
          max_depth=self.max_depth
      )
  
      estimator = sklearn.ensemble.AdaBoostClassifier(
          base_estimator=base_estimator,
          n_estimators=self.n_estimators,
          learning_rate=self.learning_rate,
          algorithm=self.algorithm,
          random_state=self.random_state,
      )
  
      estimator.fit(X, Y, sample_weight=sample_weight)
  
      self.estimator = estimator
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/classification/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 AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 2 for calling the AdaboostClassifier obj's underlying member method code with example usage: obj.fit(X, Y, sample_weight) and returns: self