code: def fit(self, X, Y, sample_weight=None):
      from sklearn.ensemble import ExtraTreesClassifier
      from sklearn.feature_selection import SelectFromModel
  
      self.n_estimators = int(self.n_estimators)
  
      if check_none(self.max_leaf_nodes):
          self.max_leaf_nodes = None
      else:
          self.max_leaf_nodes = int(self.max_leaf_nodes)
  
      if check_none(self.max_depth):
          self.max_depth = None
      else:
          self.max_depth = int(self.max_depth)
  
      self.bootstrap = check_for_bool(self.bootstrap)
      self.n_jobs = int(self.n_jobs)
      self.min_impurity_decrease = float(self.min_impurity_decrease)
      self.max_features = self.max_features
      self.min_samples_leaf = int(self.min_samples_leaf)
      self.min_samples_split = int(self.min_samples_split)
      self.verbose = int(self.verbose)
  
      max_features = int(X.shape[1] ** float(self.max_features))
      estimator = ExtraTreesClassifier(
          n_estimators=self.n_estimators,
          criterion=self.criterion,
          max_depth=self.max_depth,
          min_samples_split=self.min_samples_split,
          min_samples_leaf=self.min_samples_leaf,
          bootstrap=self.bootstrap,
          max_features=max_features,
          max_leaf_nodes=self.max_leaf_nodes,
          min_impurity_decrease=self.min_impurity_decrease,
          oob_score=self.oob_score,
          n_jobs=self.n_jobs,
          verbose=self.verbose,
          random_state=self.random_state,
          class_weight=self.class_weight,
      )
      estimator.fit(X, Y, sample_weight=sample_weight)
      self.preprocessor = SelectFromModel(
          estimator=estimator, threshold="mean", prefit=True
      )
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_classification.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, Constant, UniformFloatHyperparameter, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 2 for calling the ExtraTreesPreprocessorClassification obj's underlying member method code with example usage: obj.fit(X, Y, sample_weight) and returns: self