code: def _fit(self, X, Y=None):
      import sklearn.ensemble
  
      self.n_estimators = int(self.n_estimators)
      if check_none(self.max_depth):
          self.max_depth = None
      else:
          self.max_depth = int(self.max_depth)
      self.min_samples_split = int(self.min_samples_split)
      self.min_samples_leaf = int(self.min_samples_leaf)
      if check_none(self.max_leaf_nodes):
          self.max_leaf_nodes = None
      else:
          self.max_leaf_nodes = int(self.max_leaf_nodes)
      self.min_weight_fraction_leaf = float(
          self.min_weight_fraction_leaf
      )
      self.bootstrap = check_for_bool(self.bootstrap)
  
      self.preprocessor = sklearn.ensemble.RandomTreesEmbedding(
          n_estimators=self.n_estimators,
          max_depth=self.max_depth,
          min_samples_split=self.min_samples_split,
          min_samples_leaf=self.min_samples_leaf,
          max_leaf_nodes=self.max_leaf_nodes,
          sparse_output=self.sparse_output,
          n_jobs=self.n_jobs,
          random_state=self.random_state,
      )
      self.preprocessor.fit(X, Y)
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, Constant, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 2 for calling the RandomTreesEmbedding obj's underlying member method code with example usage: obj._fit(X, Y) and returns: self