code: def __init__(
      self,
      n_estimators,
      max_depth,
      min_samples_split,
      min_samples_leaf,
      min_weight_fraction_leaf,
      max_leaf_nodes,
      bootstrap,
      sparse_output=True,
      n_jobs=1,
      random_state=None,
  ):
      self.n_estimators = n_estimators
      self.max_depth = max_depth
      self.min_samples_split = min_samples_split
      self.min_samples_leaf = min_samples_leaf
      self.max_leaf_nodes = max_leaf_nodes
      self.min_weight_fraction_leaf = min_weight_fraction_leaf
      self.bootstrap = bootstrap
      self.sparse_output = sparse_output
      self.n_jobs = n_jobs
      self.random_state = random_state
  
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 1 to create a new RandomTreesEmbedding object from inherited base classes: AutoSklearnPreprocessingAlgorithm with example: obj = RandomTreesEmbedding(n_estimators, max_depth, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, bootstrap, sparse_output, n_jobs, random_state)