code: def __init__(
      self,
      criterion,
      max_features,
      max_depth_factor,
      min_samples_split,
      min_samples_leaf,
      min_weight_fraction_leaf,
      max_leaf_nodes,
      min_impurity_decrease,
      class_weight=None,
      random_state=None,
  ):
      self.criterion = criterion
      self.max_features = max_features
      self.max_depth_factor = max_depth_factor
      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.min_impurity_decrease = min_impurity_decrease
      self.random_state = random_state
      self.class_weight = class_weight
      self.estimator = None
  
file_path: auto-sklearn/autosklearn/pipeline/components/classification/decision_tree.py
imports: from typing import Optional
  import numpy
  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 AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel
  from autosklearn.util.common import check_none
label_desc: Use image node_id 1 to create a new DecisionTree object from inherited base classes: AutoSklearnClassificationAlgorithm with example: obj = DecisionTree(criterion, max_features, max_depth_factor, min_samples_split, min_samples_leaf, min_weight_fraction_leaf, max_leaf_nodes, min_impurity_decrease, class_weight, random_state)