code: def __init__(
      self,
      n_quantiles: int,
      output_distribution: str,
      random_state: Optional[Union[int, np.random.RandomState]] = None,
  ) -> None:
      from sklearn.preprocessing import QuantileTransformer
  
      self.n_quantiles = n_quantiles
      self.output_distribution = output_distribution
      self.preprocessor = QuantileTransformer(
          n_quantiles=n_quantiles,
          output_distribution=output_distribution,
          copy=False,
          random_state=random_state,
      )
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/rescaling/quantile_transformer.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import Rescaling
  from autosklearn.pipeline.constants import DENSE, INPUT, SIGNED_DATA, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 1 to create a new QuantileTransformerComponent object from inherited base classes: Rescaling, AutoSklearnPreprocessingAlgorithm with example: obj = QuantileTransformerComponent(n_quantiles, output_distribution, random_state)