docstr: Parameters
  ----------
  gamma: float
      Parameter of the rbf kernel to be approximated exp(-gamma * x^2)
  
  n_components: int
      Number of components (output dimensionality) used to approximate the kernel
  
  random_state: Optional[int | RandomState]
      The random state to pass to the underlying estimator
docstr_tok: ["Parameters","--","--","--","--","--","gamma",":","float","Parameter","of","the","rbf","kernel","to","be","approximated","exp","(","-gamma","*","x^2",")","n_components",":","int","Number","of","components","(","output","dimensionality",")","used","to","approximate","the","kernel","random_state",":","Optional","[","int","|","RandomState","]","The","random","state","to","pass","to","the","underlying","estimator"]
code: def __init__(
      self,
      gamma: float,
      n_components: int,
      random_state: Optional[Union[int, RandomState]] = None,
  ) -> None:
      """
      Parameters
      ----------
      gamma: float
          Parameter of the rbf kernel to be approximated exp(-gamma * x^2)
  
      n_components: int
          Number of components (output dimensionality) used to approximate the kernel
  
      random_state: Optional[int | RandomState]
          The random state to pass to the underlying estimator
      """
      self.gamma = gamma
      self.n_components = n_components
      self.random_state = random_state
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py
imports: from typing import Optional, Union
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, UniformIntegerHyperparameter
  from numpy.random import RandomState
  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
label_desc: Use image node_id 1 to create a new RandomKitchenSinks object from inherited base classes: AutoSklearnPreprocessingAlgorithm with example: obj = RandomKitchenSinks(gamma, n_components, random_state)