code: def fit(self, X, Y=None):
      import sklearn.kernel_approximation
  
      self.n_components = int(self.n_components)
      self.gamma = float(self.gamma)
  
      self.preprocessor = sklearn.kernel_approximation.RBFSampler(
          gamma=self.gamma,
          n_components=self.n_components,
          random_state=self.random_state,
      )
      self.preprocessor.fit(X)
      return self
  
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 2 for calling the RandomKitchenSinks obj's underlying member method code with example usage: obj.fit(X, Y) and returns: self