code: def fit(self, X, Y=None):
      import scipy.sparse
      import sklearn.kernel_approximation
  
      self.n_components = int(self.n_components)
      self.gamma = float(self.gamma)
      self.degree = int(self.degree)
      self.coef0 = float(self.coef0)
  
      self.preprocessor = sklearn.kernel_approximation.Nystroem(
          kernel=self.kernel,
          n_components=self.n_components,
          gamma=self.gamma,
          degree=self.degree,
          coef0=self.coef0,
          random_state=self.random_state,
      )
  
      # Because the pipeline guarantees that each feature is positive,
      # clip all values below zero to zero
      if self.kernel == "chi2":
          if scipy.sparse.issparse(X):
              X.data[X.data < 0] = 0.0
          else:
              X[X < 0] = 0.0
  
      self.preprocessor.fit(X)
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py
imports: from typing import Optional
  from ConfigSpace.conditions import EqualsCondition, InCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, INPUT, SIGNED_DATA, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 2 for calling the Nystroem obj's underlying member method code with example usage: obj.fit(X, Y) and returns: self