code: def fit(self, X, Y=None):
      import scipy.sparse
      import sklearn.decomposition
  
      self.n_components = int(self.n_components)
      self.degree = int(self.degree)
      self.gamma = float(self.gamma)
      self.coef0 = float(self.coef0)
  
      self.preprocessor = sklearn.decomposition.KernelPCA(
          n_components=self.n_components,
          kernel=self.kernel,
          degree=self.degree,
          gamma=self.gamma,
          coef0=self.coef0,
          remove_zero_eig=True,
          random_state=self.random_state,
      )
      if scipy.sparse.issparse(X):
          X = X.astype(np.float64)
      with warnings.catch_warnings():
          warnings.filterwarnings("error")
          self.preprocessor.fit(X)
      # Raise an informative error message, equation is based ~line 249 in
      # kernel_pca.py in scikit-learn
      if (
          len(self.preprocessor.alphas_ / self.preprocessor.lambdas_)
          == 0
      ):
          raise ValueError("KernelPCA removed all features!")
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/kernel_pca.py
imports: from typing import Optional
  import warnings
  import numpy
  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, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 2 for calling the KernelPCA obj's underlying member method code with example usage: obj.fit(X, Y) and returns: self