code: def fit(self, X, Y):
      import sklearn.preprocessing
  
      self.degree = int(self.degree)
      self.interaction_only = check_for_bool(self.interaction_only)
      self.include_bias = check_for_bool(self.include_bias)
  
      self.preprocessor = sklearn.preprocessing.PolynomialFeatures(
          degree=self.degree,
          interaction_only=self.interaction_only,
          include_bias=self.include_bias,
      )
      self.preprocessor.fit(X, Y)
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/polynomial.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter
  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
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 2 for calling the PolynomialFeatures obj's underlying member method code with example usage: obj.fit(X, Y) and returns: self