code: def fit(self, X, y=None):
      if check_none(self.shrinkage):
          self.shrinkage = None
      else:
          self.shrinkage = float(self.shrinkage)
      self.tol = float(self.tol)
  
      import sklearn.discriminant_analysis
  
      self.preprocessor = (
          sklearn.discriminant_analysis.LinearDiscriminantAnalysis(
              shrinkage=self.shrinkage,
              solver=self.solver,
              tol=self.tol,
          )
      )
      self.preprocessor.fit(X, y)
      return self
  
imports: from typing import Optional
  from pprint import pprint
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter
  from ConfigSpace.conditions import InCondition
  import sklearn.metrics
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.classification
  import autosklearn.pipeline.components.feature_preprocessing
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA
  from autosklearn.util.common import check_none
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 2 for calling the LDA obj's underlying member method code with example usage: obj.fit(X, y) and returns: self