code: def fit(self, X, Y):
      import sklearn.multiclass
      import sklearn.svm
  
      self.C = float(self.C)
      self.tol = float(self.tol)
  
      self.dual = check_for_bool(self.dual)
  
      self.fit_intercept = check_for_bool(self.fit_intercept)
  
      self.intercept_scaling = float(self.intercept_scaling)
  
      if check_none(self.class_weight):
          self.class_weight = None
  
      estimator = sklearn.svm.LinearSVC(
          penalty=self.penalty,
          loss=self.loss,
          dual=self.dual,
          tol=self.tol,
          C=self.C,
          class_weight=self.class_weight,
          fit_intercept=self.fit_intercept,
          intercept_scaling=self.intercept_scaling,
          multi_class=self.multi_class,
          random_state=self.random_state,
      )
  
      if len(Y.shape) == 2 and Y.shape[1] > 1:
          self.estimator = sklearn.multiclass.OneVsRestClassifier(
              estimator, n_jobs=1
          )
      else:
          self.estimator = estimator
  
      self.estimator.fit(X, Y)
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/classification/liblinear_svc.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.forbidden import ForbiddenAndConjunction, ForbiddenEqualsClause
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, Constant, UniformFloatHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.pipeline.implementations.util import softmax
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 2 for calling the LibLinear_SVC obj's underlying member method code with example usage: obj.fit(X, Y) and returns: self