code: def __init__(
      self,
      loss,
      epsilon,
      dual,
      tol,
      C,
      fit_intercept,
      intercept_scaling,
      random_state=None,
  ):
      self.epsilon = epsilon
      self.loss = loss
      self.dual = dual
      self.tol = tol
      self.C = C
      self.fit_intercept = fit_intercept
      self.intercept_scaling = intercept_scaling
      self.random_state = random_state
      self.estimator = None
  
file_path: auto-sklearn/autosklearn/pipeline/components/regression/liblinear_svr.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 AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 1 to create a new LibLinear_SVR object from inherited base classes: AutoSklearnRegressionAlgorithm with example: obj = LibLinear_SVR(loss, epsilon, dual, tol, C, fit_intercept, intercept_scaling, random_state)