code: def __init__(
      self,
      weak_learners: List[BaseEstimator],
      max_complexity: int,
      min_mult: int = 1,
      penalty="l1",
      n_estimators=100,
      tree_size=4,
      sample_fract="default",
      max_rules=30,
      memory_par=0.01,
      tree_generator=None,
      lin_trim_quantile=0.025,
      lin_standardise=True,
      exp_rand_tree_size=True,
      include_linear=False,
      alpha=None,
      random_state=None,
  ):
      super().__init__(
          n_estimators,
          tree_size,
          sample_fract,
          max_rules,
          memory_par,
          tree_generator,
          lin_trim_quantile,
          lin_standardise,
          exp_rand_tree_size,
          include_linear,
          alpha,
          random_state,
      )
      self.max_complexity = max_complexity
      self.weak_learners = weak_learners
      self.penalty = penalty
      self.min_mult = min_mult
  
file_path: imodels/imodels/experimental/stablelinear.py
imports: import numpy
  from typing import List
  from imodels.rule_set.rule_fit import RuleFit
  from imodels.util.score import score_linear
  from sklearn.base import ClassifierMixin, RegressorMixin, BaseEstimator
  from .util import extract_ensemble
label_desc: Use image node_id 1 to create a new StableLinear object from inherited base classes: RuleFit with example: obj = StableLinear(weak_learners, max_complexity, min_mult, penalty, n_estimators, tree_size, sample_fract, max_rules, memory_par, tree_generator, lin_trim_quantile, lin_standardise, exp_rand_tree_size, include_linear, alpha, random_state)