code: def _get_alpha2_lasso(self, y, alpha1):
      if self.loss == "squared":
          y_bar = 0.5 * np.mean(y**2)
  
      elif self.loss == "absolute":
          y_bar = np.mean(np.abs(y))
  
      elif self.loss in ("hinge", "squared_hinge"):
          y_bar = 1.0
  
      elif self.loss == "smooth_hinge":
          if self.gamma < 1:
              y_bar = 1 - 0.5 * self.gamma
          else:
              y_bar = 0.5 / self.gamma
  
      else:
          raise ValueError("Unknown loss.")
  
      return self.tol * (alpha1 / y_bar) ** 2
  
imports: import numpy
  from sklearn.utils import check_random_state
  from .base import BaseClassifier, BaseRegressor
  from .dataset_fast import get_dataset
  from .sdca_fast import _prox_sdca_fit
label_desc: Use image node_id 1 for calling the _BaseSDCA obj's underlying member method code with example usage: obj._get_alpha2_lasso(y, alpha1) and returns: unknown