class_docstr: Censoring-aware squared loss.
  
  Censoring is taken into account by only considering the residuals
  of samples that are not censored, or the predicted survival time
  is before the time of censoring.
class_docstr_tok: ["Censoring-aware","squared","loss",".","Censoring","is","taken","into","account","by","only","considering","the","residuals","of","samples","that","are","not","censored",",","or","the","predicted","survival","time","is","before","the","time","of","censoring","."]
docstr: Least squares does not need to update terminal regions
docstr_tok: ["Least","squares","does","not","need","to","update","terminal","regions"]
code: def _update_terminal_region(
      self,
      tree,
      terminal_regions,
      leaf,
      X,
      y,
      residual,
      raw_predictions,
      sample_weight,
  ):
      """Least squares does not need to update terminal regions"""
  
imports: from abc import ABCMeta
  import numpy
  from sklearn.dummy import DummyRegressor
  from sklearn.ensemble._gb_losses import RegressionLossFunction
  from sklearn.utils.extmath import squared_norm
  from ..nonparametric import ipc_weights
  from ._coxph_loss import coxph_loss, coxph_negative_gradient
label_desc: Use image node_id 4 for calling the CensoredSquaredLoss obj's underlying member method code with example usage: obj._update_terminal_region(tree, terminal_regions, leaf, X, y, residual, raw_predictions, sample_weight) without return types