class_docstr: Inverse probability of censoring weighted least squares error
class_docstr_tok: ["Inverse","probability","of","censoring","weighted","least","squares","error"]
code: def update_terminal_regions(
      self,
      tree,
      X,
      y,
      residual,
      y_pred,
      sample_weight,
      sample_mask,
      learning_rate=0.1,
      k=0,
  ):
      y_pred[:, k] += learning_rate * tree.predict(X).ravel()
  
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 3 for calling the IPCWLeastSquaresError obj's underlying member method code with example usage: obj.update_terminal_regions(tree, X, y, residual, y_pred, sample_weight, sample_mask, learning_rate, k) without return types