class_docstr: Inverse probability of censoring weighted least squares error
class_docstr_tok: ["Inverse","probability","of","censoring","weighted","least","squares","error"]
code: def negative_gradient(
      self, y, raw_predictions, **kwargs
  ):  # pylint: disable=unused-argument
      return y["time"] - raw_predictions.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 2 for calling the IPCWLeastSquaresError obj's underlying member method code with example usage: obj.negative_gradient(y, raw_predictions) and returns: unknown