class_docstr: Cholesky based Newton solver.
  
  Inner solver for finding the Newton step H w_newton = -g uses Cholesky based linear
  solver.
code: def update_gradient_hessian(self, X, y, sample_weight):
      _, _, self.hessian_warning = self.linear_loss.gradient_hessian(
          coef=self.coef,
          X=X,
          y=y,
          sample_weight=sample_weight,
          l2_reg_strength=self.l2_reg_strength,
          n_threads=self.n_threads,
          gradient_out=self.gradient,
          hessian_out=self.hessian,
          raw_prediction=self.raw_prediction,  # this was updated in line_search
      )
  
imports: import warnings
  from abc import ABC, abstractmethod
  import numpy
  import scipy.linalg
  import scipy.optimize
  from ..._loss.loss import HalfSquaredError
  from ...exceptions import ConvergenceWarning
  from ...utils.optimize import _check_optimize_result
  from .._linear_loss import LinearModelLoss
label_desc: Use image node_id 2 for calling the NewtonCholeskySolver obj's underlying member method code with example usage: obj.update_gradient_hessian(X, y, sample_weight) without return types