class_docstr: Base/abstract class defining the quadratic model for trust-region
  minimization. Child classes must implement the ``solve`` method.
  
  Values of the objective function, Jacobian and Hessian (if provided) at
  the current iterate ``x`` are evaluated on demand and then stored as
  attributes ``fun``, ``jac``, ``hess``.
class_docstr_tok: ["Base\/abstract","class","defining","the","quadratic","model","for","trust-region","minimization",".","Child","classes","must","implement","the","``","solve","``","method",".","Values","of","the","objective","function",",","Jacobian","and","Hessian","(","if","provided",")","at","the","current","iterate","``","x","``","are","evaluated","on","demand","and","then","stored","as","attributes","``","fun","``",",","``","jac","``",",","``","hess","``","."]
code: def __init__(self, x, fun, jac, hess=None, hessp=None):
      self._x = x
      self._f = None
      self._g = None
      self._h = None
      self._g_mag = None
      self._cauchy_point = None
      self._newton_point = None
      self._fun = fun
      self._jac = jac
      self._hess = hess
      self._hessp = hessp
  
file_path: catboost/contrib/python/scipy/py3/scipy/optimize/_trustregion.py
imports: import math
  import numpy
  import scipy.linalg
  from .optimize import _check_unknown_options, _wrap_function, _status_message, OptimizeResult, _prepare_scalar_function
  from scipy.optimize._hessian_update_strategy import HessianUpdateStrategy
  from scipy.optimize._differentiable_functions import FD_METHODS
label_desc: Use image node_id 1 to create a new BaseQuadraticSubproblem object  with example: obj = BaseQuadraticSubproblem(x, fun, jac, hess, hessp)