code: def _minimize_dogleg(
      fun, x0, args=(), jac=None, hess=None, **trust_region_options
  ):
      """
      Minimization of scalar function of one or more variables using
      the dog-leg trust-region algorithm.
  
      Options
      -------
      initial_trust_radius : float
          Initial trust-region radius.
      max_trust_radius : float
          Maximum value of the trust-region radius. No steps that are longer
          than this value will be proposed.
      eta : float
          Trust region related acceptance stringency for proposed steps.
      gtol : float
          Gradient norm must be less than `gtol` before successful
          termination.
  
      """
      if jac is None:
          raise ValueError(
              "Jacobian is required for dogleg minimization"
          )
      if not callable(hess):
          raise ValueError(
              "Hessian is required for dogleg minimization"
          )
      return _minimize_trust_region(
          fun,
          x0,
          args=args,
          jac=jac,
          hess=hess,
          subproblem=DoglegSubproblem,
          **trust_region_options,
      )
  
file_path: scipy/scipy/optimize/_trustregion_dogleg.py
imports: import numpy
  import scipy.linalg
  from ._trustregion import _minimize_trust_region, BaseQuadraticSubproblem
label_desc: Use image node_id 1 for calling a global function with example usage: _minimize_dogleg(fun, x0, args, jac, hess) and returns: _minimize_trust_region