code: def jacobian_and_hessian(func, func_out_ndim=0):
      """Return a function that returns func's jacobian and hessian.
  
      Parameters
      ----------
      func : callable
          Function whose jacobian and hessian
          will be computed. It must be real-valued.
      func_out_ndim : dim
          func output ndim.
  
      Returns
      -------
      func_with_jacobian_and_hessian : callable
          Function that returns func's jacobian and
          func's hessian at its inputs args.
      """
  
      def _jacobian_and_hessian(*args, **kwargs):
          """Return func's jacobian and func's hessian at args.
  
          Parameters
          ----------
          args : list
              Argument to function func and its gradients.
          kwargs : dict
              Keyword arguments to function func and its gradients.
  
          Returns
          -------
          jacobian : any
              Value of func's jacobian at input arguments args.
          hessian : any
              Value of func's hessian at input arguments args.
          """
          return jacobian(func)(*args), hessian(
              func, func_out_ndim=func_out_ndim
          )(*args)
  
      return _jacobian_and_hessian
  
file_path: geomstats/geomstats/_backend/pytorch/autodiff.py
imports: import functools
  import numpy
  import torch
  from torch.autograd.functional import hessian
  from torch.autograd.functional import jacobian
label_desc: Use image node_id 6 for calling a global function with example usage: jacobian_and_hessian(func, func_out_ndim) and returns: _jacobian_and_hessian, jacobian,  hessian