human
I wrote the below code

    from numdifftools import Jacobian
    model = modelcontext(model)
    if (vars is None):
        vars = model.cont_vars
    vars = inputvars(vars)
    point = Point(point, model=model)
    bij = DictToArrayBijection(ArrayOrdering(vars), point)
    dlogp = bij.mapf(model.fastdlogp(vars))
    def grad_logp(point):
        return np.nan_to_num(dlogp(point))
    '\n                Find    the    jacobian    of    the    gradient    function    at    the    current    position\n                this    should    be    the    Hessian.
   invert    it    to    find    the    approximate\n                covariance    matrix.\n                '
    return (- Jacobian(grad_logp)(bij.map(point)))

What's a good function header?
 ... Can you provide a Python program to find    the    jacobian    of    the    gradient    function    at    the    current    position\n                this    should    be    the    Hessian?
assistant
#Here is Python program to find    the    jacobian    of    the    gradient    function    at    the    current    position\n                this    should    be    the    Hessian.

def approx_hessian(point, vars=None, model=None):