Vector values gradients for vector -> scalar function

I am currently exploring boost as autodiff framework for my
application. I would like to calculate the derivative of a
vector->scalar function. However I could not find a way to evaluate
the same, rather I always get total gradient.

Example: How to get the following program yield output equivalent to JAX fwd?

#include <boost/math/differentiation/autodiff.hpp>
#include <iostream>
#include<vector>

template <typename T>
T power_sum(double p, std::vector<T> &z){
    T y=0.0;
    for (int i=0; i<6;i++){
        y += pow(z[i], p);
    }
    return y;
}

int main() {
     using namespace boost::math::differentiation;
      constexpr unsigned Order = 2;

      std::vector<double> z_v = {1.,2.,3.,4.,5.,6.};
      std::vector<autodiff_fvar<double, Order> > z;

      for (int i=0; i<6;i++){
          z.push_back(make_fvar<double, Order>(z_v[i]));
      }

    auto y = power_sum(2.0,z);

    std::cout<<y<<"\n";
    std::cout<<y.derivative(1)<<"\n";
    return 0;
}

JAX output:

import jax as jx
import jax.numpy as jnp

x = jnp.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])

def power_sum(x, p):
     y = x[0] ** p + x[1] ** p + x[2] ** p + x[3] ** p + x[4] ** p + x[5] ** p
     return y

f_prime = jx.jacfwd(power_sum)(x,2.0)

f_prime
# DeviceArray([ 2.,  4.,  6.,  8., 10., 12.], dtype=float32)

I understand I can do it by send array 6 times with all other vvector
values being zero. Is there a better way?