What is the best way to measure/benchmark the time it takes to evaluate the log probability of a Numpyro model, as well as taking the gradients with respect to the latent paramters? I am mostly interested in the context of MCMC so I would like to benchmark to the log probability and gradient functions that Numpyro uses when doing inference with `NUTS`

.

To have a specific example. How would I benchmark the `eight_schools`

model from the Getting Started section?

```
import numpy as np
import numpyro
import numpyro.distributions as dist
from jax import random
from numpyro.infer import MCMC, NUTS
J = 8
y = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])
sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])
# Eight Schools example
def eight_schools(J, sigma, y=None):
mu = numpyro.sample('mu', dist.Normal(0, 5))
tau = numpyro.sample('tau', dist.HalfCauchy(5))
with numpyro.plate('J', J):
theta = numpyro.sample('theta', dist.Normal(mu, tau))
numpyro.sample('obs', dist.Normal(theta, sigma), obs=y)
nuts_kernel = NUTS(eight_schools)
mcmc = MCMC(nuts_kernel, num_warmup=500, num_samples=1000)
rng_key = random.PRNGKey(0)
mcmc.run(rng_key, J, sigma, y=y, extra_fields=('potential_energy',))
```