Hello, I just tried the example, Example: Predator-Prey Model, with `adapt_step_size=False`

and `num_chains=2`

.

For `num_chains=1`

, `adapt_step_size=True`

works perfectly, but `num_chains > 2`

, MCMC with `adapt_step_size=True`

is peding forever, so I changed it to `False`

. Also, I added the lines to enable multiple draws on the CPU.

```
numpyro.set_host_device_count(8)
print(jax.local_device_count())
```

When I plot the trace of sigma, weird patterns are shown as follows

```
sigma = mcmc.get_samples()["sigma"]
# The number of samples = 1000.
plt.plot(sigma[:, 0])
plt.plot(sigma[:, 1])
plt.show()
```

Modification of ode options such as `rtol`

, `atol`

, `maxsteps`

dosen’t solve the problem. As far as I know, `num_chains = 2`

just performs sampling twice with difference random states (or starting potins for params maybe?). But It seems the second draw wasn’t performed. Any suggestions would be helpful.

Thank you.