In `pyro`

, I am able to scale the log probability

```
with plate('observe_data'), pyro.poutine.scale(scale=scale_factors):
pyro.sample("obs", dist.Poisson(lam), obs=values)
```

Where `scale_factors`

is a tensor the same size as `values`

. It’s unclear to me how to implement something similar with `numpyro`

. When I try to use `numpyro.handlers.scale`

in place of `poutine.scale`

in a numpyro model, I get the following error

```
523 def __init__(self, fn=None, scale=1.):
524 if not_jax_tracer(scale):
--> 525 if scale <= 0:
526 raise ValueError("'scale' argument should be a positive number.")
527 self.scale = scale
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```

Is it possible to use a vector for scaling in `numpyro`

?