I would like to sample from a distribution using NUTS, the issue is that the distribution is bounded to be strictly positive.

For instance, for a wrapped normal distribution:

```
import torch.nn.functional as F
from pyro.distributions.transforms import SoftplusTransform
from pyro.infer import MCMC, NUTS
def exp_nll(x):
return x["u"]**2
nuts = NUTS(
potential_fn=exp_nll,
transforms={"u": SoftplusTransform()}
)
mcmc = MCMC(
nuts,
num_samples=1000,
warmup_steps=100,
initial_params={
"u": torch.tensor(1.)
}
)
mcmc.run()
print(F.softplus(mcmc.get_samples()["u"]))
```

And this seems to work, but it does generate some NaNs sometimes, and it doesnâ€™t generalize at all to the exponential distribution or to my distribution where I get only NaN. Is this the right approach?

Thanks in advance.