Hi,

I would like to know why I keep getting very weird initial evaluation results even though I set the initval as 1 for all my prior distributions.

```
with pm.Model() as model:
a1 = pm.HalfNormal('a1', sigma=10, initval=1)
a2 = pm.HalfNormal('a2', sigma=10, initval=1)
a3 = pm.HalfNormal('a3', sigma=10, initval=1)
a4 = pm.HalfNormal('a4', sigma=10, initval=1)
a5 = pm.HalfNormal('a5', sigma=10, initval=1)
a6 = pm.HalfNormal('a6', sigma=10, initval=1)
sigma = pm.Exponential("sigma",1)
y_obs = np.exp((t - mean_t) / std_t)
mu = fn(a1, a2, a3, a4, a5, a6, x) # this function always produces positive values
pm.Gamma("obs", mu = mu, sigma = sigma, observed=y_obs)
idata = pm.sample_prior_predictive(samples=len(x), random_seed=rng)
idata.extend(pm.sample(4000, tune=4000, random_seed=rng, chains=2, target_accept=0.999))
```

The error message is:

```
Auto-assigning NUTS sampler...
INFO:pymc:Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
INFO:pymc:Initializing NUTS using jitter+adapt_diag...
---------------------------------------------------------------------------
SamplingError Traceback (most recent call last)
Input In [224], in <cell line: 2>()
observed=y_bos)
62 idata = pm.sample_prior_predictive(samples=len(p), random_seed=rng)
---> 63 idata.extend(pm.sample(4000, tune=4000, random_seed=rng, chains=2, target_accept=0.999))
File /opt/anaconda3/envs/pymc/lib/python3.10/site-packages/pymc/sampling.py:558, in sample(draws, step, init, n_init, initvals, trace, chain_idx, chains, cores, tune, progressbar, model, random_seed, discard_tuned_samples, compute_convergence_checks, callback, jitter_max_retries, return_inferencedata, idata_kwargs, mp_ctx, **kwargs)
556 # One final check that shapes and logps at the starting points are okay.
557 for ip in initial_points:
--> 558 model.check_start_vals(ip)
559 _check_start_shape(model, ip)
561 sample_args = {
562 "draws": draws,
563 "step": step,
(...)
573 "discard_tuned_samples": discard_tuned_samples,
574 }
File /opt/anaconda3/envs/pymc/lib/python3.10/site-packages/pymc/model.py:1725, in Model.check_start_vals(self, start)
1722 initial_eval = self.point_logps(point=elem)
1724 if not all(np.isfinite(v) for v in initial_eval.values()):
-> 1725 raise SamplingError(
1726 "Initial evaluation of model at starting point failed!\n"
1727 f"Starting values:\n{elem}\n\n"
1728 f"Initial evaluation results:\n{initial_eval}"
1729 )
SamplingError: Initial evaluation of model at starting point failed!
Starting values:
{'a1_log__': array(-3.79408301), 'a2_log__': array(-0.88779326), 'a3_log__': array(-0.49539466), 'a4_log__': array(0.7710357), 'a5_log__': array(-0.77415039), 'a6_log__': array(-0.3820694), 'sigma_log__': array(-0.28868408)}
Initial evaluation results:
{'a1': -6.32, 'a2': -3.42, 'a3': -3.03, 'a4': -1.78, 'a5': -3.3, 'a6': -2.91, 'sigma': -1.04, 'obs': nan}
```

What should I modify first?