Hi, I wanna design a special prior for my data. However, it seems PyMC3 does not sample from my prior.

I am giving a simple example below.

```
class MyPrior(pm.Continuous):
def __init__(self, mu, sd, *args, **kwargs):
super().__init__(*args, **kwargs)
self.mu = mu
self.sd = sd
self.dist = pm.Normal.dist(mu=mu, sd=sd)
def random(self, point=None, size=None):
tmp = self.dist.random(point=point, size=size)
print(tmp)
return tmp
def log_normal(self, mu, sd, val):
const = -0.5 * (np.log(2*np.pi) + 2*np.log(sd))
exp = -0.5 * (val-mu)**2 / sd**2
return const + exp
def logp(self, value):
print('## logp')
# return self.log_normal(self.mu, self.sd, value)
return self.dist.logp(value)
with pm.Model() as model:
# mu = pm.Normal('mu', mu=0, sd=10)
mu = MyPrior('mu', mu=0, sd=10, testval=0)
sd = pm.HalfNormal('sd', sd=10)
obs = pm.Normal('obs', mu=mu, sd=sd, observed=np.random.randn(100))
trace = pm.sample(500, tune=1000, cores=2)
```

When I run this code, it only prints `'## logp'`

3 times at the begining, and never prints `tmp`

, which means the model only runs my `logp`

for test values and never samples from my prior.

Do I impl the prior correctly? Thanks for the help in advance.