I would try to modify your model until you figure out what’s going on. Here’s the simplified version that I ran:
import pymc as pm
with pm.Model() as model:
a = pm.TruncatedNormal('a', mu=1/50, sigma=1, lower=1/1000, upper=1, initval=1/50)
b = pm.Normal('b', 0, 10, initval=0)
c = pm.Normal('c', 0, 10, initval=0)
ϵ = pm.HalfCauchy('ϵ', 100, initval=1)
#mu = fn(a, b, c, x)
#pm.Potential("negative_penalty", pm.math.switch(mu<0, -np.inf, 0))
mu = 1
y_pred = pm.Gamma('y_pred', mu=mu, sigma=ϵ, observed=[1,2,3])
idata = pm.sample()