How to model/deal with weighted binary outcomes

Questions
7

I tried to do what you said here

but cannot get it to work.
Here is a simple example of what I did

def test():
    N = 1000
    N2 = int(N//2)
    # create test data sets
    data = np.ones(N)
    data[N2:] = 3.0

    with pm.Model() as m1:
        
        mu1 = pm.Normal('mu1', mu=0.0, sd=100.0)
        err = pm.HalfCauchy('err1', beta=5.0)
        
        w1 = np.zeros(N)
        w1[N2:] = -1.0e10
        
        p = pm.Potential('p1', shared(w1))
        l1 = pm.Normal('lk1', mu=mu1, sd=err, observed=data)
        
        advi = pm.ADVI()
        approx = advi.fit(100000)
        
    return approx

approx = test()
s = approx.sample(1000)
pm.summary(s)

Average Loss = 5e+12: 100%|██████████| 100000/100000 [00:43<00:00, 2300.78it/s]
Finished [100%]: Average Loss = 5e+12
mean	sd	mc_error	hpd_2.5	hpd_97.5
mu1	2.003387	0.034653	0.001194	1.935714	2.069872
err1	0.999572	0.023656	0.000643	0.956357	1.047354

mu1 seems to be not affected by the potential at all.
What am I doing wrong?