How does pymc3 handle n=0 in Binomial discrete likelihood function

Questions
5

Does this extend to pymc v4 (4.0.0b2)? I can run something like pm.Binomial.dist(n=np.array([0,2,3,4,5]),p=at.expit(-0.1)).eval() with expected results, but when I put it in a model e.g.

with pm.Model() as model:
    alpha = pm.Normal("alpha", mu=0, sigma=1)
    mu = at.expit(alpha)

    pm.Binomial(
        "y",
        n=np.array([0,2,3,4,5]),
        p=mu,
        observed=np.array([0,2,2,0,2]) # or even if you omit observed
    )

I get

SamplingError: Initial evaluation of model at starting point failed!
Starting values:
{'alpha': array(0.28858972)}

Initial evaluation results:
{'alpha': -0.96, 'y': -inf}

Any ideas?