Plotting posterior predictions from new data - shape mismatch

Questions version agnostic
5

Initialized all the variables as pm.MutableData and that did the trick:

with pm.Model() as m_13_4:
    a_bar = pm.Normal("a_bar", 0.0, 1.5)
    sigma_a = pm.Exponential("sigma_a", 1.0)
    sigma_g = pm.Exponential("sigma_g", 1.0)

    a = pm.Normal("a", a_bar, sigma_a, shape=Nactor)
    g = pm.Normal("g", 0.0, sigma_g, shape=Nblock)
    b = pm.Normal("b", 0.0, 0.5, shape=Ntreatments)

    actor_ = pm.MutableData("actor", actor)
    block_ = pm.MutableData("block", block)
    treatment_ = pm.MutableData("treatment", treatment)
    data = pm.MutableData("data", d)
    p = pm.Deterministic("p", pm.math.invlogit(a[actor_] + g[block_] + b[treatment_]))
    pulled_left = pm.Binomial("pulled_left", 1, p, observed=d.pulled_left.values)

    trace_13_4 = pm.sample(tune=3000, target_accept=0.99, random_seed=RANDOM_SEED)

Are there any drawbacks to using MutableData? Seems like unless you know you won’t absolutely be using to predict on new data, no reason not to use it when specifying a model.