Pm.sample_posterior_predictive() not working with weights

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I’m trying to achieve a similar thing in the context of a Gamma GLM.
I’ve followed this topic to create a potential which I can weight.

I’ve had an idea about how to sample from the posterior predictive but I’m not convinced it’s correct: create a second model, replacing the potential with the Gamma function and use that only to sample from the posterior predictive.

For example, if this is the model I’m using for sampling:

# x = regressors
# y = target
# w = weights

with pm.Model() as m1:
    α = pm.Normal("α", 0, 1)
    β = pm.Normal("β", 0, 1)
    shape = pm.Uniform("shape", 0, 100)

    # Log-link function
    μ = at.exp(α + β*x)

    logp = w * pm.logp(pm.Gamma.dist(alpha=shape, beta=shape/μ), y)
    potential = pm.Potential("potential", logp)

    trace = pm.sample()

and then use this one for prediction:

with pm.Model() as m2:
    α = pm.Normal("α", 0, 1)
    β = pm.Normal("β", 0, 1)
    shape = pm.Uniform("shape", 0, 100)

    # Log-link function
    μ = at.exp(α + β*x)

    pm.Gamma(alpha=shape, beta=shape/μ, observed=y)

with m2:
    trace.extend(pm.sample_posterior_predictive(trace))

Would that be a correct way? I’m aware of it that the weights don’t come into account in the posterior predictive, but that makes sense - I’m giving more weight to certain observations in the fitting stage, but not in the prediction stage.

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