For loop vs shape parameter

I have a followup question. I am Doing a sample_posterior_predictive with shape parameters on the priors, and separating the observed values belonging to each prior by indexing the priors to fit a flattened observed array(originally array of arrays of observed values).

The return-value from the sample_posterior_predictive does not have a seperate posterior for each of the inferences being performed. It seems to have one trace for each observed value. Is there a way to obtain one separate trace for each posterior?