Auto generated dimensions in posterior predictive don't match with observed dimensions

[lucianopaz]

Describe the issue:

When a dimension is left unnamed, pymc will auto generate the dimension's name based on the tensor's name and the axis number. The problem is that pymc.sample_posterior_predictive returns a dimension name that is not aligned with what one gets in the observed_data group.

Reproduceable code example:

import pymc as pm

with pm.Model():
    a = pm.Normal("a")
    b = pm.Normal("b", a, observed=[-1, 2, 4])
    idata = pm.sample()
    idata.extend(pm.sample_prior_predictive())
    idata = pm.sample_posterior_predictive(idata, extend_inferencedata=True)

assert idata.observed_data.b.dims == idata.prior_predictive.b.dims[2:]  # This works!
assert idata.observed_data.b.dims == idata.posterior_predictive.b.dims[2:]  # This fails

Error message:

It's a simple `AssertionError`. The telling thing is that the posterior predictive group gets a wrong imputed name because it doesn't ignore the chain and draw dimensions


>>> print(idata.observed_data.b.dims, idata.prior_predictive.b.dims, idata.posterior_predictive.b.dims)
('b_dim_0',) ('chain', 'draw', 'b_dim_0') ('chain', 'draw', 'b_dim_2')

PyMC version information:

PyMC main

Context for the issue:

No response

[lucianopaz]

@OriolAbril, could you have a look at this? I opened a PR over at arviz trying to fix it