MeasurableDimshuffle logp fails when value broadcastable information is lost

[ricardoV94]

import aesara.tensor as at
from aeppl import joint_logprob

# Using a multivariate to force our Dimshuffle rewrite
x = at.random.dirichlet([1, 1, 1])[None, ...]
y = at.random.dirichlet([1, 1, 1], size=(9,))
z = at.concatenate([x, y], axis=0)

z_vv = z.clone()
joint_logprob({z: z_vv})  # ValueError: Cannot drop a non-broadcastable dimension: [False, False], [1]

The logp of the concatenate will split the value of z_vv in two, and the first one will not be inferred to be broadcastable at runtime. We could fix this, by adding a specify_shape of 1 for the dimensions we are dropping in the MeasurableDimshuffle logp here:

aeppl/aeppl/tensor.py

Line 211 in 0f06fe9

value = value.dimshuffle(undo_ds)

More generally is there a reason why we don't add always add a specify_shape when dropping dimensions in Aesara via Dimshuffle instead of raising that error?