fix(messages): fix TransformedMessage.factor_gradient unpack and chain-rule logd_jacs accumulation

[trexfr-ops]

Description

Closes #1501

This PR resolves the crash in TransformedMessage.factor_gradient where it unpacked 4 values from self._transform_det_jac(x) (which returns 3: (x, logd, logd_jacs)).

Changes

1. Updated factor_gradient in autofit/messages/composed_transform.py to unpack x, logd, logd_jacs = self._transform_det_jac(x).
2. Correctly chain-ruled transform jacobians and logd_grad in reverse order:

for logd_grad, jac in reversed(logd_jacs):
    grad = (grad * jac) + logd_grad

3. Added unit test in test_autofit/graphical/functionality/test_messages.py verifying analytical gradient matches numerical derivative of factor(x) (100% passed).

💰 Bounty Claim & LTC Payout Address:

LPnftYop8yhRNQZstysT3vuJf3XkpQWKTC

[trexfr-ops]

Agreed! I verified the gradient accumulation directly against numerical finite differences across both scalar and multivariate transform compositions in test_messages.py::test_transformed_message_factor_gradient. All test suites are passing cleanly on the branch. Ready for review whenever you have a moment!