fix(graphical): seed inherently-stochastic test_full_hierachical

test_full_hierachical fits a marginal hierarchical EP model whose Laplace
refinement (n_refine=3) draws samples from the global np.random state via
NormalMessage.sample, so its recovered mu_logt hyperparameter converges to a
good value or a sigma-collapsed one depending on the ambient RNG state left by
whatever ran before it. The test seeded np.random only before data generation,
not before the fit.

When #1351 added test_ep_statistics_fixes.py, its Monte-Carlo KL-direction
tests consumed np.random and shifted the ambient state into a failing region,
turning the Tests workflow red on main (green at #1347 -> red at #1351/#1354).
The fit's math is unchanged; this is a test-determinism fix. Seed np.random
immediately before the fit so the test is reproducible.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01DgCFQoGUbVkspbCjRuKnHJ

Author: 2 people

test_autofit/graphical/hierarchical/test_hierarchical.py

@@ -298,6 +298,14 @@ def test_full_hierachical(data):
         },
     )
 
+    # This fits a *marginal* hierarchical EP model whose Laplace refinement draws
+    # stochastic samples from the global np.random state (n_refine=3 via
+    # NormalMessage.sample). The fit sits near a convergence boundary, so its
+    # recovered fixed point (good vs sigma-collapse) depends on the ambient RNG
+    # state left by whatever ran before — an inherently stochastic test. Seed here
+    # so the fit is reproducible; this is the pragmatic fix (see PyAutoFit #1352).
+    np.random.seed(0)
+
     laplace = graph.LaplaceOptimiser()
     ep_opt = graph.EPOptimiser(model, default_optimiser=laplace)
     new_approx = ep_opt.run(model_approx, max_steps=10)