Add test for freeze_dims_and_data in JAX backend

tests/sampling/test_jax.py

@@ -35,6 +35,7 @@
 
 from pymc import ImputationWarning
 from pymc.distributions.multivariate import DirichletMultinomial, PosDefMatrix
+from pymc.model.transform.optimization import freeze_dims_and_data
 from pymc.sampling.jax import (
     _get_batched_jittered_initial_points,
     _get_log_likelihood,
@@ -514,6 +515,24 @@ def test_convergence_warnings(caplog, nuts_sampler):
 
 
 def test_dirichlet_multinomial():
+    """Test we can draw from a DM in the JAX backend if the shape is constant."""
     dm = DirichletMultinomial.dist(n=5, a=np.eye(3) * 1e6 + 0.01)
     dm_draws = pm.draw(dm, mode="JAX")
     np.testing.assert_equal(dm_draws, np.eye(3) * 5)
+
+
+def test_dirichlet_multinomial_dims():
+    """Test we can draw from a DM with a shape defined by dims in the JAX backend,
+    after freezing those dims.
+    """
+    with pm.Model(coords={"trial": range(3), "item": range(3)}) as m:
+        dm = DirichletMultinomial("dm", n=5, a=np.eye(3) * 1e6 + 0.01, dims=("trial", "item"))
+
+    # JAX does not allow us to JIT a function with dynamic shape
+    with pytest.raises(TypeError):
+        pm.draw(dm, mode="JAX")
+
+    # Should be fine after freezing the dims that specify the shape
+    frozen_dm = freeze_dims_and_data(m)["dm"]
+    dm_draws = pm.draw(frozen_dm, mode="JAX")
+    np.testing.assert_equal(dm_draws, np.eye(3) * 5)