Continue PyMC v5 migration: Fix remaining import issues

Fixed critical import errors:
- Updated assert_moment_is_expected → assert_support_point_is_expected
- Updated pymc_random_discrete → discrete_random_tester
- Updated pymc.sampling_jax → pymc.sampling.jax
- Fixed deprecated pytensor.tensor.var → pytensor.tensor.variable

Test Results:
- 563 tests passing ✓
- Import errors resolved
- Remaining failures are API compatibility issues

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

Author: twiecki

homepy/models/base.py

@@ -32,7 +32,7 @@
 
 from pytensor import tensor as pt
 from pytensor.compile import SharedVariable
-from pytensor.tensor.var import TensorVariable
+from pytensor.tensor.variable import TensorVariable
 from fastprogress import progress_bar as _progress_bar
 from matplotlib import pyplot as plt
 from pymc.sampling import compile_forward_sampling_function

homepy/nested_hierarchy_rvs.py

@@ -21,7 +21,7 @@
 import pymc as pm
 
 from pytensor import tensor as pt
-from pytensor.tensor.var import TensorVariable
+from pytensor.tensor.variable import TensorVariable
 from pymc.model import Model
 
 from homepy.nested_hierarchy_utils import DimName, Indexes, NestedHierarchy

homepy/tests/blocks/test_distributions.py

@@ -34,8 +34,8 @@
     BaseTestDistributionRandom,
     Domain,
     Rplus,
-    assert_moment_is_expected,
-    pymc_random_discrete,
+    assert_support_point_is_expected,
+    discrete_random_tester,
 )
 
 
@@ -217,7 +217,7 @@ class TestRandomVariable(BaseTestDistributionRandom):
         ]
 
         def test_random_matches_poisson(self):
-            pymc_random_discrete(
+            discrete_random_tester(
                 dist=self.pymc_dist,
                 paramdomains={"mu": Rplus, "lam": Domain([0], edges=(None, None))},
                 ref_rand=lambda mu, lam, size: scipy.stats.poisson.rvs(mu, size=size),
@@ -295,4 +295,4 @@ def test_logp_lam_expected_moments(self):
     def test_moment(self, mu, lam, size, expected):
         with pm.Model() as model:
             GeneralizedPoisson("x", mu=mu, lam=lam, size=size)
-        assert_moment_is_expected(model, expected)
+        assert_support_point_is_expected(model, expected)

homepy/tests/test_jax_utils.py

@@ -19,7 +19,7 @@
 import numpy as np
 
 from pytensor import tensor as pt
-from pymc.sampling_jax import get_jaxified_graph
+from pymc.sampling.jax import get_jaxified_graph
 
 
 def test_reshape():

homepy/tests/test_nested_hierarchy_rvs.py

@@ -22,7 +22,7 @@
 import pymc as pm
 import pytest
 
-from pytensor.tensor.var import TensorVariable
+from pytensor.tensor.variable import TensorVariable
 from homepy.nested_hierarchy_rvs import (
     make_hierarchical_coefs,
     make_hierarchical_weights,