Actually check types and dtypes match in numba testing helper

tests/link/numba/test_basic.py

@@ -260,9 +260,12 @@ def compare_numba_and_py(
     if assert_fn is None:
 
         def assert_fn(x, y):
-            return np.testing.assert_allclose(x, y, rtol=1e-4) and compare_shape_dtype(
-                x, y
-            )
+            np.testing.assert_allclose(x, y, rtol=1e-4, strict=True)
+            # Make sure we don't have one input be a np.ndarray while the other is not
+            if isinstance(x, np.ndarray):
+                assert isinstance(y, np.ndarray), "y is not a NumPy array, but x is"
+            else:
+                assert not isinstance(y, np.ndarray), "y is a NumPy array, but x is not"
 
     if any(
         inp.owner is not None
@@ -295,8 +298,8 @@ def compare_numba_and_py(
     test_inputs_copy = (inp.copy() for inp in test_inputs) if inplace else test_inputs
     numba_res = pytensor_numba_fn(*test_inputs_copy)
     if isinstance(graph_outputs, tuple | list):
-        for j, p in zip(numba_res, py_res, strict=True):
-            assert_fn(j, p)
+        for numba_res_i, python_res_i in zip(numba_res, py_res, strict=True):
+            assert_fn(numba_res_i, python_res_i)
     else:
         assert_fn(numba_res, py_res)