Make test_ExtractDiag_exhaustive less exhaustive

tests/link/numba/test_tensor_basic.py

@@ -15,7 +15,6 @@ from tests.tensor.test_basic import check_alloc_runtime_broadcast
 
 
 pytest.importorskip("numba")
-from pytensor.link.numba.dispatch import numba_funcify
 
 
 rng = np.random.default_rng(42849)
@@ -274,28 +273,19 @@ def test_ExtractDiag(val, offset):
     )
 
 
-@pytest.mark.parametrize("k", range(-5, 4))
-@pytest.mark.parametrize(
-    "axis1, axis2", ((0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3))
-)
-@pytest.mark.parametrize("reverse_axis", (False, True))
-def test_ExtractDiag_exhaustive(k, axis1, axis2, reverse_axis):
-    from pytensor.link.numba.dispatch.basic import numba_njit
-
-    if reverse_axis:
-        axis1, axis2 = axis2, axis1
-
+@pytest.mark.parametrize("k", (-5, -1, 0, 1, 4))
+@pytest.mark.parametrize("axis1, axis2", ((0, 1), (0, 3), (1, 2), (2, 1), (2, 3)))
+def test_ExtractDiag_exhaustive(k, axis1, axis2):
     x = pt.tensor4("x")
     x_shape = (2, 3, 4, 5)
     x_test = np.arange(np.prod(x_shape)).reshape(x_shape)
     out = pt.diagonal(x, k, axis1, axis2)
-    numba_fn = numba_funcify(out.owner.op, out.owner)
 
-    @numba_njit(no_cpython_wrapper=False)
-    def wrap(x):
-        return numba_fn(x)
-
-    np.testing.assert_allclose(wrap(x_test), np.diagonal(x_test, k, axis1, axis2))
+    compare_numba_and_py(
+        [x],
+        out,
+        [x_test],
+    )
 
 
 @pytest.mark.parametrize(