Simplify cholesky infer_shape test and remove slow mark

tests/tensor/test_slinalg.py

@@ -122,18 +122,20 @@ def test_cholesky_grad_indef():
     assert np.all(np.isnan(chol_f(mat)))
 
 
-@pytest.mark.slow
-def test_cholesky_shape():
-    rng = np.random.default_rng(utt.fetch_seed())
+def test_cholesky_infer_shape():
     x = matrix()
-    for l in (cholesky(x), Cholesky(lower=True)(x), Cholesky(lower=False)(x)):
-        f_chol = pytensor.function([x], l.shape)
+    f_chol = pytensor.function([x], [cholesky(x).shape, cholesky(x, lower=False).shape])
+    if config.mode != "FAST_COMPILE":
         topo_chol = f_chol.maker.fgraph.toposort()
-        if config.mode != "FAST_COMPILE":
-            assert sum(node.op.__class__ == Cholesky for node in topo_chol) == 0
-        for shp in [2, 3, 5]:
-            m = np.cov(rng.standard_normal((shp, shp + 10))).astype(config.floatX)
-            np.testing.assert_equal(f_chol(m), (shp, shp))
+        f_chol.dprint()
+        assert not any(
+            isinstance(getattr(node.op, "core_op", node.op), Cholesky)
+            for node in topo_chol
+        )
+    for shp in [2, 3, 5]:
+        res1, res2 = f_chol(np.eye(shp).astype(x.dtype))
+        assert tuple(res1) == (shp, shp)
+        assert tuple(res2) == (shp, shp)
 
 
 def test_eigvalsh():