Use rather than for output equality tests

tests/tensor/test_slinalg.py

@@ -533,7 +533,7 @@ def test_perform(self):
                     scipy_val = scipy.linalg.kron(a[np.newaxis, :], b).flatten()
                 else:
                     scipy_val = scipy.linalg.kron(a, b)
-                utt.assert_allclose(out, scipy_val)
+                np.testing.assert_allclose(out, scipy_val)
 
     def test_numpy_2d(self):
         for shp0 in [(2, 3)]:
@@ -578,7 +578,7 @@ def test_solve_discrete_lyapunov_via_direct_complex():
     A = rng.normal(size=(N, N)) + rng.normal(size=(N, N)) * 1j
     Q = rng.normal(size=(N, N))
     X = f(A, Q)
-    utt.assert_allclose(A @ X @ A.conj().T - X + Q, 0.0)
+    np.testing.assert_array_less(A @ X @ A.conj().T - X + Q, 1e-12)
 
     # TODO: the .conj() method currently does not have a gradient; add this test when gradients are implemented.
     # utt.verify_grad(solve_discrete_lyapunov, pt=[A, Q], rng=rng)
@@ -596,7 +596,7 @@ def test_solve_discrete_lyapunov_via_bilinear():
 
     X = f(A, Q)
 
-    utt.assert_allclose(A @ X @ A.conj().T - X + Q, 0.0)
+    np.testing.assert_array_less(A @ X @ A.conj().T - X + Q, 1e-12)
     utt.verify_grad(solve_discrete_lyapunov, pt=[A, Q], rng=rng)
 
 
@@ -611,7 +611,9 @@ def test_solve_continuous_lyapunov():
     Q = rng.normal(size=(N, N))
     X = f(A, Q)
 
-    utt.assert_allclose(A @ X + X @ A.conj().T, Q)
+    Q_recovered = A @ X + X @ A.conj().T
+
+    np.testing.assert_allclose(Q_recovered.squeeze(), Q)
     utt.verify_grad(solve_continuous_lyapunov, pt=[A, Q], rng=rng)
 
 
@@ -634,7 +636,7 @@ def test_solve_discrete_are_forward():
     )
 
     atol = 1e-4 if config.floatX == "float32" else 1e-12
-    utt.assert_allclose(res, np.zeros_like(res), atol=atol)
+    np.testing.assert_allclose(res, np.zeros_like(res), atol=atol)
 
 
 def test_solve_discrete_are_grad():