remove unncessary code

Author: Caroline Chen

test/torchaudio_unittest/rnnt/rnnt_loss_impl.py

@@ -18,23 +18,22 @@ def _test_costs_and_gradients(
         self, data, ref_costs, ref_gradients, atol=1e-6, rtol=1e-2
     ):
         logits_shape = data["logits"].shape
-        with self.subTest():
-            costs, gradients = compute_with_pytorch_transducer(data=data)
-            np.testing.assert_allclose(costs, ref_costs, atol=atol, rtol=rtol)
-            self.assertEqual(logits_shape, gradients.shape)
-            if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol):
-                for b in range(len(gradients)):
-                    T = data["logit_lengths"][b]
-                    U = data["target_lengths"][b]
-                    for t in range(gradients.shape[1]):
-                        for u in range(gradients.shape[2]):
-                            np.testing.assert_allclose(
-                                gradients[b, t, u],
-                                ref_gradients[b, t, u],
-                                atol=atol,
-                                rtol=rtol,
-                                err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}",
-                            )
+        costs, gradients = compute_with_pytorch_transducer(data=data)
+        np.testing.assert_allclose(costs, ref_costs, atol=atol, rtol=rtol)
+        self.assertEqual(logits_shape, gradients.shape)
+        if not np.allclose(gradients, ref_gradients, atol=atol, rtol=rtol):
+            for b in range(len(gradients)):
+                T = data["logit_lengths"][b]
+                U = data["target_lengths"][b]
+                for t in range(gradients.shape[1]):
+                    for u in range(gradients.shape[2]):
+                        np.testing.assert_allclose(
+                            gradients[b, t, u],
+                            ref_gradients[b, t, u],
+                            atol=atol,
+                            rtol=rtol,
+                            err_msg=f"failed on b={b}, t={t}/T={T}, u={u}/U={U}",
+                        )
 
     def test_basic_backward(self):
         rnnt_loss = RNNTLoss()