diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
index 7252ee436..5d198f806 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
@@ -452,7 +452,7 @@ def get_transducer_model(params: AttributeDict) -> nn.Module:
 def load_checkpoint_if_available(
     params: AttributeDict,
     model: nn.Module,
-    model_avg: nn.Module = None,
+    model_avg: Optional[nn.Module] = None,
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
 ) -> Optional[Dict[str, Any]]:
diff --git a/egs/librispeech/ASR/transducer_lstm/encoder.py b/egs/librispeech/ASR/transducer_lstm/encoder.py
index 296d0979f..ec00b0a7a 100644
--- a/egs/librispeech/ASR/transducer_lstm/encoder.py
+++ b/egs/librispeech/ASR/transducer_lstm/encoder.py
@@ -13,7 +13,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import warnings
 from typing import Tuple
 
 import torch
@@ -79,9 +78,8 @@ class LstmEncoder(EncoderInterface):
         x = self.encoder_embed(x)
 
         # Caution: We assume the subsampling factor is 4!
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore")
-            lengths = ((x_lens - 1) // 2 - 1) // 2
+
+        lengths = (((x_lens - 1) >> 1) - 1) >> 1
         assert x.size(1) == lengths.max().item(), (
             x.size(1),
             lengths.max(),
diff --git a/egs/librispeech/ASR/transducer_lstm/test_encoder.py b/egs/librispeech/ASR/transducer_lstm/test_encoder.py
index 2689011a3..e0e2b2747 100755
--- a/egs/librispeech/ASR/transducer_lstm/test_encoder.py
+++ b/egs/librispeech/ASR/transducer_lstm/test_encoder.py
@@ -20,11 +20,9 @@
 To run this file, do:
 
     cd icefall/egs/librispeech/ASR
-    python ./transducer_lstm/test_model.py
+    python ./transducer_lstm/test_encoder.py
 """
 
-import warnings
-
 import torch
 from train import get_encoder_model, get_params
 
@@ -47,9 +45,7 @@ def test_encoder_model():
 
     y, y_lens = encoder(x, x_lens)
     print(y.shape)
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        expected_y_lens = ((x_lens - 1) // 2 - 1) // 2
+    expected_y_lens = (((x_lens - 1) >> 1) - 1) >> 1
 
     assert torch.all(torch.eq(y_lens, expected_y_lens)), (
         y_lens,