diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/frame_reducer.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/frame_reducer.py
index b33b712e4..1517a494f 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/frame_reducer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/frame_reducer.py
@@ -74,7 +74,7 @@ class FrameReducer(nn.Module):
         padding_mask = make_pad_mask(x_lens)
         non_blank_mask = (ctc_output[:, :, blank_id] < math.log(0.9)) * (~padding_mask)
 
-        if y_lens is not None or self.training == False:
+        if y_lens is not None or self.training is False:
             # Limit the maximum number of reduced frames
             if y_lens is not None:
                 limit_lens = T - y_lens
@@ -85,15 +85,15 @@ class FrameReducer(nn.Module):
             fake_limit_indexes = torch.topk(
                 ctc_output[:, :, blank_id], max_limit_len
             ).indices
-            T = (
+            _T = (
                 torch.arange(max_limit_len)
                 .expand_as(
                     fake_limit_indexes,
                 )
                 .to(device=x.device)
             )
-            T = torch.remainder(T, limit_lens.unsqueeze(1))
-            limit_indexes = torch.gather(fake_limit_indexes, 1, torch.tensor(T))
+            _T = torch.remainder(_T, limit_lens.unsqueeze(1))
+            limit_indexes = torch.gather(fake_limit_indexes, 1, _T)
             limit_mask = (torch.full_like(
                 non_blank_mask,
                 0,