diff --git a/egs/librispeech/ASR/incremental_transf/.conformer.py.swp b/egs/librispeech/ASR/incremental_transf/.conformer.py.swp
index c70f2b921..4f9620246 100644
Binary files a/egs/librispeech/ASR/incremental_transf/.conformer.py.swp and b/egs/librispeech/ASR/incremental_transf/.conformer.py.swp differ
diff --git a/egs/librispeech/ASR/incremental_transf/.model.py.swp b/egs/librispeech/ASR/incremental_transf/.model.py.swp
index dd7695fe9..4c51a9134 100644
Binary files a/egs/librispeech/ASR/incremental_transf/.model.py.swp and b/egs/librispeech/ASR/incremental_transf/.model.py.swp differ
diff --git a/egs/librispeech/ASR/incremental_transf/model.py b/egs/librispeech/ASR/incremental_transf/model.py
index cc5fac241..396e99408 100644
--- a/egs/librispeech/ASR/incremental_transf/model.py
+++ b/egs/librispeech/ASR/incremental_transf/model.py
@@ -237,6 +237,7 @@ class Interformer(nn.Module):
 
         self.pt_encoder = pt_encoder
         self.inter_encoder = inter_encoder
+        self.mse = nn.MSELoss()
 
     def forward(
         self,
@@ -263,37 +264,4 @@ class Interformer(nn.Module):
                 return_grad=True,
             )
 
-        # ranges : [B, T, prune_range]
-        ranges = k2.get_rnnt_prune_ranges(
-            px_grad=px_grad,
-            py_grad=py_grad,
-            boundary=boundary,
-            s_range=prune_range,
-        )
-
-        # am_pruned : [B, T, prune_range, encoder_dim]
-        # lm_pruned : [B, T, prune_range, decoder_dim]
-        am_pruned, lm_pruned = k2.do_rnnt_pruning(
-            am=self.joiner.encoder_proj(encoder_out),
-            lm=self.joiner.decoder_proj(decoder_out),
-            ranges=ranges,
-        )
-
-        # logits : [B, T, prune_range, vocab_size]
-
-        # project_input=False since we applied the decoder's input projections
-        # prior to do_rnnt_pruning (this is an optimization for speed).
-        logits = self.joiner(am_pruned, lm_pruned, project_input=False)
-
-        with torch.cuda.amp.autocast(enabled=False):
-            pruned_loss = k2.rnnt_loss_pruned(
-                logits=logits.float(),
-                symbols=y_padded,
-                ranges=ranges,
-                termination_symbol=blank_id,
-                boundary=boundary,
-                delay_penalty=delay_penalty,
-                reduction=reduction,
-            )
-
         return (simple_loss, pruned_loss)