diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer_randomcombine.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer_randomcombine.py.swp
index 05be579f8..785f07df7 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer_randomcombine.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/.conformer_randomcombine.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
index 0d80f918e..121254ec6 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_gtrans/conformer_randomcombine.py
@@ -211,14 +211,25 @@ class Conformer(EncoderInterface):
             )  # (T, N, C)
 
         x = x.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
-        
+         
         layer_outputs = [x.permute(1, 0, 2) for x in layer_outputs]
-        x = self.layer_norm(1/4*(self.sigmoid(self.alpha[0])*layer_outputs[2] + \
-                                 self.sigmoid(self.alpha[1])*layer_outputs[5] + \
-                                 self.sigmoid(self.alpha[2])*layer_outputs[8] + \
-                                 self.sigmoid(self.alpha[3])*layer_outputs[11]
+        if self.group_num == 4:
+            x = self.layer_norm(1/4*(self.sigmoid(self.alpha[0])*layer_outputs[2] + \
+                                     self.sigmoid(self.alpha[1])*layer_outputs[5] + \
+                                     self.sigmoid(self.alpha[2])*layer_outputs[8] + \
+                                     self.sigmoid(self.alpha[3])*layer_outputs[11]
+                                    )
                                 )
-                            )
+        elif self.group_num == 6:
+            x = self.layer_norm(1/6*(self.sigmoid(self.alpha[0])*layer_outputs[1] + \
+                                     self.sigmoid(self.alpha[1])*layer_outputs[3] + \
+                                     self.sigmoid(self.alpha[2])*layer_outputs[5] + \
+                                     self.sigmoid(self.alpha[3])*layer_outputs[7] + \
+                                     self.sigmoid(self.alpha[4])*layer_outputs[9] + \
+                                     self.sigmoid(self.alpha[5])*layer_outputs[11]
+                                    )
+                                )
+
 
         '''
         x = self.layer_norm(1/12*(self.sigmoid(self.alpha[0])*layer_output[0] + \