Add BasicNorm on output of Conv2dSubsampling module

egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py

@@ -1821,6 +1821,7 @@ class Conv2dSubsampling(nn.Module):
 
         self.out = nn.Linear(out_height * layer3_channels, out_channels)
 
+        self.out_norm = BasicNorm(out_channels, channel_dim=-1)
         self.dropout = Dropout2(dropout)
 
 
@@ -1856,6 +1857,7 @@ class Conv2dSubsampling(nn.Module):
                                   max=float(self.scale_max))
 
         x = self.out(x)
+        x = self.out_norm(x)
         # Now x is of shape (N, ((T-1)//2 - 1))//2, odim)
         x = self.dropout(x)
         return x