diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
index a199458f0..a790ce345 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
@@ -432,8 +432,8 @@ class MaxEigLimiterFunction(torch.autograd.Function):
 
 class BasicNormFunction(torch.autograd.Function):
     # This computes:
-    #   scales = torch.mean((x + bias) ** 2, keepdim=True) + eps.exp()
-    #   return x * scales
+    #   scales = torch.mean((x - bias) ** 2, keepdim=True) + eps.exp()
+    #   return (x - bias) * scales
     # (after unsqueezing the bias), but it does it in a memory-efficient way so that
     # it can just store the returned value (chances are, this will also be needed for
     # some other reason, related to the next operation, so we can save memory).
@@ -448,9 +448,9 @@ class BasicNormFunction(torch.autograd.Function):
         ctx.channel_dim = channel_dim
         for _ in range(channel_dim + 1, x.ndim):
             bias = bias.unsqueeze(-1)
-        scales  = (torch.mean((x + bias) ** 2, dim=channel_dim, keepdim=True) + eps.exp()) ** -0.5
-        ans = x * scales - bias
-        ctx.save_for_backward(ans.detach() if store_output_for_backprop else x.detach(),
+        scales  = (torch.mean((x - bias) ** 2, dim=channel_dim, keepdim=True) + eps.exp()) ** -0.5
+        ans = x * scales
+        ctx.save_for_backward(ans.detach() if store_output_for_backprop else x,
                               scales.detach(), bias.detach(), eps.detach())
         return ans
 
@@ -468,8 +468,8 @@ class BasicNormFunction(torch.autograd.Function):
         eps.requires_grad = True
         with torch.enable_grad():
             # recompute scales from x, bias and eps.
-            scales = (torch.mean((x + bias) ** 2, dim=ctx.channel_dim, keepdim=True) + eps.exp()) ** -0.5
-            ans = x * scales - bias
+            scales = (torch.mean((x - bias) ** 2, dim=ctx.channel_dim, keepdim=True) + eps.exp()) ** -0.5
+            ans = x * scales
             ans.backward(gradient=ans_grad)
         return x.grad, bias.grad.flatten(), eps.grad, None, None