diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/model.py b/egs/librispeech/ASR/pruned_transducer_stateless7/model.py
index 0c1bd9551..ee88a9159 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/model.py
@@ -19,12 +19,10 @@ import k2
 import torch
 import torch.nn as nn
 from encoder_interface import EncoderInterface
-from scaling import random_clamp
 
 from icefall.utils import add_sos
 
 
-
 class Transducer(nn.Module):
     """It implements https://arxiv.org/pdf/1211.3711.pdf
     "Sequence Transduction with Recurrent Neural Networks"
@@ -142,12 +140,6 @@ class Transducer(nn.Module):
         lm = self.simple_lm_proj(decoder_out)
         am = self.simple_am_proj(encoder_out)
 
-        if self.training:
-            lm = random_clamp(lm, min=-8.0, max=2.0, prob=0.5,
-                              reflect=0.1)
-            am = random_clamp(am, min=-5.0, max=5.0, prob=0.5,
-                              reflect=0.1)
-
         with torch.cuda.amp.autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
@@ -183,10 +175,6 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        if self.training:
-            logits = random_clamp(logits, -8.0, 2.0, prob=0.5,
-                                  reflect=0.1)
-
         with torch.cuda.amp.autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
index 773bab4e9..280153137 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
@@ -175,7 +175,6 @@ class RandomClampFunction(torch.autograd.Function):
             ctx.reflect = reflect
         if reflect != 0.0:
             ans = ans * (1.0 + reflect) - (x * reflect)
-
         return ans
 
     @staticmethod
@@ -185,7 +184,7 @@ class RandomClampFunction(torch.autograd.Function):
         reflect = ctx.reflect
         if reflect !=  0.0:
             x_grad = x_grad * (1.0 + reflect) - (ans_grad * reflect)
-        return ans_grad * is_same.to(ans_grad.dtype), None, None, None, None
+        return x_grad, None, None, None, None
 
 def random_clamp(x: Tensor,
                  min: Optional[float] = None,