diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.model.py.swp b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.model.py.swp
index 49b562073..6108dabd4 100644
Binary files a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.model.py.swp and b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/.model.py.swp differ
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/model.py b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/model.py
index 673f4ef9a..bef12f8fd 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless_d2v_v2/model.py
@@ -26,32 +26,6 @@ from encoder_interface import EncoderInterface
 from icefall.utils import add_sos
 
 
-class AdapterHook():
-    '''
-    Implementation of the forward hook to track feature statistics and compute a loss on them.
-    Will compute mean and variance, and will use l2 as a loss
-    '''
-    def __init__(self, module):
-        self.hook = module.register_forward_hook(self.hook_fn)
-
-    def hook_fn(self, module, input, output):
-        # hook co compute deepinversion's feature distribution regularization
-        nch = input[0].shape[1]
-        mean = input[0].mean([0, 2, 3])
-        var = input[0].permute(1, 0, 2, 3).contiguous().view([nch, -1]).var(1, unbiased=False)
-
-        #forcing mean and variance to match between two distributions
-        #other ways might work better, i.g. KL divergence
-        r_feature = torch.norm(module.running_var.data - var, 2) + torch.norm(
-            module.running_mean.data - mean, 2)
-
-        self.r_feature = r_feature
-        # must have no output
-
-    def close(self):
-        self.hook.remove()
-
-
 class Transducer(nn.Module):
     """It implements https://arxiv.org/pdf/1211.3711.pdf
     "Sequence Transduction with Recurrent Neural Networks"