diff --git a/egs/librispeech/ASR/transducer_stateless/conformer.py b/egs/librispeech/ASR/transducer_stateless/conformer.py
index 4627dd147..07b80076d 100644
--- a/egs/librispeech/ASR/transducer_stateless/conformer.py
+++ b/egs/librispeech/ASR/transducer_stateless/conformer.py
@@ -15,7 +15,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
+import copy
 import math
 import warnings
 from typing import Optional, Tuple
@@ -264,13 +264,12 @@ class ConformerEncoderLayer(nn.Module):
         return src
 
 
-class ConformerEncoder(nn.TransformerEncoder):
+class ConformerEncoder(nn.Module):
     r"""ConformerEncoder is a stack of N encoder layers
 
     Args:
         encoder_layer: an instance of the ConformerEncoderLayer() class (required).
         num_layers: the number of sub-encoder-layers in the encoder (required).
-        norm: the layer normalization component (optional).
 
     Examples::
         >>> encoder_layer = ConformerEncoderLayer(d_model=512, nhead=8)
@@ -281,11 +280,12 @@ class ConformerEncoder(nn.TransformerEncoder):
     """
 
     def __init__(
-        self, encoder_layer: nn.Module, num_layers: int, norm: nn.Module = None
+        self, encoder_layer: nn.Module, num_layers: int
     ) -> None:
-        super(ConformerEncoder, self).__init__(
-            encoder_layer=encoder_layer, num_layers=num_layers, norm=norm
-        )
+        super(ConformerEncoder, self).__init__()
+        self.layers = nn.ModuleList([copy.deepcopy(encoder_layer) for i in range(num_layers)])
+        self.num_layers = num_layers
+
 
     def forward(
         self,
@@ -320,9 +320,6 @@ class ConformerEncoder(nn.TransformerEncoder):
                 src_key_padding_mask=src_key_padding_mask,
             )
 
-        if self.norm is not None:
-            output = self.norm(output)
-
         return output