diff --git a/trax/layers/rnn.py b/trax/layers/rnn.py
index 51969b417..f7b05480f 100644
--- a/trax/layers/rnn.py
+++ b/trax/layers/rnn.py
@@ -90,17 +90,41 @@ def f(x):  # pylint: disable=invalid-name
   return base.Fn('MakeZeroState', f)
 
 
-def LSTM(n_units, mode='train'):
-  """LSTM running on axis 1."""
-  zero_state = MakeZeroState(depth_multiplier=2)  # pylint: disable=no-value-for-parameter
-  return cb.Serial(
-      cb.Branch([], zero_state),
-      cb.Scan(LSTMCell(n_units=n_units), axis=1, mode=mode),
-      cb.Select([0], n_in=2),  # Drop RNN state.
-      # Set the name to LSTM and don't print sublayers.
-      name=f'LSTM_{n_units}', sublayers_to_print=[]
-  )
+def LSTM(n_units, mode='train', return_state=False, initial_state=False):
+  """LSTM running on axis 1.
+
+  Args:
+    mode: if 'predict' then we save the previous state for one-by-one inference.
+    return_state: Boolean. Whether to return the latest status in addition to the output. Default: False.
+    initial_state: Boolean. If the state RNN (c, h) is to be obtained from the stack. Default: False.
 
+  Returns:
+    A LSTM layer.
+  """
+
+  if not initial_state:
+    zero_state = MakeZeroState(depth_multiplier=2)  # pylint: disable=no-value-for-parameter
+    if return_state:
+      return cb.Serial(
+        cb.Branch([], zero_state),  
+        cb.Scan(LSTMCell(n_units=n_units), axis=1, mode=mode), 
+        name=f'LSTM_{n_units}', sublayers_to_print=[])
+    else:
+      return cb.Serial(
+        cb.Branch([], zero_state), # fill state RNN with zero.
+        cb.Scan(LSTMCell(n_units=n_units), axis=1, mode=mode),
+        cb.Select([0], n_in=2),
+        name=f'LSTM_{n_units}', sublayers_to_print=[])
+  else:
+    if return_state:
+      return cb.Serial(
+        cb.Scan(LSTMCell(n_units=n_units), axis=1, mode=mode),
+        name=f'LSTM_{n_units}', sublayers_to_print=[])
+    else:
+      return cb.Serial(
+        cb.Scan(LSTMCell(n_units=n_units), axis=1, mode=mode),
+        cb.Select([0], n_in=2),
+        name=f'LSTM_{n_units}', sublayers_to_print=[])
 
 class GRUCell(base.Layer):
   """Builds a traditional GRU cell with dense internal transformations.