diff --git a/decode_beam.py b/decode_beam.py
index c2a71ca..48f67c0 100644
--- a/decode_beam.py
+++ b/decode_beam.py
@@ -64,16 +64,20 @@ def eval(self, alpha=1.0):
 decoder = DecoderRNN()
 
 
-def beam_decode(target_tensor, decoder_hiddens, encoder_outputs=None):
+def beam_decode(target_tensor,
+                decoder_hiddens,
+                encoder_outputs=None，
+                topk=1,
+                beam_width=10,
+                SOS_token=0,
+                EOS_token=10):
     '''
     :param target_tensor: target indexes tensor of shape [B, T] where B is the batch size and T is the maximum length of the output sentence
     :param decoder_hidden: input tensor of shape [1, B, H] for start of the decoding
     :param encoder_outputs: if you are using attention mechanism you can pass encoder outputs, [T, B, H] where T is the maximum length of input sentence
+    :param topk: how many sentence do you want to generate
     :return: decoded_batch
     '''
-
-    beam_width = 10
-    topk = 1  # how many sentence do you want to generate
     decoded_batch = []
 
     # decoding goes sentence by sentence