diff --git a/texar/utils/beam_search.py b/texar/utils/beam_search.py
index 553828d2..58d341e3 100644
--- a/texar/utils/beam_search.py
+++ b/texar/utils/beam_search.py
@@ -13,7 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-# Modifications copyright (C) 2018 Texar
+# Modifications copyright (C) 2019 Texar
 # ==============================================================================
 """
 Implemetation of beam seach with penalties.
@@ -103,6 +103,7 @@ def compute_batch_indices(batch_size, beam_size):
     Args:
         batch_size: Batch size
         beam_size: Size of the beam.
+
     Returns:
         batch_pos: [batch_size, beam_size] tensor of ids
     """
@@ -142,11 +143,12 @@ def compute_topk_scores_and_seq(sequences, scores, scores_to_gather, flags,
             grow_finished, we will need to return the length penalized
             scors.
         flags: Tensor of bools for sequences that say whether a sequence
-        has reached EOS or not
+            has reached EOS or not
         beam_size: int
         batch_size: int
         prefix: string that will prefix unique names for the ops run.
         states_to_gather: dict (possibly nested) of decoding states.
+
     Returns:
         Tuple of
         (topk_seq [batch_size, beam_size, decode_length],
@@ -184,14 +186,14 @@ def gather(tensor, name):
 
 
 def beam_search(symbols_to_logits_fn,
-                                initial_ids,
-                                beam_size,
-                                decode_length,
-                                vocab_size,
-                                alpha,
-                                eos_id,
-                                states=None,
-                                stop_early=True):
+                initial_ids,
+                beam_size,
+                decode_length,
+                vocab_size,
+                alpha,
+                eos_id,
+                states=None,
+                stop_early=True):
     """Beam search with length penalties.
 
     Requires a function that can take the currently decoded sybmols and
@@ -222,20 +224,21 @@ def beam_search(symbols_to_logits_fn,
 
     Args:
         symbols_to_logits_fn: Interface to the model, to provide logits.
-                Shoud take [batch_size, decoded_ids] and return
-                [batch_size, vocab_size]
+            Should take [batch_size, decoded_ids] and return
+            [batch_size, vocab_size]
         initial_ids: Ids to start off the decoding, this will be the first
-        thing handed to symbols_to_logits_fn (after expanding to beam size)
+            thing handed to symbols_to_logits_fn (after expanding to beam size)
             [batch_size]
         beam_size: Size of the beam.
         decode_length: Number of steps to decode for.
         vocab_size: Size of the vocab, must equal the size of the logits
-        returned by symbols_to_logits_fn
+            returned by symbols_to_logits_fn
         alpha: alpha for length penalty.
         states: dict (possibly nested) of decoding states.
         eos_id: ID for end of sentence.
         stop_early: a boolean - stop once best sequence is provably
-        determined.
+            determined.
+
     Returns:
         Tuple of
         (decoded beams [batch_size, beam_size, decode_length]
@@ -282,12 +285,13 @@ def grow_finished(finished_seq, finished_scores, finished_flags,
             finished_flags: finished bools for each of these sequences.
                 [batch_size, beam_size]
             curr_seq: current topk sequence that has been grown by one
-            position.
+                position.
                 [batch_size, beam_size, current_decoded_length]
             curr_scores: scores for each of these sequences. [batch_size,
-            beam_size]
+                beam_size]
             curr_finished: Finished flags for each of these sequences.
                 [batch_size, beam_size]
+
         Returns:
             Tuple of
                 (Topk sequences based on scores,
@@ -321,7 +325,7 @@ def grow_alive(curr_seq, curr_scores, curr_log_probs, curr_finished,
 
         Args:
             curr_seq: current topk sequence that has been grown by one
-            position.
+                position.
                 [batch_size, beam_size, i+1]
             curr_scores: scores for each of these sequences. [batch_size,
                 beam_size]
@@ -330,6 +334,7 @@ def grow_alive(curr_seq, curr_scores, curr_log_probs, curr_finished,
             curr_finished: Finished flags for each of these sequences.
                 [batch_size, beam_size]
             states: dict (possibly nested) of decoding states.
+
         Returns:
             Tuple of
                 (Topk sequences based on scores,
@@ -363,6 +368,7 @@ def grow_topk(i, alive_seq, alive_log_probs, states):
             alive_log_probs: probabilities of these sequences.
                 [batch_size, beam_size]
             states: dict (possibly nested) of decoding states.
+
         Returns:
             Tuple of
                 (Topk sequences extended by the next word,
@@ -521,7 +527,7 @@ def _is_finished(i, unused_alive_seq, alive_log_probs,
             finished_scores: scores for each of these sequences.
                 [batch_size, beam_size]
             finished_in_finished: finished bools for each of these
-            sequences. [batch_size, beam_size]
+                sequences. [batch_size, beam_size]
 
         Returns:
             Bool.