diff --git a/deepspeech/modules/crf.py b/deepspeech/modules/crf.py
index 4bdc5a90..b6b481a0 100644
--- a/deepspeech/modules/crf.py
+++ b/deepspeech/modules/crf.py
@@ -24,7 +24,7 @@ __all__ = ['CRF']
 class CRF(nn.Layer):
     """
     Linear-chain Conditional Random Field (CRF).
-  
+    
     Args:
         nb_labels (int): number of labels in your tagset, including special symbols.
         bos_tag_id (int): integer representing the beginning of sentence symbol in
@@ -162,15 +162,15 @@ class CRF(nn.Layer):
         # save first and last tags to be used later
         first_tags = tags[:, 0]
         last_valid_idx = mask.int().sum(1) - 1
+
         # TODO(Hui Zhang): not support fancy index. 
         # last_tags = tags.gather(last_valid_idx.unsqueeze(1), axis=1).squeeze()
-        batch_idx = paddle.arange(batch_size)
-        gather_last_valid_idx = paddle.to_tensor(
-            list(zip(batch_idx.tolist(), last_valid_idx.tolist())))
+        batch_idx = paddle.arange(batch_size, dtype=last_valid_idx.dtype)
+        gather_last_valid_idx = paddle.stack(
+            [batch_idx, last_valid_idx], axis=-1)
         last_tags = tags.gather_nd(gather_last_valid_idx)
 
         # add the transition from BOS to the first tags for each batch
-        # TODO(Hui Zhang): not support fancy index. 
         # t_scores = self.transitions[self.BOS_TAG_ID, first_tags]
         t_scores = self.transitions[self.BOS_TAG_ID].gather(first_tags)
 
@@ -178,10 +178,8 @@ class CRF(nn.Layer):
         # for all batches, the first word, see the correspondent emissions
         # for the first tags (which is a list of ids):
         # emissions[:, 0, [tag_1, tag_2, ..., tag_nblabels]]
-        # TODO(Hui Zhang): not support fancy index. 
         # e_scores = emissions[:, 0].gather(1, first_tags.unsqueeze(1)).squeeze()
-        gather_first_tags_idx = paddle.to_tensor(
-            list(zip(batch_idx.tolist(), first_tags.tolist())))
+        gather_first_tags_idx = paddle.stack([batch_idx, first_tags], axis=-1)
         e_scores = emissions[:, 0].gather_nd(gather_first_tags_idx)
 
         # the scores for a word is just the sum of both scores
@@ -199,15 +197,13 @@ class CRF(nn.Layer):
             current_tags = tags[:, i]
 
             # calculate emission and transition scores as we did before
-            # TODO(Hui Zhang): not support fancy index. 
             # e_scores = emissions[:, i].gather(1, current_tags.unsqueeze(1)).squeeze()
-            gather_current_tags_idx = paddle.to_tensor(
-                list(zip(batch_idx.tolist(), current_tags.tolist())))
+            gather_current_tags_idx = paddle.stack(
+                [batch_idx, current_tags], axis=-1)
             e_scores = emissions[:, i].gather_nd(gather_current_tags_idx)
-            # TODO(Hui Zhang): not support fancy index. 
             # t_scores = self.transitions[previous_tags, current_tags]
-            gather_transitions_idx = paddle.to_tensor(
-                list(zip(previous_tags.tolist(), current_tags.tolist())))
+            gather_transitions_idx = paddle.stack(
+                [previous_tags, current_tags], axis=-1)
             t_scores = self.transitions.gather_nd(gather_transitions_idx)
 
             # apply the mask
@@ -300,7 +296,6 @@ class CRF(nn.Layer):
             # so far is exactly like the forward algorithm,
             # but now, instead of calculating the logsumexp,
             # we will find the highest score and the tag associated with it
-            # TODO(Hui Zhang): max not support return score and index. 
             # max_scores, max_score_tags = paddle.max(scores, axis=1)
             max_scores = paddle.max(scores, axis=1)
             max_score_tags = paddle.argmax(scores, axis=1)
@@ -319,7 +314,6 @@ class CRF(nn.Layer):
         end_scores = alphas + last_transition.unsqueeze(0)
 
         # get the final most probable score and the final most probable tag
-        # TODO(Hui Zhang): max not support return score and index. 
         # max_final_scores, max_final_tags = paddle.max(end_scores, axis=1)
         max_final_scores = paddle.max(end_scores, axis=1)
         max_final_tags = paddle.argmax(end_scores, axis=1)