Fix embedding_ops doc formatting

tensorflow/python/ops/embedding_ops.py

@@ -331,11 +331,11 @@ def embedding_lookup_sparse(params,
       representing sharded embedding tensors.  Alternatively, a
       `PartitionedVariable`, created by partitioning along dimension 0. Each
       element must be appropriately sized for the given `partition_strategy`.
-    sp_ids: N x M SparseTensor of int64 ids (typically from FeatureValueToId),
+    sp_ids: N x M `SparseTensor` of int64 ids (typically from FeatureValueToId),
       where N is typically batch size and M is arbitrary.
-    sp_weights: either a SparseTensor of float / double weights, or None to
-      indicate all weights should be taken to be 1. If specified, sp_weights
-      must have exactly the same shape and indices as sp_ids.
+    sp_weights: either a `SparseTensor` of float / double weights, or `None` to
+      indicate all weights should be taken to be 1. If specified, `sp_weights`
+      must have exactly the same shape and indices as `sp_ids`.
     partition_strategy: A string specifying the partitioning strategy, relevant
       if `len(params) > 1`. Currently `"div"` and `"mod"` are supported. Default
       is `"mod"`. See `tf.nn.embedding_lookup` for more details.
@@ -351,39 +351,43 @@ def embedding_lookup_sparse(params,
 
   Returns:
     A dense tensor representing the combined embeddings for the
-    sparse ids. For each row in the dense tensor represented by sp_ids, the op
+    sparse ids. For each row in the dense tensor represented by `sp_ids`, the op
     looks up the embeddings for all ids in that row, multiplies them by the
     corresponding weight, and combines these embeddings as specified.
 
     In other words, if
 
-      shape(combined params) = [p0, p1, ..., pm]
+      `shape(combined params) = [p0, p1, ..., pm]`
 
     and
 
-      shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn]
+      `shape(sp_ids) = shape(sp_weights) = [d0, d1, ..., dn]`
 
     then
 
-      shape(output) = [d0, d1, ..., dn-1, p1, ..., pm].
+      `shape(output) = [d0, d1, ..., dn-1, p1, ..., pm]`.
 
     For instance, if params is a 10x20 matrix, and sp_ids / sp_weights are
 
+      ```python
       [0, 0]: id 1, weight 2.0
       [0, 1]: id 3, weight 0.5
       [1, 0]: id 0, weight 1.0
       [2, 3]: id 1, weight 3.0
+      ```
 
     with `combiner`="mean", then the output will be a 3x20 matrix where
 
+      ```python
       output[0, :] = (params[1, :] * 2.0 + params[3, :] * 0.5) / (2.0 + 0.5)
       output[1, :] = (params[0, :] * 1.0) / 1.0
       output[2, :] = (params[1, :] * 3.0) / 3.0
+      ```
 
   Raises:
-    TypeError: If sp_ids is not a SparseTensor, or if sp_weights is neither
-      None nor SparseTensor.
-    ValueError: If combiner is not one of {"mean", "sqrtn", "sum"}.
+    TypeError: If `sp_ids` is not a `SparseTensor`, or if `sp_weights` is 
+      neither `None` nor `SparseTensor`.
+    ValueError: If `combiner` is not one of {"mean", "sqrtn", "sum"}.
   """
   if combiner is None:
     logging.warn("The default value of combiner will change from \"mean\" "