Update tfidf.md

docs/api_docs/python/tft/tfidf.md

@@ -21,20 +21,22 @@ The term frequency of a term in a document is calculated as
 
 The inverse document frequency of a term is, by default, calculated as
 1 + log((corpus size + 1) / (count of documents containing term + 1)).
-
+```
 Example usage:
   example strings [["I", "like", "pie", "pie", "pie"], ["yum", "yum", "pie]]
+
   in: SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [0, 3], [0, 4],
                             [1, 0], [1, 1], [1, 2]],
                    values=[1, 2, 0, 0, 0, 3, 3, 0])
+
   out: SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]],
                     values=[1, 2, 0, 3, 0])
        SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]],
                     values=[(1/5)*(log(3/2)+1), (1/5)*(log(3/2)+1), (3/5),
                             (2/3)*(log(3/2)+1), (1/3)]
   NOTE that the first doc's duplicate "pie" strings have been combined to
   one output, as have the second doc's duplicate "yum" strings.
-
+```
 #### Args:
 
 * <b>`x`</b>: A `SparseTensor` representing int64 values (most likely that are the
@@ -54,4 +56,4 @@ Example usage:
 
 Two `SparseTensor`s with indices [index_in_batch, index_in_bag_of_words].
 The first has values vocab_index, which is taken from input `x`.
-The second has values tfidf_weight.
+The second has values tfidf_weight.