Update the code sample in cn doc of dynamic_gru and gru_unit. (#1607)

doc/fluid/api_cn/layers_cn/dynamic_gru_cn.rst

@@ -60,9 +60,11 @@ dynamic_gru
     import paddle.fluid as fluid
 
     dict_dim, emb_dim = 128, 64
-    data = fluid.layers.data(name='sequence', shape=[1],
-                             dtype='int32', lod_level=1)
-    emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
+    data = fluid.data(name='sequence',
+                shape=[None],
+                dtype='int64',
+                lod_level=1)
+    emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
     hidden_dim = 512
     x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
     hidden = fluid.layers.dynamic_gru(input=x, size=hidden_dim)

doc/fluid/api_cn/layers_cn/gru_unit_cn.rst

@@ -57,12 +57,12 @@ Gated Recurrent Unit（GRU）循环神经网络计算单元。该OP用于完成
     import paddle.fluid as fluid
 
     dict_dim, emb_dim = 128, 64
-    data = fluid.layers.data(name='step_data', shape=[1], dtype='int32')
-    emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
+    data = fluid.data(name='step_data', shape=[None], dtype='int64')
+    emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
     hidden_dim = 512
     x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
-    pre_hidden = fluid.layers.data(
-        name='pre_hidden', shape=[hidden_dim], dtype='float32')
+    pre_hidden = fluid.data(
+        name='pre_hidden', shape=[None, hidden_dim], dtype='float32')
     hidden = fluid.layers.gru_unit(
         input=x, hidden=pre_hidden, size=hidden_dim * 3)