diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 2feca60430dc04dd839b163c7469a6993570178a..cafb965d406d93229a442ad8e80f262f9cc615b6 100755
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -5448,22 +5448,18 @@ def ctc_greedy_decoder(input,
 
 def transpose(x, perm, name=None):
     """
-    :alias_main: paddle.transpose
-	:alias: paddle.transpose,paddle.tensor.transpose,paddle.tensor.linalg.transpose,paddle.tensor.manipulation.transpose
-	:old_api: paddle.fluid.layers.transpose
-
     Permute the data dimensions of `input` according to `perm`.
 
     The `i`-th dimension  of the returned tensor will correspond to the
     perm[i]-th dimension of `input`.
 
     Args:
-        x (Variable): The input Tensor. It is a N-D Tensor of data types float32, float64, int32.
+        x (Tensor): The input Tensor. It is a N-D Tensor of data types float32, float64, int32.
         perm (list|tuple): Permute the input according to the data of perm.
         name (str): The name of this layer. It is optional.
 
     Returns:
-        Variable: A transposed n-D Tensor, with data type being float32, float64, int32, int64.
+        Tensor: A transposed n-D Tensor, with data type being float32, float64, int32, int64.
 
     For Example:
 
diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py
index a0e5e681c76e9fd1ab404c9c47e97c1002a0b56b..d8b8dab5252919b51acafe1ca390abefe6d9b8b2 100644
--- a/python/paddle/tensor/manipulation.py
+++ b/python/paddle/tensor/manipulation.py
@@ -1500,6 +1500,7 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
                 strides = [1, 3]
             Then:
                 result = [ [2], ]
+
     Args:
         x (Tensor): An N-D ``Tensor``. The data type is ``float32``, ``float64``, ``int32`` or ``int64``.
         axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to.
@@ -1531,7 +1532,7 @@ def strided_slice(x, axes, starts, ends, strides, name=None):
             # sliced_1 is x[:, 1:3:1, 0:2:1, 2:4:1].                                
             # example 2:
             # attr starts is a list which contain tensor Tensor.
-            minus_3 = paddle.fill_constant([1], "int32", -3)
+            minus_3 = paddle.full(shape=[1], fill_value=-3, dtype='int32')
             sliced_2 = paddle.strided_slice(x, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2)
             # sliced_2 is x[:, 1:3:1, 0:2:1, 2:4:2].
     """