diff --git a/python/paddle/nn/functional/common.py b/python/paddle/nn/functional/common.py
index 0b18dec943d5f06199d60d9be32f95f72c0c54d0..1cf3599e846b9550d8b99512cd476c05095320fc 100644
--- a/python/paddle/nn/functional/common.py
+++ b/python/paddle/nn/functional/common.py
@@ -1226,26 +1226,23 @@ def pad(x, pad, mode='constant', value=0, data_format="NCHW", name=None):
 
     Code Examples:
         .. code-block:: python
+
             import numpy as np
             import paddle
             import paddle.nn.functional as F
             
-            paddle.disable_static()
-            
             # example 1
             x_shape = (1, 1, 3)
-            x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape) + 1
-            tensor_x = paddle.to_tensor(x)
-            y = F.pad(tensor_x, pad=[2, 3], value=1, mode='constant')
-            print(y.numpy())
+            x = paddle.arange(np.prod(x_shape), dtype="float32").reshape(x_shape) + 1
+            y = F.pad(x, [2, 3], value=1, mode='constant', data_format="NCL")
+            print(y)
             # [[[1. 1. 1. 2. 3. 1. 1. 1.]]]
-            
+
             # example 2
             x_shape = (1, 1, 2, 3)
-            x = np.arange(np.prod(x_shape), dtype=np.float32).reshape(x_shape) + 1
-            tensor_x = paddle.to_tensor(x)
-            y = F.pad(tensor_x, pad=[1, 2, 1, 1], value=1, mode='circular')
-            print(y.numpy())
+            x = paddle.arange(np.prod(x_shape), dtype="float32").reshape(x_shape) + 1
+            y = F.pad(x, [1, 2, 1, 1], value=1, mode='circular')
+            print(y)
             # [[[[6. 4. 5. 6. 4. 5.]
             #    [3. 1. 2. 3. 1. 2.]
             #    [6. 4. 5. 6. 4. 5.]
@@ -1361,6 +1358,7 @@ def cosine_similarity(x1, x2, axis=1, eps=1e-8):
 
     Examples:
         .. code-block:: text
+        
             Case 0:
                 x1 = [[0.8024077  0.9927354  0.27238318 0.8344984 ]
                      [0.48949873 0.5797396  0.65444374 0.66510963]
@@ -1376,10 +1374,10 @@ def cosine_similarity(x1, x2, axis=1, eps=1e-8):
 
     Code Examples:
         .. code-block:: python
+        
             import paddle
             import paddle.nn as nn
             import numpy as np
-            paddle.disable_static()
 
             np.random.seed(0)
             x1 = np.random.rand(2,3)
@@ -1387,7 +1385,7 @@ def cosine_similarity(x1, x2, axis=1, eps=1e-8):
             x1 = paddle.to_tensor(x1)
             x2 = paddle.to_tensor(x2)
             result = paddle.nn.functional.cosine_similarity(x1, x2, axis=0)
-            print(result.numpy())
+            print(result)
             # [0.99806249 0.9817672  0.94987036]
             
     """
diff --git a/python/paddle/nn/layer/common.py b/python/paddle/nn/layer/common.py
index ad8263e48356c2cbef7bdcd4b03890999f9a2e4b..6e3910745e1579aae17a9bd4e3ba7dc1bbb75f77 100644
--- a/python/paddle/nn/layer/common.py
+++ b/python/paddle/nn/layer/common.py
@@ -744,7 +744,6 @@ class Pad1D(layers.Layer):
             import paddle
             import paddle.nn as nn
             import numpy as np
-            paddle.disable_static()
 
             input_shape = (1, 2, 3)
             pad = [1, 2]
@@ -752,7 +751,7 @@ class Pad1D(layers.Layer):
             data = paddle.arange(np.prod(input_shape), dtype="float32").reshape(input_shape) + 1
             my_pad = nn.Pad1D(padding=pad, mode=mode)
             result = my_pad(data)
-            print(result.numpy())
+            print(result)
             # [[[0. 1. 2. 3. 0. 0.]
             #   [0. 4. 5. 6. 0. 0.]]]
     """
@@ -821,14 +820,13 @@ class Pad2D(layers.Layer):
             import paddle
             import paddle.nn as nn
             import numpy as np
-            paddle.disable_static()
             input_shape = (1, 1, 2, 3)
             pad = [1, 0, 1, 2]
             mode = "constant"
             data = paddle.arange(np.prod(input_shape), dtype="float32").reshape(input_shape) + 1
             my_pad = nn.Pad2D(padding=pad, mode=mode)
             result = my_pad(data)
-            print(result.numpy())
+            print(result)
             # [[[[0. 0. 0. 0.]
             #    [0. 1. 2. 3.]
             #    [0. 4. 5. 6.]
@@ -906,7 +904,7 @@ class Pad3D(layers.Layer):
             data = paddle.arange(np.prod(input_shape), dtype="float32").reshape(input_shape) + 1
             my_pad = nn.Pad3D(padding=pad, mode=mode)
             result = my_pad(data)
-            print(result.numpy())
+            print(result)
             # [[[[[0. 0. 0. 0.]
             #     [0. 1. 2. 3.]
             #     [0. 4. 5. 6.]
@@ -968,7 +966,6 @@ class CosineSimilarity(layers.Layer):
             import paddle
             import paddle.nn as nn
             import numpy as np
-            paddle.disable_static()
 
             np.random.seed(0)
             x1 = np.random.rand(2,3)
@@ -978,7 +975,7 @@ class CosineSimilarity(layers.Layer):
 
             cos_sim_func = nn.CosineSimilarity(axis=0)
             result = cos_sim_func(x1, x2)
-            print(result.numpy())
+            print(result)
             # [0.99806249 0.9817672  0.94987036]
     """