diff --git a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md
index 1142684a4ef8d9f5b052995d07280ac6c9c7d00d..e1a0ca07a2ef05e6b87028fb98d6817379981cfe 100644
--- a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md
+++ b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md
@@ -243,7 +243,7 @@ print(z.asnumpy())
 
 ## Debugging Network Train Model
 
-In PyNative mode, the gradient can be calculated separately. As shown in the following example, `GradOperation` is used to calculate all input gradients of the function or the network.
+In PyNative mode, the gradient can be calculated separately. As shown in the following example, `GradOperation` is used to calculate all input gradients of the function or the network. Note that the inputs have to be Tensor.
 
 **Example Code**
 
@@ -259,7 +259,7 @@ def mul(x, y):
 def mainf(x, y):
     return C.GradOperation('get_all', get_all=True)(mul)(x, y)
 
-print(mainf(1,2))
+print(mainf(Tensor(1, mstype.int32), Tensor(2, mstype.int32)))
 ```
 
 **Output**
diff --git a/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md
index 4ab62931237f86a53455adfe30cf48accaf28103..ba3ea2089a3e2cffe86de8e89fa92a18b08a2767 100644
--- a/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md
+++ b/tutorials/source_zh_cn/advanced_use/debugging_in_pynative_mode.md
@@ -245,7 +245,7 @@ print(z.asnumpy())
 
 ## 调试网络训练模型
 
-PyNative模式下，还可以支持单独求梯度的操作。如下例所示，可通过`GradOperation`求该函数或者网络所有的输入梯度。
+PyNative模式下，还可以支持单独求梯度的操作。如下例所示，可通过`GradOperation`求该函数或者网络所有的输入梯度。需要注意，输入类型仅支持Tensor。
 
 **示例代码**
 
@@ -261,7 +261,7 @@ def mul(x, y):
 def mainf(x, y):
     return C.GradOperation('get_all', get_all=True)(mul)(x, y)
 
-print(mainf(1,2))
+print(mainf(Tensor(1, mstype.int32), Tensor(2, mstype.int32)))
 ```
 
 **输出**