diff --git a/tutorials/notebook/debugging_in_pynative_mode.ipynb b/tutorials/notebook/debugging_in_pynative_mode.ipynb
index d6c6f81bf49ab31481c9ee4f64375f31ff49f9f8..b068dddd05fc8bde544cf34f234b405b02db40dd 100644
--- a/tutorials/notebook/debugging_in_pynative_mode.ipynb
+++ b/tutorials/notebook/debugging_in_pynative_mode.ipynb
@@ -373,7 +373,7 @@
     "\n",
     "    def construct(self, x, label):\n",
     "        weights = self.weights\n",
-    "        return C.GradOperation('get_by_list', get_by_list=True)(self.network, weights)(x, label)"
+    "        return C.GradOperation(get_by_list=True)(self.network, weights)(x, label)"
    ]
   },
   {
diff --git a/tutorials/notebook/linear_regression.ipynb b/tutorials/notebook/linear_regression.ipynb
index 1d301aa1d00b589dcf06d3664424524e848f6339..b67175937c7ec66376c1431e764e81fbfb150e7e 100644
--- a/tutorials/notebook/linear_regression.ipynb
+++ b/tutorials/notebook/linear_regression.ipynb
@@ -524,7 +524,7 @@
     "\n",
     "    def construct(self, data, label):\n",
     "        weights = self.weights\n",
-    "        return C.GradOperation('get_by_list', get_by_list=True) \\\n",
+    "        return C.GradOperation(get_by_list=True) \\\n",
     "            (self.network, weights)(data, label)\n"
    ]
   },
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 5769bbfb6333a74959eaafeedaef67ddf855be26..877b07e0a6dcc8e7980a883927f5d1f235bcd213 100644
--- a/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md
+++ b/tutorials/source_en/advanced_use/debugging_in_pynative_mode.md
@@ -262,7 +262,7 @@ def mul(x, y):
     return x * y
 
 def mainf(x, y):
-    return C.GradOperation('get_all', get_all=True)(mul)(x, y)
+    return C.GradOperation(get_all=True)(mul)(x, y)
 
 print(mainf(Tensor(1, mstype.int32), Tensor(2, mstype.int32)))
 ```
@@ -357,7 +357,7 @@ class GradWrap(nn.Cell):
 
     def construct(self, x, label):
         weights = self.weights
-        return C.GradOperation('get_by_list', get_by_list=True)(self.network, weights)(x, label)
+        return C.GradOperation(get_by_list=True)(self.network, weights)(x, label)
 
 net = LeNet5()
 optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
diff --git a/tutorials/source_en/use/custom_operator.md b/tutorials/source_en/use/custom_operator.md
index bbb0ac9b352cf5f88885dbf77f98b9d2b8b9f499..cff9317dd13be9efd5d52200234cc4256eecf862 100644
--- a/tutorials/source_en/use/custom_operator.md
+++ b/tutorials/source_en/use/custom_operator.md
@@ -232,7 +232,7 @@ def test_grad_net():
     x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
     sens = np.array([1.0, 1.0, 1.0]).astype(np.float32)
     square = Net()
-    grad = C.GradOperation('grad_with_sens', sens_param=True)
+    grad = C.GradOperation(sens_param=True)
     dx = grad(square)(Tensor(x), Tensor(sens))
     print("x: ", x)
     print("dx: ", dx)
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 4289a3fd501f8247312f7c62f2d5fd4b34e3d7bb..a8c87f9ba8f6df44d9f5c4193b4d2b14ba1db147 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
@@ -264,7 +264,7 @@ def mul(x, y):
     return x * y
 
 def mainf(x, y):
-    return C.GradOperation('get_all', get_all=True)(mul)(x, y)
+    return C.GradOperation(get_all=True)(mul)(x, y)
 
 print(mainf(Tensor(1, mstype.int32), Tensor(2, mstype.int32)))
 ```
@@ -359,7 +359,7 @@ class GradWrap(nn.Cell):
 
     def construct(self, x, label):
         weights = self.weights
-        return C.GradOperation('get_by_list', get_by_list=True)(self.network, weights)(x, label)
+        return C.GradOperation(get_by_list=True)(self.network, weights)(x, label)
 
 net = LeNet5()
 optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), 0.1, 0.9)
diff --git a/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md b/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md
index 2526411468dd2fee7a376f703caec139734642cc..5ea2290e4647d865fdef6ddaf50032efb22a5fe8 100644
--- a/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md
+++ b/tutorials/source_zh_cn/advanced_use/gradient_accumulation.md
@@ -100,7 +100,7 @@ class TrainForwardBackward(Cell):
         self.weights = ParameterTuple(network.trainable_params())
         self.optimizer = optimizer
         self.grad_sum = grad_sum
-        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.grad = C.GradOperation(get_by_list=True, sens_param=True)
         self.sens = sens
         self.hyper_map = C.HyperMap()
 
diff --git a/tutorials/source_zh_cn/quick_start/linear_regression.md b/tutorials/source_zh_cn/quick_start/linear_regression.md
index 18ea005e760af3f6c6cebb7d218811c6ddb65d71..9f7ab617c692f9e04a200e87f2a5203a9d1bbae8 100644
--- a/tutorials/source_zh_cn/quick_start/linear_regression.md
+++ b/tutorials/source_zh_cn/quick_start/linear_regression.md
@@ -297,7 +297,7 @@ class GradWrap(nn.Cell):
 
     def construct(self, data, label):
         weights = self.weights
-        return C.GradOperation('get_by_list', get_by_list=True) \
+        return C.GradOperation(get_by_list=True) \
             (self.network, weights)(data, label)
 
 ```
diff --git a/tutorials/source_zh_cn/use/custom_operator.md b/tutorials/source_zh_cn/use/custom_operator.md
index 9eb6dec635b3bbe89d9a723570fc7e6601bc450c..5ca1b6d2103d261a5c0c5fd643583d12c13925ce 100644
--- a/tutorials/source_zh_cn/use/custom_operator.md
+++ b/tutorials/source_zh_cn/use/custom_operator.md
@@ -232,7 +232,7 @@ def test_grad_net():
     x = np.array([1.0, 4.0, 9.0]).astype(np.float32)
     sens = np.array([1.0, 1.0, 1.0]).astype(np.float32)
     square = Net()
-    grad = C.GradOperation('grad_with_sens', sens_param=True)
+    grad = C.GradOperation(sens_param=True)
     dx = grad(square)(Tensor(x), Tensor(sens))
     print("x: ", x)
     print("dx: ", dx)
diff --git a/tutorials/tutorial_code/gradient_accumulation/train.py b/tutorials/tutorial_code/gradient_accumulation/train.py
index a26e79f3102e3d141f65318800b59fae6584d29e..123bf80a6c88bf324e8b1465e1029b8af68452d8 100644
--- a/tutorials/tutorial_code/gradient_accumulation/train.py
+++ b/tutorials/tutorial_code/gradient_accumulation/train.py
@@ -41,7 +41,7 @@ class TrainForwardBackward(Cell):
         self.weights = ParameterTuple(network.trainable_params())
         self.optimizer = optimizer
         self.grad_sum = grad_sum
-        self.grad = C.GradOperation('grad', get_by_list=True, sens_param=True)
+        self.grad = C.GradOperation(get_by_list=True, sens_param=True)
         self.sens = sens
         self.hyper_map = C.HyperMap()
 
diff --git a/tutorials/tutorial_code/linear_regression.py b/tutorials/tutorial_code/linear_regression.py
index 952607a19d43e1ed0e739d8f606681ab65881d60..bfed5b389e5d1de61e14336c14b5ab1663437784 100644
--- a/tutorials/tutorial_code/linear_regression.py
+++ b/tutorials/tutorial_code/linear_regression.py
@@ -41,7 +41,7 @@ class GradWrap(nn.Cell):
 
     def construct(self, data, label):
         weights = self.weights
-        return C.GradOperation('get_by_list', get_by_list=True) \
+        return C.GradOperation(get_by_list=True) \
             (self.network, weights)(data, label)
 
 # Initializing model functions