diff --git a/docs/mindspore/source_en/model_train/program_form/static_graph.rst b/docs/mindspore/source_en/model_train/program_form/static_graph.rst
index ce490801335d48a9c5451618bf5c740febedfd5b..b1a3c36f3917757c4a5270bf5214584e526c6930 100644
--- a/docs/mindspore/source_en/model_train/program_form/static_graph.rst
+++ b/docs/mindspore/source_en/model_train/program_form/static_graph.rst
@@ -106,16 +106,20 @@ Constants Generate Scenes
    (without using the mutable interface). For example:
 
    .. code:: python
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-      from mindspore import Tensor, jit
+      context.set_context(mode=ms.GRAPH_MODE)
 
       a = 1
       b = [Tensor([1]), Tensor([2])]
       c = ["a", "b", "c"]
 
-      @jit
-      def foo(a, b, c):
-          return a, b, c
+      class Net(nn.Cell):
+         def construct(self, a, b, c):
+            return a, b, c
 
    In the above code, enter ``a``, ``b``, ``c`` are constants.
 
@@ -123,14 +127,19 @@ Constants Generate Scenes
 
    .. code:: python
 
-      from mindspore import jit, Tensor
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @jit
-      def foo():
-          a = 1
-          b = "2"
-          c = Tensor([1, 2, 3])
-          return a, b, c
+      class Net(nn.Cell):
+         def construct(self):
+            a = 1
+            b = "2"
+            c = Tensor([1, 2, 3])
+            return a, b, c
 
    In the above code, enter ``a``, ``b``, ``c`` are constants.
 
@@ -138,14 +147,19 @@ Constants Generate Scenes
 
    .. code:: python
 
-      from mindspore import jit, Tensor
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @jit
-      def foo():
-          a = Tensor([1, 2, 3])
-          b = Tensor([1, 1, 1])
-          c = a + b
-          return c
+      class Net(nn.Cell):
+         def construct(self):
+            a = Tensor([1, 2, 3])
+            b = Tensor([1, 1, 1])
+            c = a + b
+            return c
 
    In the above code, ``a`` and ``b`` are constants of Tensor generated
    in the graph mode, so the result of their calculation is also
@@ -160,16 +174,22 @@ Variables Generate Scenes
 
    .. code:: python
 
-      from mindspore import Tensor, jit
       from mindspore import mutable
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       a = mutable([Tensor([1]), Tensor([2])])
 
-      @jit
-      def foo(a):
-          b = mutable(Tensor([3]))
-          c = mutable((Tensor([1]), Tensor([2])))
-          return a, b, c
+
+      class Net(nn.Cell):
+         def construct(self, a):
+            b = mutable(Tensor([3]))
+            c = mutable((Tensor([1]), Tensor([2])))
+            return a, b, c
 
    In the above code, ``a`` is generated by calling the mutable
    interface outside the graph, ``b`` and ``c`` are generated by calling
@@ -180,14 +200,20 @@ Variables Generate Scenes
 
    .. code:: python
 
-      from mindspore import Tensor, jit
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       a = Tensor([1])
       b = (Tensor([1]), Tensor([2]))
 
-      @jit
-      def foo(a, b):
-          return a, b
+
+      class Net(nn.Cell):
+         def construct(self, a, b):
+            return a, b
 
    In the above code, ``a`` is the Tensor input as the graph pattern, so
    it is a variable. But ``b`` is a tuple that is input to the graph
@@ -201,15 +227,20 @@ Variables Generate Scenes
 
    .. code:: python
 
-      from mindspore import Tensor, jit, ops
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       a = Tensor([1])
       b = Tensor([2])
 
-      @jit
-      def foo(a, b):
-          c = a + b
-          return c
+      class Net(nn.Cell):
+         def construct(self, a, b):
+            c = a + b
+            return c
 
    In this case , ``c`` is the result of calculations of ``a`` and ``b``
    , and the inputs ``a`` and ``b`` used for the calculation are
@@ -243,16 +274,23 @@ bool(). For example:
 
 .. code:: python
 
-   from mindspore import Tensor, jit
+   import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @jit
-   def foo(x):
-       out1 = int(11.1)
-       out2 = int(Tensor([10]))
-       out3 = int(x.asnumpy())
-       return out1, out2, out3
 
-   res = foo(Tensor(2))
+   class Net(nn.Cell):
+      def construct(self, x):
+         out1 = int(11.1)
+         out2 = int(Tensor([10]))
+         out3 = int(x.asnumpy())
+         return out1, out2, out3
+
+   net = Net()
+   res = net(Tensor(2))
    print("res[0]:", res[0])
    print("res[1]:", res[1])
    print("res[2]:", res[2])
@@ -270,12 +308,19 @@ Supporting returning Number. For example:
 .. code:: python
 
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
 
-   @ms.jit
-   def test_return_scalar(x, y):
-       return x + y
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   res = test_return_scalar(ms.mutable(1), ms.mutable(2))
+
+   class Net(nn.Cell):
+      def construct(self, x, y):
+         return x + y
+
+   net = Net()
+   res = net(ms.mutable(1), ms.mutable(2))
    print(res)
 
 The results are as follows:
@@ -301,17 +346,23 @@ For example:
 
 .. code:: python
 
-   from mindspore import jit
+   import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @jit
-   def foo():
-       var1 = 'Hello!'
-       var2 = "MindSpore"
-       var3 = str(123)
-       var4 = "{} is {}".format("string", var3)
-       return var1[0], var2[4:9], var1 + var2, var2 * 2, "H" in var1, "My name is %s!" % var2, var4
+   class Net(nn.Cell):
+      def construct(self):
+         var1 = 'Hello!'
+         var2 = "MindSpore"
+         var3 = str(123)
+         var4 = "{} is {}".format("string", var3)
+         return var1[0], var2[4:9], var1 + var2, var2 * 2, "H" in var1, "My name is %s!" % var2, var4
 
-   res = foo()
+   net = Net()
+   res = net()
    print("res:", res)
 
 The results are as follows:
@@ -340,14 +391,19 @@ The basic usage scenarios of 'List' are as follows:
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit
-      def generate_list():
-          a = [1, 2, 3, 4]
-          b = ["1", "2", "a"]
-          c = [ms.Tensor([1]), ms.Tensor([2])]
-          d = [a, b, c, (4, 5)]
-          return d
+      class Net(nn.Cell):
+         def construct(self):
+            a = [1, 2, 3, 4]
+            b = ["1", "2", "a"]
+            c = [ms.Tensor([1]), ms.Tensor([2])]
+            d = [a, b, c, (4, 5)]
+            return d
 
    The above sample code, all ``List`` objects can be created normally.
 
@@ -360,13 +416,20 @@ The basic usage scenarios of 'List' are as follows:
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
+
 
-      @ms.jit
-      def list_func():
-          a = [1, 2, 3, 4]
-          return a
+      class Net(nn.Cell):
+         def construct(self):
+            a = [1, 2, 3, 4]
+            return a
 
-      output = list_func()  # output: [1, 2, 3, 4]
+      net = Net()
+      output = net()  # output: [1, 2, 3, 4]
 
    In the same way that a ``List`` is created within a graph mode, the
    graph mode returns a ``List`` object that can include any of the
@@ -379,15 +442,22 @@ The basic usage scenarios of 'List' are as follows:
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       global_list = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func():
-          global_list.reverse()
-          return global_list
 
-      output = list_func()  # output: [4, 3, 2, 1]
+      class Net(nn.Cell):
+         def construct(self):
+            global_list.reverse()
+            return global_list
+
+      net = Net()
+      output = net()  # output: [4, 3, 2, 1]
 
    It should be noted that the list returned in the following pattern in
    the basic scenario is not the same object as the list of global
@@ -404,14 +474,21 @@ The basic usage scenarios of 'List' are as follows:
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       list_input = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func(x):
-          return x
 
-      output = list_func(list_input)  # output: [1, 2, 3, 4]
+      class Net(nn.Cell):
+         def construct(self, x):
+            return x
+
+      net = Net()
+      output = net(list_input)  # output: [1, 2, 3, 4]
 
    It should be noted that when 'List' is input as a static graph, it is
    always treated as a constant, regardless of the type of element
@@ -439,15 +516,22 @@ The basic usage scenarios of 'List' are as follows:
       .. code:: python
 
          import mindspore as ms
-         @ms.jit()
-         def list_getitem_func():
-             x = [[1, 2], 3, 4]
-             a = x[0]
-             b = x[0][ms.Tensor([1])]
-             c = x[1:3:1]
-             return a, b, c
-
-         a, b, c = list_getitem_func()
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [[1, 2], 3, 4]
+               a = x[0]
+               b = x[0][ms.Tensor([1])]
+               c = x[1:3:1]
+               return a, b, c
+
+         net = Net()
+         a, b, c = net()
          print('a:{}'.format(a))
          print('b:{}'.format(b))
          print('c:{}'.format(c))
@@ -486,17 +570,24 @@ The basic usage scenarios of 'List' are as follows:
       .. code:: python
 
          import mindspore as ms
-
-         @ms.jit()
-         def test_setitem_func():
-             x = [[0, 1], 2, 3, 4]
-             x[1] = 10
-             x[2] = "ok"
-             x[3] = (1, 2, 3)
-             x[0][1] = 88
-             return x
-
-         output = test_setitem_func()
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
+
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [[0, 1], 2, 3, 4]
+               x[1] = 10
+               x[2] = "ok"
+               x[3] = (1, 2, 3)
+               x[0][1] = 88
+               return x
+
+         net = Net()
+         output = net()
          print('output:{}'.format(output))
 
       The results are as follows:
@@ -521,14 +612,21 @@ The basic usage scenarios of 'List' are as follows:
       .. code:: python
 
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         @ms.jit()
-         def test_list():
-             x = [1, 2, 3]
-             x.append(4)
-             return x
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 2, 3]
+               x.append(4)
+               return x
 
-         x = test_list()
+         net = Net()
+         x = net()
          print('x:{}'.format(x))
 
       The results are as follows:
@@ -553,14 +651,21 @@ The basic usage scenarios of 'List' are as follows:
       .. code:: python
 
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
 
-         @ms.jit()
-         def test_list_clear():
-             x = [1, 3, 4]
-             x.clear()
-             return x
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         x = test_list_clear()
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 3, 4]
+               x.clear()
+               return x
+
+         net = Net()
+         x = net()
          print('x:{}'.format(x))
 
       The results are as follows:
@@ -585,16 +690,22 @@ The basic usage scenarios of 'List' are as follows:
       .. code:: python
 
          import mindspore as ms
-
-         @ms.jit()
-         def test_list_extend():
-             x1 = [1, 2, 3]
-             x1.extend((4, "a"))
-             x2 = [1, 2, 3]
-             x2.extend(ms.Tensor([4, 5]))
-             return x1, x2
-
-         output1, output2 = test_list_extend()
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
+
+         class Net(nn.Cell):
+            def construct(self):
+               x1 = [1, 2, 3]
+               x1.extend((4, "a"))
+               x2 = [1, 2, 3]
+               x2.extend(ms.Tensor([4, 5]))
+               return x1, x2
+
+         net = Net()
+         output1, output2 = net()
          print('output1:{}'.format(output1))
          print('output2:{}'.format(output2))
 
@@ -622,14 +733,21 @@ The basic usage scenarios of 'List' are as follows:
       .. code:: python
 
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         @ms.jit()
-         def test_list_pop():
-             x = [1, 2, 3]
-             b = x.pop()
-             return b, x
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 2, 3]
+               b = x.pop()
+               return b, x
 
-         pop_element, res_list = test_list_pop()
+         net = Net()
+         pop_element, res_list = net()
          print('pop_element:{}'.format(pop_element))
          print('res_list:{}'.format(res_list))
 
@@ -652,14 +770,21 @@ The basic usage scenarios of 'List' are as follows:
       .. code:: python
 
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         @ms.jit()
-         def test_list_reverse():
-             x = [1, 2, 3]
-             x.reverse()
-             return x
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 2, 3]
+               x.reverse()
+               return x
 
-         output = test_list_reverse()
+         net = Net()
+         output = net()
          print('output:{}'.format(output))
 
       The results are as follows:
@@ -686,14 +811,21 @@ The basic usage scenarios of 'List' are as follows:
       .. code:: python
 
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
 
-         @ms.jit()
-         def test_list_insert():
-             x = [1, 2, 3]
-             x.insert(3, 4)
-             return x
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         output = test_list_insert()
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 2, 3]
+               x.insert(3, 4)
+               return x
+
+         net = Net()
+         output = net()
          print('output:{}'.format(output))
 
       The results are as follows:
@@ -738,19 +870,25 @@ supported.
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-      t = ms.Tensor(np.array([1, 2, 3]))
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit()
-      def test_index():
-          x = (1, (2, 3, 4), 3, 4, t)
-          y = x[1][1]
-          z = x[4]
-          m = x[1:4]
-          n = x[-4]
-          return y, z, m, n
+      t = ms.Tensor(np.array([1, 2, 3]))
+      
+      class Net(nn.Cell):
+         def construct(self):
+            x = (1, (2, 3, 4), 3, 4, t)
+            y = x[1][1]
+            z = x[4]
+            m = x[1:4]
+            n = x[-4]
+            return y, z, m, n
 
-      y, z, m, n = test_index()
+      net = Net()
+      y, z, m, n = net()
       print('y:{}'.format(y))
       print('z:{}'.format(z))
       print('m:{}'.format(m))
@@ -805,14 +943,21 @@ supported.
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit()
-      def test_index():
-          x = (1, 2, 3)
-          y = (4, 5, 6)
-          return x + y, x * 2
 
-      out1, out2 = test_index()
+      class Net(nn.Cell):
+         def construct(self):
+            x = (1, 2, 3)
+            y = (4, 5, 6)
+            return x + y, x * 2
+
+      net = Net()
+      out1, out2 = net()
       print('out1:{}'.format(out1))
       print('out2:{}'.format(out2))
 
@@ -878,24 +1023,31 @@ The ``value`` can be ``Number``, ``Tuple``, ``Tensor``, ``List`` or
 
    .. code:: python
 
-      import mindspore as ms
       import numpy as np
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       x = {"a": ms.Tensor(np.array([1, 2, 3])), "b": ms.Tensor(np.array([4, 5, 6])), "c": ms.Tensor(np.array([7, 8, 9]))}
 
-      @ms.jit()
-      def test_dict():
-          x_keys = x.keys()
-          x_values = x.values()
-          x_items = x.items()
-          value_a = x.get("a")
-          check_key = x.has_key("a")
-          y = {"a": ms.Tensor(np.array([0, 0, 0]))}
-          x.update(y)
-          new_dict = x.fromkeys("abcd", 123)
-          return x_keys, x_values, x_items, value_a, check_key, x, new_dict
-
-      x_keys, x_values, x_items, value_a, check_key, new_x, new_dict = test_dict()
+
+      class Net(nn.Cell):
+         def construct(self):
+            x_keys = x.keys()
+            x_values = x.values()
+            x_items = x.items()
+            value_a = x.get("a")
+            check_key = x.has_key("a")
+            y = {"a": ms.Tensor(np.array([0, 0, 0]))}
+            x.update(y)
+            new_dict = x.fromkeys("abcd", 123)
+            return x_keys, x_values, x_items, value_a, check_key, x, new_dict
+
+      net = Net()
+      x_keys, x_values, x_items, value_a, check_key, new_x, new_dict = net()
       print('x_keys:{}'.format(x_keys))
       print('x_values:{}'.format(x_values))
       print('x_items:{}'.format(x_items))
@@ -943,7 +1095,6 @@ follows.
        def __init__(self):
            super(Net, self).__init__()
 
-       @ms.jit
        def construct(self, x):
            return ms.tensor(x.asnumpy(), dtype=ms.float32)
 
@@ -1293,17 +1444,24 @@ compilation <https://www.mindspore.cn/docs/en/master/faq/network_compilation.htm
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-      @ms.jit
-      def test_try_except(x, y):
-          global_out = 1
-          try:
-              global_out = x / y
-          except ZeroDivisionError:
-              print("division by zero, y is zero.")
-          return global_out
+      context.set_context(mode=ms.GRAPH_MODE)
+
+
+      class Net(nn.Cell):
+         def construct(self, x, y):
+            global_out = 1
+            try:
+               global_out = x / y
+            except ZeroDivisionError:
+               print("division by zero, y is zero.")
+            return global_out
 
-      test_try_except_out = test_try_except(1, 0)
+      net = Net()
+      test_try_except_out = net(1, 0)
       print("out:", test_try_except_out)
 
    The error is reported as follows:
@@ -1412,15 +1570,22 @@ Calling the Third-party Libraries
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
+
 
-      @ms.jit
-      def func():
-          a = np.array([1, 2, 3])
-          b = np.array([4, 5, 6])
-          out = a + b
-          return out
+      class Net(nn.Cell):
+         def construct(self):
+            a = np.array([1, 2, 3])
+            b = np.array([4, 5, 6])
+            out = a + b
+            return out
 
-      print(func())
+      net = Net()
+      print(net())
 
    The results are as follows:
 
@@ -1436,13 +1601,20 @@ Calling the Third-party Libraries
 
       from scipy import linalg
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit
-      def func():
-          x = [[1, 2], [3, 4]]
-          return linalg.qr(x)
 
-      out = func()
+      class Net(nn.Cell):
+         def construct(self):
+            x = [[1, 2], [3, 4]]
+            return linalg.qr(x)
+
+      net = Net()
+      out = net()
       print(out[0].shape)
 
    The results are as follows:
@@ -1460,14 +1632,21 @@ Calling the Third-party Libraries
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit
-      def func():
-          x = np.array([1, 2, 3])
-          out = ms.Tensor(x) + 1
-          return out
 
-      print(func())
+      class Net(nn.Cell):
+         def construct(self):
+            x = np.array([1, 2, 3])
+            out = ms.Tensor(x) + 1
+            return out
+
+      net = Net()
+      print(net())
 
    The results are as follows:
 
@@ -1484,14 +1663,20 @@ Calling the Third-party Libraries
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-      @ms.jit
-      def func():
-          x = np.array([1, 2, 3])
-          x[0] += 1
-          return ms.Tensor(x)
+      context.set_context(mode=ms.GRAPH_MODE)
+
+      class Net(nn.Cell):
+         def construct(self):
+            x = np.array([1, 2, 3])
+            x[0] += 1
+            return ms.Tensor(x)
 
-      res = func()
+      net = Net()
+      res = net()
       print("res: ", res)
 
    The error message is reported as follows:
@@ -1643,15 +1828,22 @@ The specific usage scenarios are as follows:
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       global_list = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func():
-          global_list.reverse()
-          return global_list
 
-      output = list_func()  # output: [4, 3, 2, 1]
+      class Net(nn.Cell):
+         def construct(self):
+            global_list.reverse()
+            return global_list
+
+      net = Net()
+      output = net()  # output: [4, 3, 2, 1]
       assert id(global_list) == id(output)
 
 -  Inplace operations on input 'List' objects are not supported
@@ -1664,15 +1856,22 @@ The specific usage scenarios are as follows:
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       list_input = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func(x):
-          x.reverse()
-          return x
+ 
+      class Net(nn.Cell):
+         def construct(self, x):
+            x.reverse()
+            return x
 
-      output = list_func(list_input)  # output: [4, 3, 2, 1]  list_input: [1, 2, 3, 4]
+      net = Net()
+      output = net(list_input)  # output: [4, 3, 2, 1]  list_input: [1, 2, 3, 4]
       assert id(output) != id(list_input)
 
    As shown in the above use case, the 'List' object cannot be inplaced
@@ -1696,15 +1895,21 @@ The specific usage scenarios are as follows:
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       list_input = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func():
-          list_input.reverse()
-          return list_input
+      class Net(nn.Cell):
+         def construct(self):
+            list_input.reverse()
+            return list_input
 
-      output = list_func()  # output: [4, 3, 2, 1]  list_input: [4, 3, 2, 1]
+      net = Net()
+      output = net()  # output: [4, 3, 2, 1]  list_input: [4, 3, 2, 1]
       assert id(output) == id(list_input)
 
 Supporting the High-Level Usage of Dictionary
@@ -1717,15 +1922,22 @@ Supporting the High-Level Usage of Dictionary
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit()
-      def test_dict():
-          x = {'a': 'a', 'b': 'b'}
-          y = x.get('a')
-          z = dict(y=y)
-          return z
 
-      out = test_dict()
+      class Net(nn.Cell):
+         def construct(self):
+            x = {'a': 'a', 'b': 'b'}
+            y = x.get('a')
+            z = dict(y=y)
+            return z
+
+      net = Net()
+      out = net()
       print("out:", out)
 
    The results are as follows:
@@ -1740,18 +1952,25 @@ Supporting the High-Level Usage of Dictionary
 
    .. code:: python
 
-      import mindspore as ms
       import numpy as np
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       x = {"a": ms.Tensor(np.array([1, 2, 3])), "b": ms.Tensor(np.array([4, 5, 6])), "c": ms.Tensor(np.array([7, 8, 9]))}
 
-      @ms.jit()
-      def test_dict():
-          y = x["b"]
-          x["a"] = (2, 3, 4)
-          return x, y
 
-      out1, out2 = test_dict()
+      class Net(nn.Cell):
+         def construct(self):
+            y = x["b"]
+            x["a"] = (2, 3, 4)
+            return x, y
+
+      net = Net()
+      out1, out2 = net()
       print('out1:{}'.format(out1))
       print('out2:{}'.format(out2))
 
@@ -1777,12 +1996,19 @@ Examples are as follows:
 .. code:: python
 
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @ms.jit
-   def test_return_none():
-       return 1, "a", None
 
-   res = test_return_none()
+   class Net(nn.Cell):
+      def construct(self):
+         return 1, "a", None
+
+   net = Net()
+   res = net()
    print(res)
 
 The results are as follows:
@@ -1797,13 +2023,20 @@ default.
 .. code:: python
 
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @ms.jit
-   def foo():
-       x = 3
-       print("x:", x)
 
-   res = foo()
+   class Net(nn.Cell):
+      def construct(self):
+         x = 3
+         print("x:", x)
+
+   net = Net()
+   res = net()
    assert res is None
 
 As in the example below, 'None' is used as the default input parameter
@@ -1812,18 +2045,24 @@ for the top graph.
 .. code:: python
 
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
 
-   @ms.jit
-   def foo(x, y=None):
-       if y is not None:
-           print("y:", y)
-       else:
-           print("y is None")
-       print("x:", x)
-       return y
+   context.set_context(mode=ms.GRAPH_MODE)
+
+   class Net(nn.Cell):
+      def construct(self, x, y=None):
+         if y is not None:
+            print("y:", y)
+         else:
+            print("y is None")
+         print("x:", x)
+         return y
 
    x = [1, 2]
-   res = foo(x)
+   net = Net()
+   res = net(x)
    assert res is None
 
 Built-in Functions Support More Data Types
@@ -1869,15 +2108,21 @@ in control flow scenarios. The code use cases are as follows:
 
    import numpy as np
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @ms.jit
-   def func():
-       x = np.array(1)
-       if x <= 1:
-           x += 1
-       return ms.Tensor(x)
+   class Net(nn.Cell):
+      def construct(self):
+         x = np.array(1)
+         if x <= 1:
+            x += 1
+         return ms.Tensor(x)
 
-   res = func()
+   net = Net()
+   res = net()
    print("res: ", res)
 
 The results are as follows:
@@ -1899,21 +2144,26 @@ objects, such as:
 
 .. code:: python
 
-   from mindspore import jit
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-   class AssignClass():
-       def __init__(self):
-           self.x = 1
+      context.set_context(mode=ms.GRAPH_MODE)
 
-   obj = AssignClass()
+      class AssignClass():
+          def __init__(self):
+              self.x = 1
 
-   @jit
-   def foo():
-       obj.x = 100
-       return
+      obj = AssignClass()
+
+      class Net(nn.Cell):
+         def construct(self):
+            obj.x = 100
 
-   foo()
-   print(f"obj.x is: {obj.x}")
+      net = Net()
+      net()
+      print(f"obj.x is: {obj.x}")
 
 The results are as follows:
 
@@ -1926,17 +2176,23 @@ library objects, such as:
 
 .. code:: python
 
-   from mindspore import jit
-   import numpy as np
+      import numpy as np
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-   @jit
-   def foo():
-       a = np.array([1, 2, 3, 4])
-       a.shape = (2, 2)
-       return a.shape
+      context.set_context(mode=ms.GRAPH_MODE)
 
-   shape = foo()
-   print(f"shape is {shape}")
+      class Net(nn.Cell):
+         def construct(self):
+            a = np.array([1, 2, 3, 4])
+            a.shape = (2, 2)
+            return a.shape
+
+      net = Net()
+      shape = net()
+      print(f"shape is {shape}")
 
 The results are as follows:
 
@@ -2037,15 +2293,16 @@ in derivation, such as:
 .. code:: python
 
    import mindspore as ms
-   from mindspore import ops, set_context
+   from mindspore import ops, set_context, nn
    set_context(mode=ms.GRAPH_MODE)
 
-   @ms.jit
-   def dict_net(a):
-       x = {'a': a, 'b': 2}
-       return a, (x, (1, 2))
+   class Net(nn.Cell):
+      def construct(self, a):
+         x = {'a': a, 'b': 2}
+         return a, (x, (1, 2))
 
-   out = ops.grad(dict_net)(ms.Tensor([1]))
+   net = Net()
+   out = ops.grad(net)(ms.Tensor([1]))
    assert out == 2
 
 Annotation Type
@@ -2089,7 +2346,6 @@ The code example is as follows.
            super(Net, self).__init__()
            self.abs = ops.Abs()
 
-       @ms.jit
        def construct(self, x, y):
            y1 = ms.tensor(x.asnumpy() + y.asnumpy(), dtype=ms.float32)
            y2 = ms.Tensor(x.asnumpy() + y.asnumpy(), dtype=ms.float32) # @jit.typing: () -> tensor_type[float32]
diff --git a/docs/mindspore/source_zh_cn/model_train/program_form/static_graph.rst b/docs/mindspore/source_zh_cn/model_train/program_form/static_graph.rst
index ada604d2e7885e4f7b07c59a8eee1aaf79e4480f..3d9e5a6746d7424f264cc7d8e0ff9ece19973f6e 100644
--- a/docs/mindspore/source_zh_cn/model_train/program_form/static_graph.rst
+++ b/docs/mindspore/source_zh_cn/model_train/program_form/static_graph.rst
@@ -65,15 +65,20 @@ API文档 <https://www.mindspore.cn/docs/zh-CN/master/api_python/nn/mindspore.nn
 
    .. code:: python
 
-      from mindspore import Tensor, jit
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       a = 1
       b = [Tensor([1]), Tensor([2])]
       c = ["a", "b", "c"]
 
-      @jit
-      def foo(a, b, c):
-          return a, b, c
+      class Net(nn.Cell):
+         def construct(self, a, b, c):
+            return a, b, c
 
    上述代码中，输入\ ``a``\ ，\ ``b``\ ，\ ``c``\ 均为常量。
 
@@ -81,14 +86,19 @@ API文档 <https://www.mindspore.cn/docs/zh-CN/master/api_python/nn/mindspore.nn
 
    .. code:: python
 
-      from mindspore import jit, Tensor
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @jit
-      def foo():
-          a = 1
-          b = "2"
-          c = Tensor([1, 2, 3])
-          return a, b, c
+      class Net(nn.Cell):
+         def construct(self):
+            a = 1
+            b = "2"
+            c = Tensor([1, 2, 3])
+            return a, b, c
 
    上述代码中， ``a``\ ，\ ``b``\ ，\ ``c``\ 均为常量。
 
@@ -96,14 +106,19 @@ API文档 <https://www.mindspore.cn/docs/zh-CN/master/api_python/nn/mindspore.nn
 
    .. code:: python
 
-      from mindspore import jit, Tensor
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @jit
-      def foo():
-          a = Tensor([1, 2, 3])
-          b = Tensor([1, 1, 1])
-          c = a + b
-          return c
+      class Net(nn.Cell):
+         def construct(self):
+            a = Tensor([1, 2, 3])
+            b = Tensor([1, 1, 1])
+            c = a + b
+            return c
 
    上述代码中，\ ``a``\ 、\ ``b``\ 均为图模式内产生的Tensor为常量，因此其计算得到的结果也是常量。但如果其中之一为变量时，其返回值也会为变量。
 
@@ -114,16 +129,22 @@ API文档 <https://www.mindspore.cn/docs/zh-CN/master/api_python/nn/mindspore.nn
 
    .. code:: python
 
-      from mindspore import Tensor, jit
       from mindspore import mutable
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       a = mutable([Tensor([1]), Tensor([2])])
 
-      @jit
-      def foo(a):
-          b = mutable(Tensor([3]))
-          c = mutable((Tensor([1]), Tensor([2])))
-          return a, b, c
+
+      class Net(nn.Cell):
+         def construct(self, a):
+            b = mutable(Tensor([3]))
+            c = mutable((Tensor([1]), Tensor([2])))
+            return a, b, c
 
    上述代码中，\ ``a``\ 是在图外调用mutable接口的，\ ``b``\ 和\ ``c``\ 是在图内调用mutable接口生成的，\ ``a``\ 、\ ``b``\ 、\ ``c``\ 均为变量。
 
@@ -131,14 +152,20 @@ API文档 <https://www.mindspore.cn/docs/zh-CN/master/api_python/nn/mindspore.nn
 
    .. code:: python
 
-      from mindspore import Tensor, jit
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       a = Tensor([1])
       b = (Tensor([1]), Tensor([2]))
 
-      @jit
-      def foo(a, b):
-          return a, b
+
+      class Net(nn.Cell):
+         def construct(self, a, b):
+            return a, b
 
    上述代码中，\ ``a``\ 是作为图模式输入的Tensor，因此其为变量。但\ ``b``\ 是作为图模式输入的元组，非Tensor类型，即使其内部的元素均为Tensor，\ ``b``\ 也是常量。
  
@@ -148,15 +175,20 @@ API文档 <https://www.mindspore.cn/docs/zh-CN/master/api_python/nn/mindspore.nn
 
    .. code:: python
 
-      from mindspore import Tensor, jit, ops
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       a = Tensor([1])
       b = Tensor([2])
 
-      @jit
-      def foo(a, b):
-          c = a + b
-          return c
+      class Net(nn.Cell):
+         def construct(self, a, b):
+            c = a + b
+            return c
 
    在这种情况下，\ ``c``\ 是\ ``a``\ 和\ ``b``\ 计算来的结果，且用来计算的输入\ ``a``\ 、\ ``b``\ 均为变量，因此\ ``c``\ 也是变量。
 
@@ -179,17 +211,22 @@ Number
 当数据为变量时，即需要在运行时期才可以获取到数值，也支持使用int()，float()，bool()等内置函数\ `Python内置函数 <https://www.mindspore.cn/docs/zh-CN/master/model_train/program_form/static_graph_syntax/python_builtin_functions.html>`_\ 进行数据类型的转换。例如：
 
 .. code:: python
+   import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
 
-   from mindspore import Tensor, jit
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @jit
-   def foo(x):
-       out1 = int(11.1)
-       out2 = int(Tensor([10]))
-       out3 = int(x.asnumpy())
-       return out1, out2, out3
+   class Net(nn.Cell):
+      def construct(self, x):
+         out1 = int(11.1)
+         out2 = int(Tensor([10]))
+         out3 = int(x.asnumpy())
+         return out1, out2, out3
 
-   res = foo(Tensor(2))
+   net = Net()
+   res = net(Tensor(2))
    print("res[0]:", res[0])
    print("res[1]:", res[1])
    print("res[2]:", res[2])
@@ -207,12 +244,19 @@ Number
 .. code:: python
 
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
 
-   @ms.jit
-   def test_return_scalar(x, y):
-       return x + y
+   context.set_context(mode=ms.GRAPH_MODE)
+
+
+   class Net(nn.Cell):
+      def construct(self, x, y):
+         return x + y
 
-   res = test_return_scalar(ms.mutable(1), ms.mutable(2))
+   net = Net()
+   res = net(ms.mutable(1), ms.mutable(2))
    print(res)
 
 运行结果如下：
@@ -230,17 +274,23 @@ String
 
 .. code:: python
 
-   from mindspore import jit
+   import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @jit
-   def foo():
-       var1 = 'Hello!'
-       var2 = "MindSpore"
-       var3 = str(123)
-       var4 = "{} is {}".format("string", var3)
-       return var1[0], var2[4:9], var1 + var2, var2 * 2, "H" in var1, "My name is %s!" % var2, var4
+   class Net(nn.Cell):
+      def construct(self):
+         var1 = 'Hello!'
+         var2 = "MindSpore"
+         var3 = str(123)
+         var4 = "{} is {}".format("string", var3)
+         return var1[0], var2[4:9], var1 + var2, var2 * 2, "H" in var1, "My name is %s!" % var2, var4
 
-   res = foo()
+   net = Net()
+   res = net()
    print("res:", res)
 
 运行结果如下：
@@ -264,14 +314,19 @@ List
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit
-      def generate_list():
-          a = [1, 2, 3, 4]
-          b = ["1", "2", "a"]
-          c = [ms.Tensor([1]), ms.Tensor([2])]
-          d = [a, b, c, (4, 5)]
-          return d
+      class Net(nn.Cell):
+         def construct(self):
+            a = [1, 2, 3, 4]
+            b = ["1", "2", "a"]
+            c = [ms.Tensor([1]), ms.Tensor([2])]
+            d = [a, b, c, (4, 5)]
+            return d
 
    上述示例代码中，所有的\ ``List``\ 对象都可以被正常的创建。
 
@@ -281,15 +336,21 @@ List
    对象时，\ ``List``\ 会被转换为\ ``Tuple``\ 。MindSpore2.0版本已经可以支持返回\ ``List``\ 对象。例如：
 
    .. code:: python
-
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
+
 
-      @ms.jit
-      def list_func():
-          a = [1, 2, 3, 4]
-          return a
+      class Net(nn.Cell):
+         def construct(self):
+            a = [1, 2, 3, 4]
+            return a
 
-      output = list_func()  # output: [1, 2, 3, 4]
+      net = Net()
+      output = net()  # output: [1, 2, 3, 4]
 
    与图模式内创建\ ``List``
    相同，图模式返回\ ``List``\ 对象可以包括任意图模式支持的类型，也支持多层嵌套。
@@ -297,17 +358,23 @@ List
 -  图模式支持从全局变量中获取\ ``List``\ 对象。
 
    .. code:: python
-
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       global_list = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func():
-          global_list.reverse()
-          return global_list
 
-      output = list_func()  # output: [4, 3, 2, 1]
+      class Net(nn.Cell):
+         def construct(self):
+            global_list.reverse()
+            return global_list
+
+      net = Net()
+      output = net()  # output: [4, 3, 2, 1]
 
    需要注意的是，在基础场景下图模式返回的列表与全局变量的列表不是同一个对象，当\ ``JIT_SYNTAX_LEVEL``\ 设置为\ ``LAX``\ 时，返回的对象与全局对象为统一对象。
 
@@ -316,16 +383,22 @@ List
    图模式支持\ ``List``\ 作为静态图的输入，作为输入的\ ``List``\ 对象的元素必须为图模式支持的输入类型，也支持多层嵌套。
 
    .. code:: python
-
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       list_input = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func(x):
-          return x
 
-      output = list_func(list_input)  # output: [1, 2, 3, 4]
+      class Net(nn.Cell):
+         def construct(self, x):
+            return x
+
+      net = Net()
+      output = net(list_input)  # output: [1, 2, 3, 4]
 
    需要注意的是，\ ``List``\ 作为静态图输入时，无论其内部的元素是什么类型，一律被视为常量。
 
@@ -344,17 +417,23 @@ List
       示例如下：
 
       .. code:: python
-
          import mindspore as ms
-         @ms.jit()
-         def list_getitem_func():
-             x = [[1, 2], 3, 4]
-             a = x[0]
-             b = x[0][ms.Tensor([1])]
-             c = x[1:3:1]
-             return a, b, c
-
-         a, b, c = list_getitem_func()
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [[1, 2], 3, 4]
+               a = x[0]
+               b = x[0][ms.Tensor([1])]
+               c = x[1:3:1]
+               return a, b, c
+
+         net = Net()
+         a, b, c = net()
          print('a:{}'.format(a))
          print('b:{}'.format(b))
          print('c:{}'.format(c))
@@ -387,17 +466,24 @@ List
       .. code:: python
 
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
 
-         @ms.jit()
-         def test_setitem_func():
-             x = [[0, 1], 2, 3, 4]
-             x[1] = 10
-             x[2] = "ok"
-             x[3] = (1, 2, 3)
-             x[0][1] = 88
-             return x
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         output = test_setitem_func()
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [[0, 1], 2, 3, 4]
+               x[1] = 10
+               x[2] = "ok"
+               x[3] = (1, 2, 3)
+               x[0][1] = 88
+               return x
+
+         net = Net()
+         output = net()
          print('output:{}'.format(output))
 
       运行结果如下：
@@ -418,16 +504,22 @@ List
       示例如下：
 
       .. code:: python
-
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         @ms.jit()
-         def test_list():
-             x = [1, 2, 3]
-             x.append(4)
-             return x
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 2, 3]
+               x.append(4)
+               return x
 
-         x = test_list()
+         net = Net()
+         x = net()
          print('x:{}'.format(x))
 
       运行结果如下：
@@ -448,16 +540,22 @@ List
       示例如下：
 
       .. code:: python
-
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         @ms.jit()
-         def test_list_clear():
-             x = [1, 3, 4]
-             x.clear()
-             return x
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 3, 4]
+               x.clear()
+               return x
 
-         x = test_list_clear()
+         net = Net()
+         x = net()
          print('x:{}'.format(x))
 
       运行结果如下：
@@ -477,18 +575,23 @@ List
       示例如下：
 
       .. code:: python
-
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
 
-         @ms.jit()
-         def test_list_extend():
-             x1 = [1, 2, 3]
-             x1.extend((4, "a"))
-             x2 = [1, 2, 3]
-             x2.extend(ms.Tensor([4, 5]))
-             return x1, x2
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         output1, output2 = test_list_extend()
+         class Net(nn.Cell):
+            def construct(self):
+               x1 = [1, 2, 3]
+               x1.extend((4, "a"))
+               x2 = [1, 2, 3]
+               x2.extend(ms.Tensor([4, 5]))
+               return x1, x2
+
+         net = Net()
+         output1, output2 = net()
          print('output1:{}'.format(output1))
          print('output2:{}'.format(output2))
 
@@ -512,14 +615,20 @@ List
       .. code:: python
 
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
 
-         @ms.jit()
-         def test_list_pop():
-             x = [1, 2, 3]
-             b = x.pop()
-             return b, x
+         context.set_context(mode=ms.GRAPH_MODE)
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 2, 3]
+               b = x.pop()
+               return b, x
 
-         pop_element, res_list = test_list_pop()
+         net = Net()
+         pop_element, res_list = net()
          print('pop_element:{}'.format(pop_element))
          print('res_list:{}'.format(res_list))
 
@@ -539,16 +648,22 @@ List
       示例如下：
 
       .. code:: python
-
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
+
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         @ms.jit()
-         def test_list_reverse():
-             x = [1, 2, 3]
-             x.reverse()
-             return x
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 2, 3]
+               x.reverse()
+               return x
 
-         output = test_list_reverse()
+         net = Net()
+         output = net()
          print('output:{}'.format(output))
 
       运行结果如下：
@@ -570,14 +685,21 @@ List
       .. code:: python
 
          import mindspore as ms
+         from mindspore import nn
+         from mindspore import context
+         from mindspore import Tensor
 
-         @ms.jit()
-         def test_list_insert():
-             x = [1, 2, 3]
-             x.insert(3, 4)
-             return x
+         context.set_context(mode=ms.GRAPH_MODE)
 
-         output = test_list_insert()
+         
+         class Net(nn.Cell):
+            def construct(self):
+               x = [1, 2, 3]
+               x.insert(3, 4)
+               return x
+
+         net = Net()
+         output = net()
          print('output:{}'.format(output))
 
       运行结果如下：
@@ -609,11 +731,17 @@ Tuple
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       t = ms.Tensor(np.array([1, 2, 3]))
 
-      @ms.jit()
-      def test_index():
+      
+      class Net(nn.Cell):
+         def construct(self):
             x = (1, (2, 3, 4), 3, 4, t)
             y = x[1][1]
             z = x[4]
@@ -621,7 +749,8 @@ Tuple
             n = x[-4]
             return y, z, m, n
 
-      y, z, m, n = test_index()
+      net = Net()
+      y, z, m, n = net()
       print('y:{}'.format(y))
       print('z:{}'.format(z))
       print('m:{}'.format(m))
@@ -641,9 +770,11 @@ Tuple
    .. code:: python
 
       import mindspore as ms
-      from mindspore import nn, set_context
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-      set_context(mode=ms.GRAPH_MODE)
+      context.set_context(mode=ms.GRAPH_MODE)
 
       class Net(nn.Cell):
             def __init__(self):
@@ -673,16 +804,22 @@ Tuple
    与字符串\ ``String``\ 类似，元组支持使用\ ``+``\ 和\ ``*``\ 进行组合，得到一个新的元组\ ``Tuple``\ ，例如：
 
    .. code:: python
-
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit()
-      def test_index():
-          x = (1, 2, 3)
-          y = (4, 5, 6)
-          return x + y, x * 2
 
-      out1, out2 = test_index()
+      class Net(nn.Cell):
+         def construct(self):
+            x = (1, 2, 3)
+            y = (4, 5, 6)
+            return x + y, x * 2
+
+      net = Net()
+      out1, out2 = net()
       print('out1:{}'.format(out1))
       print('out2:{}'.format(out2))
 
@@ -722,24 +859,31 @@ Dictionary
 
    .. code:: python
 
-      import mindspore as ms
       import numpy as np
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       x = {"a": ms.Tensor(np.array([1, 2, 3])), "b": ms.Tensor(np.array([4, 5, 6])), "c": ms.Tensor(np.array([7, 8, 9]))}
 
-      @ms.jit()
-      def test_dict():
-          x_keys = x.keys()
-          x_values = x.values()
-          x_items = x.items()
-          value_a = x.get("a")
-          check_key = x.has_key("a")
-          y = {"a": ms.Tensor(np.array([0, 0, 0]))}
-          x.update(y)
-          new_dict = x.fromkeys("abcd", 123)
-          return x_keys, x_values, x_items, value_a, check_key, x, new_dict
-
-      x_keys, x_values, x_items, value_a, check_key, new_x, new_dict = test_dict()
+
+      class Net(nn.Cell):
+         def construct(self):
+            x_keys = x.keys()
+            x_values = x.values()
+            x_items = x.items()
+            value_a = x.get("a")
+            check_key = x.has_key("a")
+            y = {"a": ms.Tensor(np.array([0, 0, 0]))}
+            x.update(y)
+            new_dict = x.fromkeys("abcd", 123)
+            return x_keys, x_values, x_items, value_a, check_key, x, new_dict
+
+      net = Net()
+      x_keys, x_values, x_items, value_a, check_key, new_x, new_dict = net()
       print('x_keys:{}'.format(x_keys))
       print('x_values:{}'.format(x_values))
       print('x_items:{}'.format(x_items))
@@ -782,7 +926,6 @@ API文档 <https://mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore
        def __init__(self):
            super(Net, self).__init__()
 
-       @ms.jit
        def construct(self, x):
            return ms.tensor(x.asnumpy(), dtype=ms.float32)
 
@@ -1080,19 +1223,25 @@ Python内置函数
    如果使用Try语句，示例如下：
 
    .. code:: python
-
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
+
 
-      @ms.jit
-      def test_try_except(x, y):
-          global_out = 1
-          try:
-              global_out = x / y
-          except ZeroDivisionError:
-              print("division by zero, y is zero.")
-          return global_out
+      class Net(nn.Cell):
+         def construct(self, x, y):
+            global_out = 1
+            try:
+               global_out = x / y
+            except ZeroDivisionError:
+               print("division by zero, y is zero.")
+            return global_out
 
-      test_try_except_out = test_try_except(1, 0)
+      net = Net()
+      test_try_except_out = net(1, 0)
       print("out:", test_try_except_out)
 
    结果报错如下：
@@ -1163,15 +1312,22 @@ Python内置函数
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
+
 
-      @ms.jit
-      def func():
-          a = np.array([1, 2, 3])
-          b = np.array([4, 5, 6])
-          out = a + b
-          return out
+      class Net(nn.Cell):
+         def construct(self):
+            a = np.array([1, 2, 3])
+            b = np.array([4, 5, 6])
+            out = a + b
+            return out
 
-      print(func())
+      net = Net()
+      print(net())
 
    运行结果如下：
 
@@ -1187,13 +1343,20 @@ Python内置函数
 
       from scipy import linalg
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit
-      def func():
-          x = [[1, 2], [3, 4]]
-          return linalg.qr(x)
 
-      out = func()
+      class Net(nn.Cell):
+         def construct(self):
+            x = [[1, 2], [3, 4]]
+            return linalg.qr(x)
+
+      net = Net()
+      out = net()
       print(out[0].shape)
 
    运行结果如下：
@@ -1210,14 +1373,20 @@ Python内置函数
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-      @ms.jit
-      def func():
-          x = np.array([1, 2, 3])
-          out = ms.Tensor(x) + 1
-          return out
+      context.set_context(mode=ms.GRAPH_MODE)
+
+      class Net(nn.Cell):
+         def construct(self):
+            x = np.array([1, 2, 3])
+            out = ms.Tensor(x) + 1
+            return out
 
-      print(func())
+      net = Net()
+      print(net())
 
    运行结果如下：
 
@@ -1233,14 +1402,20 @@ Python内置函数
 
       import numpy as np
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit
-      def func():
-          x = np.array([1, 2, 3])
-          x[0] += 1
-          return ms.Tensor(x)
+      class Net(nn.Cell):
+         def construct(self):
+            x = np.array([1, 2, 3])
+            x[0] += 1
+            return ms.Tensor(x)
 
-      res = func()
+      net = Net()
+      res = net()
       print("res: ", res)
 
    报错信息如下:
@@ -1368,15 +1543,22 @@ Python内置函数
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       global_list = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func():
-          global_list.reverse()
-          return global_list
 
-      output = list_func()  # output: [4, 3, 2, 1]
+      class Net(nn.Cell):
+         def construct(self):
+            global_list.reverse()
+            return global_list
+
+      net = Net()
+      output = net()  # output: [4, 3, 2, 1]
       assert id(global_list) == id(output)
 
 -  不支持对输入\ ``List``\ 对象进行inplace操作。
@@ -1386,15 +1568,22 @@ Python内置函数
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       list_input = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func(x):
-      x.reverse()
-      return x
+ 
+      class Net(nn.Cell):
+         def construct(self, x):
+            x.reverse()
+            return x
 
-      output = list_func(list_input)  # output: [4, 3, 2, 1]  list_input: [1, 2, 3, 4]
+      net = Net()
+      output = net(list_input)  # output: [4, 3, 2, 1]  list_input: [1, 2, 3, 4]
       assert id(output) != id(list_input)
 
    如上述用例所示，\ ``List``\ 对象作为图模式输入时无法在原有对象上进行inplace操作。图模式返回的对象与输入的对象id不同，为不同对象。
@@ -1412,15 +1601,21 @@ Python内置函数
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       list_input = [1, 2, 3, 4]
 
-      @ms.jit
-      def list_func():
-          list_input.reverse()
-          return list_input
+      class Net(nn.Cell):
+         def construct(self):
+            list_input.reverse()
+            return list_input
 
-      output = list_func()  # output: [4, 3, 2, 1]  list_input: [4, 3, 2, 1]
+      net = Net()
+      output = net()  # output: [4, 3, 2, 1]  list_input: [4, 3, 2, 1]
       assert id(output) == id(list_input)
 
 支持Dictionary的高阶用法
@@ -1433,15 +1628,22 @@ Python内置函数
    .. code:: python
 
       import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      @ms.jit()
-      def test_dict():
-          x = {'a': 'a', 'b': 'b'}
-          y = x.get('a')
-          z = dict(y=y)
-          return z
 
-      out = test_dict()
+      class Net(nn.Cell):
+         def construct(self):
+            x = {'a': 'a', 'b': 'b'}
+            y = x.get('a')
+            z = dict(y=y)
+            return z
+
+      net = Net()
+      out = net()
       print("out:", out)
 
    运行结果如下：
@@ -1455,19 +1657,25 @@ Python内置函数
    示例如下：
 
    .. code:: python
-
-      import mindspore as ms
       import numpy as np
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       x = {"a": ms.Tensor(np.array([1, 2, 3])), "b": ms.Tensor(np.array([4, 5, 6])), "c": ms.Tensor(np.array([7, 8, 9]))}
 
-      @ms.jit()
-      def test_dict():
-          y = x["b"]
-          x["a"] = (2, 3, 4)
-          return x, y
 
-      out1, out2 = test_dict()
+      class Net(nn.Cell):
+         def construct(self):
+            y = x["b"]
+            x["a"] = (2, 3, 4)
+            return x, y
+
+      net = Net()
+      out1, out2 = net()
       print('out1:{}'.format(out1))
       print('out2:{}'.format(out2))
 
@@ -1488,12 +1696,19 @@ Python内置函数
 .. code:: python
 
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
+
 
-   @ms.jit
-   def test_return_none():
-       return 1, "a", None
+   class Net(nn.Cell):
+      def construct(self):
+         return 1, "a", None
 
-   res = test_return_none()
+   net = Net()
+   res = net()
    print(res)
 
 运行结果如下：
@@ -1507,13 +1722,20 @@ Python内置函数
 .. code:: python
 
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
+
 
-   @ms.jit
-   def foo():
-       x = 3
-       print("x:", x)
+   class Net(nn.Cell):
+      def construct(self):
+         x = 3
+         print("x:", x)
 
-   res = foo()
+   net = Net()
+   res = net()
    assert res is None
 
 运行结果如下：
@@ -1526,20 +1748,26 @@ Python内置函数
 如下面例子，\ ``None``\ 作为顶图的默认入参。
 
 .. code:: python
-
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @ms.jit
-   def foo(x, y=None):
-       if y is not None:
-           print("y:", y)
-       else:
-           print("y is None")
-       print("x:", x)
-       return y
+   
+   class Net(nn.Cell):
+      def construct(self, x, y=None):
+         if y is not None:
+            print("y:", y)
+         else:
+            print("y is None")
+         print("x:", x)
+         return y
 
    x = [1, 2]
-   res = foo(x)
+   net = Net()
+   res = net(x)
    assert res is None
 
 运行结果如下：
@@ -1583,15 +1811,21 @@ Python内置函数
 
    import numpy as np
    import mindspore as ms
+   from mindspore import nn
+   from mindspore import context
+   from mindspore import Tensor
+
+   context.set_context(mode=ms.GRAPH_MODE)
 
-   @ms.jit
-   def func():
-       x = np.array(1)
-       if x <= 1:
-           x += 1
-       return ms.Tensor(x)
+   class Net(nn.Cell):
+      def construct(self):
+         x = np.array(1)
+         if x <= 1:
+            x += 1
+         return ms.Tensor(x)
 
-   res = func()
+   net = Net()
+   res = net()
    print("res: ", res)
 
 运行结果如下：
@@ -1611,7 +1845,12 @@ Python内置函数
 
    .. code:: python
 
-      from mindspore import jit
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
+
+      context.set_context(mode=ms.GRAPH_MODE)
 
       class AssignClass():
           def __init__(self):
@@ -1619,11 +1858,12 @@ Python内置函数
 
       obj = AssignClass()
 
-      @jit
-      def foo():
-          obj.x = 100
+      class Net(nn.Cell):
+         def construct(self):
+            obj.x = 100
 
-      foo()
+      net = Net()
+      net()
       print(f"obj.x is: {obj.x}")
 
    运行结果如下：
@@ -1636,16 +1876,22 @@ Python内置函数
 
    .. code:: python
 
-      from mindspore import jit
       import numpy as np
+      import mindspore as ms
+      from mindspore import nn
+      from mindspore import context
+      from mindspore import Tensor
 
-      @jit
-      def foo():
-          a = np.array([1, 2, 3, 4])
-          a.shape = (2, 2)
-          return a.shape
+      context.set_context(mode=ms.GRAPH_MODE)
 
-      shape = foo()
+      class Net(nn.Cell):
+         def construct(self):
+            a = np.array([1, 2, 3, 4])
+            a.shape = (2, 2)
+            return a.shape
+
+      net = Net()
+      shape = net()
       print(f"shape is {shape}")
 
    运行结果如下：
@@ -1750,15 +1996,16 @@ Python内置函数
 .. code:: python
 
    import mindspore as ms
-   from mindspore import ops, set_context
+   from mindspore import ops, set_context, nn
    set_context(mode=ms.GRAPH_MODE)
 
-   @ms.jit
-   def dict_net(a):
-       x = {'a': a, 'b': 2}
-       return a, (x, (1, 2))
+   class Net(nn.Cell):
+      def construct(self, a):
+         x = {'a': a, 'b': 2}
+         return a, (x, (1, 2))
 
-   out = ops.grad(dict_net)(ms.Tensor([1]))
+   net = Net()
+   out = ops.grad(net)(ms.Tensor([1]))
    assert out == 2
 
 Annotation Type
@@ -1784,7 +2031,6 @@ Type机制。当\ ``tensor``\ 函数的\ ``dtype``\ 确定时，函数内部会
            super(Net, self).__init__()
            self.abs = ops.Abs()
 
-       @ms.jit
        def construct(self, x, y):
            y1 = ms.tensor(x.asnumpy() + y.asnumpy(), dtype=ms.float32)
            y2 = ms.Tensor(x.asnumpy() + y.asnumpy(), dtype=ms.float32) # @jit.typing: () -> tensor_type[float32]
diff --git a/docs/mindspore/source_zh_cn/model_train/program_form/static_graph_syntax/static_graph_syntax_support.ipynb b/docs/mindspore/source_zh_cn/model_train/program_form/static_graph_syntax/static_graph_syntax_support.ipynb
index debc6658fd39b80b515f133499300e3498348001..9e7b076fd0c1a45a5e20d0b6bc17ba2c4ddf8eb4 100644
--- a/docs/mindspore/source_zh_cn/model_train/program_form/static_graph_syntax/static_graph_syntax_support.ipynb
+++ b/docs/mindspore/source_zh_cn/model_train/program_form/static_graph_syntax/static_graph_syntax_support.ipynb
@@ -54,7 +54,7 @@
     "import mindspore as ms\n",
     "from mindspore import nn\n",
     "from mindspore import context\n",
-    "from mindspore import Tensor, jit\n",
+    "from mindspore import Tensor\n",
     "\n",
     "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
@@ -84,14 +84,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mindspore import jit, Tensor\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
     "\n",
-    "@jit\n",
-    "def foo():\n",
-    "    a = 1\n",
-    "    b = \"2\"\n",
-    "    c = Tensor([1, 2, 3])\n",
-    "    return a, b, c"
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        a = 1\n",
+    "        b = \"2\"\n",
+    "        c = Tensor([1, 2, 3])\n",
+    "        return a, b, c"
    ]
   },
   {
@@ -111,14 +116,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mindspore import jit, Tensor\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@jit\n",
-    "def foo():\n",
-    "    a = Tensor([1, 2, 3])\n",
-    "    b = Tensor([1, 1, 1])\n",
-    "    c = a + b\n",
-    "    return c"
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        a = Tensor([1, 2, 3])\n",
+    "        b = Tensor([1, 1, 1])\n",
+    "        c = a + b\n",
+    "        return c"
    ]
   },
   {
@@ -140,16 +150,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mindspore import Tensor, jit\n",
     "from mindspore import mutable\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "a = mutable([Tensor([1]), Tensor([2])])\n",
     "\n",
-    "@jit\n",
-    "def foo(a):\n",
-    "    b = mutable(Tensor([3]))\n",
-    "    c = mutable((Tensor([1]), Tensor([2])))\n",
-    "    return a, b, c"
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self, a):\n",
+    "        b = mutable(Tensor([3]))\n",
+    "        c = mutable((Tensor([1]), Tensor([2])))\n",
+    "        return a, b, c"
    ]
   },
   {
@@ -169,14 +185,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mindspore import Tensor, jit\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "a = Tensor([1])\n",
     "b = (Tensor([1]), Tensor([2]))\n",
     "\n",
-    "@jit\n",
-    "def foo(a, b):\n",
-    "    return a, b"
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self, a, b):\n",
+    "        return a, b"
    ]
   },
   {
@@ -198,15 +220,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mindspore import Tensor, jit, ops\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "a = Tensor([1])\n",
     "b = Tensor([2])\n",
     "\n",
-    "@jit\n",
-    "def foo(a, b):\n",
-    "    c = a + b\n",
-    "    return c"
+    "class Net(nn.Cell):\n",
+    "    def construct(self, a, b):\n",
+    "        c = a + b\n",
+    "        return c"
    ]
   },
   {
@@ -249,16 +276,22 @@
     }
    ],
    "source": [
-    "from mindspore import Tensor, jit\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
     "\n",
-    "@jit\n",
-    "def foo(x):\n",
-    "    out1 = int(11.1)\n",
-    "    out2 = int(Tensor([10]))\n",
-    "    out3 = int(x.asnumpy())\n",
-    "    return out1, out2, out3\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self, x):\n",
+    "        out1 = int(11.1)\n",
+    "        out2 = int(Tensor([10]))\n",
+    "        out3 = int(x.asnumpy())\n",
+    "        return out1, out2, out3\n",
     "\n",
-    "res = foo(Tensor(2))\n",
+    "net = Net()\n",
+    "res = net(Tensor(2))\n",
     "print(\"res[0]:\", res[0])\n",
     "print(\"res[1]:\", res[1])\n",
     "print(\"res[2]:\", res[2])"
@@ -288,12 +321,19 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
     "\n",
-    "@ms.jit\n",
-    "def test_return_scalar(x, y):\n",
-    "    return x + y\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
+    "\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self, x, y):\n",
+    "        return x + y\n",
     "\n",
-    "res = test_return_scalar(ms.mutable(1), ms.mutable(2))\n",
+    "net = Net()\n",
+    "res = net(ms.mutable(1), ms.mutable(2))\n",
     "print(res)"
    ]
   },
@@ -324,17 +364,22 @@
     }
    ],
    "source": [
-    "from mindspore import jit\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@jit\n",
-    "def foo():\n",
-    "    var1 = 'Hello!'\n",
-    "    var2 = \"MindSpore\"\n",
-    "    var3 = str(123)\n",
-    "    var4 = \"{} is {}\".format(\"string\", var3)\n",
-    "    return var1[0], var2[4:9], var1 + var2, var2 * 2, \"H\" in var1, \"My name is %s!\" % var2, var4\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        var1 = 'Hello!'\n",
+    "        var2 = \"MindSpore\"\n",
+    "        var3 = str(123)\n",
+    "        var4 = \"{} is {}\".format(\"string\", var3)\n",
+    "        return var1[0], var2[4:9], var1 + var2, var2 * 2, \"H\" in var1, \"My name is %s!\" % var2, var4\n",
     "\n",
-    "res = foo()\n",
+    "net = Net()\n",
+    "res = net()\n",
     "print(\"res:\", res)"
    ]
   },
@@ -363,14 +408,19 @@
    "source": [
     "import numpy as np\n",
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
     "\n",
-    "@ms.jit\n",
-    "def generate_list():\n",
-    "    a = [1, 2, 3, 4]\n",
-    "    b = [\"1\", \"2\", \"a\"]\n",
-    "    c = [ms.Tensor([1]), ms.Tensor([2])]\n",
-    "    d = [a, b, c, (4, 5)]\n",
-    "    return d"
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        a = [1, 2, 3, 4]\n",
+    "        b = [\"1\", \"2\", \"a\"]\n",
+    "        c = [ms.Tensor([1]), ms.Tensor([2])]\n",
+    "        d = [a, b, c, (4, 5)]\n",
+    "        return d"
    ]
   },
   {
@@ -393,13 +443,20 @@
    "outputs": [],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
     "\n",
-    "@ms.jit\n",
-    "def list_func():\n",
-    "    a = [1, 2, 3, 4]\n",
-    "    return a\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
+    "\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        a = [1, 2, 3, 4]\n",
+    "        return a\n",
     "\n",
-    "output = list_func()  # output: [1, 2, 3, 4]"
+    "net = Net()\n",
+    "output = net()  # output: [1, 2, 3, 4]"
    ]
   },
   {
@@ -420,15 +477,22 @@
    "outputs": [],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "global_list = [1, 2, 3, 4]\n",
     "\n",
-    "@ms.jit\n",
-    "def list_func():\n",
-    "    global_list.reverse()\n",
-    "    return global_list\n",
     "\n",
-    "output = list_func()  # output: [4, 3, 2, 1]"
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        global_list.reverse()\n",
+    "        return global_list\n",
+    "\n",
+    "net = Net()\n",
+    "output = net()  # output: [4, 3, 2, 1]"
    ]
   },
   {
@@ -451,14 +515,21 @@
    "outputs": [],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "list_input = [1, 2, 3, 4]\n",
     "\n",
-    "@ms.jit\n",
-    "def list_func(x):\n",
-    "    return x\n",
     "\n",
-    "output = list_func(list_input)  # output: [1, 2, 3, 4]"
+    "class Net(nn.Cell):\n",
+    "    def construct(self, x):\n",
+    "        return x\n",
+    "\n",
+    "net = Net()\n",
+    "output = net(list_input)  # output: [1, 2, 3, 4]"
    ]
   },
   {
@@ -500,16 +571,22 @@
     }
    ],
    "source": [
-    "import mindspore as ms\n",
-    "@ms.jit()\n",
-    "def list_getitem_func():\n",
-    "    x = [[1, 2], 3, 4]\n",
-    "    a = x[0]\n",
-    "    b = x[0][ms.Tensor([1])]\n",
-    "    c = x[1:3:1]\n",
-    "    return a, b, c\n",
-    "\n",
-    "a, b, c = list_getitem_func()\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = [[1, 2], 3, 4]\n",
+    "        a = x[0]\n",
+    "        b = x[0][ms.Tensor([1])]\n",
+    "        c = x[1:3:1]\n",
+    "        return a, b, c\n",
+    "\n",
+    "net = Net()\n",
+    "a, b, c = net()\n",
     "print('a:{}'.format(a))\n",
     "print('b:{}'.format(b))\n",
     "print('c:{}'.format(c))"
@@ -551,17 +628,23 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_setitem_func():\n",
-    "    x = [[0, 1], 2, 3, 4]\n",
-    "    x[1] = 10\n",
-    "    x[2] = \"ok\"\n",
-    "    x[3] = (1, 2, 3)\n",
-    "    x[0][1] = 88\n",
-    "    return x\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = [[0, 1], 2, 3, 4]\n",
+    "        x[1] = 10\n",
+    "        x[2] = \"ok\"\n",
+    "        x[3] = (1, 2, 3)\n",
+    "        x[0][1] = 88\n",
+    "        return x\n",
     "\n",
-    "output = test_setitem_func()\n",
+    "net = Net()\n",
+    "output = net()\n",
     "print('output:{}'.format(output))"
    ]
   },
@@ -597,14 +680,19 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_list():\n",
-    "    x = [1, 2, 3]\n",
-    "    x.append(4)\n",
-    "    return x\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = [1, 2, 3]\n",
+    "        x.append(4)\n",
+    "        return x\n",
     "\n",
-    "x = test_list()\n",
+    "net = Net()\n",
+    "x = net()\n",
     "print('x:{}'.format(x))"
    ]
   },
@@ -640,14 +728,19 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_list_clear():\n",
-    "    x = [1, 3, 4]\n",
-    "    x.clear()\n",
-    "    return x\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "x = test_list_clear()\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = [1, 3, 4]\n",
+    "        x.clear()\n",
+    "        return x\n",
+    "\n",
+    "net = Net()\n",
+    "x = net()\n",
     "print('x:{}'.format(x))"
    ]
   },
@@ -684,16 +777,21 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_list_extend():\n",
-    "    x1 = [1, 2, 3]\n",
-    "    x1.extend((4, \"a\"))\n",
-    "    x2 = [1, 2, 3]\n",
-    "    x2.extend(ms.Tensor([4, 5]))\n",
-    "    return x1, x2\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x1 = [1, 2, 3]\n",
+    "        x1.extend((4, \"a\"))\n",
+    "        x2 = [1, 2, 3]\n",
+    "        x2.extend(ms.Tensor([4, 5]))\n",
+    "        return x1, x2\n",
     "\n",
-    "output1, output2 = test_list_extend()\n",
+    "net = Net()\n",
+    "output1, output2 = net()\n",
     "print('output1:{}'.format(output1))\n",
     "print('output2:{}'.format(output2))"
    ]
@@ -729,14 +827,20 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_list_pop():\n",
-    "    x = [1, 2, 3]\n",
-    "    b = x.pop()\n",
-    "    return b, x\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "pop_element, res_list = test_list_pop()\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = [1, 2, 3]\n",
+    "        b = x.pop()\n",
+    "        return b, x\n",
+    "\n",
+    "net = Net()\n",
+    "pop_element, res_list = net()\n",
     "print('pop_element:{}'.format(pop_element))\n",
     "print('res_list:{}'.format(res_list))"
    ]
@@ -770,15 +874,19 @@
     }
    ],
    "source": [
-    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_list_reverse():\n",
-    "    x = [1, 2, 3]\n",
-    "    x.reverse()\n",
-    "    return x\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = [1, 2, 3]\n",
+    "        x.reverse()\n",
+    "        return x\n",
     "\n",
-    "output = test_list_reverse()\n",
+    "net = Net()\n",
+    "output = net()\n",
     "print('output:{}'.format(output))"
    ]
   },
@@ -814,14 +922,19 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_list_insert():\n",
-    "    x = [1, 2, 3]\n",
-    "    x.insert(3, 4)\n",
-    "    return x\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = [1, 2, 3]\n",
+    "        x.insert(3, 4)\n",
+    "        return x\n",
     "\n",
-    "output = test_list_insert()\n",
+    "net = Net()\n",
+    "output = net()\n",
     "print('output:{}'.format(output))"
    ]
   },
@@ -875,19 +988,24 @@
    "source": [
     "import numpy as np\n",
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "t = ms.Tensor(np.array([1, 2, 3]))\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_index():\n",
-    "    x = (1, (2, 3, 4), 3, 4, t)\n",
-    "    y = x[1][1]\n",
-    "    z = x[4]\n",
-    "    m = x[1:4]\n",
-    "    n = x[-4]\n",
-    "    return y, z, m, n\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = (1, (2, 3, 4), 3, 4, t)\n",
+    "        y = x[1][1]\n",
+    "        z = x[4]\n",
+    "        m = x[1:4]\n",
+    "        n = x[-4]\n",
+    "        return y, z, m, n\n",
     "\n",
-    "y, z, m, n = test_index()\n",
+    "net = Net()\n",
+    "y, z, m, n = net()\n",
     "print('y:{}'.format(y))\n",
     "print('z:{}'.format(z))\n",
     "print('m:{}'.format(m))\n",
@@ -967,14 +1085,19 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_index():\n",
-    "    x = (1, 2, 3)\n",
-    "    y = (4, 5, 6)\n",
-    "    return x + y, x * 2\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = (1, 2, 3)\n",
+    "        y = (4, 5, 6)\n",
+    "        return x + y, x * 2\n",
     "\n",
-    "out1, out2 = test_index()\n",
+    "net = Net()\n",
+    "out1, out2 = net()\n",
     "print('out1:{}'.format(out1))\n",
     "print('out2:{}'.format(out2))"
    ]
@@ -1032,24 +1155,30 @@
     }
    ],
    "source": [
-    "import mindspore as ms\n",
     "import numpy as np\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "x = {\"a\": ms.Tensor(np.array([1, 2, 3])), \"b\": ms.Tensor(np.array([4, 5, 6])), \"c\": ms.Tensor(np.array([7, 8, 9]))}\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_dict():\n",
-    "    x_keys = x.keys()\n",
-    "    x_values = x.values()\n",
-    "    x_items = x.items()\n",
-    "    value_a = x.get(\"a\")\n",
-    "    check_key = x.has_key(\"a\")\n",
-    "    y = {\"a\": ms.Tensor(np.array([0, 0, 0]))}\n",
-    "    x.update(y)\n",
-    "    new_dict = x.fromkeys(\"abcd\", 123)\n",
-    "    return x_keys, x_values, x_items, value_a, check_key, x, new_dict\n",
-    "\n",
-    "x_keys, x_values, x_items, value_a, check_key, new_x, new_dict = test_dict()\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x_keys = x.keys()\n",
+    "        x_values = x.values()\n",
+    "        x_items = x.items()\n",
+    "        value_a = x.get(\"a\")\n",
+    "        check_key = x.has_key(\"a\")\n",
+    "        y = {\"a\": ms.Tensor(np.array([0, 0, 0]))}\n",
+    "        x.update(y)\n",
+    "        new_dict = x.fromkeys(\"abcd\", 123)\n",
+    "        return x_keys, x_values, x_items, value_a, check_key, x, new_dict\n",
+    "\n",
+    "net = Net()\n",
+    "x_keys, x_values, x_items, value_a, check_key, new_x, new_dict = net()\n",
     "print('x_keys:{}'.format(x_keys))\n",
     "print('x_values:{}'.format(x_values))\n",
     "print('x_items:{}'.format(x_items))\n",
@@ -1097,7 +1226,6 @@
     "    def __init__(self):\n",
     "        super(Net, self).__init__()\n",
     "\n",
-    "    @ms.jit\n",
     "    def construct(self, x):\n",
     "        return ms.tensor(x.asnumpy(), dtype=ms.float32)\n",
     "\n",
@@ -1554,15 +1682,21 @@
    "source": [
     "import numpy as np\n",
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit\n",
-    "def func():\n",
-    "    a = np.array([1, 2, 3])\n",
-    "    b = np.array([4, 5, 6])\n",
-    "    out = a + b\n",
-    "    return out\n",
     "\n",
-    "print(func())"
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        a = np.array([1, 2, 3])\n",
+    "        b = np.array([4, 5, 6])\n",
+    "        out = a + b\n",
+    "        return out\n",
+    "\n",
+    "net = Net()\n",
+    "print(net())"
    ]
   },
   {
@@ -1592,13 +1726,19 @@
    "source": [
     "from scipy import linalg\n",
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit\n",
-    "def func():\n",
-    "    x = [[1, 2], [3, 4]]\n",
-    "    return linalg.qr(x)\n",
     "\n",
-    "out = func()\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = [[1, 2], [3, 4]]\n",
+    "        return linalg.qr(x)\n",
+    "\n",
+    "net = Net()\n",
+    "out = net()\n",
     "print(out[0].shape)"
    ]
   },
@@ -1629,14 +1769,19 @@
    "source": [
     "import numpy as np\n",
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
     "\n",
-    "@ms.jit\n",
-    "def func():\n",
-    "    x = np.array([1, 2, 3])\n",
-    "    out = ms.Tensor(x) + 1\n",
-    "    return out\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
+    "\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = np.array([1, 2, 3])\n",
+    "        out = ms.Tensor(x) + 1\n",
+    "        return out\n",
     "\n",
-    "print(func())"
+    "net = Net()\n",
+    "print(net())"
    ]
   },
   {
@@ -1837,15 +1982,22 @@
    "outputs": [],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "global_list = [1, 2, 3, 4]\n",
     "\n",
-    "@ms.jit\n",
-    "def list_func():\n",
-    "    global_list.reverse()\n",
-    "    return global_list\n",
     "\n",
-    "output = list_func()  # output: [4, 3, 2, 1]\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        global_list.reverse()\n",
+    "        return global_list\n",
+    "\n",
+    "net = Net()\n",
+    "output = net()  # output: [4, 3, 2, 1]\n",
     "assert id(global_list) == id(output)"
    ]
   },
@@ -1891,15 +2043,21 @@
    "outputs": [],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "list_input = [1, 2, 3, 4]\n",
     "\n",
-    "@ms.jit\n",
-    "def list_func():\n",
-    "    list_input.reverse()\n",
-    "    return list_input\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        list_input.reverse()\n",
+    "        return list_input\n",
     "\n",
-    "output = list_func()  # output: [4, 3, 2, 1]  list_input: [4, 3, 2, 1]\n",
+    "net = Net()\n",
+    "output = net()  # output: [4, 3, 2, 1]  list_input: [4, 3, 2, 1]\n",
     "assert id(output) == id(list_input)"
    ]
   },
@@ -1931,15 +2089,21 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_dict():\n",
-    "    x = {'a': 'a', 'b': 'b'}\n",
-    "    y = x.get('a')\n",
-    "    z = dict(y=y)\n",
-    "    return z\n",
     "\n",
-    "out = test_dict()\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = {'a': 'a', 'b': 'b'}\n",
+    "        y = x.get('a')\n",
+    "        z = dict(y=y)\n",
+    "        return z\n",
+    "\n",
+    "net = Net()\n",
+    "out = net()\n",
     "print(\"out:\", out)"
    ]
   },
@@ -1969,18 +2133,25 @@
     }
    ],
    "source": [
-    "import mindspore as ms\n",
     "import numpy as np\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "x = {\"a\": ms.Tensor(np.array([1, 2, 3])), \"b\": ms.Tensor(np.array([4, 5, 6])), \"c\": ms.Tensor(np.array([7, 8, 9]))}\n",
     "\n",
-    "@ms.jit()\n",
-    "def test_dict():\n",
-    "    y = x[\"b\"]\n",
-    "    x[\"a\"] = (2, 3, 4)\n",
-    "    return x, y\n",
     "\n",
-    "out1, out2 = test_dict()\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        y = x[\"b\"]\n",
+    "        x[\"a\"] = (2, 3, 4)\n",
+    "        return x, y\n",
+    "\n",
+    "net = Net()\n",
+    "out1, out2 = net()\n",
     "print('out1:{}'.format(out1))\n",
     "print('out2:{}'.format(out2))"
    ]
@@ -2013,12 +2184,17 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
     "\n",
-    "@ms.jit\n",
-    "def test_return_none():\n",
-    "    return 1, \"a\", None\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "res = test_return_none()\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        return 1, \"a\", None\n",
+    "\n",
+    "net = Net()\n",
+    "res = net()\n",
     "print(res)"
    ]
   },
@@ -2047,13 +2223,18 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
     "\n",
-    "@ms.jit\n",
-    "def foo():\n",
-    "    x = 3\n",
-    "    print(\"x:\", x)\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "res = foo()\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = 3\n",
+    "        print(\"x:\", x)\n",
+    "\n",
+    "net = Net()\n",
+    "res = net()\n",
     "assert res is None"
    ]
   },
@@ -2083,18 +2264,23 @@
    ],
    "source": [
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit\n",
-    "def foo(x, y=None):\n",
-    "    if y is not None:\n",
-    "        print(\"y:\", y)\n",
-    "    else:\n",
-    "        print(\"y is None\")\n",
-    "    print(\"x:\", x)\n",
-    "    return y\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self, x, y=None):\n",
+    "        if y is not None:\n",
+    "            print(\"y:\", y)\n",
+    "        else:\n",
+    "            print(\"y is None\")\n",
+    "        print(\"x:\", x)\n",
+    "        return y\n",
     "\n",
     "x = [1, 2]\n",
-    "res = foo(x)\n",
+    "net = Net()\n",
+    "res = net(x)\n",
     "assert res is None"
    ]
   },
@@ -2160,15 +2346,21 @@
    "source": [
     "import numpy as np\n",
     "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit\n",
-    "def func():\n",
-    "    x = np.array(1)\n",
-    "    if x <= 1:\n",
-    "        x += 1\n",
-    "    return ms.Tensor(x)\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        x = np.array(1)\n",
+    "        if x <= 1:\n",
+    "            x += 1\n",
+    "        return ms.Tensor(x)\n",
     "\n",
-    "res = func()\n",
+    "net = Net()\n",
+    "res = net()\n",
     "print(\"res: \", res)"
    ]
   },
@@ -2201,7 +2393,13 @@
     }
    ],
    "source": [
-    "from mindspore import jit\n",
+    "\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
+    "from mindspore import Tensor\n",
+    "\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
     "class AssignClass():\n",
     "    def __init__(self):\n",
@@ -2209,11 +2407,12 @@
     "\n",
     "obj = AssignClass()\n",
     "\n",
-    "@jit\n",
-    "def foo():\n",
-    "    obj.x = 100\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        obj.x = 100\n",
     "\n",
-    "foo()\n",
+    "net = Net()\n",
+    "net()\n",
     "print(f\"obj.x is: {obj.x}\")"
    ]
   },
@@ -2241,18 +2440,22 @@
     }
    ],
    "source": [
-    "from mindspore import jit\n",
     "import numpy as np\n",
+    "import mindspore as ms\n",
+    "from mindspore import nn\n",
+    "from mindspore import context\n",
     "\n",
-    "@jit\n",
-    "def foo():\n",
-    "    a = np.array([1, 2, 3, 4])\n",
-    "    a.shape = (2, 2)\n",
-    "    return a.shape\n",
+    "context.set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "shape = foo()\n",
-    "print(f\"shape is {shape}\")\n",
-    "\n"
+    "class Net(nn.Cell):\n",
+    "    def construct(self):\n",
+    "        a = np.array([1, 2, 3, 4])\n",
+    "        a.shape = (2, 2)\n",
+    "        return a.shape\n",
+    "\n",
+    "net = Net()\n",
+    "shape = net()\n",
+    "print(f\"shape is {shape}\")"
    ]
   },
   {
@@ -2407,15 +2610,16 @@
    "outputs": [],
    "source": [
     "import mindspore as ms\n",
-    "from mindspore import ops, set_context\n",
+    "from mindspore import ops, set_context, nn\n",
     "set_context(mode=ms.GRAPH_MODE)\n",
     "\n",
-    "@ms.jit\n",
-    "def dict_net(a):\n",
-    "    x = {'a': a, 'b': 2}\n",
-    "    return a, (x, (1, 2))\n",
+    "class Net(nn.Cell):\n",
+    "    def construct(self, a):\n",
+    "        x = {'a': a, 'b': 2}\n",
+    "        return a, (x, (1, 2))\n",
     "\n",
-    "out = ops.grad(dict_net)(ms.Tensor([1]))\n",
+    "net = Net()\n",
+    "out = ops.grad(net)(ms.Tensor([1]))\n",
     "assert out == 2"
    ]
   },
@@ -2460,7 +2664,6 @@
     "        super(Net, self).__init__()\n",
     "        self.abs = ops.Abs()\n",
     "\n",
-    "    @ms.jit\n",
     "    def construct(self, x, y):\n",
     "        y1 = ms.tensor(x.asnumpy() + y.asnumpy(), dtype=ms.float32)\n",
     "        y2 = ms.Tensor(x.asnumpy() + y.asnumpy(), dtype=ms.float32) # @jit.typing: () -> tensor_type[float32]\n",
@@ -2524,4 +2727,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
\ No newline at end of file
+}