fix

docs/api/paddle/LazyGuard_cn.rst

@@ -13,15 +13,4 @@ LazyGuard 是一个用于设置模型（继承自 ``paddle.nn.Layer`` ） 中参
 代码示例
 ::::::::::::
 
-.. code-block:: python
-
-    from paddle import LazyGuard
-    from paddle.nn import Linear
-
-    with LazyGuard():
-        # w and b are initialized lazily and have no memory.
-        net = Linear(10, 10)
-
-    for param in net.parameters():
-        # Initialize param and allocate memory explicitly.
-        param.initialize()
+COPY-FROM: paddle.LazyGuard

docs/api/paddle/Tensor_cn.rst

@@ -182,12 +182,7 @@ shape
 查看一个 Tensor 的 shape，shape 是 Tensor 的一个重要的概念，其描述了 tensor 在每个维度上的元素数量。
 
 **代码示例**
-
-    .. code-block:: python
-
-        import paddle
-        print("Tensor's shape: ", paddle.to_tensor([[1, 2], [3, 4]]).shape)
-        # Tensor's shape: [2, 2]
+COPY-FROM: paddle.shape
 
 stop_gradient
 :::::::::
@@ -625,14 +620,7 @@ cosh(name=None)
 请参考 :ref:`cn_api_fluid_layers_cosh`
 
 **代码示例**
-    .. code-block:: python
-
-        import paddle
-
-        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
-        out = paddle.cosh(x)
-        print(out)
-        # [1.08107237 1.02006674 1.00500417 1.04533851]
+COPY-FROM: paddle.cosh
 
 count_nonzero(axis=None, keepdim=False, name=None)
 :::::::::
@@ -2034,14 +2022,7 @@ sinh(name=None)
 对该 Tensor 中逐个元素求双曲正弦。
 
 **代码示例**
-    .. code-block:: python
-
-        import paddle
-
-        x = paddle.to_tensor([-0.4, -0.2, 0.1, 0.3])
-        out = x.sinh()
-        print(out)
-        # [-0.41075233 -0.201336    0.10016675  0.30452029]
+COPY-FROM: paddle.sinh
 
 size()
 :::::::::

docs/api/paddle/grad_cn.rst

@@ -30,80 +30,8 @@ tuple(Tensor)，其长度等于 `inputs` 中的变量个数，且第 i 个返回
 
 代码示例 1
 :::::::::
-  .. code-block:: python
-
-        import paddle
-
-        def test_dygraph_grad(create_graph):
-            x = paddle.ones(shape=[1], dtype='float32')
-            x.stop_gradient = False
-            y = x * x
-
-            # Since y = x * x, dx = 2 * x
-            dx = paddle.grad(
-                    outputs=[y],
-                    inputs=[x],
-                    create_graph=create_graph,
-                    retain_graph=True)[0]
-
-            z = y + dx
-
-            # If create_graph = False, the gradient of dx
-            # would not be backpropagated. Therefore,
-            # z = x * x + dx, and x.gradient() = 2 * x = 2.0
-
-            # If create_graph = True, the gradient of dx
-            # would be backpropagated. Therefore,
-            # z = x * x + dx = x * x + 2 * x, and
-            # x.gradient() = 2 * x + 2 = 4.0
-
-            z.backward()
-            return x.gradient()
-
-        print(test_dygraph_grad(create_graph=False)) # [2.]
-        print(test_dygraph_grad(create_graph=True)) # [4.]
+COPY-FROM: paddle.grad:code-example-1
 
 代码示例 2
 :::::::::
-  .. code-block:: python
-
-        import paddle
-
-        def test_dygraph_grad(grad_outputs=None):
-            x = paddle.to_tensor(2.0)
-            x.stop_gradient = False
-
-            y1 = x * x
-            y2 = x * 3
-
-            # If grad_outputs=None, dy1 = [1], dy2 = [1].
-            # If grad_outputs=[g1, g2], then:
-            #    - dy1 = [1] if g1 is None else g1
-            #    - dy2 = [1] if g2 is None else g2
-
-            # Since y1 = x * x, dx = 2 * x * dy1.
-            # Since y2 = x * 3, dx = 3 * dy2.
-            # Therefore, the final result would be:
-            # dx = 2 * x * dy1 + 3 * dy2 = 4 * dy1 + 3 * dy2.
-
-            dx = paddle.grad(
-                outputs=[y1, y2],
-                inputs=[x],
-                grad_outputs=grad_outputs)[0]
-
-            return dx.numpy()
-
-        grad_value = paddle.to_tensor(4.0)
-
-        # dy1 = [1], dy2 = [1]
-        print(test_dygraph_grad(None)) # [7.]
-
-        # dy1 = [1], dy2 = [4]
-        print(test_dygraph_grad([None, grad_value])) # [16.]
-
-        # dy1 = [4], dy2 = [1]
-        print(test_dygraph_grad([grad_value, None])) # [19.]
-
-        # dy1 = [3], dy2 = [4]
-        grad_y1 = paddle.to_tensor(3.0)
-        print(test_dygraph_grad([grad_y1, grad_value])) # [24.]
+COPY-FROM: paddle.grad:code-example-2

docs/api/paddle/scatter_cn.rst

@@ -7,28 +7,7 @@ scatter
 
 通过基于 ``updates`` 来更新选定索引 ``index`` 上的输入来获得输出。具体行为如下：
 
-    .. code-block:: python
-
-        import paddle
-        #input:
-        x = paddle.to_tensor([[1, 1], [2, 2], [3, 3]], dtype='float32')
-        index = paddle.to_tensor([2, 1, 0, 1], dtype='int64')
-        # shape of updates should be the same as x
-        # shape of updates with dim > 1 should be the same as input
-        updates = paddle.to_tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32')
-        overwrite = False
-        # calculation:
-        if not overwrite:
-            for i in range(len(index)):
-                x[index[i]] = paddle.zeros([2])
-        for i in range(len(index)):
-            if (overwrite):
-                x[index[i]] = updates[i]
-            else:
-                x[index[i]] += updates[i]
-        # output:
-        out = paddle.to_tensor([[3, 3], [6, 6], [1, 1]])
-        out.shape # [3, 2]
+COPY-FROM: paddle.scatter:code-example1
 
 **Notice：**
 因为 ``updates`` 的应用顺序是不确定的，因此，如果索引 ``index`` 包含重复项，则输出将具有不确定性。