update demo code test=develop

doc/fluid/api_cn/dygraph_cn/Flatten_cn.rst

@@ -26,12 +26,13 @@ Flatten
 .. code-block:: python
     
     import paddle
-    from paddle.imperative import to_variable
+    from paddle import to_variable
     import numpy as np
+
     inp_np = np.ones([5, 2, 3, 4]).astype('float32')
-    
-    paddle.enable_imperative()
-    
+
+    paddle.disable_static()
+
     inp_np = to_variable(inp_np)
     flatten = paddle.nn.Flatten(start_axis=1, stop_axis=2)
     flatten_res = flatten(inp_np)

doc/fluid/api_cn/tensor_cn/addmm_cn.rst

@@ -37,14 +37,19 @@ addmm
 
     import numpy as np
     import paddle
+
     data_x = np.ones((2, 2)).astype(np.float32)
     data_y = np.ones((2, 2)).astype(np.float32)
     data_input = np.ones((2, 2)).astype(np.float32)
-    paddle.enable_imperative()
-    x = paddle.imperative.to_variable(data_x)
-    y = paddle.imperative.to_variable(data_y)
-    input = paddle.imperative.to_variable(data_input)
+
+    paddle.disable_static()
+
+    x = paddle.to_variable(data_x)
+    y = paddle.to_variable(data_y)
+    input = paddle.to_variable(data_input)
+
     out = paddle.tensor.addmm( input=input, x=x, y=y, beta=0.5, alpha=5.0 )
+
     print( out.numpy() )
     # [[10.5 10.5]
     # [10.5 10.5]]

doc/fluid/api_cn/tensor_cn/bmm_cn.rst

@@ -33,14 +33,16 @@ bmm
 .. code-block:: python
     
     import paddle
+
     # In imperative mode:
     # size input1: (2, 2, 3) and input2: (2, 3, 2)
     input1 = np.array([[[1.0, 1.0, 1.0],[2.0, 2.0, 2.0]],[[3.0, 3.0, 3.0],[4.0, 4.0, 4.0]]])
     input2 = np.array([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],[[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])
-    paddle.enable_imperative()
+
+    paddle.disable_static()
     
-    x = paddle.imperative.to_variable(input1)
-    y = paddle.imperative.to_variable(input2)
+    x = paddle.to_variable(input1)
+    y = paddle.to_variable(input2)
     out = paddle.bmm(x, y)
     #output size: (2, 2, 2)
     #output value:

doc/fluid/api_cn/tensor_cn/flatten_cn.rst

@@ -58,12 +58,14 @@ flatten op 根据给定的start_axis 和 stop_axis 将连续的维度展平
 
     import paddle
     import numpy as np
-    paddle.enable_imperative()
+
+    paddle.disable_static()
+
     image_shape=(2, 3, 4, 4)
     x = np.arange(image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3]).reshape(image_shape) / 100.
     x = x.astype('float32')
     
-    img = paddle.imperative.to_variable(x)
+    img = paddle.to_variable(x)
     out = paddle.flatten(img, start_axis=1, stop_axis=2)
     # out shape is [2, 12, 4]
         

doc/fluid/api_cn/tensor_cn/tril_cn.rst

@@ -28,22 +28,27 @@ tril
 
     import numpy as np
     import paddle
+
     data = np.arange(1, 13, dtype="int64").reshape(3,-1)
     # array([[ 1,  2,  3,  4],
     #        [ 5,  6,  7,  8],
     #        [ 9, 10, 11, 12]])
-    paddle.enable_imperative()
-    x = paddle.imperative.to_variable(data)
+
+    paddle.disable_static()
+
+    x = paddle.to_variable(data)
     
     tril1 = paddle.tensor.tril(x)
     # array([[ 1,  0,  0,  0],
     #        [ 5,  6,  0,  0],
     #        [ 9, 10, 11,  0]])
+
     # example 2, positive diagonal value
     tril2 = paddle.tensor.tril(x, diagonal=2)
     # array([[ 1,  2,  3,  0], 
     #        [ 5,  6,  7,  8],
     #        [ 9, 10, 11, 12]])
+
     # example 3, negative diagonal value
     tril3 = paddle.tensor.tril(x, diagonal=-1)
     # array([[ 0,  0,  0,  0],

doc/fluid/api_cn/tensor_cn/triu_cn.rst

@@ -29,22 +29,27 @@ triu
 
     import numpy as np
     import paddle
+
     data = np.arange(1, 13, dtype="int64").reshape(3,-1)
     # array([[ 1,  2,  3,  4],
     #        [ 5,  6,  7,  8],
     #        [ 9, 10, 11, 12]])
-    paddle.enable_imperative()
+
+    paddle.disable_static()
+
     # example 1, default diagonal
-    x = paddle.imperative.to_variable(data)
+    x = paddle.to_variable(data)
     triu1 = paddle.tensor.triu(x)
     # array([[ 1,  2,  3,  4],
     #        [ 0,  6,  7,  8],
     #        [ 0,  0, 11, 12]])
+
     # example 2, positive diagonal value
     triu2 = paddle.tensor.triu(x, diagonal=2)
     # array([[0, 0, 3, 4],
     #        [0, 0, 0, 8],
     #        [0, 0, 0, 0]])
+
     # example 3, negative diagonal value
     triu3 = paddle.tensor.triu(x, diagonal=-1)
     # array([[ 1,  2,  3,  4],