fix examples, test=document_fix (#29019)

* fix examples, test=document_fix

* fix, test=document_fix

python/paddle/fluid/layers/nn.py

@@ -3126,7 +3126,7 @@ def instance_norm(input,
         `H` means height of feature map, `W` means width of feature map.
 
     Args:
-        input(variable): The rank of input variable can be 2, 3, 4, 5.
+        input(Tensor): The rank of input tensor can be 2, 3, 4, 5.
             The data type is float32 or float64.
         epsilon(float, Default 1e-05): A value added to the denominator for
             numerical stability. Default is 1e-5.
@@ -3146,19 +3146,18 @@ def instance_norm(input,
             will be named automatically.
 
     Returns:
-        A Variable holding Tensor which is the result after applying instance normalization on the input,
+        A Tensor which is the result after applying instance normalization on the input,
         has same shape and data type with input.
 
     Examples:
 
         .. code-block:: python
 
-            import paddle.fluid as fluid
             import paddle
             paddle.enable_static()
-            x = fluid.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
-            hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
-            hidden2 = fluid.layers.instance_norm(input=hidden1)
+            x = paddle.static.data(name='x', shape=[3, 7, 3, 7], dtype='float32')
+            hidden1 = paddle.static.nn.fc(x, size=200)
+            hidden2 = paddle.static.nn.instance_norm(hidden1)
     """
     check_variable_and_dtype(input, 'input', ['float32', 'float64'],
                              'instance_norm')

python/paddle/nn/layer/activation.py

@@ -515,9 +515,6 @@ class LeakyReLU(layers.Layer):
         .. code-block:: python
 
             import paddle
-            import numpy as np
-
-            paddle.disable_static()
 
             m = paddle.nn.LeakyReLU()
             x = paddle.to_tensor(np.array([-2, 0, 1], 'float32'))

python/paddle/nn/layer/norm.py

@@ -996,12 +996,12 @@ class SyncBatchNorm(_BatchNormBase):
           import numpy as np
 
           x = np.array([[[[0.3, 0.4], [0.3, 0.07]], [[0.83, 0.37], [0.18, 0.93]]]]).astype('float32')
-          paddle.disable_static()
           x = paddle.to_tensor(x)
-          if paddle.fluid.is_compiled_with_cuda():
+
+          if paddle.is_compiled_with_cuda():
               sync_batch_norm = nn.SyncBatchNorm(2)
               hidden1 = sync_batch_norm(x)
-              print(hidden1.numpy())
+              print(hidden1)
               # [[[[0.26824948, 1.0936325],[0.26824948, -1.6301316]],[[ 0.8095662, -0.665287],[-1.2744656, 1.1301866 ]]]]
     """
 
@@ -1096,7 +1096,6 @@ class SyncBatchNorm(_BatchNormBase):
                 import paddle
                 import paddle.nn as nn
 
-                paddle.disable_static()
                 model = nn.Sequential(nn.Conv2D(3, 5, 3), nn.BatchNorm2D(5))
                 sync_model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
 

python/paddle/nn/utils/weight_norm_hook.py

@@ -188,7 +188,6 @@ def weight_norm(layer, name='weight', dim=0):
           from paddle.nn.utils import weight_norm
 
           x = np.array([[[[0.3, 0.4], [0.3, 0.07]], [[0.83, 0.37], [0.18, 0.93]]]]).astype('float32')
-          paddle.disable_static()
           conv = Conv2D(3, 5, 3)
           wn = weight_norm(conv)
           print(conv.weight_g.shape)
@@ -217,7 +216,6 @@ def remove_weight_norm(layer, name='weight'):
           from paddle.nn import Conv2D
           from paddle.nn.utils import weight_norm, remove_weight_norm
 
-          paddle.disable_static()
           conv = Conv2D(3, 5, 3)
           wn = weight_norm(conv)
           remove_weight_norm(conv)