Fix api 1128 (#29174)

* fix 2.0 api, test=develop

* fix api, test=develop

python/paddle/nn/functional/norm.py

@@ -150,7 +150,6 @@ def batch_norm(x,
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           x = np.random.seed(123)
           x = np.random.random(size=(2, 1, 2, 3)).astype('float32')
           running_mean = np.random.random(size=1).astype('float32')
@@ -163,7 +162,7 @@ def batch_norm(x,
           w = paddle.to_tensor(weight_data)
           b = paddle.to_tensor(bias_data)
           batch_norm_out = paddle.nn.functional.batch_norm(x, rm, rv, w, b)
-          print(batch_norm_out.numpy())
+          print(batch_norm_out)
     """
 
     assert len(x.shape) >= 2, "input dim must be larger than 1"
@@ -269,14 +268,13 @@ def layer_norm(x,
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 2, 2, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           layer_norm = paddle.nn.functional.layer_norm(x, x.shape[1:])
           layer_norm_out = layer_norm(x)
 
-          print(layer_norm_out.numpy())
+          print(layer_norm_out)
     """
     input_shape = list(x.shape)
     input_ndim = len(input_shape)
@@ -362,13 +360,12 @@ def instance_norm(x,
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 2, 2, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           instance_norm_out = paddle.nn.functional.instancenorm(x)
 
-          print(instance_norm_out.numpy())
+          print(instance_norm_out)
 
     """
 

python/paddle/nn/layer/norm.py

@@ -163,14 +163,13 @@ class InstanceNorm1D(_InstanceNormBase):
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 2, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           instance_norm = paddle.nn.InstanceNorm1D(2)
           instance_norm_out = instance_norm(x)
 
-          print(instance_norm_out.numpy())
+          print(instance_norm_out)
 
     """
 
@@ -235,14 +234,13 @@ class InstanceNorm2D(_InstanceNormBase):
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 2, 2, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           instance_norm = paddle.nn.InstanceNorm2D(2)
           instance_norm_out = instance_norm(x)
 
-          print(instance_norm_out.numpy())
+          print(instance_norm_out)
     """
 
     def _check_input_dim(self, input):
@@ -306,14 +304,13 @@ class InstanceNorm3D(_InstanceNormBase):
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 2, 2, 2, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           instance_norm = paddle.nn.InstanceNorm3D(2)
           instance_norm_out = instance_norm(x)
 
-          print(instance_norm_out.numpy())
+          print(instance_norm_out.numpy)
     """
 
     def _check_input_dim(self, input):
@@ -352,6 +349,7 @@ class GroupNorm(layers.Layer):
 
     Examples:
         .. code-block:: python
+
           import paddle
           import numpy as np
 
@@ -492,14 +490,13 @@ class LayerNorm(layers.Layer):
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 2, 2, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           layer_norm = paddle.nn.LayerNorm(x_data.shape[1:])
           layer_norm_out = layer_norm(x)
 
-          print(layer_norm_out.numpy())
+          print(layer_norm_out)
     """
 
     def __init__(self,
@@ -714,14 +711,13 @@ class BatchNorm1D(_BatchNormBase):
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 1, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           batch_norm = paddle.nn.BatchNorm1D(1)
           batch_norm_out = batch_norm(x)
 
-          print(batch_norm_out.numpy())
+          print(batch_norm_out)
     """
 
     def _check_data_format(self, input):
@@ -804,14 +800,13 @@ class BatchNorm2D(_BatchNormBase):
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 1, 2, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           batch_norm = paddle.nn.BatchNorm2D(1)
           batch_norm_out = batch_norm(x)
 
-          print(batch_norm_out.numpy())
+          print(batch_norm_out)
     """
 
     def _check_data_format(self, input):
@@ -893,14 +888,13 @@ class BatchNorm3D(_BatchNormBase):
           import paddle
           import numpy as np
 
-          paddle.disable_static()
           np.random.seed(123)
           x_data = np.random.random(size=(2, 1, 2, 2, 3)).astype('float32')
           x = paddle.to_tensor(x_data) 
           batch_norm = paddle.nn.BatchNorm3D(1)
           batch_norm_out = batch_norm(x)
 
-          print(batch_norm_out.numpy())
+          print(batch_norm_out)
     """
 
     def _check_data_format(self, input):

python/paddle/optimizer/adagrad.py

@@ -50,8 +50,8 @@ class Adagrad(Optimizer):
 	    The default value is None in static mode, at this time all parameters will be updated.
 	weight_decay (float|WeightDecayRegularizer, optional): The strategy of regularization. \
 	    It canbe a float value as coeff of L2 regularization or \
-	    :ref:`api_fluid_regularizer_L1Decay`, :ref:`api_fluid_regularizer_L2Decay`.
-	    If a parameter has set regularizer using :ref:`api_fluid_ParamAttr` already, \
+	    :ref:`api_paddle_regularizer_L1Decay`, :ref:`api_paddle_regularizer_L2Decay`.
+	    If a parameter has set regularizer using :ref:`api_paddle_fluid_param_attr_aramAttr` already, \
 	    the regularization setting here in optimizer will be ignored for this parameter. \
 	    Otherwise, the regularization setting here in optimizer will take effect. \
 	    Default None, meaning there is no regularization.
@@ -71,7 +71,6 @@ class Adagrad(Optimizer):
             import paddle
             import numpy as np
 
-            paddle.disable_static()
             inp = paddle.rand(shape=[10, 10])
             linear = paddle.nn.Linear(10, 10)
             out = linear(inp)