fix code example (PaddlePaddle#28636)

* fix code example, test=document_fix

python/paddle/fluid/layers/loss.py

@@ -329,10 +329,15 @@ def square_error_cost(input, label):
             input = paddle.to_tensor([1.1, 1.9])
             label = paddle.to_tensor([1.0, 2.0])
             output = paddle.nn.functional.square_error_cost(input, label)
-            print(output.numpy())
+            print(output)
             # [0.01, 0.01]
 
     """
+    if in_dygraph_mode():
+        minus_out = core.ops.elementwise_sub(input, label)
+        square_out = core.ops.square(minus_out)
+        return square_out
+
     check_variable_and_dtype(input, "input", ['float32', 'float64'],
                              'square_error_cost')
     check_variable_and_dtype(label, "label", ['float32', 'float64'],

python/paddle/fluid/layers/nn.py

@@ -5698,16 +5698,18 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
         ${out_comment}.
 
     Examples:
-        >>>  # for LodTensor inputs
-        >>> import paddle.fluid as fluid
-        >>> import paddle
-        >>> paddle.enable_static()
-        >>> x = fluid.data(name='x', shape=[9, 16],
-        >>>                        dtype='float32', lod_level=1)
-        >>> out = fluid.layers.row_conv(input=x, future_context_size=2)
-        >>> # for Tensor inputs
-        >>> x = fluid.data(name='x', shape=[9, 4, 16], dtype='float32')
-        >>> out = fluid.layers.row_conv(input=x, future_context_size=2)
+
+      .. code-block:: python
+
+        # for LodTensor inputs
+        import paddle
+        paddle.enable_static()
+        x = paddle.static.data(name='x', shape=[9, 16],
+                               dtype='float32', lod_level=1)
+        out = paddle.static.nn.row_conv(input=x, future_context_size=2)
+        # for Tensor inputs
+        x = paddle.static.data(name='x', shape=[9, 4, 16], dtype='float32')
+        out = paddle.static.nn.row_conv(input=x, future_context_size=2)
     """
     helper = LayerHelper('row_conv', **locals())
     check_variable_and_dtype(input, 'input', ['float32'], 'row_conv')

python/paddle/nn/functional/loss.py

@@ -989,32 +989,11 @@ def mse_loss(input, label, reduction='mean', name=None):
         .. code-block:: python
 
             import paddle
-
-
-            # static graph mode
-            paddle.enable_static()
             mse_loss = paddle.nn.loss.MSELoss()
-            input = paddle.fluid.data(name="input", shape=[1])
-            label = paddle.fluid.data(name="label", shape=[1])
-            place = paddle.CPUPlace()
-
-            output = mse_loss(input,label)
-            exe = paddle.static.Executor(place)
-            exe.run(paddle.static.default_startup_program())
-            output_data = exe.run(
-                paddle.static.default_main_program(),
-                feed={"input":input_data, "label":label_data},
-                fetch_list=[output],
-                return_numpy=True)
-            print(output_data)
-            # [array([0.04000002], dtype=float32)]
-
-            # dynamic graph mode
-            paddle.disable_static()
             input = paddle.to_tensor(1.5)
             label = paddle.to_tensor(1.7)
             output = mse_loss(input, label)
-            print(output.numpy())
+            print(output)
             # [0.04000002]
 
     """

python/paddle/nn/functional/pooling.py

@@ -887,6 +887,7 @@ def adaptive_avg_pool1d(x, output_size, name=None):
             ValueError: 'output_size' should be an integer.
     Examples:
         .. code-block:: python
+
               # average adaptive pool1d
               # suppose input data in shape of [N, C, L], `output_size` is m or [m],
               # output shape is [N, C, m], adaptive pool divide L dimension
@@ -961,6 +962,7 @@ def adaptive_avg_pool2d(x, output_size, data_format='NCHW', name=None):
         ValueError: If `data_format` is not "NCHW" or "NHWC".
     Examples:
         .. code-block:: python
+
             # adaptive avg pool2d
             # suppose input data in shape of [N, C, H, W], `output_size` is [m, n],
             # output shape is [N, C, m, n], adaptive pool divide H and W dimensions
@@ -1062,6 +1064,7 @@ def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None):
         ValueError: If `data_format` is not "NCDHW" or "NDHWC".
     Examples:
         .. code-block:: python
+
             # adaptive avg pool3d
             # suppose input data in shape of [N, C, D, H, W], `output_size` is [l, m, n],
             # output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimensions
@@ -1082,7 +1085,6 @@ def adaptive_avg_pool3d(x, output_size, data_format='NCDHW', name=None):
             #                     avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
             import paddle
             import numpy as np
-
             input_data = np.random.rand(2, 3, 8, 32, 32)
             x = paddle.to_tensor(input_data)
             # x.shape is [2, 3, 8, 32, 32]