[xdoctest] reformat example code with google style in No.344-No.352 (#…

…57134)

* fix sample codes

* fix bug

* fix bug

python/paddle/base/lod_tensor.py

@@ -62,10 +62,10 @@ def create_lod_tensor(data, recursive_seq_lens, place):
 
         .. code-block:: python
 
-            import paddle.base as base
-            import numpy as np
+            >>> import paddle.base as base
+            >>> import numpy as np
 
-            t = base.create_lod_tensor(np.ndarray([5, 30]), [[2, 3]], base.CPUPlace())
+            >>> t = base.create_lod_tensor(np.ndarray([5, 30]), [[2, 3]], base.CPUPlace())
     """
     if isinstance(data, core.LoDTensor):
         return create_lod_tensor(np.array(data), recursive_seq_lens, place)
@@ -154,13 +154,15 @@ def create_random_int_lodtensor(
         is inside [low, high].
 
     Examples:
+
         .. code-block:: python
 
-          import paddle.base as base
+            >>> import paddle.base as base
 
-          t = base.create_random_int_lodtensor(recursive_seq_lens=[[2, 3]],
-                    base_shape=[30], place=base.CPUPlace(), low=0, high=10)
-          print(t.shape()) # [5, 30]
+            >>> t = base.create_random_int_lodtensor(recursive_seq_lens=[[2, 3]],
+            ...         base_shape=[30], place=base.CPUPlace(), low=0, high=10)
+            >>> print(t.shape())
+            [5, 30]
     """
     assert isinstance(base_shape, list), "base_shape should be a list"
     # append the total number of basic elements to the front of its shape

python/paddle/base/log_helper.py

@@ -32,10 +32,15 @@ def get_logger(name, level, fmt=None):
         logging.Logger: logging logger with given settings
 
     Examples:
+
         .. code-block:: python
 
-            logger = log_helper.get_logger(__name__, logging.INFO,
-                            fmt='%(asctime)s-%(levelname)s: %(message)s')
+            >>> import paddle
+            >>> import logging
+            >>> from paddle.base import log_helper
+
+            >>> logger = log_helper.get_logger(__name__, logging.INFO,
+            ...                 fmt='%(asctime)s-%(levelname)s: %(message)s')
     """
 
     logger = logging.getLogger(name)

python/paddle/base/param_attr.py

@@ -61,14 +61,15 @@ class ParamAttr:
 
         .. code-block:: python
 
-            import paddle
-
-            weight_attr = paddle.ParamAttr(name="weight",
-                                           learning_rate=0.5,
-                                           regularizer=paddle.regularizer.L2Decay(1.0),
-                                           trainable=True)
-            print(weight_attr.name) # "weight"
-            paddle.nn.Linear(3, 4, weight_attr=weight_attr)
+            >>> import paddle
+
+            >>> weight_attr = paddle.ParamAttr(name="weight",
+            ...                                 learning_rate=0.5,
+            ...                                 regularizer=paddle.regularizer.L2Decay(1.0),
+            ...                                 trainable=True)
+            >>> print(weight_attr.name)
+            weight
+            >>> paddle.nn.Linear(3, 4, weight_attr=weight_attr)
     """
 
     def __init__(
@@ -259,24 +260,24 @@ class WeightNormParamAttr(ParamAttr):
 
         .. code-block:: python
 
-            import paddle
+            >>> import paddle
 
-            paddle.enable_static()
+            >>> paddle.enable_static()
 
-            data = paddle.static.data(name="data", shape=[3, 32, 32], dtype="float32")
-
-            fc = paddle.static.nn.fc(x=data,
-                                     size=1000,
-                                     weight_attr=paddle.static.WeightNormParamAttr(
-                                         dim=None,
-                                         name='weight_norm_param',
-                                         initializer=paddle.nn.initializer.Constant(1.0),
-                                         learning_rate=1.0,
-                                         regularizer=paddle.regularizer.L2Decay(0.1),
-                                         trainable=True,
-                                         do_model_average=False,
-                                         need_clip=True))
+            >>> data = paddle.static.data(name="data", shape=[3, 32, 32], dtype="float32")
 
+            >>> fc = paddle.static.nn.fc(x=data,
+            ...                             size=1000,
+            ...                             weight_attr=paddle.static.WeightNormParamAttr(
+            ...                                 dim=None,
+            ...                                 name='weight_norm_param',
+            ...                                 initializer=paddle.nn.initializer.Constant(1.0),
+            ...                                 learning_rate=1.0,
+            ...                                 regularizer=paddle.regularizer.L2Decay(0.1),
+            ...                                 trainable=True,
+            ...                                 do_model_average=False,
+            ...                                 need_clip=True))
+            ...
     """
     # List to record the parameters reparameterized by weight normalization.
     # If these parameters are treated as Variable rather than Parameter,