[xdoctest][task 331] reformat example code with google style in python/paddle/base/data_feeder.py

python/paddle/base/data_feeder.py

@@ -350,40 +350,40 @@ class DataFeeder:
         :code:`ValueError` - If some Variables are not in this Program.
 
     Example:
-        ..  code-block:: python
+        .. code-block:: python
 
-            import numpy as np
-            import paddle
-            import paddle.base as base
+            >>> import numpy as np
+            >>> import paddle
+            >>> import paddle.base as base
 
-            place = base.CPUPlace()
-            def reader():
-                for _ in range(4):
-                    yield np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32'),
+            >>> place = base.CPUPlace()
+            >>> def reader():
+            ...     for _ in range(4):
+            ...         yield np.random.random([4]).astype('float32'), np.random.random([3]).astype('float32'),
+            ... 
+            >>> main_program = base.Program()
+            >>> startup_program = base.Program()
 
-            main_program = base.Program()
-            startup_program = base.Program()
+            >>> with base.program_guard(main_program, startup_program):
+            ...     data_1 = paddle.static.data(name='data_1', shape=[None, 2, 2], dtype='float32')
+            ...     data_2 = paddle.static.data(name='data_2', shape=[None, 1, 3], dtype='float32')
+            ...     out = paddle.static.nn.fc(x=[data_1, data_2], size=2)
+            ...     # ...
+            >>> feeder = base.DataFeeder([data_1, data_2], place)
 
-            with base.program_guard(main_program, startup_program):
-                data_1 = paddle.static.data(name='data_1', shape=[None, 2, 2], dtype='float32')
-                data_2 = paddle.static.data(name='data_2', shape=[None, 1, 3], dtype='float32')
-                out = paddle.static.nn.fc(x=[data_1, data_2], size=2)
-                # ...
-            feeder = base.DataFeeder([data_1, data_2], place)
+            >>> exe = base.Executor(place)
+            >>> exe.run(startup_program)
 
-            exe = base.Executor(place)
-            exe.run(startup_program)
+            >>> feed_data = feeder.feed(reader())
 
-            feed_data = feeder.feed(reader())
+            >>> # print feed_data to view feed results
+            >>> # print(feed_data['data_1'])
+            >>> # print(feed_data['data_2'])
 
-            # print feed_data to view feed results
-            # print(feed_data['data_1'])
-            # print(feed_data['data_2'])
-
-            outs = exe.run(program=main_program,
-                            feed=feed_data,
-                            fetch_list=[out])
-            print(outs)
+            >>> outs = exe.run(program=main_program,
+            ...                 feed=feed_data,
+            ...                 fetch_list=[out])
+            >>> print(outs)
 
     """
 
@@ -418,30 +418,30 @@ def feed(self, iterable):
             :code:`dict`: a :code:`dict` that contains (variable name - converted tensor) pairs
 
         Example:
-            ..  code-block:: python
-
-                # In this example, reader - generator will return a list of ndarray of 3 elements
-                # feed API will convert each ndarray input into a tensor
-                # the return result is a dict with keys: data_1, data_2, data_3
-                # result['data_1']  a LoD-Tensor with shape of  [5, 2, 1, 3]. 5 is batch size, and [2, 1, 3] is the real shape of data_1.
-                # result['data_2'], result['data_3'] are similar.
-                import numpy as np
-                import paddle.base as base
-
-                def reader(limit=5):
-                    for i in range(1, limit + 1):
-                        yield np.ones([6]).astype('float32') * i , np.ones([1]).astype('int64') * i, np.random.random([9]).astype('float32')
-
-                data_1 = paddle.static.data(name='data_1', shape=[None, 2, 1, 3])
-                data_2 = paddle.static.data(name='data_2', shape=[None, 1], dtype='int64')
-                data_3 = paddle.static.data(name='data_3', shape=[None, 3, 3], dtype='float32')
-                feeder = base.DataFeeder(['data_1','data_2', 'data_3'], base.CPUPlace())
-
-
-                result = feeder.feed(reader())
-                print(result['data_1'])
-                print(result['data_2'])
-                print(result['data_3'])
+            .. code-block:: python
+
+                >>> # In this example, reader - generator will return a list of ndarray of 3 elements
+                >>> # feed API will convert each ndarray input into a tensor
+                >>> # the return result is a dict with keys: data_1, data_2, data_3
+                >>> # result['data_1']  a LoD-Tensor with shape of  [5, 2, 1, 3]. 5 is batch size, and [2, 1, 3] is the real shape of data_1.
+                >>> # result['data_2'], result['data_3'] are similar.
+                >>> import numpy as np
+                >>> import paddle.base as base
+
+                >>> def reader(limit=5):
+                ...     for i in range(1, limit + 1):
+                ...         yield np.ones([6]).astype('float32') * i , np.ones([1]).astype('int64') * i, np.random.random([9]).astype('float32')
+                ... 
+                >>> data_1 = paddle.static.data(name='data_1', shape=[None, 2, 1, 3])
+                >>> data_2 = paddle.static.data(name='data_2', shape=[None, 1], dtype='int64')
+                >>> data_3 = paddle.static.data(name='data_3', shape=[None, 3, 3], dtype='float32')
+                >>> feeder = base.DataFeeder(['data_1','data_2', 'data_3'], base.CPUPlace())
+
+
+                >>> result = feeder.feed(reader())
+                >>> print(result['data_1'])
+                >>> print(result['data_2'])
+                >>> print(result['data_3'])
 
         """
         converter = []