[xdoctest][task 60-64] reformat example code with google style in...

[xdoctest][task 60-64] reformat example code with google style in `geometric/*` ,`hapi/callbacks.py` (#55919)

* [Doctest]fix No.21, test=docs_preview

* Revert "[Doctest]fix No.21, test=docs_preview"

This reverts commit 76bcdb280e254d682be6fc6f85588f1940bb1ade.

* [Doctest]fix No.60-64, test=docs_preview

python/paddle/geometric/math.py

@@ -43,11 +43,13 @@ def segment_sum(data, segment_ids, name=None):
     Examples:
         .. code-block:: python
 
-            import paddle
-            data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
-            segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
-            out = paddle.geometric.segment_sum(data, segment_ids)
-            #Outputs: [[4., 4., 4.], [4., 5., 6.]]
+            >>> import paddle
+            >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
+            >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
+            >>> out = paddle.geometric.segment_sum(data, segment_ids)
+            >>> print(out.numpy())
+            [[4. 4. 4.]
+             [4. 5. 6.]]
 
     """
     if in_dynamic_mode():
@@ -99,11 +101,13 @@ def segment_mean(data, segment_ids, name=None):
     Examples:
         .. code-block:: python
 
-            import paddle
-            data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
-            segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
-            out = paddle.geometric.segment_mean(data, segment_ids)
-            #Outputs: [[2., 2., 2.], [4., 5., 6.]]
+            >>> import paddle
+            >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
+            >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
+            >>> out = paddle.geometric.segment_mean(data, segment_ids)
+            >>> print(out.numpy())
+            [[2. 2. 2.]
+             [4. 5. 6.]]
 
     """
 
@@ -155,11 +159,13 @@ def segment_min(data, segment_ids, name=None):
     Examples:
         .. code-block:: python
 
-            import paddle
-            data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
-            segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
-            out = paddle.geometric.segment_min(data, segment_ids)
-            #Outputs:  [[1., 2., 1.], [4., 5., 6.]]
+            >>> import paddle
+            >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
+            >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
+            >>> out = paddle.geometric.segment_min(data, segment_ids)
+            >>> print(out.numpy())
+            [[1. 2. 1.]
+             [4. 5. 6.]]
 
     """
 
@@ -211,11 +217,13 @@ def segment_max(data, segment_ids, name=None):
     Examples:
         .. code-block:: python
 
-            import paddle
-            data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
-            segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
-            out = paddle.geometric.segment_max(data, segment_ids)
-            #Outputs: [[3., 2., 3.], [4., 5., 6.]]
+            >>> import paddle
+            >>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
+            >>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
+            >>> out = paddle.geometric.segment_max(data, segment_ids)
+            >>> print(out.numpy())
+            [[3. 2. 3.]
+             [4. 5. 6.]]
 
     """
 

python/paddle/geometric/message_passing/send_recv.py

@@ -88,26 +88,34 @@ def send_u_recv(
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
-            src_index, dst_index = indexes[:, 0], indexes[:, 1]
-            out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum")
-            # Outputs: [[0., 2., 3.], [2., 8., 10.], [1., 4., 5.]]
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
-            src_index, dst_index = indexes[:, 0], indexes[:, 1]
-            out_size = paddle.max(dst_index) + 1
-            out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum", out_size=out_size)
-            # Outputs: [[0., 2., 3.], [[2., 8., 10.]]]
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
-            src_index, dst_index = indexes[:, 0], indexes[:, 1]
-            out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum")
-            # Outputs: [[0., 2., 3.], [2., 8., 10.], [0., 0., 0.]]
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
+            >>> out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum")
+            >>> print(out.numpy())
+            [[ 0. 2. 3.]
+             [ 2. 8. 10.]
+             [ 1. 4. 5.]]
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
+            >>> out_size = paddle.max(dst_index) + 1
+            >>> out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum", out_size=out_size)
+            >>> print(out.numpy())
+            [[ 0. 2. 3.]
+             [ 2. 8. 10.]]
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
+            >>> out = paddle.geometric.send_u_recv(x, src_index, dst_index, reduce_op="sum")
+            >>> print(out.numpy())
+            [[ 0. 2. 3.]
+             [ 2. 8. 10.]
+             [ 0. 0. 0.]]
 
     """
 
@@ -247,29 +255,37 @@ def send_ue_recv(
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            y = paddle.to_tensor([1, 1, 1, 1], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
-            src_index, dst_index = indexes[:, 0], indexes[:, 1]
-            out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum")
-            # Outputs: [[1., 3., 4.], [4., 10., 12.], [2., 5., 6.]]
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            y = paddle.to_tensor([1, 1, 1], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
-            src_index, dst_index = indexes[:, 0], indexes[:, 1]
-            out_size = paddle.max(dst_index) + 1
-            out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum", out_size=out_size)
-            # Outputs: [[1., 3., 4.], [[4., 10., 12.]]]
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            y = paddle.to_tensor([1, 1, 1], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
-            src_index, dst_index = indexes[:, 0], indexes[:, 1]
-            out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum")
-            # Outputs: [[1., 3., 4.], [4., 10., 12.], [0., 0., 0.]]
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> y = paddle.to_tensor([1, 1, 1, 1], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
+            >>> out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum")
+            >>> print(out.numpy())
+            [[ 1. 3. 4.]
+             [ 4. 10. 12.]
+             [ 2. 5. 6.]]
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> y = paddle.to_tensor([1, 1, 1], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
+            >>> out_size = paddle.max(dst_index) + 1
+            >>> out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum", out_size=out_size)
+            >>> print(out.numpy())
+            [[ 1. 3. 4.]
+             [ 4. 10. 12.]]
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> y = paddle.to_tensor([1, 1, 1], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index, dst_index = indexes[:, 0], indexes[:, 1]
+            >>> out = paddle.geometric.send_ue_recv(x, y, src_index, dst_index, message_op="add", reduce_op="sum")
+            >>> print(out.numpy())
+            [[ 1. 3. 4.]
+             [ 4. 10. 12.]
+             [ 0. 0. 0.]]
 
     """
 
@@ -425,15 +441,19 @@ def send_uv(x, y, src_index, dst_index, message_op="add", name=None):
 
         .. code-block:: python
 
-            import paddle
-
-            x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
-            y = paddle.to_tensor([[0, 1, 2], [2, 3, 4], [4, 5, 6]], dtype="float32")
-            indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
-            src_index = indexes[:, 0]
-            dst_index = indexes[:, 1]
-            out = paddle.geometric.send_uv(x, y, src_index, dst_index, message_op="add")
-            # Outputs: [[2., 5., 7.], [5., 9., 11.], [4., 9., 11.], [0., 3., 5.]]
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([[0, 2, 3], [1, 4, 5], [2, 6, 7]], dtype="float32")
+            >>> y = paddle.to_tensor([[0, 1, 2], [2, 3, 4], [4, 5, 6]], dtype="float32")
+            >>> indexes = paddle.to_tensor([[0, 1], [1, 2], [2, 1], [0, 0]], dtype="int32")
+            >>> src_index = indexes[:, 0]
+            >>> dst_index = indexes[:, 1]
+            >>> out = paddle.geometric.send_uv(x, y, src_index, dst_index, message_op="add")
+            >>> print(out.numpy())
+            [[ 2. 5. 7.]
+             [ 5. 9. 11.]
+             [ 4. 9. 11.]
+             [ 0. 3. 5.]]
 
     """
 

python/paddle/geometric/reindex.py

@@ -69,18 +69,21 @@ def reindex_graph(
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            x = [0, 1, 2]
-            neighbors = [8, 9, 0, 4, 7, 6, 7]
-            count = [2, 3, 2]
-            x = paddle.to_tensor(x, dtype="int64")
-            neighbors = paddle.to_tensor(neighbors, dtype="int64")
-            count = paddle.to_tensor(count, dtype="int32")
-            reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_graph(x, neighbors, count)
-            # reindex_src: [3, 4, 0, 5, 6, 7, 6]
-            # reindex_dst: [0, 0, 1, 1, 1, 2, 2]
-            # out_nodes: [0, 1, 2, 8, 9, 4, 7, 6]
+            >>> import paddle
+
+            >>> x = [0, 1, 2]
+            >>> neighbors = [8, 9, 0, 4, 7, 6, 7]
+            >>> count = [2, 3, 2]
+            >>> x = paddle.to_tensor(x, dtype="int64")
+            >>> neighbors = paddle.to_tensor(neighbors, dtype="int64")
+            >>> count = paddle.to_tensor(count, dtype="int32")
+            >>> reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_graph(x, neighbors, count)
+            >>> print(reindex_src.numpy())
+            [3 4 0 5 6 7 6]
+            >>> print(reindex_dst.numpy())
+            [0 0 1 1 1 2 2]
+            >>> print(out_nodes.numpy())
+            [0 1 2 8 9 4 7 6]
 
     """
     use_buffer_hashtable = (
@@ -182,24 +185,27 @@ def reindex_heter_graph(
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            x = [0, 1, 2]
-            neighbors_a = [8, 9, 0, 4, 7, 6, 7]
-            count_a = [2, 3, 2]
-            x = paddle.to_tensor(x, dtype="int64")
-            neighbors_a = paddle.to_tensor(neighbors_a, dtype="int64")
-            count_a = paddle.to_tensor(count_a, dtype="int32")
-            neighbors_b = [0, 2, 3, 5, 1]
-            count_b = [1, 3, 1]
-            neighbors_b = paddle.to_tensor(neighbors_b, dtype="int64")
-            count_b = paddle.to_tensor(count_b, dtype="int32")
-            neighbors = [neighbors_a, neighbors_b]
-            count = [count_a, count_b]
-            reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_heter_graph(x, neighbors, count)
-            # reindex_src: [3, 4, 0, 5, 6, 7, 6, 0, 2, 8, 9, 1]
-            # reindex_dst: [0, 0, 1, 1, 1, 2, 2, 0, 1, 1, 1, 2]
-            # out_nodes: [0, 1, 2, 8, 9, 4, 7, 6, 3, 5]
+            >>> import paddle
+
+            >>> x = [0, 1, 2]
+            >>> neighbors_a = [8, 9, 0, 4, 7, 6, 7]
+            >>> count_a = [2, 3, 2]
+            >>> x = paddle.to_tensor(x, dtype="int64")
+            >>> neighbors_a = paddle.to_tensor(neighbors_a, dtype="int64")
+            >>> count_a = paddle.to_tensor(count_a, dtype="int32")
+            >>> neighbors_b = [0, 2, 3, 5, 1]
+            >>> count_b = [1, 3, 1]
+            >>> neighbors_b = paddle.to_tensor(neighbors_b, dtype="int64")
+            >>> count_b = paddle.to_tensor(count_b, dtype="int32")
+            >>> neighbors = [neighbors_a, neighbors_b]
+            >>> count = [count_a, count_b]
+            >>> reindex_src, reindex_dst, out_nodes = paddle.geometric.reindex_heter_graph(x, neighbors, count)
+            >>> print(reindex_src.numpy())
+            [3 4 0 5 6 7 6 0 2 8 9 1]
+            >>> print(reindex_dst.numpy())
+            [0 0 1 1 1 2 2 0 1 1 1 2]
+            >>> print(out_nodes.numpy())
+            [0 1 2 8 9 4 7 6 3 5]
 
     """
     use_buffer_hashtable = (

python/paddle/geometric/sampling/neighbors.py

@@ -77,18 +77,18 @@ def sample_neighbors(
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            # edges: (3, 0), (7, 0), (0, 1), (9, 1), (1, 2), (4, 3), (2, 4),
-            #        (9, 5), (3, 5), (9, 6), (1, 6), (9, 8), (7, 8)
-            row = [3, 7, 0, 9, 1, 4, 2, 9, 3, 9, 1, 9, 7]
-            colptr = [0, 2, 4, 5, 6, 7, 9, 11, 11, 13, 13]
-            nodes = [0, 8, 1, 2]
-            sample_size = 2
-            row = paddle.to_tensor(row, dtype="int64")
-            colptr = paddle.to_tensor(colptr, dtype="int64")
-            nodes = paddle.to_tensor(nodes, dtype="int64")
-            out_neighbors, out_count = paddle.geometric.sample_neighbors(row, colptr, nodes, sample_size=sample_size)
+            >>> import paddle
+
+            >>> # edges: (3, 0), (7, 0), (0, 1), (9, 1), (1, 2), (4, 3), (2, 4),
+            >>> #        (9, 5), (3, 5), (9, 6), (1, 6), (9, 8), (7, 8)
+            >>> row = [3, 7, 0, 9, 1, 4, 2, 9, 3, 9, 1, 9, 7]
+            >>> colptr = [0, 2, 4, 5, 6, 7, 9, 11, 11, 13, 13]
+            >>> nodes = [0, 8, 1, 2]
+            >>> sample_size = 2
+            >>> row = paddle.to_tensor(row, dtype="int64")
+            >>> colptr = paddle.to_tensor(colptr, dtype="int64")
+            >>> nodes = paddle.to_tensor(nodes, dtype="int64")
+            >>> out_neighbors, out_count = paddle.geometric.sample_neighbors(row, colptr, nodes, sample_size=sample_size)
 
     """
 
@@ -228,20 +228,20 @@ def weighted_sample_neighbors(
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            # edges: (3, 0), (7, 0), (0, 1), (9, 1), (1, 2), (4, 3), (2, 4),
-            #        (9, 5), (3, 5), (9, 6), (1, 6), (9, 8), (7, 8)
-            row = [3, 7, 0, 9, 1, 4, 2, 9, 3, 9, 1, 9, 7]
-            colptr = [0, 2, 4, 5, 6, 7, 9, 11, 11, 13, 13]
-            weight = [0.1, 0.5, 0.2, 0.5, 0.9, 1.9, 2.0, 2.1, 0.01, 0.9, 0,12, 0.59, 0.67]
-            nodes = [0, 8, 1, 2]
-            sample_size = 2
-            row = paddle.to_tensor(row, dtype="int64")
-            colptr = paddle.to_tensor(colptr, dtype="int64")
-            weight = paddle.to_tensor(weight, dtype="float32")
-            nodes = paddle.to_tensor(nodes, dtype="int64")
-            out_neighbors, out_count = paddle.geometric.weighted_sample_neighbors(row, colptr, weight, nodes, sample_size=sample_size)
+            >>> import paddle
+
+            >>> # edges: (3, 0), (7, 0), (0, 1), (9, 1), (1, 2), (4, 3), (2, 4),
+            >>> #        (9, 5), (3, 5), (9, 6), (1, 6), (9, 8), (7, 8)
+            >>> row = [3, 7, 0, 9, 1, 4, 2, 9, 3, 9, 1, 9, 7]
+            >>> colptr = [0, 2, 4, 5, 6, 7, 9, 11, 11, 13, 13]
+            >>> weight = [0.1, 0.5, 0.2, 0.5, 0.9, 1.9, 2.0, 2.1, 0.01, 0.9, 0,12, 0.59, 0.67]
+            >>> nodes = [0, 8, 1, 2]
+            >>> sample_size = 2
+            >>> row = paddle.to_tensor(row, dtype="int64")
+            >>> colptr = paddle.to_tensor(colptr, dtype="int64")
+            >>> weight = paddle.to_tensor(weight, dtype="float32")
+            >>> nodes = paddle.to_tensor(nodes, dtype="int64")
+            >>> out_neighbors, out_count = paddle.geometric.weighted_sample_neighbors(row, colptr, weight, nodes, sample_size=sample_size)
 
     """
 

python/paddle/hapi/callbacks.py

@@ -137,19 +137,19 @@ class Callback:
 
         .. code-block:: python
 
-            import paddle
-
-            # build a simple model checkpoint callback
-            class ModelCheckpoint(paddle.callbacks.Callback):
-                def __init__(self, save_freq=1, save_dir=None):
-                    self.save_freq = save_freq
-                    self.save_dir = save_dir
-
-                def on_epoch_end(self, epoch, logs=None):
-                    if self.model is not None and epoch % self.save_freq == 0:
-                        path = '{}/{}'.format(self.save_dir, epoch)
-                        print('save checkpoint at {}'.format(path))
-                        self.model.save(path)
+            >>> import paddle
+
+            >>> # build a simple model checkpoint callback
+            >>> class ModelCheckpoint(paddle.callbacks.Callback):
+            ...     def __init__(self, save_freq=1, save_dir=None):
+            ...         self.save_freq = save_freq
+            ...         self.save_dir = save_dir
+            ...
+            ...     def on_epoch_end(self, epoch, logs=None):
+            ...         if self.model is not None and epoch % self.save_freq == 0:
+            ...             path = '{}/{}'.format(self.save_dir, epoch)
+            ...             print('save checkpoint at {}'.format(path))
+            ...             self.model.save(path)
 
     """
 
@@ -314,31 +314,31 @@ class ProgBarLogger(Callback):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.vision.transforms as T
-            from paddle.vision.datasets import MNIST
-            from paddle.static import InputSpec
+            >>> import paddle
+            >>> import paddle.vision.transforms as T
+            >>> from paddle.vision.datasets import MNIST
+            >>> from paddle.static import InputSpec
 
-            inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
-            labels = [InputSpec([None, 1], 'int64', 'label')]
+            >>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+            >>> labels = [InputSpec([None, 1], 'int64', 'label')]
 
-            transform = T.Compose([
-                T.Transpose(),
-                T.Normalize([127.5], [127.5])
-            ])
-            train_dataset = MNIST(mode='train', transform=transform)
+            >>> transform = T.Compose([
+            ...     T.Transpose(),
+            ...     T.Normalize([127.5], [127.5])
+            ... ])
+            >>> train_dataset = MNIST(mode='train', transform=transform)
 
-            lenet = paddle.vision.models.LeNet()
-            model = paddle.Model(lenet,
-                inputs, labels)
+            >>> lenet = paddle.vision.models.LeNet()
+            >>> model = paddle.Model(lenet,
+            ...     inputs, labels)
 
-            optim = paddle.optimizer.Adam(0.001, parameters=lenet.parameters())
-            model.prepare(optimizer=optim,
-                        loss=paddle.nn.CrossEntropyLoss(),
-                        metrics=paddle.metric.Accuracy())
+            >>> optim = paddle.optimizer.Adam(0.001, parameters=lenet.parameters())
+            >>> model.prepare(optimizer=optim,
+            ...             loss=paddle.nn.CrossEntropyLoss(),
+            ...             metrics=paddle.metric.Accuracy())
 
-            callback = paddle.callbacks.ProgBarLogger(log_freq=10)
-            model.fit(train_dataset, batch_size=64, callbacks=callback)
+            >>> callback = paddle.callbacks.ProgBarLogger(log_freq=10)
+            >>> model.fit(train_dataset, batch_size=64, callbacks=callback)
     """
 
     def __init__(self, log_freq=1, verbose=2):
@@ -562,31 +562,31 @@ class ModelCheckpoint(Callback):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.vision.transforms as T
-            from paddle.vision.datasets import MNIST
-            from paddle.static import InputSpec
+            >>> import paddle
+            >>> import paddle.vision.transforms as T
+            >>> from paddle.vision.datasets import MNIST
+            >>> from paddle.static import InputSpec
 
-            inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
-            labels = [InputSpec([None, 1], 'int64', 'label')]
+            >>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+            >>> labels = [InputSpec([None, 1], 'int64', 'label')]
 
-            transform = T.Compose([
-                T.Transpose(),
-                T.Normalize([127.5], [127.5])
-            ])
-            train_dataset = MNIST(mode='train', transform=transform)
+            >>> transform = T.Compose([
+            ...     T.Transpose(),
+            ...     T.Normalize([127.5], [127.5])
+            ... ])
+            >>> train_dataset = MNIST(mode='train', transform=transform)
 
-            lenet = paddle.vision.models.LeNet()
-            model = paddle.Model(lenet,
-                inputs, labels)
+            >>> lenet = paddle.vision.models.LeNet()
+            >>> model = paddle.Model(lenet,
+            ...     inputs, labels)
 
-            optim = paddle.optimizer.Adam(0.001, parameters=lenet.parameters())
-            model.prepare(optimizer=optim,
-                        loss=paddle.nn.CrossEntropyLoss(),
-                        metrics=paddle.metric.Accuracy())
+            >>> optim = paddle.optimizer.Adam(0.001, parameters=lenet.parameters())
+            >>> model.prepare(optimizer=optim,
+            ...             loss=paddle.nn.CrossEntropyLoss(),
+            ...             metrics=paddle.metric.Accuracy())
 
-            callback = paddle.callbacks.ModelCheckpoint(save_dir='./temp')
-            model.fit(train_dataset, batch_size=64, callbacks=callback)
+            >>> callback = paddle.callbacks.ModelCheckpoint(save_dir='./temp')
+            >>> model.fit(train_dataset, batch_size=64, callbacks=callback)
     """
 
     def __init__(self, save_freq=1, save_dir=None):
@@ -628,58 +628,58 @@ class LRScheduler(Callback):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.vision.transforms as T
-            from paddle.static import InputSpec
-
-            inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
-            labels = [InputSpec([None, 1], 'int64', 'label')]
-
-            transform = T.Compose([
-                T.Transpose(),
-                T.Normalize([127.5], [127.5])
-            ])
-            train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
-
-            lenet = paddle.vision.models.LeNet()
-            model = paddle.Model(lenet,
-                inputs, labels)
-
-            base_lr = 1e-3
-            boundaries = [5, 8]
-            wamup_steps = 4
-
-            def make_optimizer(parameters=None):
-                momentum = 0.9
-                weight_decay = 5e-4
-                values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
-                learning_rate = paddle.optimizer.lr.PiecewiseDecay(
-                    boundaries=boundaries, values=values)
-                learning_rate = paddle.optimizer.lr.LinearWarmup(
-                    learning_rate=learning_rate,
-                    warmup_steps=wamup_steps,
-                    start_lr=base_lr / 5.,
-                    end_lr=base_lr,
-                    verbose=True)
-                optimizer = paddle.optimizer.Momentum(
-                    learning_rate=learning_rate,
-                    weight_decay=weight_decay,
-                    momentum=momentum,
-                    parameters=parameters)
-                return optimizer
-
-            optim = make_optimizer(parameters=lenet.parameters())
-            model.prepare(optimizer=optim,
-                        loss=paddle.nn.CrossEntropyLoss(),
-                        metrics=paddle.metric.Accuracy())
-
-            # if LRScheduler callback not set, an instance LRScheduler update by step
-            # will be created auto.
-            model.fit(train_dataset, batch_size=64)
-
-            # create a learning rate scheduler update by epoch
-            callback = paddle.callbacks.LRScheduler(by_step=False, by_epoch=True)
-            model.fit(train_dataset, batch_size=64, callbacks=callback)
+            >>> import paddle
+            >>> import paddle.vision.transforms as T
+            >>> from paddle.static import InputSpec
+
+            >>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+            >>> labels = [InputSpec([None, 1], 'int64', 'label')]
+
+            >>> transform = T.Compose([
+            ...     T.Transpose(),
+            ...     T.Normalize([127.5], [127.5])
+            ... ])
+            >>> train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
+
+            >>> lenet = paddle.vision.models.LeNet()
+            >>> model = paddle.Model(lenet,
+            ...     inputs, labels)
+
+            >>> base_lr = 1e-3
+            >>> boundaries = [5, 8]
+            >>> wamup_steps = 4
+
+            >>> def make_optimizer(parameters=None):
+            ...     momentum = 0.9
+            ...     weight_decay = 5e-4
+            ...     values = [base_lr * (0.1**i) for i in range(len(boundaries) + 1)]
+            ...     learning_rate = paddle.optimizer.lr.PiecewiseDecay(
+            ...         boundaries=boundaries, values=values)
+            ...     learning_rate = paddle.optimizer.lr.LinearWarmup(
+            ...         learning_rate=learning_rate,
+            ...         warmup_steps=wamup_steps,
+            ...         start_lr=base_lr / 5.,
+            ...         end_lr=base_lr,
+            ...         verbose=True)
+            ...     optimizer = paddle.optimizer.Momentum(
+            ...         learning_rate=learning_rate,
+            ...         weight_decay=weight_decay,
+            ...         momentum=momentum,
+            ...         parameters=parameters)
+            ...     return optimizer
+
+            >>> optim = make_optimizer(parameters=lenet.parameters())
+            >>> model.prepare(optimizer=optim,
+            ...             loss=paddle.nn.CrossEntropyLoss(),
+            ...             metrics=paddle.metric.Accuracy())
+
+            >>> # if LRScheduler callback not set, an instance LRScheduler update by step
+            >>> # will be created auto.
+            >>> model.fit(train_dataset, batch_size=64)
+
+            >>> # create a learning rate scheduler update by epoch
+            >>> callback = paddle.callbacks.LRScheduler(by_step=False, by_epoch=True)
+            >>> model.fit(train_dataset, batch_size=64, callbacks=callback)
     """
 
     def __init__(self, by_step=True, by_epoch=False):
@@ -744,50 +744,50 @@ class EarlyStopping(Callback):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle import Model
-            from paddle.static import InputSpec
-            from paddle.vision.models import LeNet
-            from paddle.vision.datasets import MNIST
-            from paddle.metric import Accuracy
-            from paddle.nn import CrossEntropyLoss
-            import paddle.vision.transforms as T
-
-            device = paddle.set_device('cpu')
-            sample_num = 200
-            save_dir = './best_model_checkpoint'
-            transform = T.Compose(
-                [T.Transpose(), T.Normalize([127.5], [127.5])])
-            train_dataset = MNIST(mode='train', transform=transform)
-            val_dataset = MNIST(mode='test', transform=transform)
-            net = LeNet()
-            optim = paddle.optimizer.Adam(
-                learning_rate=0.001, parameters=net.parameters())
-
-            inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
-            labels = [InputSpec([None, 1], 'int64', 'label')]
-
-            model = Model(net, inputs=inputs, labels=labels)
-            model.prepare(
-                optim,
-                loss=CrossEntropyLoss(reduction="sum"),
-                metrics=[Accuracy()])
-            callbacks = paddle.callbacks.EarlyStopping(
-                'loss',
-                mode='min',
-                patience=1,
-                verbose=1,
-                min_delta=0,
-                baseline=None,
-                save_best_model=True)
-            model.fit(train_dataset,
-                      val_dataset,
-                      batch_size=64,
-                      log_freq=200,
-                      save_freq=10,
-                      save_dir=save_dir,
-                      epochs=20,
-                      callbacks=[callbacks])
+            >>> import paddle
+            >>> from paddle import Model
+            >>> from paddle.static import InputSpec
+            >>> from paddle.vision.models import LeNet
+            >>> from paddle.vision.datasets import MNIST
+            >>> from paddle.metric import Accuracy
+            >>> from paddle.nn import CrossEntropyLoss
+            >>> import paddle.vision.transforms as T
+
+            >>> device = paddle.set_device('cpu')
+            >>> sample_num = 200
+            >>> save_dir = './best_model_checkpoint'
+            >>> transform = T.Compose(
+            ...     [T.Transpose(), T.Normalize([127.5], [127.5])])
+            >>> train_dataset = MNIST(mode='train', transform=transform)
+            >>> val_dataset = MNIST(mode='test', transform=transform)
+            >>> net = LeNet()
+            >>> optim = paddle.optimizer.Adam(
+            ...     learning_rate=0.001, parameters=net.parameters())
+
+            >>> inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
+            >>> labels = [InputSpec([None, 1], 'int64', 'label')]
+
+            >>> model = Model(net, inputs=inputs, labels=labels)
+            >>> model.prepare(
+            ...     optim,
+            ...     loss=CrossEntropyLoss(reduction="sum"),
+            ...     metrics=[Accuracy()])
+            >>> callbacks = paddle.callbacks.EarlyStopping(
+            ...     'loss',
+            ...     mode='min',
+            ...     patience=1,
+            ...     verbose=1,
+            ...     min_delta=0,
+            ...     baseline=None,
+            ...     save_best_model=True)
+            >>> model.fit(train_dataset,
+            ...           val_dataset,
+            ...           batch_size=64,
+            ...           log_freq=200,
+            ...           save_freq=10,
+            ...           save_dir=save_dir,
+            ...           epochs=20,
+            ...           callbacks=[callbacks])
     """
 
     def __init__(
@@ -890,31 +890,31 @@ class VisualDL(Callback):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.vision.transforms as T
-            from paddle.static import InputSpec
+            >>> import paddle
+            >>> import paddle.vision.transforms as T
+            >>> from paddle.static import InputSpec
 
-            inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
-            labels = [InputSpec([None, 1], 'int64', 'label')]
+            >>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+            >>> labels = [InputSpec([None, 1], 'int64', 'label')]
 
-            transform = T.Compose([
-                T.Transpose(),
-                T.Normalize([127.5], [127.5])
-            ])
-            train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
-            eval_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
+            >>> transform = T.Compose([
+            ...     T.Transpose(),
+            ...     T.Normalize([127.5], [127.5])
+            ... ])
+            >>> train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
+            >>> eval_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
 
-            net = paddle.vision.models.LeNet()
-            model = paddle.Model(net, inputs, labels)
+            >>> net = paddle.vision.models.LeNet()
+            >>> model = paddle.Model(net, inputs, labels)
 
-            optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
-            model.prepare(optimizer=optim,
-                        loss=paddle.nn.CrossEntropyLoss(),
-                        metrics=paddle.metric.Accuracy())
+            >>> optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
+            >>> model.prepare(optimizer=optim,
+            ...             loss=paddle.nn.CrossEntropyLoss(),
+            ...             metrics=paddle.metric.Accuracy())
 
-            ## uncomment following lines to fit model with visualdl callback function
-            # callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')
-            # model.fit(train_dataset, eval_dataset, batch_size=64, callbacks=callback)
+            >>> ## uncomment following lines to fit model with visualdl callback function
+            >>> # callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')
+            >>> # model.fit(train_dataset, eval_dataset, batch_size=64, callbacks=callback)
 
     """
 
@@ -1019,31 +1019,31 @@ class WandbCallback(Callback):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.vision.transforms as T
-            from paddle.static import InputSpec
+            >>> import paddle
+            >>> import paddle.vision.transforms as T
+            >>> from paddle.static import InputSpec
 
-            inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
-            labels = [InputSpec([None, 1], 'int64', 'label')]
+            >>> inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
+            >>> labels = [InputSpec([None, 1], 'int64', 'label')]
 
-            transform = T.Compose([
-                T.Transpose(),
-                T.Normalize([127.5], [127.5])
-            ])
-            train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
-            eval_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
+            >>> transform = T.Compose([
+            ...     T.Transpose(),
+            ...     T.Normalize([127.5], [127.5])
+            ... ])
+            >>> train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=transform)
+            >>> eval_dataset = paddle.vision.datasets.MNIST(mode='test', transform=transform)
 
-            net = paddle.vision.models.LeNet()
-            model = paddle.Model(net, inputs, labels)
+            >>> net = paddle.vision.models.LeNet()
+            >>> model = paddle.Model(net, inputs, labels)
 
-            optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
-            model.prepare(optimizer=optim,
-                        loss=paddle.nn.CrossEntropyLoss(),
-                        metrics=paddle.metric.Accuracy())
+            >>> optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
+            >>> model.prepare(optimizer=optim,
+            ...             loss=paddle.nn.CrossEntropyLoss(),
+            ...             metrics=paddle.metric.Accuracy())
 
-            ## uncomment following lines to fit model with wandb callback function
-            # callback = paddle.callbacks.WandbCallback(project='paddle_mnist')
-            # model.fit(train_dataset, eval_dataset, batch_size=64, callbacks=callback)
+            >>> ## uncomment following lines to fit model with wandb callback function
+            >>> # callback = paddle.callbacks.WandbCallback(project='paddle_mnist')
+            >>> # model.fit(train_dataset, eval_dataset, batch_size=64, callbacks=callback)
 
     """
 
@@ -1200,37 +1200,37 @@ class ReduceLROnPlateau(Callback):
     Examples:
         .. code-block:: python
 
-              import paddle
-              from paddle import Model
-              from paddle.static import InputSpec
-              from paddle.vision.models import LeNet
-              from paddle.vision.datasets import MNIST
-              from paddle.metric import Accuracy
-              from paddle.nn.layer.loss import CrossEntropyLoss
-              import paddle.vision.transforms as T
-              sample_num = 200
-              transform = T.Compose(
-                  [T.Transpose(), T.Normalize([127.5], [127.5])])
-              train_dataset = MNIST(mode='train', transform=transform)
-              val_dataset = MNIST(mode='test', transform=transform)
-              net = LeNet()
-              optim = paddle.optimizer.Adam(
-                  learning_rate=0.001, parameters=net.parameters())
-              inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
-              labels = [InputSpec([None, 1], 'int64', 'label')]
-              model = Model(net, inputs=inputs, labels=labels)
-              model.prepare(
-                  optim,
-                  loss=CrossEntropyLoss(),
-                  metrics=[Accuracy()])
-              callbacks = paddle.callbacks.ReduceLROnPlateau(patience=3, verbose=1)
-              model.fit(train_dataset,
-                          val_dataset,
-                          batch_size=64,
-                          log_freq=200,
-                          save_freq=10,
-                          epochs=20,
-                          callbacks=[callbacks])
+            >>> import paddle
+            >>> from paddle import Model
+            >>> from paddle.static import InputSpec
+            >>> from paddle.vision.models import LeNet
+            >>> from paddle.vision.datasets import MNIST
+            >>> from paddle.metric import Accuracy
+            >>> from paddle.nn.layer.loss import CrossEntropyLoss
+            >>> import paddle.vision.transforms as T
+            >>> sample_num = 200
+            >>> transform = T.Compose(
+            ...      [T.Transpose(), T.Normalize([127.5], [127.5])])
+            >>> train_dataset = MNIST(mode='train', transform=transform)
+            >>> val_dataset = MNIST(mode='test', transform=transform)
+            >>> net = LeNet()
+            >>> optim = paddle.optimizer.Adam(
+            ...     learning_rate=0.001, parameters=net.parameters())
+            >>> inputs = [InputSpec([None, 1, 28, 28], 'float32', 'x')]
+            >>> labels = [InputSpec([None, 1], 'int64', 'label')]
+            >>> model = Model(net, inputs=inputs, labels=labels)
+            >>> model.prepare(
+            ...     optim,
+            ...     loss=CrossEntropyLoss(),
+            ...     metrics=[Accuracy()])
+            >>> callbacks = paddle.callbacks.ReduceLROnPlateau(patience=3, verbose=1)
+            >>> model.fit(train_dataset,
+            ...             val_dataset,
+            ...             batch_size=64,
+            ...             log_freq=200,
+            ...             save_freq=10,
+            ...             epochs=20,
+            ...             callbacks=[callbacks])
 
     """