[xdoctest] reformat example code with google style No.186-190 (#56166)

* fix: updated code examples.

* fix: added paddle.seed

* fix: updated code style

* Apply suggestions from code review

* refactor: refine detail of code examples

* Update python/paddle/distributed/auto_parallel/static/process_mesh_v2.py

* fix: refine detail

* fix: refine detail

* Update python/paddle/distributed/auto_parallel/static/process_mesh_v2.py
Co-authored-by: NNyakku Shigure <sigure.qaq@gmail.com>

* refactor: refine detail

* refactor: refine detail

* fix: refine doc

---------
Co-authored-by: NNyakku Shigure <sigure.qaq@gmail.com>

python/paddle/distributed/auto_parallel/static/engine.py

@@ -79,39 +79,39 @@ class Engine:
 
         .. code-block:: python
 
-            import paddle
-            import paddle.vision.transforms as T
-            from paddle.distributed.fleet import auto
-            from paddle.vision.datasets import MNIST
-
-            transform = T.Compose([
-                T.Transpose(),
-                T.Normalize([127.5], [127.5])
-            ])
-            train_dataset = MNIST(mode='train', transform=transform)
-            valid_dataset = MNIST(mode='test', transform=transform)
-
-            model = paddle.vision.models.LeNet()
-            loss = paddle.nn.CrossEntropyLoss()
-            optimizer = paddle.optimizer.Adam(
-                learning_rate=0.001, parameters=model.parameters())
-            metrics = paddle.metric.Accuracy(topk=(1, 2))
-
-            engine = auto.Engine(model, loss, optimizer, metrics)
-            # fit
-            engine.fit(train_dataset,
-                       epochs=2,
-                       batch_size=64)
-            # evaluate
-            engine.evaluate(valid_dataset,
-                            batch_size=64)
-            # predict
-            engine.predict(valid_dataset,
-                           batch_size=64)
-            # save
-            engine.save("./my_model")
-            # load
-            engine.load("./my_model")
+            >>> import paddle
+            >>> import paddle.vision.transforms as T
+            >>> from paddle.distributed.fleet import auto
+            >>> from paddle.vision.datasets import MNIST
+
+            >>> transform = T.Compose([
+            ...     T.Transpose(),
+            ...     T.Normalize([127.5], [127.5])
+            >>> ])
+            >>> train_dataset = MNIST(mode='train', transform=transform)
+            >>> valid_dataset = MNIST(mode='test', transform=transform)
+
+            >>> model = paddle.vision.models.LeNet()
+            >>> loss = paddle.nn.CrossEntropyLoss()
+            >>> optimizer = paddle.optimizer.Adam(
+            ...     learning_rate=0.001, parameters=model.parameters())
+            >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
+
+            >>> engine = auto.Engine(model, loss, optimizer, metrics)
+            >>> # fit
+            >>> engine.fit(train_dataset,
+            ...            epochs=2,
+            ...            batch_size=64)
+            >>> # evaluate
+            >>> engine.evaluate(valid_dataset,
+            ...                 batch_size=64)
+            >>> # predict
+            >>> engine.predict(valid_dataset,
+            ...                batch_size=64)
+            >>> # save
+            >>> engine.save("./my_model")
+            >>> # load
+            >>> engine.load("./my_model")
 
     """
 
@@ -918,27 +918,27 @@ class Engine:
 
             .. code-block:: python
 
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
-
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                train_dataset = MNIST(mode='train', transform=transform)
-
-                model = paddle.vision.models.LeNet()
-                loss = paddle.nn.CrossEntropyLoss()
-                optimizer = paddle.optimizer.Adam(
-                    learning_rate=0.001, parameters=model.parameters())
-                metrics = paddle.metric.Accuracy(topk=(1, 2))
-
-                engine = auto.Engine(model, loss, optimizer, metrics)
-                engine.fit(train_dataset,
-                           epochs=2,
-                           batch_size=64)
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
+
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> train_dataset = MNIST(mode='train', transform=transform)
+
+                >>> model = paddle.vision.models.LeNet()
+                >>> loss = paddle.nn.CrossEntropyLoss()
+                >>> optimizer = paddle.optimizer.Adam(
+                ...     learning_rate=0.001, parameters=model.parameters())
+                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
+
+                >>> engine = auto.Engine(model, loss, optimizer, metrics)
+                >>> engine.fit(train_dataset,
+                ...             epochs=2,
+                ...             batch_size=64)
         """
         self._mode = 'train'
         self._inputs_spec, self._labels_spec = self._prepare_data_spec(
@@ -1071,23 +1071,23 @@ class Engine:
 
             .. code-block:: python
 
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
 
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                valid_dataset = MNIST(mode='test', transform=transform)
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> valid_dataset = MNIST(mode='test', transform=transform)
 
-                model = paddle.vision.models.LeNet()
-                loss = paddle.nn.CrossEntropyLoss()
-                metrics = paddle.metric.Accuracy(topk=(1, 2))
+                >>> model = paddle.vision.models.LeNet()
+                >>> loss = paddle.nn.CrossEntropyLoss()
+                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
 
-                engine = auto.Engine(model, loss, metrics=metrics)
-                engine.evaluate(valid_dataset, batch_size=64)
+                >>> engine = auto.Engine(model, loss, metrics=metrics)
+                >>> engine.evaluate(valid_dataset, batch_size=64)
 
         """
         self._mode = 'eval'
@@ -1181,21 +1181,21 @@ class Engine:
 
             .. code-block:: python
 
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
 
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                valid_dataset = MNIST(mode='test', transform=transform)
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> valid_dataset = MNIST(mode='test', transform=transform)
 
-                model = paddle.vision.models.LeNet()
+                >>> model = paddle.vision.models.LeNet()
 
-                engine = auto.Engine(model)
-                engine.predict(valid_dataset, batch_size=64)
+                >>> engine = auto.Engine(model)
+                >>> engine.predict(valid_dataset, batch_size=64)
         """
         self._mode = 'predict'
         self._inputs_spec, self._labels_spec = self._prepare_data_spec(
@@ -1650,28 +1650,29 @@ class Engine:
         Examples:
 
             .. code-block:: python
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
-
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                train_dataset = MNIST(mode='train', transform=transform)
-
-                model = paddle.vision.models.LeNet()
-                loss = paddle.nn.CrossEntropyLoss()
-                optimizer = paddle.optimizer.Adam(
-                    learning_rate=0.001, parameters=model.parameters())
-                metrics = paddle.metric.Accuracy(topk=(1, 2))
-
-                engine = auto.Engine(model, loss, optimizer, metrics)
-                engine.fit(train_dataset,
-                           epochs=1,
-                           batch_size=64)
-                engine.save("./my_model")
+
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
+
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> train_dataset = MNIST(mode='train', transform=transform)
+
+                >>> model = paddle.vision.models.LeNet()
+                >>> loss = paddle.nn.CrossEntropyLoss()
+                >>> optimizer = paddle.optimizer.Adam(
+                ...     learning_rate=0.001, parameters=model.parameters())
+                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
+
+                >>> engine = auto.Engine(model, loss, optimizer, metrics)
+                >>> engine.fit(train_dataset,
+                ...             epochs=1,
+                ...             batch_size=64)
+                >>> engine.save("./my_model")
 
         """
         if training:
@@ -1734,29 +1735,30 @@ class Engine:
         Examples:
 
             .. code-block:: python
-                import paddle
-                import paddle.vision.transforms as T
-                from paddle.distributed.fleet import auto
-                from paddle.vision.datasets import MNIST
-
-                transform = T.Compose([
-                    T.Transpose(),
-                    T.Normalize([127.5], [127.5])
-                ])
-                train_dataset = MNIST(mode='train', transform=transform)
-
-                model = paddle.vision.models.LeNet()
-                loss = paddle.nn.CrossEntropyLoss()
-                optimizer = paddle.optimizer.Adam(
-                    learning_rate=0.001, parameters=model.parameters())
-                metrics = paddle.metric.Accuracy(topk=(1, 2))
-
-                engine = auto.Engine(model, loss, optimizer, metrics)
-                engine.fit(train_dataset,
-                           epochs=1,
-                           batch_size=64)
-                engine.save("./my_model")
-                engine.load("./my_model")
+
+                >>> import paddle
+                >>> import paddle.vision.transforms as T
+                >>> from paddle.distributed.fleet import auto
+                >>> from paddle.vision.datasets import MNIST
+
+                >>> transform = T.Compose([
+                ...     T.Transpose(),
+                ...     T.Normalize([127.5], [127.5])
+                >>> ])
+                >>> train_dataset = MNIST(mode='train', transform=transform)
+
+                >>> model = paddle.vision.models.LeNet()
+                >>> loss = paddle.nn.CrossEntropyLoss()
+                >>> optimizer = paddle.optimizer.Adam(
+                ...     learning_rate=0.001, parameters=model.parameters())
+                >>> metrics = paddle.metric.Accuracy(topk=(1, 2))
+
+                >>> engine = auto.Engine(model, loss, optimizer, metrics)
+                >>> engine.fit(train_dataset,
+                ...             epochs=1,
+                ...             batch_size=64)
+                >>> engine.save("./my_model")
+                >>> engine.load("./my_model")
 
         """
         self._strict = strict

python/paddle/distributed/auto_parallel/static/process_mesh_v2.py

@@ -33,14 +33,13 @@ class ProcessMesh(core.ProcessMesh):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.distributed as dist
+            >>> import paddle
+            >>> import paddle.distributed as dist
+            >>> paddle.enable_static()
 
-            paddle.enable_static()
-
-            mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]])
-            assert mesh.shape == [2, 3]
-            assert mesh.processe_ids == [2, 4, 5, 0, 1, 3]
+            >>> mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]])
+            >>> assert mesh.shape == [2, 3]
+            >>> assert mesh.process_ids == [2, 4, 5, 0, 1, 3]
 
     """
 

python/paddle/distributed/auto_parallel/static/utils.py

@@ -611,10 +611,18 @@ def save_distributed_checkpoint(
     Examples:
         .. code-block:: python
 
-            path = os.path.join("./output", "step_%d" % step)
-            os.makedirs(path, exist_ok=True)
-            add_info = {'batch': step, "batch_size": global_batch_size}
-            save_distributed_checkpoint(program, path, path, add_info)
+            >>> import os
+            >>> from paddle.distributed.auto_parallel.static.utils import save_distributed_checkpoint
+
+            >>> step = 16000
+            >>> global_batch_size = 32
+            >>> path = os.path.join("./output", "step_%d" % step)
+            >>> os.makedirs(path, exist_ok=True)
+            >>> program = paddle.static.Program()
+
+            >>> add_info = {'batch': step, "batch_size": global_batch_size}
+            >>> save_distributed_checkpoint(program, path, path, add_info)
+
     """
     from .dist_context import get_default_distributed_context
 
@@ -653,11 +661,18 @@ def load_distributed_checkpoint(checkpoint_path, dist_attr_path):
     Examples:
         .. code-block:: python
 
-            ckpt_path = ['./model_state_rank0.pdmodel',
-                         './model_state_rank1.pdmodel']
-            dist_attr_path = ['./dist_attr_rank0.pdattr',
-                              './dist_attr_rank1.pdattr']
-            param_dict, dist_attr, add_info = load_distributed_checkpoint(ckpt_path, dist_attr_path)
+            >>> # doctest: +SKIP('Depends on external files.')
+            >>> from paddle.distributed.auto_parallel.static.utils import load_distributed_checkpoint
+
+            >>> ckpt_path = [
+            ...     './model_state_rank0.pdmodel',
+            ...     './model_state_rank1.pdmodel',
+            ... ]
+            >>> dist_attr_path = [
+            ...     './dist_attr_rank0.pdattr',
+            ...     './dist_attr_rank1.pdattr',
+            ... ]
+            >>> param_dict, dist_attr, add_info = load_distributed_checkpoint(ckpt_path, dist_attr_path)
     """
     assert _check_valid_path(
         checkpoint_path
@@ -692,12 +707,19 @@ def load_checkpoint_into_program(
     Examples:
         .. code-block:: python
 
-            exe.run(startup_program)
-            ckpt_path = ['./model_state_rank0.pdmodel',
-                         './model_state_rank1.pdmodel']
-            dist_attr_path = ['./dist_attr_rank0.pdattr',
-                              './dist_attr_rank1.pdattr']
-            load_checkpoint_into_program(ckpt_path, dist_attr_path, main_program)
+            >>> # doctest: +SKIP('Depends on external files.')
+            >>> from paddle.distributed.auto_parallel.static.utils import load_checkpoint_into_program
+
+            >>> exe.run(startup_program)
+            >>> ckpt_path = [
+            ...     './model_state_rank0.pdmodel',
+            ...     './model_state_rank1.pdmodel',
+            ... ]
+            >>> dist_attr_path = [
+            ...     './dist_attr_rank0.pdattr',
+            ...     './dist_attr_rank1.pdattr',
+            ... ]
+            >>> load_checkpoint_into_program(ckpt_path, dist_attr_path, main_program)
     """
     from .dist_context import get_default_distributed_context
 
@@ -999,13 +1021,18 @@ def _merge_parameter(
     Examples:
         .. code-block:: python
 
-            import numpy as np
-            partition_param_list = [(np.array([[[1.11, 1.12]]]), [[0,1],[0,1],[0,2]])]
-            param = np.array([[[1.13, 1.14]]])
-            partition_index = [[0,1],[0,1],[2,4]]
+            >>> import numpy as np
+            >>> from paddle.distributed.auto_parallel.static.utils import _merge_parameter
+
+            >>> partition_param_list = [(np.array([[[1.11, 1.12]]]), [[0, 1],[0, 1],[0, 2]])]
+            >>> param = np.array([[[1.13, 1.14]]])
+            >>> partition_index = [[0, 1],[0, 1],[2, 4]]
+            >>> complete_shape = [2, 2, 4]
+
+            >>> _merge_parameter(partition_param_list, param, partition_index, complete_shape)
+            >>> print(partition_param_list)
+            [(array([[[1.11, 1.12, 1.13, 1.14]]]), [[0, 1],[0, 1],[0, 4]])]
 
-            _merge_parameter(partition_param_list, param, partition_index)
-            # partition_param_list: [(np.array([[[1.11, 1.12, 1.13, 1.14]]]), [[0,1],[0,1],[0,4]])]
     """
     from .reshard import Resharder
 
@@ -1061,16 +1088,20 @@ def _slice_parameter(complete_param, partition_index_list, length):
     Examples:
         .. code-block:: python
 
-            import numpy as np
-            complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
-            rank = 2
-            complete_shape = [1, 1, 6]
-            dims_mapping = [-1, -1, 0]
-            process_shape = [3]
-            process_group = [0, 1, 2]
+            >>> import numpy as np
+            >>> from paddle.distributed.auto_parallel.static.utils import _slice_parameter
+
+            >>> complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
+            >>> rank = 2
+            >>> complete_shape = [1, 1, 6]
+            >>> dims_mapping = [-1, -1, 0]
+            >>> process_shape = [3]
+            >>> process_group = [0, 1, 2]
+
+            >>> sliced_param_list = _slice_parameter(complete_param, [[], [], [2, 4]], 3)
+            >>> print(sliced_param_list)
+            [array([[[1.11, 1.12]]]), array([[[1.13, 1.14]]]), array([[[1.15, 1.16]]])]
 
-            sliced_param_list = _slice_parameter(complete_param, [[], [], [2, 4]], 3)
-            # [array([[[1.11, 1.12]]]), array([[[1.13, 1.14]]]), array([[[1.15, 1.16]]])]
     """
     sliced_param_list = []
     axis = len(complete_param.shape) - length
@@ -1098,21 +1129,24 @@ def _get_sliced_param_index(
     Examples:
         .. code-block:: python
 
-            import numpy as np
-            complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
-            rank = 2
-            complete_shape = [1, 1, 6]
-            dims_mapping = [-1, -1, 0]
-            process_shape = [3]
-            process_group = [0, 1, 2]
-
-            slice_param = _slice_parameter(complete_param, [[], [], [2, 4]], 3)
-            # slice_param:
-            # [array([[[1.11, 1.12]]]), array([[[1.13, 1.14]]]), array([[[1.15, 1.16]]])]
-
-            index = _get_sliced_param_index(rank, complete_shape, dims_mapping
-                                            process_shape, process_group)
-            # index: 2
+            >>> import numpy as np
+            >>> from paddle.distributed.auto_parallel.static.utils import _get_sliced_param_index
+
+            >>> complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
+            >>> rank = 2
+            >>> complete_shape = [1, 1, 6]
+            >>> dims_mapping = [-1, -1, 0]
+            >>> process_shape = [3]
+            >>> process_group = [0, 1, 2]
+
+            >>> slice_param = _slice_parameter(complete_param, [[], [], [2, 4]], 3)
+            >>> print(slice_param)
+            [array([[[1.11, 1.12]]]), array([[[1.13, 1.14]]]), array([[[1.15, 1.16]]])]
+
+            >>> index = _get_sliced_param_index(rank, complete_shape, dims_mapping,
+            ...                                 process_shape, process_group)
+            >>> print(index)
+            2
     """
     from .reshard import Resharder
 
@@ -1145,15 +1179,18 @@ def _get_split_indices(
     Examples:
         .. code-block:: python
 
-            import numpy as np
-            complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
-            complete_shape = [1, 1, 6]
-            dims_mapping = [-1, -1, 0]
-            process_shape = [3]
-            process_group = [0, 1, 2]
+            >>> import numpy as np
+            >>> from paddle.distributed.auto_parallel.static.utils import _get_split_indices
+
+            >>> complete_param = np.array([[[1.11, 1.12, 1.13, 1.14, 1.15, 1.16]]])
+            >>> complete_shape = [1, 1, 6]
+            >>> dims_mapping = [-1, -1, 0]
+            >>> process_shape = [3]
+            >>> process_group = [0, 1, 2]
 
-            index = _get_split_indices(complete_shape, dims_mapping, process_shape, process_group)
-            # index: [[], [], [2, 4]]
+            >>> index = _get_split_indices(complete_shape, dims_mapping, process_shape, process_group)
+            >>> print(index)
+            [[], [], [2, 4]]
     """
     from .reshard import Resharder
 
@@ -2410,19 +2447,26 @@ def wrap_data_for_completion(
       output_specs: list, DistTensorSpec for each output tensor of the dist_op
       attrs: dict, attribute map of the dist op
 
-    Usage:
-      op_desc = dist_op.serial_op.desc
-      input_name_list = []
-      output_name_list = []
-      input_name_list.append(op_desc.input('X')[0]) # 'X' is the arg name for op
-      input_name_list.append(op_desc.input('Y')[0])
-      output_name_list.append(op_desc.output('Out')[0])
-      attr_name_list = ['trans_x', 'trans_y']
-      input_specs, output_specs, attrs = wrap_data_for_completion(
-          dist_op,
-          input_name_list,
-          output_name_list,
-          attr_name_list)
+    Examples:
+        .. code-block:: python
+
+            >>> # doctest: +SKIP('Depends on other ops.')
+            >>> from paddle.distributed.auto_parallel.static.utils import wrap_data_for_completion
+
+            >>> op_desc = dist_op.serial_op.desc
+            >>> input_name_list = []
+            >>> output_name_list = []
+
+            >>> input_name_list.append(op_desc.input('X')[0]) # 'X' is the arg name for op
+            >>> input_name_list.append(op_desc.input('Y')[0])
+            >>> output_name_list.append(op_desc.output('Out')[0])
+
+            >>> attr_name_list = ['trans_x', 'trans_y']
+            >>> input_specs, output_specs, attrs = wrap_data_for_completion(
+            ...        dist_op,
+            ...        input_name_list,
+            ...        output_name_list,
+            ...        attr_name_list)
 
     """
 

python/paddle/distributed/auto_parallel/strategy.py

@@ -152,20 +152,21 @@ class Strategy(BaseConfig):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.distributed.fleet import auto
-
-            strategy = auto.Strategy()
-            sharding = strategy.sharding
-            self.assertEqual(sharding.enabled, False)
-            self.assertEqual(sharding.stage, 1)
-            self.assertEqual(sharding.degree, 8)
-            sharding.enabled = True
-            sharding.stage = 2
-            sharding.degree = 2
-            self.assertEqual(sharding.enabled, True)
-            self.assertEqual(sharding.stage, 2)
-            self.assertEqual(sharding.degree, 2)
+            >>> import paddle
+            >>> from paddle.distributed.fleet import auto
+
+            >>> strategy = auto.Strategy()
+            >>> sharding = strategy.sharding
+            >>> assert sharding.enable == False
+            >>> assert sharding.stage == 1
+            >>> assert sharding.degree == 8
+
+            >>> sharding.enable = True
+            >>> sharding.stage = 2
+            >>> sharding.degree = 2
+            >>> assert sharding.enable == True
+            >>> assert sharding.stage == 2
+            >>> assert sharding.degree == 2
 
     """
 

python/paddle/distributed/collective.py

@@ -188,12 +188,13 @@ def new_group(ranks=None, backend=None, timeout=_default_timeout):
     Examples:
         .. code-block:: python
 
-            import paddle
+            >>> # doctest: +REQUIRES(env: DISTRIBUTED)
+            >>> import paddle
 
-            paddle.distributed.init_parallel_env()
-            tindata = paddle.randn(shape=[2, 3])
-            gp = paddle.distributed.new_group([2,4,6])
-            paddle.distributed.all_reduce(tindata, group=gp, sync_op=False)
+            >>> paddle.distributed.init_parallel_env()
+            >>> tindata = paddle.randn(shape=[2, 3])
+            >>> gp = paddle.distributed.new_group([2, 4, 6])
+            >>> paddle.distributed.all_reduce(tindata, group=gp, sync_op=False)
 
     """
     global _custom_gid
@@ -310,9 +311,8 @@ def is_available():
     Examples:
         .. code-block:: python
 
-            import paddle
-
-            print(paddle.distributed.is_available())
+            >>> import paddle
+            >>> print(paddle.distributed.is_available())
 
     """
     return core.is_compiled_with_dist()