[xdoctest][task 181-183] reformat example code with google style in...

[xdoctest][task 181-183] reformat example code with google style in `sparse/multiary.py`,`distributed/auto_parallel/*` (#56665)

* [Doctest]fix No.181-183, test=docs_preview

* add env skip

python/paddle/distributed/auto_parallel/interface.py

@@ -56,13 +56,14 @@ def shard_tensor(x, process_mesh=None, shard_spec=None):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.distributed.fleet import auto
+            >>> # doctest: +REQUIRES(env:DISTRIBUTED)
+            >>> import paddle
+            >>> from paddle.distributed.fleet import auto
 
-            mesh = auto.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
-            x = paddle.ones([4, 6])
-            shard_spec = ["x", "y"]
-            auto.shard_tensor(x, mesh, shard_spec)
+            >>> mesh = auto.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
+            >>> x = paddle.ones([4, 6])
+            >>> shard_spec = ["x", "y"]
+            >>> auto.shard_tensor(x, mesh, shard_spec)
 
     """
 
@@ -145,16 +146,17 @@ def shard_op(op, process_mesh=None, in_shard_specs=None, out_shard_specs=None):
     Examples:
         .. code-block:: python
 
-            import paddle
-            from paddle.distributed.fleet import auto
-
-            x = paddle.ones([4, 6])
-            y = paddle.zeros([4, 6])
-            mesh = auto.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
-            dist_add = auto.shard_op(paddle.add,
-                                     in_shard_specs=[["x", "y"], ["y", None]],
-                                     out_shard_specs=[[None, "x"]])
-            dist_add(x, y)
+            >>> import paddle
+            >>> from paddle.distributed.fleet import auto
+
+            >>> x = paddle.ones([4, 6])
+            >>> y = paddle.zeros([4, 6])
+            >>> mesh = auto.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
+            >>> dist_add = auto.shard_op(paddle.add,
+            ...                          mesh,
+            ...                          in_shard_specs=[["x", "y"], ["y", None]],
+            ...                          out_shard_specs=[[None, "x"]])
+            >>> dist_add(x, y)
 
     """
 

python/paddle/distributed/auto_parallel/process_mesh.py

@@ -81,12 +81,12 @@ class ProcessMesh(core.ProcessMesh):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.distributed as dist
+            >>> import paddle
+            >>> import paddle.distributed as dist
 
-            mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]], dim_names=["x", "y"])
-            assert mesh.shape == [2, 3]
-            assert mesh.process_ids == [2, 4, 5, 0, 1, 3]
+            >>> mesh = dist.ProcessMesh([[2, 4, 5], [0, 1, 3]], dim_names=["x", "y"])
+            >>> assert mesh.shape == [2, 3]
+            >>> assert mesh.process_ids == [2, 4, 5, 0, 1, 3]
 
     """
 

python/paddle/sparse/multiary.py

@@ -58,25 +58,26 @@ def addmm(input, x, y, beta=1.0, alpha=1.0, name=None):
 
         .. code-block:: python
 
-            # required: gpu
-            import paddle
-
-            # dense + csr @ dense -> dense
-            input = paddle.rand([3, 2])
-            crows = [0, 1, 2, 3]
-            cols = [1, 2, 0]
-            values = [1., 2., 3.]
-            x = paddle.sparse.sparse_csr_tensor(crows, cols, values, [3, 3])
-            y = paddle.rand([3, 2])
-            out = paddle.sparse.addmm(input, x, y, 3.0, 2.0)
-
-            # dense + coo @ dense -> dense
-            input = paddle.rand([3, 2])
-            indices = [[0, 1, 2], [1, 2, 0]]
-            values = [1., 2., 3.]
-            x = paddle.sparse.sparse_coo_tensor(indices, values, [3, 3])
-            y = paddle.rand([3, 2])
-            out = paddle.sparse.addmm(input, x, y, 3.0, 2.0)
+            >>> # doctest: +REQUIRES(env:GPU)
+            >>> import paddle
+            >>> paddle.device.set_device('gpu')
+
+            >>> # dense + csr @ dense -> dense
+            >>> input = paddle.rand([3, 2])
+            >>> crows = [0, 1, 2, 3]
+            >>> cols = [1, 2, 0]
+            >>> values = [1., 2., 3.]
+            >>> x = paddle.sparse.sparse_csr_tensor(crows, cols, values, [3, 3])
+            >>> y = paddle.rand([3, 2])
+            >>> out = paddle.sparse.addmm(input, x, y, 3.0, 2.0)
+
+            >>> # dense + coo @ dense -> dense
+            >>> input = paddle.rand([3, 2])
+            >>> indices = [[0, 1, 2], [1, 2, 0]]
+            >>> values = [1., 2., 3.]
+            >>> x = paddle.sparse.sparse_coo_tensor(indices, values, [3, 3])
+            >>> y = paddle.rand([3, 2])
+            >>> out = paddle.sparse.addmm(input, x, y, 3.0, 2.0)
 
     """
     return _C_ops.sparse_addmm(input, x, y, beta, alpha)