!10191 update the example of some operations.

From: @wangshuide2020
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/nn/layer/lstm.py

@@ -343,6 +343,8 @@ class LSTMCell(Cell):
         >>> c = Tensor(np.ones([1, 3, 12]).astype(np.float32))
         >>> w = Tensor(np.ones([1152, 1, 1]).astype(np.float32))
         >>> output, h, c, _, _ = net(input, h, c, w)
+        >>> print(output.shape)
+        (3, 5, 12)
     """
 
     def __init__(self,

mindspore/ops/composite/array_ops.py

@@ -59,10 +59,10 @@ def repeat_elements(x, rep, axis=0):
     Repeat elements of a tensor along an axis, like np.repeat.
 
     Args:
-        - **x** (Tensor) - The tensor to repeat values for. Must be of type: float16,
+        x (Tensor): The tensor to repeat values for. Must be of type: float16,
           float32, int8, uint8, int16, int32, or int64.
-        - **rep** (int) - The number of times to repeat, must be positive, required.
-        - **axis** (int) - The axis along which to repeat, default 0.
+        rep (int): The number of times to repeat, must be positive, required.
+        axis (int): The axis along which to repeat, default 0.
 
     Outputs:
         One tensor with values repeated along the specified axis. If x has shape

mindspore/ops/operations/comm_ops.py

@@ -142,16 +142,19 @@ class AllGather(PrimitiveWithInfer):
         ``Ascend`` ``GPU``
 
     Examples:
+        >>> # This example should be run with two devices. Refer to the tutorial > Distirbuted Training on mindspore.cn.
+        >>> import numpy as np
         >>> import mindspore.ops.operations as ops
         >>> import mindspore.nn as nn
         >>> from mindspore.communication import init
-        >>> from mindspore import Tensor
+        >>> from mindspore import Tensor, context
         >>>
+        >>> context.set_context(mode=context.GRAPH_MODE)
         >>> init()
         ... class Net(nn.Cell):
         ...     def __init__(self):
         ...         super(Net, self).__init__()
-        ...         self.allgather = ops.AllGather(group="nccl_world_group")
+        ...         self.allgather = ops.AllGather()
         ...
         ...     def construct(self, x):
         ...         return self.allgather(x)
@@ -160,6 +163,10 @@ class AllGather(PrimitiveWithInfer):
         >>> net = Net()
         >>> output = net(input_)
         >>> print(output)
+        [[1. 1. 1. 1. 1. 1. 1. 1.]
+         [1. 1. 1. 1. 1. 1. 1. 1.]
+         [1. 1. 1. 1. 1. 1. 1. 1.]
+         [1. 1. 1. 1. 1. 1. 1. 1.]]
     """
 
     @prim_attr_register
@@ -255,16 +262,18 @@ class ReduceScatter(PrimitiveWithInfer):
         ValueError: If the first dimension of the input cannot be divided by the rank size.
 
     Supported Platforms:
-        ``GPU``
+        ``Ascend`` ``GPU``
 
     Examples:
-        >>> from mindspore import Tensor
+        >>> # This example should be run with two devices. Refer to the tutorial > Distirbuted Training on mindspore.cn.
+        >>> from mindspore import Tensor, context
         >>> from mindspore.communication import init
         >>> from mindspore.ops.operations.comm_ops import ReduceOp
         >>> import mindspore.nn as nn
         >>> import mindspore.ops.operations as ops
         >>> import numpy as np
         >>>
+        >>> context.set_context(mode=context.GRAPH_MODE)
         >>> init()
         >>> class Net(nn.Cell):
         ...     def __init__(self):
@@ -278,6 +287,10 @@ class ReduceScatter(PrimitiveWithInfer):
         >>> net = Net()
         >>> output = net(input_)
         >>> print(output)
+        [[2. 2. 2. 2. 2. 2. 2. 2.]
+         [2. 2. 2. 2. 2. 2. 2. 2.]
+         [2. 2. 2. 2. 2. 2. 2. 2.]
+         [2. 2. 2. 2. 2. 2. 2. 2.]]
     """
 
     @prim_attr_register

mindspore/ops/operations/debug_ops.py

@@ -350,7 +350,12 @@ class Print(PrimitiveWithInfer):
         >>> x = Tensor(np.ones([2, 1]).astype(np.int32))
         >>> y = Tensor(np.ones([2, 2]).astype(np.int32))
         >>> net = PrintDemo()
-        >>> output = net(x, y)
+        >>> result = net(x, y)
+        Print Tensor x and Tensor y:
+        [[1]
+         [1]]
+        [[1 1]
+         [1 1]]
     """
 
     @prim_attr_register