!10458 update the description and example of applyftrl operator.

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

mindspore/ops/operations/nn_ops.py

@@ -5764,7 +5764,12 @@ class ApplyFtrl(PrimitiveWithInfer):
           Default: -0.5. It must be a float number or a scalar tensor with float16 or float32 data type.
 
     Outputs:
-        Tensor, represents the updated `var`.
+        There are three outputs for Ascend environment.
+        - **var** (Tensor) - represents the updated `var`.
+        - **accum** (Tensor) - represents the updated `accum`.
+        - **linear** (Tensor) - represents the updated `linear`.
+        There is only one output for GPU environment.
+        - **var** (Tensor) - This value is alwalys zero and the input parameters has been updated in-place.
 
     Supported Platforms:
         ``Ascend`` ``GPU``
@@ -5773,8 +5778,8 @@ class ApplyFtrl(PrimitiveWithInfer):
         >>> import mindspore
         >>> import mindspore.nn as nn
         >>> import numpy as np
-        >>> from mindspore import Parameter
-        >>> from mindspore import Tensor
+        >>> from mindspore import Parameter, Tensor
+        >>> import mindspore.context as context
         >>> from mindspore.ops import operations as ops
         >>> class ApplyFtrlNet(nn.Cell):
         ...     def __init__(self):
@@ -5797,7 +5802,9 @@ class ApplyFtrl(PrimitiveWithInfer):
         >>> net = ApplyFtrlNet()
         >>> input_x = Tensor(np.random.randint(-4, 4, (2, 2)), mindspore.float32)
         >>> output = net(input_x)
-        >>> print(output)
+        >>> is_tbe = context.get_context("device_target") == "Ascend"
+        >>> if is_tbe:
+        ...     print(output)
         (Tensor(shape=[2, 2], dtype=Float32, value=
         [[ 4.61418092e-01,  5.30964255e-01],
          [ 2.68715084e-01,  3.82065028e-01]]), Tensor(shape=[2, 2], dtype=Float32, value=
@@ -5805,6 +5812,16 @@ class ApplyFtrl(PrimitiveWithInfer):
          [ 1.43758726e+00,  9.89177322e+00]]), Tensor(shape=[2, 2], dtype=Float32, value=
         [[-1.86994812e+03, -1.64906018e+03],
          [-3.22187836e+02, -1.20163989e+03]]))
+         >>> else:
+         ...    print(net.var.asnumpy())
+         [[0.4614181  0.5309642 ]
+          [0.2687151  0.38206503]]
+         ...    print(net.accum.asnumpy())
+         [[16.423655  9.645894 ]
+          [ 1.4375873 9.891773 ]]
+         ...    print(net.linear.asnumpy())
+         [[-1869.9479 -1649.0599]
+          [ -322.1879 -1201.6399]]
     """
 
     @prim_attr_register