!42033 modify format

Merge pull request !42033 from 俞涵/code_docs_0819

docs/api/api_python/ops/mindspore.ops.func_relu.rst

@@ -1,7 +1,7 @@
 mindspore.ops.relu
 ==================
 
-.. py:class:: mindspore.ops.relu(input_x)
+.. py:function:: mindspore.ops.relu(input_x)
 
     线性修正单元激活函数（Rectified Linear Unit）。
 

docs/api/api_python/ops/mindspore.ops.func_relu6.rst

@@ -1,7 +1,7 @@
 mindspore.ops.relu6
 ====================
 
-.. py:class:: mindspore.ops.relu6(input_x)
+.. py:function:: mindspore.ops.relu6(input_x)
 
     计算输入Tensor的ReLU（修正线性单元），其上限为6。
 

docs/api/api_python/ops/mindspore.ops.func_std.rst

@@ -1,12 +1,12 @@
 mindspore.ops.std
 ==================
 
-.. py:function:: mindspore.ops.std(x, axis=(), unbiased=True, keep_dims=False)
+.. py:function:: mindspore.ops.std(input_x, axis=(), unbiased=True, keep_dims=False)
 
     默认情况下，输出Tensor各维度上的标准差与均值，也可以对指定维度求标准差与均值。如果 `axis` 是维度列表，则减少对应的维度。
 
     参数：
-        - **x** (Tensor[Number]) - 输入Tensor，其数据类型为数值型。shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。秩应小于8。
+        - **input_x** (Tensor[Number]) - 输入Tensor，其数据类型为数值型。shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。秩应小于8。
         - **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: ()，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
         - **unbiased** (bool) - 如果为True，使用贝塞尔校正。否则不使用贝塞尔校正。默认值：True。
         - **keep_dims** (bool) - 如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
@@ -19,7 +19,7 @@ mindspore.ops.std
         - 如果 `axis` 为tuple(int)或list(int)，取值为(1, 2)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_0, x_3, ..., x_R)` 。
 
     异常：
-        - **TypeError** - `x` 不是Tensor。
+        - **TypeError** - `input_x` 不是Tensor。
         - **TypeError** - `axis` 不是以下数据类型之一：int、Tuple或List。
         - **TypeError** - `keep_dims` 不是bool类型。
         - **ValueError** - `axis` 超出范围。

mindspore/python/mindspore/common/tensor.py

@@ -5535,7 +5535,7 @@ class Tensor(Tensor_):
         Computes matrix multiplication between two tensors by batch.
 
         .. math::
-        \text{output}[..., :, :] = \text{matrix}(input_x[..., :, :]) * \text{matrix}(mat2[..., :, :])
+            \text{output}[..., :, :] = \text{matrix}(input_x[..., :, :]) * \text{matrix}(mat2[..., :, :])
 
         The first input tensor must be not less than `3` and the second input must be not less than `2`.
 

mindspore/python/mindspore/ops/function/math_func.py

@@ -3388,9 +3388,10 @@ def std(input_x, axis=(), unbiased=True, keep_dims=False):
         A tuple (output_std, output_mean) containing the standard deviation and mean.
 
     Raises:
-        TypeError: If `keep_dims` is not a bool.
         TypeError: If `input_x` is not a Tensor.
-        ValueError: If `axis` is not one of the following: int, tuple or list.
+        TypeError: If `axis` is not one of the following: int, tuple or list.
+        TypeError: If `keep_dims` is not a bool.
+        ValueError: If `axis` is out of range.
 
     Supported Platforms:
         ``Ascend`` ``CPU``

mindspore/python/mindspore/ops/operations/math_ops.py

@@ -2281,28 +2281,7 @@ class ReduceStd(Primitive):
     Returns the standard-deviation and mean of each row of the input tensor in the dimension `axis`.
     If `axis` is a list of dimensions, reduce over all of them.
 
-    Args:
-        keep_dims (bool): Whether the output tensor has dim retained or not.
-                          If true, keep these reduced dimensions and the length is 1.
-                          If false, don't keep these dimensions.
-        unbiased (bool):  Whether to use Bessel’s correction.
-                          If true, will use the Bessel correction unbiased estimation.
-                          If false, will through the biased estimation to calculate the standard deviation.
-        axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Default: (), reduce all dimensions.
-                                                  Only constant value is allowed.
-                                                  Must be in the range [-rank(`input_x`), rank(`input_x`)).
-
-    Inputs:
-        - **input_x** (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number.
-          :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
-
-    Outputs:
-        A tuple (output_std, output_mean) containing the standard deviation and mean.
-
-    Raises:
-        TypeError: If `keep_dims` is not a bool.
-        TypeError: If `input_x` is not a Tensor.
-        ValueError: If `axis` is not one of the following: int, tuple or list.
+    Refer to :func:`mindspore.ops.std` for more detail.
 
     Supported Platforms:
         ``Ascend`` ``CPU``

mindspore/python/mindspore/ops/operations/sparse_ops.py

@@ -573,7 +573,7 @@ class SparseTensorDenseAdd(Primitive):
           The shape should be :math:`(n,)`.
         - **x1_shape** (tuple(int)) - A positive int tuple which specifies the shape of sparse tensor,
           should have 2 elements, represent sparse tensor shape is :math:`(N, C)`.
-        -**x2** (Tensor)- A dense Tensor, the dtype is same as `values`.
+        - **x2** (Tensor) - A dense Tensor, the dtype is same as `values`.
 
     Returns:
         Tensor, add result of sparse tensor and dense tensor. The dtype is same as `values`,