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!48680 modify document duplication 

Merge pull request !48680 from 冯一航/code_docs_modify_document_duplication
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2
docs/api/api_python/ops/mindspore.ops.func_arange.rst
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@ -11,7 +11,7 @@ mindspore.ops.arange
- **step** (Union[float, int, Tensor], 可选) - 表述序列中数值的步长。如果为Tensor，则shape必须为()。默认值：1。
关键字参数：
- **dtype** (mindspore.dtype, 可选) - 返回序列的数据类型。默认值：None。如果未指定或者为None，将会被推断为 `start` 、 `end` 和 `step` 参数中精度最高的类型。
- **dtype** (mindspore.dtype, 可选) - 返回Tensor的所需数据类型。默认值：None。如果未指定或者为None，将会被推断为 `start` 、 `end` 和 `step` 参数中精度最高的类型。
返回：
一维Tensor，数据类型与输入数据类型一致。

4
docs/api/api_python/ops/mindspore.ops.func_cov.rst
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@ -3,9 +3,9 @@ mindspore.ops.cov
.. py:function:: mindspore.ops.cov(x, *, correction=1, fweights=None, aweights=None)
给定输入 `x` 和权重，估计输入 `x` 的协方差矩阵，其中输入的行是变量，列是观察值。
给定输入 `x` 和权重，返回输入 `x` 的协方差矩阵(每对变量的协方差的方阵)，其中输入行是变量，列是观察值。
协方差矩阵是每对变量的协方差的方阵。对角线包含每个变量与其自身的协方差。若 `x` 表示单个变量(标量或一维)，则返回其方差。
对角线包含每个变量及其自身的协方差。如果 `x` 是单个变量的标量或一维向量，则将返回其方差。
变量 :math:`a` 和 :math:`b` 的无偏样本协方差由下式给出：

2
docs/api/api_python/ops/mindspore.ops.func_nan_to_num.rst
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@ -3,7 +3,7 @@ mindspore.ops.nan_to_num
.. py:function:: mindspore.ops.nan_to_num(x, nan=0.0, posinf=None, neginf=None)
将 `x` 中的 `NaN` 、正无穷大和负无穷大值分别替换为 `nan`, `posinf`, 和 `neginf` 指定的值。默认情况下，NaN替换为0，正无穷替换为 `x` 类型支持的上限，负无穷替换为由 `x` 类型支持的下限。
将 `x` 中的 `NaN` 、正无穷大和负无穷大值分别替换为 `nan`, `posinf`, 和 `neginf` 指定的值。
参数：
- **x** (Tensor) - shape为 :math:`(x_1, x_2, ..., x_R)` 的tensor。类型必须为float32或float16。

4
docs/api/api_python/ops/mindspore.ops.func_sgn.rst
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@ -3,7 +3,9 @@ mindspore.ops.sgn
.. py:function:: mindspore.ops.sgn(x)
此方法为 :func:`mindspore.ops.sign` 在复数Tensor上的扩展。
:func:`mindspore.ops.sign` 在复数上的扩展。
对于实数输入，此方法与 :func:`mindspore.ops.sign` 一致。
对于复数输入，此方法按照如下公式计算。
.. math::
\text{out}_{i} = \begin{cases}

2
docs/api/api_python/ops/mindspore.ops.func_tanhshrink.rst
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@ -3,6 +3,6 @@ mindspore.ops.tanhshrink
.. py:function:: mindspore.ops.tanhshrink(x)
按元素计算 :math:`Tanhshrink(x)=x-Tanh(x)` 。
Tanhshrink激活函数， :math:`Tanhshrink(x)=x-Tanh(x)` 。
详情请查看 :class:`mindspore.nn.Tanhshrink` 。

4
mindspore/python/mindspore/common/tensor.py
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@ -3873,8 +3873,8 @@ class Tensor(Tensor_):
@property
def mT(self):
r"""
Returns a view of this tensor with the last two dimensions transposed.
x.mT is equivalent to x.swapaxes(-2, -1).
Returns the Tensor that exchanges the last two dimensions.
Accessing the attribute, x.mT, is equal to calling the method, x.swapaxes(-2, -1).
For details, please refer to :func:`mindspore.Tensor.swapaxes`.
"""
return self.swapaxes(-2, -1)

7
mindspore/python/mindspore/ops/composite/math_ops.py
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@ -659,12 +659,13 @@ def mm(mat1, mat2):
:math:`(m \times p)` Tensor, `out` will be a :math:`(n \times p)` Tensor.
Note:
This function does not broadcast. For broadcasting matrix products, see :func:`mindspore.ops.matmul`.
This function cannot support broadcasting.
Refer to :func:`mindspore.ops.matmul` instead if you need a broadcastable function.
Args:
mat1 (Tensor): The first matrix to be matrix multiplied.
mat1 (Tensor): The first matrix of matrix multiplication.
The last dimension of `mat1` must be the same size as the first dimension of `mat2`.
mat2 (Tensor): The second matrix to be matrix multiplied.
mat2 (Tensor): The second matrix of matrix multiplication.
The last dimension of `mat1` must be the same size as the first dimension of `mat2`.
Returns:

6
mindspore/python/mindspore/ops/function/array_func.py
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@ -175,9 +175,9 @@ def arange(start=0, end=None, step=1, *, dtype=None):
If Tensor, the shape must be (). Default: 1.
Keyword Args:
dtype (mindspore.dtype, optional): The desired data type of returned tensor. Default: None.
If dtype is not given or None, the dtype is inferred to be the type with the highest precision among
the `start`, `end` and `step` parameters.
dtype (mindspore.dtype, optional): The required data type of returned Tensor. Default: None.
If the value is not specified or is None, the type with the highest precision in the
`start`, `end`, and `step` parameters is inferred.
Returns:
A 1-D Tensor, with the same type as the inputs.

55
mindspore/python/mindspore/ops/function/math_func.py
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@ -1716,10 +1716,10 @@ def signbit(x):
Examples:
>>> import mindspore as ms
>>> import mindspore.ops as ops
>>> x = ms.Tensor([0.7, -1.2, 0., 2.3])
>>> x = ms.Tensor([0.3, 1.2, 0., -2.5])
>>> output = ops.signbit(x)
>>> print(output)
[False True False False]
[False False False True]
"""
if not isinstance(x, Tensor):
raise TypeError(f"For signbit, the input must be a Tensor, but got {type(x)}")
@ -1729,7 +1729,9 @@ def signbit(x):
def sgn(x):
r"""
This function is an extension of :func:`mindspore.ops.sign` to complex tensors.
Extension of :func:`mindspore.ops.sign` in the plural.
For real number input, this function is the same as :func:`mindspore.ops.sign`.
For complex input, this function is calculated according to the following formula.
.. math::
\text{out}_{i} = \begin{cases}
@ -1869,7 +1871,7 @@ def cos(x):
def cosine_similarity(x1, x2, dim=1, eps=1e-08):
r"""
Returns cosine similarity between x1 and x2, computed along dim. x1 and x2 must be broadcastable to a common shape.
Calculate cosine similarity between `x1` and `x2` along the axis, `dim`. `x1` and `x2` must be broadcastable.
.. math::
\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
@ -1877,8 +1879,8 @@ def cosine_similarity(x1, x2, dim=1, eps=1e-08):
Args:
x1 (Tensor): The first input Tensor.
x2 (Tensor): The second input Tensor.
dim (int, optional): Dimension along which cosine similarity is computed. Default: 1.
eps (float, optional): Small value to avoid division by zero. Default: 1e-8.
dim (int, optional): Axis for calculating cosine similarity. Default: 1.
eps (float, optional): Minimal value to avoid division by zero. Default: 1e-8.
Returns:
Tensor, cosine similarity between x1 and x2.
@ -1923,14 +1925,13 @@ def _check_cov_weights(weights, weights_name, num_observations, valid_type, vali
def cov(x, *, correction=1, fweights=None, aweights=None):
r"""
Given the input 'x' and weight, estimate the covariance matrix of the input 'x', where the input row is the variable
and the column is the observation value.
Given the input `x` and weights, returns the covariance matrix (the square matrix of the covariance of each pair of
variables) of x, where the input row is the variable and the column is the observation value.
The covariance matrix is the square matrix of the covariance of each pair of variables. The diagonal contains the
covariance of each variable and its own. If 'x' represents a single variable (scalar or 1D),
its variance is returned.
The diagonal contains each variable and its own covariance. If x is a scalar or 1D vector of a single variable,
its variance will be returned.
The unbiased sample covariance of the variables :math:`a` and :math:`b` is given by:
The unbiased sample covariance of the variables :math:`a` and :math:`b` is given by the following formula:
.. math::
\text{cov}_w(a,b) = \frac{\sum^{N}_{i = 1}(a_{i} - \bar{a})(b_{i} - \bar{b})}{N~-~1}
@ -1954,12 +1955,12 @@ def cov(x, *, correction=1, fweights=None, aweights=None):
x (Tensor): A 2D matrix, or a scalar or 1D vector of a single variable
Keyword Args:
correction (int, optional): difference between the sample size and sample degrees of freedom.
correction (int, optional): The difference between sample size and sample degrees of freedom.
Defaults to Bessel's correction, `correction = 1` which returns the unbiased estimate,
even if both `fweights` and `aweights` are specified. `correction = 0`
will return the simple average. Default: 1.
fweights (Tensor, optional): A scalar or 1D Tensor containing integer frequency weights represents
the number of repeats of each observation vector. Its numel must equal the number of columns of `x`.
fweights (Tensor, optional): Scalar or one-dimensional Tensor containing integer frequency weight, indicating
the number of repetition of each observation vector. Its numel must equal the number of columns of `x`.
Ignored if `None`. Default: None.
aweights (Tensor, optional): A scalar or 1D Tensor containing float observation weights represents
the importance of each observation vector. The higher the importance, the greater the corresponding value.
@ -4136,19 +4137,17 @@ def is_complex(x):
def nan_to_num(x, nan=0.0, posinf=None, neginf=None):
"""
Replaces `NaN`, positive infinity, and negative infinity values in the `x` with the values
specified by `nan`, `posinf`, and `neginf`, respectively. By default, NaN is replaced by 0,
positive infinity is replaced by the largest finite value representable by the x dtype,
and negative infinity is replaced by the smallest finite value representable by the x dtype.
Replace the `NaN`, positive infinity and negative infinity values in 'x' with the
specified values, `nan`, `posinf`, and `neginf` respectively.
Args:
x (Tensor): The shape of tensor is :math:`(x_1, x_2, ..., x_R)`. With float32 or float16 data type.
nan (float): The value to replace `NaN`. Default value is 0.0.
posinf (float): If a Number, the value to replace positive infinity values with. If None, positive
infinity values are replaced with the greatest finite value representable by `x`'s dtype.
nan (float): The replace value of 'NaN'. Default value is 0.0.
posinf (float): the value to replace positive infinity values with. Default: None,
replacing positive infinity with the maximum value supported by the data type of `x`.
Default value is None.
neginf (float): if a Number, the value to replace negative infinity values with. If None, negative
infinity values are replaced with the lowest finite value representable by `x`'s dtype.
neginf (float): the value to replace negative infinity values with. Default: None,
replacing negative infinity with the minimum value supported by the data type of `x`.
Default value is None.
Returns:
@ -4162,10 +4161,10 @@ def nan_to_num(x, nan=0.0, posinf=None, neginf=None):
``CPU``
Examples:
>>> x = Tensor(np.array([float('nan'), float('inf'), -float('inf'), 3.14]), mindspore.float32)
>>> x = Tensor(np.array([float('nan'), float('inf'), -float('inf'), 5.0]), mindspore.float32)
>>> output = ops.nan_to_num(x, 1.0, 2.0, 3.0)
>>> print(output)
[1. 2. 3. 3.14]
[1. 2. 3. 5.0]
"""
if not isinstance(x, (Tensor, Tensor_)):
raise TypeError("the input x must be Tensor!")
@ -8315,7 +8314,7 @@ def roll(x, shifts, dims=None):
TypeError: If `dims` is not an int, a tuple or a list.
Supported Platforms:
``Ascend`` ``GPU``
``GPU``
Examples:
>>> import numpy as np
@ -10014,7 +10013,7 @@ def sum(x, dim=None, keepdim=False, *, dtype=None):
def tanhshrink(x):
'''
Applies element-wise, :math:`Tanhshrink(x)=x-Tanh(x)` .
Tanhshrink Activation, :math:`Tanhshrink(x)=x-Tanh(x)` .
See :class:`mindspore.nn.Tanhshrink` for more details.
Supported Platforms:

2
mindspore/python/mindspore/ops/function/nn_func.py
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@ -5708,7 +5708,7 @@ def mse_loss(input_x, target, reduction='mean'):
def msort(x):
r"""
Sorts the elements of the input tensor along its first dimension in ascending order by value.
Sorts the elements in Tensor in ascending order of value along its first dimension.
ops.msort(t) is equivalent to ops.Sort(axis=0)(t)[0]. See also :class:`mindspore.ops.Sort()`.