!48383 fix API docs issues

Merge pull request !48383 from luojianing/code_docs_master

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

@@ -3,9 +3,9 @@ mindspore.ops.clamp
 
 .. py:function:: mindspore.ops.clamp(x, min=None, max=None)
 
-    将输入Tensor值裁剪到指定的最小值和最大值之间。
+    将输入Tensor的值裁剪到指定的最小值和最大值之间。
 
-    限制 :math:`x` 的范围，其 :math:`x` 的最小值为 `min` ，最大值为 `max` 。
+    限制 :math:`x` 的范围，其最小值为 `min` ，最大值为 `max` 。
 
     .. math::
         out_i= \left\{

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

@@ -3,7 +3,7 @@ mindspore.ops.full_like
 
 .. py:function:: mindspore.ops.full_like(x, fill_value, *, dtype=None)
 
-    返回一个与输入相同大小的Tensor，填充 `fill_value`。'ops.full_like(x, fill_value)'相当于'ops.full(x.shape, fill_value, dtype=x.dtype)'。
+    返回一个与输入相同大小的Tensor，填充 `fill_value` 。 `ops.full_like(x, fill_value)` 相当于 `ops.full(x.shape, fill_value, dtype=x.dtype)` 。
 
     参数：
         - **x** (Tensor) - `x` 的shape决定输出Tensor的shape。

mindspore/python/mindspore/ops/function/array_func.py

@@ -717,8 +717,8 @@ def full(size, fill_value, *, dtype=None): # pylint: disable=redefined-outer-nam
 
 def full_like(x, fill_value, *, dtype=None):
     """
-    Returns  a Tensor with the same size as `x` filled with `fill_value`. 'ops.full_like(x, fill_value)' is
-    equivalent to 'ops.full(x.shape, fill_value, dtype=x.dtype)'.
+    Returns  a Tensor with the same size as `x` filled with `fill_value`. `ops.full_like(x, fill_value)` is
+    equivalent to `ops.full(x.shape, fill_value, dtype=x.dtype)` .
 
     Args:
         x (Tensor): The shape of `x` will determine shape of the output Tensor.
@@ -5862,7 +5862,7 @@ def diagonal(input, offset=0, dim1=0, dim2=1):
     Returns specified diagonals of `input`.
 
     If `input` is 2-D, returns the diagonal of `input` with the given offset.
-    If `a` has more than two
+    If `input` has more than two
     dimensions, then the axes specified by `dim1` and `dim2` are used to determine
     the 2-D sub-array whose diagonal is returned. The shape of the resulting
     array can be determined by removing `dim1` and `dim2` and appending an index

mindspore/python/mindspore/ops/function/clip_func.py

@@ -153,7 +153,7 @@ def clip_by_value(x, clip_value_min=None, clip_value_max=None):
 
 def clamp(x, min=None, max=None):
     r"""
-    Clamps tensor values to a specified min and max.
+    Clamps tensor values between the specified minimum value and maximum value.
 
     Limits the value of :math:`x` to a range, whose lower limit is `min` and upper limit is `max` .
 
@@ -181,7 +181,7 @@ def clamp(x, min=None, max=None):
           max (Union(Tensor, float, int)): The maximum value. Default: None.
 
     Returns:
-          (Union(Tensor, tuple[Tensor], list[Tensor])), a clipped Tensor or a tuple or a list of clipped Tensor.
+          Union(Tensor, tuple[Tensor], list[Tensor]), a clipped Tensor or a tuple or a list of clipped Tensor.
           The data type and shape are the same as x.
 
     Raises:

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

@@ -1293,7 +1293,7 @@ def logdet(x):
 
     Returns:
         Tensor, the log determinant of `x`. If the matrix determinant is smaller than 0, nan will be returned. If the
-            matrix determinant is 0, -inf will be returned.
+        matrix determinant is 0, -inf will be returned.
 
     Raises:
         TypeError: If dtype of `x` is not float32, float64, Complex64 or Complex128.
@@ -4503,11 +4503,11 @@ def heaviside(x, values):
 
     Args:
         x (Tensor): The input tensor. With real number data type.
-        values (Tensor): The values to use where x is zero. Values can be broadcast with x.
-            'x' should have the same dtype with 'values'.
+        values (Tensor): The values to use where `x` is zero. Values can be broadcast with `x` .
+            `x` should have the same dtype with 'values'.
 
     Returns:
-        Tensor, has the same type as 'x' and 'values'.
+        Tensor, has the same type as `x` and `values`.
 
     Raises:
         TypeError: If `x` or `values` is not Tensor.

mindspore/python/mindspore/ops/function/nn_func.py

@@ -686,8 +686,8 @@ def max_unpool1d(x, indices, kernel_size, stride=None, padding=0, output_size=No
 
     max_unpool1d takes the output of maxpool1d as input including the indices of the maximal values
     and computes a partial inverse in which all non-maximal values are set to zero. Typically the input
-    is of shape :math:`(N, C, H_{in})` or :math:`(C, H_{in})`, and the output is of shape :math:`(N, C, H_{out}`
-    or :math:`(C, H_{out}`. The operation is as follows.
+    is of shape :math:`(N, C, H_{in})` or :math:`(C, H_{in})`, and the output is of shape :math:`(N, C, H_{out})`
+    or :math:`(C, H_{out})`. The operation is as follows.
 
     .. math::
         \begin{array}{ll} \\
@@ -4607,7 +4607,7 @@ def batch_norm(input_x, running_mean, running_var, weight, bias, training=False,
 
     .. warning::
         - If this operation is used for inferring and output "reserve_space_1" and "reserve_space_2" are usable,
-            then "reserve_space_1" and "reserve_space_2" have the same value as "mean" and "variance" respectively.
+          then "reserve_space_1" and "reserve_space_2" have the same value as "mean" and "variance" respectively.
         - For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction.
 
     Note:
@@ -5480,7 +5480,7 @@ def lp_pool2d(x, norm_type, kernel_size, stride=None, ceil_mode=False):
         stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents
             the height and width of movement are both strides, or a tuple of two int numbers that
             represent height and width of movement respectively, if the value is None,
-            the default value `kernel_size` is used;
+            the default value `kernel_size` is used.
         ceil_mode (bool): Whether to use ceil or floor to calculate output shape. Default: False.
 
     Returns: