!46021 modify the format of files 1125

Merge pull request !46021 from 宦晓玲/code_docs_1125

docs/api/api_python/ops/mindspore.ops.Dilation2D.rst

@@ -52,7 +52,7 @@
         - **TypeError** - 如果参数 `stride` 或者 `dilation` 不是一个整数或者包含两个整数的元组或者包含四个整数的元组。
         - **ValueError** - 如果参数 `stride` 或者 `dilation` 是一个元组，并且它的长度不是2或者4。
         - **ValueError** - 如果参数 `stride` 或者 `dilation` 是一个包含四个整数的元组，它的shape不是 `(1, 1, height, width)`。
-        - **ValueError** - 如果参数 `stride` 的取值范围不是`[1, 255]`。
+        - **ValueError** - 如果参数 `stride` 的取值范围不是 `[1, 255]` 。
         - **ValueError** - 如果参数 `dilation` 的值小于1。
         - **ValueError** - 如果参数 `pad_mode` 不是 `same` 、 `valid` 、 `SAME` 或者 `VALID`。
         - **ValueError** - 如果参数 `data_format` 不是字符串`NCHW`。

docs/api/api_python/ops/mindspore.ops.PadV3.rst

@@ -28,5 +28,6 @@ mindspore.ops.PadV3
         - **ValueError** - `mode` 是"edge"或"reflect"， `x` 的维度是4， `paddings` 元素个数是4。
         - **ValueError** - `mode` 是"edge"或"reflect"的同时 `x` 的维度小于3。
         - **ValueError** - `mode` 是"edge"的同时 `x` 的维度大于5。
+        - **ValueError** - `mode` 是"reflect"的同时 `x` 的维度大于4。
         - **ValueError** - `mode` 是"reflect"的同时填充值大于对应 `x` 的维度。
         - **ValueError** - 填充之后，输出shape数不大于零。

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

@@ -13,5 +13,5 @@ mindspore.ops.angle
         Tensor，类型为float32或float64，shape与输入相同。
 
     异常：
-        TypeError：如果 `x` 不是Tensor。
-        TypeError：如果输入的数据类型不是complex64或complex128。
+        - **TypeError** - 如果 `x` 不是Tensor。
+        - **TypeError** - 如果输入的数据类型不是complex64或complex128。

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

@@ -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。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。
 
     参数：
         - **x** (Tensor) - shape为 :math:`(x_1, x_2, ..., x_R)` 的tensor。类型必须为float32或float16。

mindspore/python/mindspore/ops/function/debug_func.py

@@ -37,7 +37,7 @@ def print_(*input_x):
         `Summary <https://www.mindspore.cn/mindinsight/docs/en/master/summary_record.html?highlight=summary#>`_.
 
     Args:
-        input_x (Union[Tensor, bool, int, float, str]): The inputs of print_.
+        input_x (Union[Tensor, bool, int, float, str, tuple, list]): The inputs of print_.
             Supports multiple inputs which are separated by ','.
 
     Returns:

mindspore/python/mindspore/ops/operations/array_ops.py

@@ -441,9 +441,6 @@ class Im2Col(Primitive):
     Outputs:
         Tensor, a 4-D Tensor with same type of input `x`.
 
-    Supported Platforms:
-        ``Ascend`` ``CPU``
-
     Raises:
         TypeError: If `ksizes` data type is not in Union[int, tuple[int], list[int]].
         TypeError: If `strides` data type is not in Union[int, tuple[int], list[int]].
@@ -457,6 +454,9 @@ class Im2Col(Primitive):
         ValueError: If `padding_mode` value is not in ["SAME", "VALID", "CALCULATED"].
         ValueError: If `pads` value is not greater than zero.
 
+    Supported Platforms:
+        ``Ascend`` ``CPU``
+
     Examples:
         >>> x = Tensor(input_data=np.random.rand(4, 4, 32, 32), dtype=mstype.float64)
         >>> im2col = P.Im2Col(ksizes=3, strides=1, dilations=1)
@@ -555,9 +555,6 @@ class Col2Im(Primitive):
     Outputs:
         Tensor, a 4-D Tensor with same type of input `x`.
 
-    Supported Platforms:
-        ``CPU``
-
     Raises:
         TypeError: If :attr:`kernel_size` data type is not in Union[int, tuple[int], list[int]].
         TypeError: If :attr:`dilation` data type is not in Union[int, tuple[int], list[int]].
@@ -570,6 +567,9 @@ class Col2Im(Primitive):
         ValueError: If x.shape[2] != kernel_size[0] * kernel_size[1].
         ValueError: If x.shape[3] does not match the calculated number of sliding blocks.
 
+    Supported Platforms:
+        ``CPU``
+
     Examples:
         >>> import numpy as np
         >>> from mindspore import Tensor
@@ -7451,7 +7451,7 @@ class FillDiagonal(Primitive):
 
     Args:
         fill_value (float): The fill value.
-        wrap (bool): the diagonal ‘wrapped’ after N columns for tall matrices. Default: False.
+        wrap (bool, optional): the diagonal ‘wrapped’ after N columns for tall matrices. Default: False.
 
     Inputs:
         - **input_x** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.

mindspore/python/mindspore/ops/operations/image_ops.py

@@ -184,15 +184,15 @@ class ExtractGlimpse(Primitive):
         If the window and input image tensor not overlap, random noise is filled.
 
     Args:
-        centered (bool): An optional `bool`. Indicates if the offset coordinates
+        centered (bool, optional): An optional `bool`. Indicates if the offset coordinates
             are centered relative to the image, in which case the (0, 0) offset is relative to the center of
             the center of the input images. If false, the (0, 0) offset corresponds to the upper left corner
             of the input images. Defaults to `True`.
-        normalized (bool): An optional `bool`. indicates if the offset
+        normalized (bool, optional): An optional `bool`. indicates if the offset
             coordinates are normalized. Defaults to `True`.
-        uniform_noise (bool): An optional `bool`. indicates if the noise should be
+        uniform_noise (bool, optional): An optional `bool`. indicates if the noise should be
             generated using a uniform distribution or a Gaussian distribution. Defaults to `True`.
-        noise (str): An optional string. The value can be 'uniform', 'gaussian'
+        noise (str, optional): An optional string. The value can be 'uniform', 'gaussian'
             and 'zero'. The window is determined by size and offsets.
             When the window and input image tensor not overlap, random noise is filled.
             The result is variable when noise is equal to 'uniform' and 'gaussian'.
@@ -352,7 +352,7 @@ class CropAndResize(Primitive):
 
 class NonMaxSuppressionV3(Primitive):
     r"""
-        Greedily selects a subset of bounding boxes in descending order of score.
+    Greedily selects a subset of bounding boxes in descending order of score.
 
     .. warning::
         When input `max_output_size` is negative, it will be treated as 0.
@@ -384,7 +384,7 @@ class NonMaxSuppressionV3(Primitive):
         TypeError: If the dtype of `boxes` and `scores` are different.
         TypeError: If the dtype of `iou_threshold` and `score_threshold` are different.
         TypeError: If `boxes` is not tensor or its dtype is not float16 or float32.
-        TypeEroor: If `scores` is not tensor or its dtype is not float16 or float32.
+        TypeError: If `scores` is not tensor or its dtype is not float16 or float32.
         TypeError: If `max_output_size` is not tensor or scalar or its date type is not int32 or int64.
         TypeError: If `iou_threshold` is not tensor or scalar or its type is neither float16 or float32.
         TypeError: If `score_threshold` is not tensor or scalar or its type is neither float16 or float32.

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

@@ -542,7 +542,7 @@ class EuclideanNorm(Primitive):
     Reduces input along the dimensions given in axis.
 
     Args:
-        keep_dims (bool): If true, the reduceed dimensions are retained with length 1.
+        keep_dims (bool, optional): If true, the reduceed dimensions are retained with length 1.
                           If false, don't keep these dimensions. Default: False.
 
     Inputs:

mindspore/python/mindspore/ops/operations/nn_ops.py

@@ -9134,7 +9134,7 @@ class FractionalMaxPool3DWithFixedKsize(Primitive):
             output_shape can be a tuple, or a single H for H x H x H.
             specifying the size (D, H, W) of the output tensor.
 
-        data_format (str) : The optional value for data format.
+        data_format (str, optional) : The optional value for data format.
             Currently support 'NCDHW' and 'NHDWC'. Default: 'NCDHW'.
 
     Inputs:
@@ -10198,7 +10198,7 @@ class FractionalMaxPoolWithFixedKsize(Primitive):
         output_shape (Union[int, tuple[int]]): The target output size is H x W.
             output_shape can be a tuple, or a single H for H x H.
             specifying the size (H, W) of the output tensor.
-        data_format (str): The optional value for data format, is 'NCHW'.
+        data_format (str, optional): The optional value for data format, is 'NCHW'.
             Default: "NCHW".
 
     Inputs:

mindspore/python/mindspore/ops/operations/random_ops.py

@@ -86,7 +86,7 @@ class TruncatedNormal(Primitive):
         seed (int, optional): An optional int. Defaults to 0. If either `seed` or `seed2` are set to be non-zero,
             the seed is set by the given seed. Otherwise, it is seeded by a random seed.
         seed2 (int, optional): An optional int. Defaults to 0. A second seed to avoid seed collision.
-        dtype (mindspore.dtype): Specified output data type. Must be one of the following types:
+        dtype (mindspore.dtype, optional): Specified output data type. Must be one of the following types:
             mindspore.float16, mindspore.float32 and mindspore.float64. Default: mindspore.float32.
 
     Inputs: