!38642 Add functional doc

Merge pull request !38642 from ZPaC/add-argmin-func-doc

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

@@ -233,6 +233,7 @@ Reduction函数
     mindspore.ops.logsumexp
     mindspore.ops.max
     mindspore.ops.norm
+    mindspore.ops.min
 
 .. list-table::
    :widths: 50 50

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

@@ -0,0 +1,34 @@
+mindspore.ops.min
+==============================
+
+.. py:function:: mindspore.ops.min(x, axis=0, keep_dims=False)
+
+    根据指定的索引计算最小值，并返回索引和值。
+
+    在给定轴上计算输入Tensor的最小值，并且返回最小值和索引。
+
+    .. note::
+        在auto_parallel和semi_auto_parallel模式下，不能使用第一个输出索引。
+
+    .. warning::
+        - 如果有多个最小值，则取第一个最小值的索引。
+        - "axis"的取值范围为[-dims, dims - 1]。"dims"为"x"的维度长度。
+
+    **参数：**
+
+    - **x** (Tensor) - 输入任意维度的Tensor。将输入Tensor的shape设为 :math:`(x_1, x_2, ..., x_N)` 。数据类型为mindspore.uint16，mindspore.uint32，mindspore.int16，mindspore.int32，mindspore.float16或者mindspore.float32。
+    - **axis** (int) - 指定计算维度。默认值：0。
+    - **keep_dims** (bool) - 表示是否减少维度，如果为True，输出将与输入保持相同的维度；如果为False，输出将减少维度。默认值：False。
+
+    **返回：**
+
+    tuple (Tensor)，表示2个Tensor组成的tuple，包含对应的索引和输入Tensor的最小值。
+
+    - **index** (Tensor) - 输入Tensor最小值的索引。如果 `keep_dims` 为True，则输出Tensor的shape为 :math:`(x_1, x_2, ..., x_{axis-1}, 1, x_{axis+1}, ..., x_N)` 。否则，shape为 :math:`(x_1, x_2, ..., x_{axis-1}, x_{axis+1}, ..., x_N)` 。
+    - **output_x** (Tensor) - 输入Tensor的最小值，其shape与索引相同。
+
+    **异常：**
+
+    - **TypeError** - `input_x` 的数据类型非uint16，uint32，int16，int32，float16，float32。
+    - **TypeError** - `keep_dims` 不是bool。
+    - **TypeError** - `axis` 不是int。

docs/api/api_python_en/mindspore.ops.functional.rst

@@ -236,6 +236,7 @@ Reduction Functions
     mindspore.ops.logsumexp
     mindspore.ops.max
     mindspore.ops.norm
+    mindspore.ops.min
 
 .. list-table::
    :widths: 50 50

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

@@ -3856,7 +3856,7 @@ def max(x, axis=0, keep_dims=False):
     Also see: class: `mindspore.ops.ArgMaxWithValue`.
 
     Args:
-        x (Tensor) - The input tensor, can be any dimension. Set the shape of input tensor as
+        x (Tensor): The input tensor, can be any dimension. Set the shape of input tensor as
           :math:`(x_1, x_2, ..., x_N)`. And the data type only support mindspore.float16 or float32.
         axis (int): The dimension to reduce. Default: 0.
         keep_dims (bool): Whether to reduce dimension, if true, the output will keep same dimension with the input,
@@ -3909,7 +3909,7 @@ def min(x, axis=0, keep_dims=False):
     Also see: class: `mindspore.ops.ArgMinWithValue`.
 
     Args:
-        x (Tensor) - The input tensor, can be any dimension. Set the shape of input tensor as
+        x (Tensor): The input tensor, can be any dimension. Set the shape of input tensor as
           :math:`(x_1, x_2, ..., x_N)` . And the data type only support
           mindspore.uint16, mindspore.uint32, mindspore.int16, mindspore.int32, mindspore.float16, mindspore.float32.
         axis (int): The dimension to reduce. Default: 0.
@@ -3926,6 +3926,7 @@ def min(x, axis=0, keep_dims=False):
         - **values** (Tensor) - The minimum value of input tensor, with the same shape as index.
 
     Raises:
+        TypeError: If data type `x` is not uint16, uint32, int16, int32, float16, float32.
         TypeError: If `keep_dims` is not a bool.
         TypeError: If `axis` is not an int.