fix Tensor min api docs

docs/api/api_python/mindspore/Tensor/mindspore.Tensor.min.rst

@@ -3,4 +3,16 @@ mindspore.Tensor.min
 
 .. py:method:: mindspore.Tensor.min(axis=None, keepdims=False, initial=None, where=True)
 
-    详情请参考 :func:`mindspore.ops.min`。
+    返回Tensor元素中的最小值或沿 `axis` 轴方向上的最小值。
+
+    参数：
+        - **axis** (Union[None, int, list, tuple of ints], 可选) - 轴，在该轴方向上进行操作。默认情况下，使用扁平输入。如果该参数为整数元组，则在多个轴上选择最小值，而不是在单个轴或所有轴上进行选择。默认值：None。
+        - **keepdims** (bool, 可选) - 如果这个参数为True，被删去的维度保留在结果中，且维度设为1。有了这个选项，结果就可以与输入数组进行正确的广播运算。默认值：False。
+        - **initial** (scalar, 可选) - 输出元素的最小值。如果对空切片进行计算，则该参数必须设置。默认值：None。
+        - **where** (Tensor[bool], 可选) - 一个bool类型的Tensor，被广播以匹配数组维度和选择包含在降维中的元素。如果传递了一个非默认值，则必须提供初始值。默认值：True。
+
+    返回：
+        Tensor或标量，输入Tensor的最小值。如果 `axis` 为None，则结果是一个标量值。如果提供了 `axis` ，则结果是Tensor ndim - 1维度的一个数组。
+
+    异常：
+        - **TypeError** - 参数的数据类型与上述不一致。

mindspore/python/mindspore/common/tensor.py

@@ -2033,7 +2033,58 @@ class Tensor(Tensor_):
 
     def min(self, axis=None, keepdims=False, initial=None, where=True):
         """
-        For details, please refer to :func:`mindspore.ops.min`.
+        Return the minimum of a tensor or minimum along an axis.
+
+        Args:
+            axis (Union[None, int, list, tuple of ints], optional): An axis or
+                axes along which to operate. By default, flattened input is used. If
+                `axis` is a tuple of ints, the minimum is selected over multiple axes,
+                instead of a single axis or all the axes as before. Default: None.
+            keepdims (bool, optional):
+                If True, the axes which are reduced are left in the
+                result as dimensions with size one. With this option, the result will
+                broadcast correctly against the input array. Default: False.
+            initial (scalar, optional):
+                The minimum value of an output element. Must be present to allow
+                computation on empty slice. Default: None.
+            where (bool Tensor, optional):
+                A boolean tensor which is broadcasted to match the dimensions of array,
+                and selects elements to include in the reduction. If non-default value
+                is passed, initial must also be provided. Default: True.
+
+        Returns:
+            Tensor or scalar, minimum of input tensor. If `axis` is None, the result is a scalar
+            value. If `axis` is given, the result is a tensor of dimension ``self.ndim - 1``.
+
+        Raises:
+            TypeError: If arguments have types not specified above.
+
+        Supported Platforms:
+            ``Ascend`` ``GPU`` ``CPU``
+
+        See also:
+            :func:`mindspore.Tensor.argmin`: Return the indices of the minimum values along an axis.
+
+            :func:`mindspore.Tensor.argmax`: Return the indices of the maximum values along an axis.
+
+            :func:`mindspore.Tensor.max`: Return the minimum of a tensor or minimum along an axis.
+
+        Examples:
+            >>> import numpy as np
+            >>> from mindspore import Tensor
+            >>> a = Tensor(np.arange(4).reshape((2, 2)).astype('float32'))
+            >>> output = a.min()
+            >>> print(output)
+            0.0
+            >>> output = a.min(axis=0)
+            >>> print(output)
+            [0. 1.]
+            >>> output = a.min(axis=0, initial=9, where=Tensor([False]))
+            >>> print(output)
+            [9. 9.]
+            >>> output = a.min(axis=0, initial=9, where=Tensor([False, True]))
+            >>> print(output)
+            [9. 1.]
         """
         reduce_ = tensor_operator_registry.get("reduce")
         reduce_min = tensor_operator_registry.get("reduce_min")