!15531 Update MindSpore Doc Topk,Unique,UniqueWithPad

From: @dinglinhe123
Reviewed-by: @liangchenghui,@wuxuejian
Signed-off-by: @liangchenghui

mindspore/ops/operations/array_ops.py

@@ -748,6 +748,12 @@ class Unique(Primitive):
     Returns the unique elements of input tensor and also return a tensor containing the index of each value of input
     tensor corresponding to the output unique tensor.
 
+    This operation returns a tuple that contains the tensor `y` and the tensor `idx`;
+    where the tensor `y` contains unique elements along the `axis` of the tensor.
+    The sorting order of the unique elements is the same as the order that
+    they appear along the `axis` in `x`. Another tensor `idx` has the same size as the number of elements in `x`
+    along the `axis` dimension. It contains the index in the unique output `y`.
+
     Inputs:
         - **x** (Tensor) - The input tensor.
 
@@ -964,6 +970,12 @@ class UniqueWithPad(PrimitiveWithInfer):
     """
     Returns unique elements and relative indexes in 1-D tensor, filled with padding num.
 
+    The basic function is the same as the Unique operator, but the operator adds a Pad function.
+    The returned tuple(`y`,`idx`) after the input tensor x is processed by the unique operator,
+    in which the shapes of `y` and `idx` are mostly not equal. Therefore, in order to solve the above situation,
+    the UniqueWithPad operator will fill the `y` tensor with the number specified by the user
+    to make it have the same shape as the tensor `idx`.
+
     Inputs:
         - **x** (Tensor) - The tensor need to be unique. Must be 1-D vector with types: int32, int64.
         - **pad_num** (int) - Pad num.

mindspore/ops/operations/math_ops.py

@@ -1421,6 +1421,10 @@ class SquaredDifference(_MathBinaryOp):
     When the inputs are one tensor and one scalar,
     the scalar could only be a constant.
 
+     .. math::
+
+        out_{i} = (x_{i} + y_{i}) * (x_{i} - y_{i}) = x_{i}^2 - y_{i}^2
+
     Inputs:
         - **input_x** (Union[Tensor, Number, bool]) - The first input is a number, or a bool,
           or a tensor whose data type is float16, float32, int32 or bool.

mindspore/ops/operations/nn_ops.py

@@ -2079,6 +2079,16 @@ class TopK(PrimitiveWithInfer):
     """
     Finds values and indices of the `k` largest entries along the last dimension.
 
+    If the input is a one-dimensional Tensor, find the k largest entries in the Tensor,
+    and output its value and index as a Tensor. Therefore, "values [k]" is the "k" largest item in "input",
+    and its index is "indices [k]".
+
+    For a multi-dimensional matrix,
+    calculate the first k entries in each row (corresponding vector along the last dimension), therefore:
+    values.shape = indices.shape = input.shape[:-1] + [k].
+
+    If the two compared elements are the same, the one with the smaller index value is returned first.
+
     Args:
         sorted (bool): If true, the obtained elements will
             be sorted by the values in descending order. Default: False.