update the description of input data type of mulnonan.

mindspore/ops/operations/array_ops.py

@@ -2731,14 +2731,12 @@ class Select(PrimitiveWithInfer):
     Returns the selected elements, either from input :math:`x` or input :math:`y`, depending on the `condition`.
 
     Given a tensor as input, this operation inserts a dimension of 1 at the dimension,
-    it was invalid when both math: 'x' and math: 'y' are none,
-    the coordinates are returned as a two-dimensional tensor, where the first dimension (row) represents
-    the number of true elements and the second dimension (columns) represents the coordinates of the true
-    elements. Keep in mind that the shape of the output tensor can vary depending
+    it was invalid when both math: 'x' and math: 'y' are none.
+    Keep in mind that the shape of the output tensor can vary depending
     on how many true values are in the input. Indexes are output in row-first
     order.
 
-    math:`x` and :math:`y` must have the same shape. If :math:`x` and :math:`y` are
+    If neither is None, math:`x` and :math:`y` must have the same shape. If :math:`x` and :math:`y` are
     scalars, the conditional tensor must be a scalar. If :math:`x` and :math:`y` are
     higher-dimensional vectors, the `condition` must be a vector whose size matches the
     first dimension of :math:`x`, or must have the same shape as :math:`y`.

mindspore/ops/operations/math_ops.py

@@ -2249,8 +2249,10 @@ class MulNoNan(_MathBinaryOp):
         The shapes of `input_x` and `input_y` should be same or can be broadcasted.
 
     Inputs:
-        - **input_x** (Union[Tensor]) - The first input is a tensor whose data type is number or scalar.
-        - **input_y** (Union[Tensor]) - The second input is a tensor whose data type is number or scalar.
+        - **input_x** (Union[Tensor]) - The first input is a tensor whose data type is one of
+          flota16, float32, int32, int64 currently or scalar.
+        - **input_y** (Union[Tensor]) - The second input is a tensor whose data type is one of
+          flota16, float32, int32, int64 currently or scalar.
 
     Outputs:
         Tensor, the shape is same to the shape after broadcasting,