update docs for its format or some examples are wrong

mindspore/ops/operations/array_ops.py

@@ -1760,7 +1760,7 @@ class ArgMaxWithValue(PrimitiveWithInfer):
 
     Inputs:
         - **input_x** (Tensor) - The input tensor, can be any dimension. Set the shape of input tensor as
-          :math:`(x_1, x_2, ..., x_N)`.
+          :math:`(x_1, x_2, ..., x_N)`. And the data type only support mindspore.float16 or float32.
 
     Outputs:
         tuple (Tensor), tuple of 2 tensors, containing the corresponding index and the maximum value of the input
@@ -2294,7 +2294,7 @@ class Concat(PrimitiveWithInfer):
           Suppose there are two tensors in this tuple or list, namely x1 and x2.
           To perform `Concat` in the axis 0 direction, except for the 0th axis, all other axes should be equal,
           that is, :math:`x1.shape[1] == x2.shape[1], x1.shape[2] == x2.shape[2], ..., x1.shape[R] == x2.shape[R]`,
-          where the :math:`R' indicates the last axis.
+          where the :math:`R` indicates the last axis.
 
     Outputs:
         Tensor, the shape is :math:`(x_1, x_2, ..., \sum_{i=1}^Nx_{mi}, ..., x_R)`.

mindspore/ops/operations/nn_ops.py

@@ -8683,6 +8683,7 @@ class Conv3DTranspose(PrimitiveWithInfer):
 class SoftShrink(Primitive):
     r"""
     Applies the soft shrinkage function elementwise.
+
     .. math::
         \text{SoftShrink}(x) =
         \begin{cases}
@@ -8690,6 +8691,7 @@ class SoftShrink(Primitive):
         x + \lambda, & \text{ if } x < -\lambda \\
         0, & \text{ otherwise }
         \end{cases}
+
     Args:
         lambd: the :math:`\lambda` must be no less than zero value for the Softshrink formulation. Default: 0.5.