update the documentation of MatrixSetDiag, MatrixDiag, Unfold and Pad operators.

mindspore/nn/layer/basic.py

@@ -571,7 +571,7 @@ class OneHot(Cell):
 
 
 class Pad(Cell):
-    """
+    r"""
     Pads the input tensor according to the paddings and mode.
 
     Args:
@@ -579,7 +579,7 @@ class Pad(Cell):
             paddings are int type. For `D` th dimension of input, paddings[D, 0] indicates how many sizes to be
             extended ahead of the `D` th dimension of the input tensor, and paddings[D, 1] indicates how many sizes to
             be extended behind of the `D` th dimension of the input tensor. The padded size of each dimension D of the
-            output is: :math:`paddings[D, 0] + input_x.dim_size(D) + paddings[D, 1]`
+            output is: :math:`paddings[D, 0] + input\_x.dim\_size(D) + paddings[D, 1]`
 
         mode (str): Specifies padding mode. The optional values are "CONSTANT", "REFLECT", "SYMMETRIC".
             Default: "CONSTANT".
@@ -729,7 +729,7 @@ class ResizeBilinear(Cell):
 
 
 class Unfold(Cell):
-    """
+    r"""
     Extract patches from images.
     The input tensor must be a 4-D tensor and the data format is NCHW.
 
@@ -755,11 +755,11 @@ class Unfold(Cell):
         Tensor, a 4-D tensor whose data type is same as `input_x`,
         and the shape is [out_batch, out_depth, out_row, out_col] where `out_batch` is the same as the `in_batch`.
 
-        :math:`out_depth = ksize_row * ksize_col * in_depth`
+        :math:`out\_depth = ksize\_row * ksize\_col * in\_depth`
 
-        :math:`out_row = (in_row - (ksize_row + (ksize_row - 1) * (rate_row - 1))) // stride_row + 1`
+        :math:`out\_row = (in\_row - (ksize\_row + (ksize\_row - 1) * (rate\_row - 1))) // stride\_row + 1`
 
-        :math:`out_col = (in_col - (ksize_col + (ksize_col - 1) * (rate_col - 1))) // stride_col + 1`
+        :math:`out\_col = (in\_col - (ksize\_col + (ksize\_col - 1) * (rate\_col - 1))) // stride\_col + 1`
 
     Raises:
         TypeError: If `ksizes`, `strides` or `rates` is neither a tuple nor list.
@@ -919,7 +919,7 @@ class MatrixDiag(Cell):
 
     Assume `x` has :math:`k` dimensions :math:`[I, J, K, ..., N]`, then the output is a tensor of rank
     :math:`k+1` with dimensions :math:`[I, J, K, ..., N, N]` where:
-    :math:`output[i, j, k, ..., m, n] = 1{m=n} * x[i, j, k, ..., n]`
+    :math:`output[i, j, k, ..., m, n] = 1\{m=n\} * x[i, j, k, ..., n]`
 
     Inputs:
         - **x** (Tensor) - The diagonal values. It can be one of the following data types:
@@ -1007,9 +1007,9 @@ class MatrixSetDiag(Cell):
     Assume `x` has :math:`k+1` dimensions :math:`[I, J, K, ..., M, N]` and `diagonal` has :math:`k`
     dimensions :math:`[I, J, K, ..., min(M, N)]`. Then the output is a tensor of rank :math:`k+1` with dimensions
     :math:`[I, J, K, ..., M, N]` where:
-    :math:`output[i, j, k, ..., m, n] = diagnoal[i, j, k, ..., n] for m == n`
+    :math:`output[i, j, k, ..., m, n] = diagnoal[i, j, k, ..., n]\ for\ m == n`
 
-    :math:`output[i, j, k, ..., m, n] = x[i, j, k, ..., m, n] for m != n`
+    :math:`output[i, j, k, ..., m, n] = x[i, j, k, ..., m, n]\ for\ m != n`
 
     Inputs:
         - **x** (Tensor) - The batched tensor. Rank k+1, where k >= 1. It can be one of the following data types: