Push docs and doxygen updates into gh-pages branch

Dialects/onnx.md

@@ -529,7 +529,7 @@ AveragePool consumes an input tensor X and applies average pooling across
  ```
  output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)
  ```
- if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.
+ if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.
 
  `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:
  ```
@@ -4201,7 +4201,9 @@ This is layer normalization defined in ONNX as function.
       Let `d[i]` indicate the i-th dimension of `X`.
       If `X`'s shape is `[d[0], ..., d[axis-1], d[axis], ..., d[rank-1]]`,
       the shape of `Mean` and `InvStdDev` is `[d[0], ..., d[axis-1], 1, ..., 1]`.
-      `Y` and `X` have the same shape.
+      `Y` and `X` have the same shape. This operator supports unidirectional broadcasting
+      (tensors `Scale` and `B` should be unidirectional broadcastable to tensor `X`);
+      for more details please check [the doc](Broadcasting.md).
 
 Traits: `AlwaysSpeculatableImplTrait`
 
@@ -4851,7 +4853,7 @@ MaxPool consumes an input tensor X and applies max pooling across
  ```
  output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)
  ```
- if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.
+ if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.
 
  `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:
  ```
@@ -10294,11 +10296,11 @@ Effects: `MemoryEffects::Effect{}`
 _ONNX TopK operation_
 
 Retrieve the top-K largest or smallest elements along a specified axis. Given an input tensor of
-shape [a_1, a_2, ..., a_n, r] and integer argument k, return two outputs:
+shape [a_0, a_1, ..., a_{n-1\}\] and integer argument k, return two outputs:
 
-* Value tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n]
+* Value tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1\}\]
   which contains the values of the top k elements along the specified axis
-* Index tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] which
+* Index tensor of shape [a_0, a_1, ..., a_{axis-1}, k, a_{axis+1}, ... a_{n-1\}\] which
   contains the indices of the top k elements (original indices from the input
   tensor).