From 4791f3bfd74180f27a84a67cce8f6e518cb95b73 Mon Sep 17 00:00:00 2001
From: Jozef Daniecki <jozef.daniecki@intel.com>
Date: Wed, 10 Mar 2021 07:57:55 +0100
Subject: [PATCH] SquaredDifference operation specification refactoring.
 (#4567)

* SquaredDifference operation specification refactoring.

* Add dummy broadcast_rules.md.

* Minor fixes, e.g. capitalize operation names, typos.

Co-authored-by: Szymon Durawa <szymon.durawa@intel.com>
---
 docs/ops/arithmetic/SquaredDifference_1.md | 34 +++++++++++-----------
 1 file changed, 17 insertions(+), 17 deletions(-)

diff --git a/docs/ops/arithmetic/SquaredDifference_1.md b/docs/ops/arithmetic/SquaredDifference_1.md
index 565dc00f0fc..5e5b89b5727 100644
--- a/docs/ops/arithmetic/SquaredDifference_1.md
+++ b/docs/ops/arithmetic/SquaredDifference_1.md
@@ -4,7 +4,14 @@
 
 **Category**: Arithmetic binary operation
 
-**Short description**: *SquaredDifference* performs element-wise subtraction operation with two given tensors applying multi-directional broadcast rules, after that each result of the subtraction is squared.
+**Short description**: *SquaredDifference* performs element-wise subtract and square the result operation with two given tensors applying broadcasting rule specified in the *auto_broadcast* attribute.
+
+**Detailed description**
+As a first step input tensors *a* and *b* are broadcasted if their shapes differ. Broadcasting is performed according to `auto_broadcast` attribute specification. As a second step *Substract* and *Square* the result operation is computed element-wise on the input tensors *a* and *b* according to the formula below:
+
+\f[
+o_{i} = (a_{i} - b_{i})^2
+\f] 
 
 **Attributes**:
 
@@ -12,40 +19,32 @@
 
   * **Description**: specifies rules used for auto-broadcasting of input tensors.
   * **Range of values**:
-    * *none* - no auto-broadcasting is allowed, all input shapes should match
-    * *numpy* - numpy broadcasting rules, aligned with ONNX Broadcasting. Description is available in <a href="https://github.com/onnx/onnx/blob/master/docs/Broadcasting.md">ONNX docs</a>.
+    * *none* - no auto-broadcasting is allowed, all input shapes must match
+    * *numpy* - numpy broadcasting rules, description is available in [Broadcast Rules For Elementwise Operations](../broadcast_rules.md)
   * **Type**: string
   * **Default value**: "numpy"
   * **Required**: *no*
 
 **Inputs**
 
-* **1**: A tensor of type T. **Required.**
-* **2**: A tensor of type T. **Required.**
+* **1**: A tensor of type T and arbitrary shape. Required.
+* **2**: A tensor of type T and arbitrary shape. Required.
 
 **Outputs**
 
-* **1**: The result of element-wise SquaredDifference operation. A tensor of type T.
+* **1**: The result of element-wise subtract and square the result operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
 
 **Types**
 
 * *T*: any numeric type.
 
-**Detailed description**
-Before performing arithmetic operation, input tensors *a* and *b* are broadcasted if their shapes are different and `auto_broadcast` attributes is not `none`. Broadcasting is performed according to `auto_broadcast` value.
-
-After broadcasting *SquaredDifference* does the following with the input tensors *a* and *b*:
-
-\f[
-o_{i} = (a_{i} - b_{i})^2
-\f]
-
 **Examples**
 
-*Example 1*
+*Example 1 - no broadcasting*
 
 ```xml
 <layer ... type="SquaredDifference">
+    <data auto_broadcast="none"/>
     <input>
         <port id="0">
             <dim>256</dim>
@@ -64,9 +63,10 @@ o_{i} = (a_{i} - b_{i})^2
     </output>
 </layer>
 ```
-*Example 2: broadcast*
+*Example 2: numpy broadcasting*
 ```xml
 <layer ... type="SquaredDifference">
+    <data auto_broadcast="numpy"/>
     <input>
         <port id="0">
             <dim>8</dim>