Maximum operation specification refactoring. (#4572)

* Maximum 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/Maximum_1.md

@@ -4,35 +4,10 @@
 
 **Category**: Arithmetic binary operation
 
-**Short description**: *Maximum* performs element-wise maximum operation with two given tensors applying multi-directional broadcast rules.
+**Short description**: *Maximum* performs element-wise maximum operation with two given tensors applying broadcasting rule specified in the *auto_broadcast* attribute.
-
-**Attributes**:
-
-* *auto_broadcast*
-
-  * **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>.
-  * **Type**: string
-  * **Default value**: "numpy"
-  * **Required**: *no*
-
-**Inputs**
-
-* **1**: First input tensor of type T. Required.
-* **2**: Second input tensor of type T. Required.
-
-**Outputs**
-
-* **1**: The result of element-wise maximum operation. A tensor of type T.
-
-**Types**
-
-* *T*: arbitrary type, which supports less/greater comparison.
 
 **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.
+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 *Maximum* operation is computed element-wise on the input tensors *a* and *b* according to the formula below:
 
 After broadcasting *Maximum* does the following with the input tensors *a* and *b*:
 
@@ -40,12 +15,38 @@ After broadcasting *Maximum* does the following with the input tensors *a* and *
 o_{i} = max(a_{i}, b_{i})
 \f] 
 
+**Attributes**:
+
+* *auto_broadcast*
+
+  * **Description**: specifies rules used for auto-broadcasting of input tensors.
+  * **Range of values**:
+    * *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 and arbitrary shape. Required.
+* **2**: A tensor of type T and arbitrary shape. Required.
+
+**Outputs**
+
+* **1**: The result of element-wise maximum operation. A tensor of type T with shape equal to broadcasted shape of two inputs.
+
+**Types**
+
+* *T*: any numeric type.
+
 **Examples**
 
-*Example 1*
+*Example 1 - no broadcasting*
 
 ```xml
 <layer ... type="Maximum">
+    <data auto_broadcast="none"/>
     <input>
         <port id="0">
             <dim>256</dim>
@@ -65,9 +66,10 @@ o_{i} = max(a_{i}, b_{i})
 </layer>
 ```
 
-*Example 2: broadcast*
+*Example 2: numpy broadcasting*
 ```xml
 <layer ... type="Maximum">
+    <data auto_broadcast="numpy"/>
     <input>
         <port id="0">
             <dim>8</dim>