Add specification for SegmentMax-16

docs/articles_en/documentation/openvino-ir-format/operation-sets/operation-specs/arithmetic/segment-max-16.rst

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+SegmentMax
+==========
+
+
+.. meta::
+  :description: Learn about SegmentMax-16 - an arithmetic operation which computes the maximum values along segments of a tensor.
+
+**Versioned name**: *SegmentMax-16*
+
+**Category**: *Arithmetic*
+
+**Short description**: *SegmentMax-16* operation finds the maximum value in each specified segment of the ``input`` tensor.
+
+**Detailed description**
+
+For each index in ``segment_ids`` the operator gets values from ``data`` input tensor and calculates the maximum value for each segment.
+
+For example ``segments_ids`` with value ``[0,0,0,1,1,3,5,5]`` defines 4 non-empty segments. When coupled with a 1D data tensor ``data``, the segments are as follows:
+
+* Segment_0: ``[data[0], data[1], data[2]]``
+* Segment_1: ``[data[3], data[4]]``
+* Segment_2: ``[]``
+* Segment_3: ``[data[5]]``
+* Segment_4: ``[]``
+* Segment_5: ``[data[6], data[7]]``
+
+When there are no values in a segment, ``output[segment]`` is defined by ``fill_mode`` attribute.
+
+For ``fill_mode`` equal to ``ZERO`` , the operation output would be ``[max(Segment_0), max(Segment_1), 0, max(Segment_3), 0, max(Segment_5)]``.
+
+**Attributes**:
+
+* **1**: *fill_mode*
+
+  * **Description**: The value assigned to segments which are empty. **Required.**
+  * **Range of values**: Name of the mode in string format:
+
+    * ``ZERO`` - the empty segments are filled with zeros.
+    * ``LOWEST`` - the empty segments are filled with the lowest value of the data type *T*.
+  * **Type**: ``string``
+
+**Inputs**
+
+* **1**: ``data`` - ND tensor of type *T*, the numerical data on which SegmentMax operation will be performed. **Required.**
+
+* **2**: ``segment_ids`` - 1D Tensor of sorted non-negative numbers of type *T_IDX1*. Its size is equal to the size of the first dimension of the ``data`` input tensor. The values must be smaller than ``num_segments``. **Required.**
+
+* **3**: ``num_segments`` - A scalar value of type *T_IDX2* representing the segments count, used for shape inference. Defaults to ``max(segment_ids) + 1``. If ``num_segments < max(segment_ids) + 1`` then the extra segments defined in ``segment_ids`` are not included in the output.  **Optional.**
+
+**Outputs**
+
+* **1**: The output tensor has same rank and dimensions as the ``data`` input tensor except for the first dimension which is equal to the value of ``num_segments``.
+
+**Types**
+
+* *T*: any supported numerical data type.
+* *T_IDX1*, *T_IDX2*: ``int64`` or ``int32``.
+
+**Examples**
+
+*Example 1: 1D input data*
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="SegmentMax" ... >
+        <data empty_segment_value="ZERO">
+        <input>
+            <port id="0" precision="F32">   <!-- data -->
+                <dim>8</dim>
+            </port>
+            <port id="1" precision="I32">   <!-- segment_ids with 4 segments: [0, 0, 2, 3, 3] -->
+                <dim>5</dim> 
+            </port>
+            <port id="2" precision="I64">   <!-- number of segments: 8 -->
+                <dim>0</dim> 
+            </port>
+        </input>
+        <output>
+            <port id="3" precision="F32">
+                <dim>8</dim>
+            </port>
+        </output>
+    </layer>
+
+*Example 2: 2D input data*
+
+.. code-block:: xml
+   :force:
+
+    <layer ... type="SegmentMax" ... >
+        <data empty_segment_value="LOWEST">
+        <input>
+            <port id="0" precision="I32">   <!-- data -->
+                <dim>3</dim>
+                <dim>4</dim>
+            </port>
+            <port id="1" precision="I64">   <!-- segment_ids with 2 segments: [0, 1, 1] -->
+                <dim>3</dim>
+            </port>
+        </input>
+        <output>
+            <port id="2" precision="I32">
+                <dim>2</dim>
+                <dim>4</dim>
+            </port>
+        </output>
+    </layer>