.. SPDX-FileCopyrightText: 2020-2021 Intel Corporation
..
.. SPDX-License-Identifier: CC-BY-4.0
--------
ReduceL2
--------
**Versioned name**: *ReduceL2-1*
**Category**: *Reduction*
**Short description**: *ReduceL2* operation performs the reduction with finding
the L2 norm (square root of sum of squares) on a given input data along
dimensions specified by axes input.
**OpenVINO description**: This OP is as same as `OpenVINO OP
`__
**Attributes**
* *axes*
* **Description**: specify indices of input data, along which the reduction is
performed. If axes is a list, reduce over all of them. If axes is empty,
corresponds to the identity operation. If axes contains all dimensions of
input tensor, a single reduction value is calculated for the entire input
tensor. Exactly one of attribute *axes* and the second input tensor *axes*
should be available.
* **Range of values**: ``[-r, r-1]`` where ``r`` = rank(``input``)
* **Type**: s64[]
* **Default value**: empty list
* **Required**: *no*
* *keep_dims*
* **Description**: If set to ``True`` it holds axes that are used for
reduction. For each such axes, output dimension is equal to 1.
* **Range of values**: True or False
* **Type**: bool
* **Default value**: False
* **Required**: *no*
**Inputs**
* **1**: ``input`` - input tensor. **Required.**
* **Type**: T1
* **2**: ``axes`` - 1-D tensor specifying the axis along which the reduction is
performed. 1D tensor of unique elements. The range of elements is
``[-r, r-1]``, where ``r`` is the rank of input tensor. Exactly one of
attribute *axes* and the second input tensor *axes* should be available.
**Optional.**.
* **Type**: T2
**Outputs**
* **1**: ``output`` - the he result of ReduceL2 function applied to input tensor.
``shape[i] = shapeOf(data)[i]`` for all ``i`` that is not in the list of
axes from the second input. For dimensions from ``axes``, ``shape[i] == 1``
if ``keep_dims == True``, or ``i``-th dimension is removed from the output
otherwise.
* **Type**: T1
**Types**:
* **T1**: f32, f16, bf16.
* **T2**: s32.
* **Note**: The input tensor and the result tensor have the same data type
denoted by *T1*. For example, if input is f32 tensor, then result tensor has
f32 data type.