.. SPDX-FileCopyrightText: 2021 Intel Corporation .. .. SPDX-License-Identifier: CC-BY-4.0 --------- ReduceSum --------- **Versioned name**: *ReduceSum-1* **Category**: *Reduction* **Short description**: *ReduceSum* operation performs the reduction with addition on a given input data along dimensions specified by axes. **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 **data**, 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 data 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 ReduceSum 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.