* Copyright (c) 2025 Huawei Technologies Co., Ltd.
* This program is free software, you can redistribute it and/or modify it under the terms and conditions of
* CANN Open Software License Agreement Version 2.0 (the "License").
* Please refer to the License for details. You may not use this file except in compliance with the License.
* THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED,
* INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.
* See LICENSE in the root of the software repository for the full text of the License.
*/
* \file squared_difference_proto.h
* \brief
*/
#ifndef OPS_OP_PROTO_INC_SQUARED_DIFFERENCE_H_
#define OPS_OP_PROTO_INC_SQUARED_DIFFERENCE_H_
#include "graph/operator_reg.h"
#include "graph/types.h"
namespace ge {
*@brief Returns (x1 - x2)(x1 - x2) element-wise. Support broadcasting operations.
*@par Inputs:
*Two inputs, including: \n
*@li x1: A ND Tensor. Must be one of the following types: bfloat16, float16, float32,
* float64, int32, int64, complex64, complex128.
*@li x2: A ND Tensor. Has the same dtype as "x1".
* The shape of x1 and x2 must meet the requirements of the broadcast relationship. \n
*@par Outputs:
*y: A ND Tensor. Has the same dtype as "x1". \n
*@par Third-party framework compatibility
* Compatible with TensorFlow operator SquaredDifference.
*/
REG_OP(SquaredDifference)
.INPUT(x1, TensorType({DT_BF16, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_COMPLEX64, DT_COMPLEX128}))
.INPUT(x2, TensorType({DT_BF16, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_COMPLEX64, DT_COMPLEX128}))
.OUTPUT(y, TensorType({DT_BF16, DT_FLOAT16, DT_FLOAT, DT_DOUBLE, DT_INT32, DT_INT64, DT_COMPLEX64, DT_COMPLEX128}))
.OP_END_FACTORY_REG(SquaredDifference)
}
#endif
