/**
 * Copyright (c) 2026 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.
 */

/* Generated By CANNBot */

/*!
 * \file sparse_apply_proximal_adagrad_proto.h
 * \brief
 */
#ifndef OPS_OP_PROTO_INC_SPARSE_APPLY_PROXIMAL_ADAGRAD_H_
#define OPS_OP_PROTO_INC_SPARSE_APPLY_PROXIMAL_ADAGRAD_H_

#include "graph/operator_reg.h"
#include "graph/types.h"

namespace ge {

/**
*@brief Updates var and accum by applying ProximalAdagrad algorithm for sparse indices.
*@par Inputs:
*Seven inputs, including:
* @li var: A ND Tensor. Must be one of the following types: float32, float16, bfloat16.
* @li accum: A ND Tensor. Must be one of the following types: float32, float16, bfloat16.
* @li lr: A scalar Tensor. Must be one of the following types: float32, float16, bfloat16.
* @li l1: A scalar Tensor. Must be one of the following types: float32, float16, bfloat16.
* @li l2: A scalar Tensor. Must be one of the following types: float32, float16, bfloat16.
* @li grad: A ND Tensor. Must be one of the following types: float32, float16, bfloat16.
* @li indices: A 1D Tensor. Must be one of the following types: int32, int64. \n

*@par Outputs:
*var: A ND Tensor. Must be one of the following types: float32, float16, bfloat16.
*accum: A ND Tensor. Must be one of the following types: float32, float16, bfloat16.
*@par Third-party framework compatibility
*Compatible with the TensorFlow operator SparseApplyProximalAdagrad.
*/
REG_OP(SparseApplyProximalAdagrad)
    .INPUT(var, TensorType::NumberType())
    .INPUT(accum, TensorType::NumberType())
    .INPUT(lr, TensorType::NumberType())
    .INPUT(l1, TensorType::NumberType())
    .INPUT(l2, TensorType::NumberType())
    .INPUT(grad, TensorType::NumberType())
    .INPUT(indices, TensorType::IndexNumberType())
    .OUTPUT(var, TensorType::NumberType())
    .OUTPUT(accum, TensorType::NumberType())
    .ATTR(use_locking, Bool, false)
    .OP_END_FACTORY_REG(SparseApplyProximalAdagrad)

} // namespace ge

#endif // OPS_OP_PROTO_INC_SPARSE_APPLY_PROXIMAL_ADAGRAD_H_