text: // Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved.
  
  // Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"),
  // to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
  // and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
  
  // The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
  
  // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
  // WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
  // COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  
  #ifndef REDUCE_MEAN_LAYER_H
  #define REDUCE_MEAN_LAYER_H
  
  #include "ReduceArithmeticLayer.h"
  
  namespace raul
  {
  
  /**
   * @brief Reduce Mean Layer
   * Computes the mean of elements across dimensions of a tensor.
   *
   * @see
   * https://pytorch.org/docs/master/generated/torch.mean.html
   */
  class ReduceMeanLayer : public ReduceArithmeticLayer
  {
    public:
      ReduceMeanLayer(const Name& name, const BasicParamsWithDim& params, NetworkParameters& networkParameters);
  
      ReduceMeanLayer(ReduceMeanLayer&&) = default;
      ReduceMeanLayer(const ReduceMeanLayer&) = delete;
      ReduceMeanLayer& operator=(const ReduceMeanLayer&) = delete;
  };
  
  } // raul namespace
  
  #endif