text: // Copyright 2018 The AITS DNNC Authors.All Rights Reserved.
  //
  // Licensed to the Apache Software Foundation(ASF) under one
  // or more contributor license agreements.See the NOTICE file
  // distributed with this work for additional information
  // regarding copyright ownership.The ASF licenses this file
  // to you under the Apache License, Version 2.0 (the
  // "License"); you may not use this file except in compliance
  // with the License.You may obtain a copy of the License at
  //
  //   http://www.apache.org/licenses/LICENSE-2.0
  //
  // Unless required by applicable law or agreed to in writing,
  // software distributed under the License is distributed on an
  // "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  // KIND, either express or implied.See the License for the
  // specific language governing permissionsand limitations
  // under the License.
  //
  // This file is part of AITS DNN compiler maintained at
  // https://github.com/ai-techsystems/dnnCompiler
  //
  
  #pragma once
  #include "operators/baseOperator.h"
  #include <string>
  
  using namespace Eigen;
  
  namespace dnnc {
  
  /*! Dropout takes one input floating tensor and produces two tensor outputs,
      output (floating tensor) and mask (Tensor<bool>). Depending on whether
      it is in test mode or not, the output Y will either be a random dropout
      or a simple copy of the input. Note that our implementation of Dropout
      does scaling in the training phase, so during testing nothing needs to be
     done.*/
  
  template <typename T> class Dropout : public baseOperator<T, T, T> {
  protected:
    float ratio = 0.5; /*!< The ratio of random dropout. */
    //  Dropout attributes
  public:
    Dropout(std::string name = "opDropout", float ratio = 0.5)
        : baseOperator<T, T, T>(opDropout, name) {
      this->ratio = ratio;
    }
  
    bool getAttribute(OPATTR attrName, float &obj) override {
      if (attrName == attr_ratio) {
        obj = ratio;
        return true;
      }
      return false;
    }
    bool setAttribute(OPATTR attrName, float obj) override {
      if (attrName == attr_ratio) {
        ratio = obj;
        return true;
      }
      return false;
    }
  
    tensor<T> compute(tensor<T> &a /*!<[float,double]: ND tensor*/) {
  
      if (!(this->template type_check<T, float, double>())) {
        SPDLOG_ERROR("Constrain input and output types to float tensors.");
        return NULL_TENSOR<T>;
      }
  
      // Dropout is a NOOP for compiler. During training, it zeros
      // a fraction (attribute ratio) of the tensor a.
      return a;
    }
    /*!<
    \return The output tensor of the same shape and dtype as input.
    */
  };
  } // namespace dnnc
  