text: //*****************************************************************************
  // Copyright 2017-2021 Intel Corporation
  //
  // Licensed 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 permissions and
  // limitations under the License.
  //*****************************************************************************
  
  #pragma once
  
  #include "ngraph/node.hpp"
  #include "ngraph/op/op.hpp"
  
  namespace ngraph
  {
      namespace op
      {
          namespace v4
          {
              /// \brief A Swish Activation Function
              /// f(x) =  x / (1.0 + exp(-beta * x)) or
              /// f(x) = x * sigmoid(beta * x)
              ///
              class NGRAPH_API Swish : public ngraph::op::Op
              {
              public:
                  static constexpr NodeTypeInfo type_info{"Swish", 4};
                  const NodeTypeInfo& get_type_info() const override { return type_info; }
                  Swish() = default;
  
                  /// \brief Constructs an Swish operation.
                  ///
                  /// \param data Input tensor
                  /// \param beta Scalar with beta value. If the argument is not specified then use
                  /// the default value 1.0
                  Swish(const Output<Node>& arg, const Output<Node>& beta);
                  explicit Swish(const Output<Node>& arg);
  
                  bool visit_attributes(AttributeVisitor& visitor) override;
                  void validate_and_infer_types() override;
  
                  virtual std::shared_ptr<Node>
                      clone_with_new_inputs(const OutputVector& new_args) const override;
                  bool evaluate(const HostTensorVector& outputs,
                                const HostTensorVector& inputs) const override;
              };
          }
      }
  }
  