text: //*****************************************************************************
  // Copyright 2017-2020 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"
  #include "ngraph/op/util/fused_op.hpp"
  
  namespace ngraph
  {
      namespace op
      {
          namespace v0
          {
              /// \brief Layer Normalization
              ///
              class NGRAPH_API LayerNorm : public ngraph::op::util::FusedOp
              {
              public:
                  static constexpr NodeTypeInfo type_info{"LayerNorm", 0};
                  const NodeTypeInfo& get_type_info() const override { return type_info; }
                  LayerNorm() = default;
                  /// \brief Constructs an LayerNorm operation.
                  ///
                  /// \param data Input tensor
                  /// \param scale Scale tensor
                  /// \param bias Bias tensor
                  /// \param keep_stats Generated addition output mean and variance, default true
                  /// \param begin_norm_axis Axis where normalization starts, default - -1
                  /// \param epsilon Small number to add for stability of rsqrt, default 1e-5
                  LayerNorm(const Output<Node>& data,
                            const Output<Node>& scale,
                            const Output<Node>& bias,
                            bool keep_stats = true,
                            int64_t begin_norm_axis = 1,
                            double epsilon = 1e-5);
  
                  LayerNorm(const Output<Node>& data,
                            bool keep_stats = true,
                            int64_t begin_norm_axis = 1,
                            double epsilon = 1e-5);
  
                  virtual OutputVector decompose_op() const override;
  
                  void pre_validate_and_infer_types() override;
  
                  virtual std::shared_ptr<Node>
                      clone_with_new_inputs(const OutputVector& new_args) const override;
  
                  bool get_keep_stats() const { return m_keep_stats; }
                  bool get_use_affine() const { return m_use_affine; }
                  double get_epsilon() const { return m_epsilon; }
                  int64_t get_begin_norm_axis() const { return m_begin_norm_axis; }
  
              protected:
                  virtual void generate_adjoints(autodiff::Adjoints& adjoints,
                                                 const OutputVector& deltas) override;
  
              private:
                  bool m_keep_stats{true};
                  bool m_use_affine{true};
                  int64_t m_begin_norm_axis{1};
                  double m_epsilon{1e-5};
              };
  
              /// \brief Layer Normalization Backprop
              ///
              class NGRAPH_API LayerNormBackprop : public ngraph::op::util::FusedOp
              {
              public:
                  static constexpr NodeTypeInfo type_info{"LayerNormBackprop", 0};
                  const NodeTypeInfo& get_type_info() const override { return type_info; }
                  LayerNormBackprop() = default;
                  /// \brief Constructs an LayerNormBackprop operation.
                  ///
                  /// \param data Input tensor
                  /// \param mean Mean tensor from fprop
                  /// \param variance Variance tensor from fprop
                  /// \param delta Delta tensor
                  /// \param scale Scale tensor
                  /// \param begin_norm_axis Axis where normalization starts, default - -1
                  /// \param epsilon Small number to add for stability of rsqrt, default 1e-5
                  LayerNormBackprop(const Output<Node>& data,
                                    const Output<Node>& delta,
                                    const Output<Node>& mean,
                                    const Output<Node>& variance,
                                    const Output<Node>& scale,
                                    int64_t begin_norm_axis = 1,
                                    double epsilon = 1e-5);
  
                  LayerNormBackprop(const Output<Node>& data,
                                    const Output<Node>& delta,
                                    const Output<Node>& mean,
                                    const Output<Node>& variance,
                                    int64_t begin_norm_axis = 1,
                                    double epsilon = 1e-5);
  
                  LayerNormBackprop(const Output<Node>& data,
                                    const Output<Node>& delta,
                                    const Output<Node>& scale,
                                    int64_t begin_norm_axis = 1,
                                    double epsilon = 1e-5);
  
                  LayerNormBackprop(const Output<Node>& data,
                                    const Output<Node>& delta,
                                    int64_t begin_norm_axis = 1,
                                    double epsilon = 1e-5);
  
                  virtual OutputVector decompose_op() const override;
  
                  void pre_validate_and_infer_types() override;
  
                  virtual std::shared_ptr<Node>
                      clone_with_new_inputs(const OutputVector& new_args) const override;
  
                  bool get_use_stats() const { return m_use_stats; }
                  bool get_use_affine() const { return m_use_affine; }
                  double get_epsilon() const { return m_epsilon; }
                  int64_t get_begin_norm_axis() const { return m_begin_norm_axis; }
  
              private:
                  bool m_use_stats{true};
                  bool m_use_affine{true};
                  int64_t m_begin_norm_axis{1};
                  double m_epsilon{1e-5};
              };
          }
      }
  }
  