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 #ifndef TMVA_SOFIE_ROPERATOR_RNN
 #define TMVA_SOFIE_ROPERATOR_RNN
 
 #include "TMVA/RModel.hxx"
 #include "TMVA/ROperator.hxx"
 #include "TMVA/SOFIE_common.hxx"
 
 #include <memory>
 #include <sstream>
 #include <vector>
 
 namespace TMVA::Experimental::SOFIE {
 
 /*! \brief Recurrent Neural Network operator
  *
  * Inference code generation for one-layer vanilla RNN. Supports forward, reverse and bidirectional RNNs.
  * See the <a href="https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN">ONNX documentation</a>
  * for details about the supported RNN architectures.
  */
 template <typename T> class ROperator_RNN final : public ROperator {
  private:
    std::vector<float> fAttrActivationAlpha;   ///< Scaling values used by some activation functions
    std::vector<float> fAttrActivationBeta;    ///< Scaling values used by some activation functions
    std::vector<std::string> fAttrActivations; ///< Activation functions
    float fAttrClip;                           ///< Clip threshold
    std::string fAttrDirection;                ///< Direction of processing
    size_t fAttrHiddenSize;                    ///< Number of the hidden layers
    size_t fAttrLayout;                        ///< Data layout
 
    std::string fNX;                           ///< Name of the input
    std::string fNW;                           ///< Name of the weights
    std::string fNR;                           ///< Name of the recurrence
    std::string fNB;                           ///< Name of the bias
    std::string fNSequence_lens;               ///< Name of the length of the sequences
    std::string fNInitial_h;                   ///< Name of the initial value of the hidden states
    std::string fNY;                           ///< Name of the output
    std::string fNY_h;                         ///< Name of the last sequence of the output
 
    std::vector<size_t> fShapeX;               ///< Shape of the input
    std::vector<size_t> fShapeW;               ///< Shape of the weights
    std::vector<size_t> fShapeR;               ///< Shape of the recurrence
    std::vector<size_t> fShapeB;               ///< Shape of the bias
    std::vector<size_t> fShapeSequence_lens;   ///< Shape of the length of the sequences
    std::vector<size_t> fShapeInitial_h;       ///< Shape of the initial value of the hidden states
    std::vector<size_t> fShapeY;               ///< Shape of the output
    std::vector<size_t> fShapeY_h;             ///< Shape of the last sequence of the output
 
    std::string fType; ///< Type of the tensors
 
  public:
    /*! Default constructor of ROperator_RNN */
    ROperator_RNN() {}
 
    /*! \brief Constructor of ROperator_RNN from the attributes
     *
     * \param activation_alpha scaling values used by some activation functions
     * \param activation_beta scaling values used by some activation functions
     * \param activations activation functions
     * \param clip clip threshold
     * \param direction direction of processing of the sequneces
     * \param hidden_size number of hidden layers
     * \param layout data layout
     * \param nameX name of the input tensor
     * \param nameW name of the weight tensor
     * \param nameR name of the recurrence tensor
     * \param nameB name of the bias tensor
     * \param nameSequence_lens name of the length of the sequences
     * \param nameInitial_h name of the initial value of the hidden states
     * \param nameY name of the output
     * \param nameY_h name of the last sequence of the output
     */
    ROperator_RNN(std::vector<float> activation_alpha,
                  std::vector<float> activation_beta,
                  std::vector<std::string> activations, float clip,
                  std::string direction, size_t hidden_size, size_t layout,
                  std::string nameX, std::string nameW, std::string nameR,
                  std::string nameB, std::string nameSequence_lens,
                  std::string nameInitial_h, std::string nameY,
                  std::string nameY_h)
        : fAttrActivationAlpha(activation_alpha),
          fAttrActivationBeta(activation_beta), fAttrActivations(activations),
          fAttrClip(clip), fAttrDirection(direction),
          fAttrHiddenSize(hidden_size), fAttrLayout(layout),
          fNX(UTILITY::Clean_name(nameX)), fNW(UTILITY::Clean_name(nameW)),
          fNR(UTILITY::Clean_name(nameR)), fNB(UTILITY::Clean_name(nameB)),
          fNSequence_lens(UTILITY::Clean_name(nameSequence_lens)),
          fNInitial_h(UTILITY::Clean_name(nameInitial_h)),
          fNY(UTILITY::Clean_name(nameY)), fNY_h(UTILITY::Clean_name(nameY_h)) {
       if (std::is_same<T, float>::value) {
          fType = "float";
       } else {
          throw std::runtime_error(
              "TMVA SOFIE Encountered unsupported type parsing a RNN operator");
       }
 
       fInputTensorNames = { fNX, fNW, fNR };
       if(!fNB.empty()){
          fInputTensorNames.emplace_back(fNB);
       }
       if(!fNSequence_lens.empty()){
          fInputTensorNames.emplace_back(fNSequence_lens);
       }
       if(!fNInitial_h.empty()){
          fInputTensorNames.emplace_back(fNInitial_h);
       }
 
       fOutputTensorNames = { };
       if(!fNY.empty()){
          fOutputTensorNames.emplace_back(fNY);
       }
       if(!fNY_h.empty()){
          fOutputTensorNames.emplace_back(fNY_h);
       }
    }
 
    /*! \brief Infers the type of the output tensors
     *
     * \param input type of the input tensors
     */
    std::vector<ETensorType> TypeInference(std::vector<ETensorType> input) override;
 
    /*! \brief Infers the shape of the output tensors
     *
     * \param input shape of the input tensors
     */
    std::vector<std::vector<size_t>>
    ShapeInference(std::vector<std::vector<size_t>> input) override;
 
    /*! \brief Initialize the model
     *
     * \param model Model
     */
    void Initialize(RModel &) override;
 
    /*! \brief Generates the inference code
     *
     * \param OpName name of the operator
     */
    std::string Generate(std::string OpName) override;
 
    // generate code for Session data members (e.g. internal vectors)
    std::string GenerateSessionMembersCode(std::string opName) override;
 
    /*! \brief Returns the blas routines needed to compile the generated code
     */
    std::vector<std::string> GetBlasRoutines() override { return { std::string("Gemm"), std::string("Axpy") }; }
 };
 
 template <typename T>
 auto ROperator_RNN<T>::TypeInference(std::vector<ETensorType> input) -> std::vector<ETensorType>
 {
    ETensorType out = input[0];
    return {out, out};
 }
 
 template <typename T>
 auto ROperator_RNN<T>::ShapeInference(std::vector<std::vector<size_t>> input) -> std::vector<std::vector<size_t>>
 {
    size_t num_directions = input[1][0];
    size_t hidden_size = input[1][1];
    if (fAttrLayout == 0) {
       size_t seq_length = input[0][0];
       size_t batch_size = input[0][1];
       std::vector<std::vector<size_t>> ret(
          {{seq_length, num_directions, batch_size, hidden_size}, {num_directions, batch_size, hidden_size}});
       return ret;
    } else {
       size_t batch_size = input[0][0];
       size_t seq_length = input[0][1];
       std::vector<std::vector<size_t>> ret(
          {{batch_size, seq_length, num_directions, hidden_size}, {batch_size, num_directions, hidden_size}});
       return ret;
    }
 }
 
 template <typename T>
 auto ROperator_RNN<T>::Initialize(RModel &model) -> void
 {
    fUseSession = model.UseSession();
    // Check the input and output tensors
    if (!model.CheckIfTensorAlreadyExist(fNX)) {
       throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNX + "  is not found in model.");
    }
    fShapeX = model.GetTensorShape(fNX);
    if (fShapeX.size() != 3) {
       throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNX + " is not of 3 dimensions.");
    }
    if (!model.CheckIfTensorAlreadyExist(fNW)) {
       throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNW + "  is not found in model.");
    }
    fShapeW = model.GetTensorShape(fNW);
    if (fShapeW.size() != 3) {
       throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNW + " is not of 3 dimensions.");
    }
    if (!model.CheckIfTensorAlreadyExist(fNR)) {
       throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNR + "  is not found in model.");
    }
    fShapeR = model.GetTensorShape(fNR);
    if (fShapeR.size() != 3) {
       throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNR + " is not of 3 dimensions.");
    }
    if (!fNB.empty()) {
       if (!model.CheckIfTensorAlreadyExist(fNB)) {
          throw std::runtime_error("TMVA SOFIE RNN op input tensor " + fNB + " is not  found in model.");
       }
       fShapeB = model.GetTensorShape(fNB);
       if (fShapeB.size() != 2 && fShapeB.size() != 4) {
          throw std::runtime_error("TMVA SOFIE RNN op input tensor " + fNB + " is not of 2 or 4 dimensions.");
       }
       if (fShapeB.size() == 2) {
          // Broadcasting the bias
          auto original_data = model.GetInitializedTensorData(fNB);
          size_t num_directions = fShapeW[0];
          size_t seq_length = (fAttrLayout == 0) ? fShapeX[0] : fShapeX[1];
          size_t batch_size = (fAttrLayout == 0) ? fShapeX[1] : fShapeX[0];
          if (fType == "float") {
             float *original_bias = static_cast<float *>(original_data.get());
             float *new_bias = new float[num_directions * seq_length * batch_size * fAttrHiddenSize];
             std::vector<float> sum(fAttrHiddenSize);
             for (size_t direction = 0; direction < num_directions; direction++) {
                for (size_t h = 0; h < fAttrHiddenSize; h++) {
                   sum[h] = original_bias[direction * 2 * fAttrHiddenSize + h] +
                            original_bias[(2 * direction + 1) * fAttrHiddenSize + h];
                }
                for (size_t seq = 0; seq < seq_length; seq++) {
                   for (size_t batch = 0; batch < batch_size; batch++) {
                      size_t bias_offset = direction * seq_length * batch_size * fAttrHiddenSize +
                                           seq * batch_size * fAttrHiddenSize + batch * fAttrHiddenSize;
                      std::copy(sum.begin(), sum.end(), new_bias + bias_offset);
                   }
                }
             }
             std::vector<size_t> new_bias_shape = {num_directions, seq_length, batch_size, fAttrHiddenSize};
             std::shared_ptr<void> new_bias_ptr(new_bias, std::default_delete<float[]>());
             model.UpdateInitializedTensor(fNB, model.GetTensorType(fNB), new_bias_shape, new_bias_ptr);
             fShapeB = model.GetTensorShape(fNB);
          }
       }
    }
    if (!fNSequence_lens.empty()) {
       if (!model.CheckIfTensorAlreadyExist(fNSequence_lens)) {
          throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNSequence_lens + "is not found in model.");
       }
       fShapeSequence_lens = model.GetTensorShape(fNSequence_lens);
       if (fShapeSequence_lens.size() != 1) {
          throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNSequence_lens + " is not of 1 dimension.");
       }
    }
    if (!fNInitial_h.empty()) {
       if (!model.CheckIfTensorAlreadyExist(fNInitial_h)) {
          throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNInitial_h + " is not found in model.");
       }
       fShapeInitial_h = model.GetTensorShape(fNInitial_h);
       if (fShapeInitial_h.size() != 3) {
          throw std::runtime_error("TMVA SOFIE RNN Op input tensor " + fNInitial_h + " is not of 3 dimensions.");
       }
    }
    if (!fNY.empty()) {
       fShapeY = ShapeInference({fShapeX, fShapeW})[0];
       if (!model.CheckIfTensorAlreadyExist(fNY)) {
          model.AddIntermediateTensor(fNY, model.GetTensorType(fNX), fShapeY);
       }
    }
    if (!fNY_h.empty()) {
       fShapeY_h = ShapeInference({fShapeX, fShapeW})[1];
       if (!model.CheckIfTensorAlreadyExist(fNY_h)) {
          model.AddIntermediateTensor(fNY_h, model.GetTensorType(fNX), fShapeY_h);
       }
    }
    // Check the attributes
    for (auto &activation : fAttrActivations) {
       if (activation != "Relu" && activation != "Tanh" && activation != "Sigmoid" && activation != "Affine" &&
           activation != "LeakyRelu" && activation != "ThresholdRelu" && activation != "ScaledTanh" &&
           activation != "HardSigmoid" && activation != "Elu" && activation != "Softsign" && activation != "Softplus") {
          throw std::runtime_error("TMVA SOFIE - Activation function " + activation + " not implemented");
       }
    }
    if (fAttrDirection != "forward" && fAttrDirection != "backward" && fAttrDirection != "bidirectional") {
       throw std::runtime_error("TMVA SOFIE - Invalid RNN direction fAttrDirection = " + fAttrDirection);
    }
    if (fAttrHiddenSize != fShapeW[1]) {
       throw std::runtime_error("TMVA SOFIE - fAttrHiddenSize must be equal to " + std::to_string(fShapeW[1]));
    }
    if (fAttrLayout > 1) {
       throw std::runtime_error("TMVA SOFIE - Layout fAttrLayout = " + std::to_string(fAttrLayout) +
                                " must be 0 (timewise) or 1 (batchwise)");
    }
    if (fAttrActivations.empty()) {
       if (fAttrDirection == "bidirectional") {
          fAttrActivations = {"Tanh", "Tanh"};
       } else {
          fAttrActivations = {"Tanh"};
       }
    }
    // Add needed standard library headers
    model.AddNeededStdLib("cmath");
 }
 
 // generate code for Session data members (e.g. internal vectors)
 template <typename T>
 std::string ROperator_RNN<T>::GenerateSessionMembersCode(std::string opName)
 {
    opName = "op_" + opName;
    std::stringstream out;
 
    size_t num_directions = fShapeW[0];
    size_t seq_length = (fAttrLayout == 0) ? fShapeX[0] : fShapeX[1];
    size_t batch_size = (fAttrLayout == 0) ? fShapeX[1] : fShapeX[0];
    size_t input_size = fShapeX[2];
 
    struct Block {
       std::string name;
       size_t size;
    };
 
    std::vector<Block> blocks;
 
    if (fAttrLayout != 0) {
       blocks.push_back({"input", seq_length * batch_size * input_size});
       blocks.push_back({"initial_hidden_state", num_directions * batch_size * fAttrHiddenSize});
    }
    blocks.push_back({"feedforward", seq_length * batch_size * fAttrHiddenSize});
    if (fAttrLayout != 0 || fNY.empty()) {
       blocks.push_back({"hidden_state", seq_length * num_directions * batch_size * fAttrHiddenSize});
    }
 
    // Compute total size
    size_t total_size = 0;
    for (const auto &b : blocks) {
       total_size += b.size;
    }
 
    // Emit backing storage
    out << "std::vector<" << fType << "> fVec_" << opName << "_buffer = std::vector<" << fType << ">(" << total_size
        << ");\n";
 
    // Emit pointers
    std::size_t offset = 0;
    for (const auto &b : blocks) {
       out << fType << "* fVec_" << opName << "_" << b.name << " = fVec_" << opName << "_buffer.data() + " << offset
           << ";\n";
       offset += b.size;
    }
 
    out << "\n";
 
    return out.str();
 }
 
 //////////////////////////////////////////////////////////////////////////////////////////////////
 template <typename T>
 auto ROperator_RNN<T>::Generate(std::string OpName) -> std::string
 {
    OpName = "op_" + OpName;
    std::stringstream out;
 
    size_t seq_length = (fAttrLayout == 0) ? fShapeX[0] : fShapeX[1];
    size_t batch_size = (fAttrLayout == 0) ? fShapeX[1] : fShapeX[0];
    size_t input_size = fShapeX[2];
    size_t num_directions = fShapeW[0];
 
    // set the input
    if (fAttrLayout == 0) {
       if (fType == "float") {
          out << SP << "float const*" << OpName << "_input = tensor_" << fNX << ";\n";
       }
    } else {
       if (fUseSession)
          out << SP << fType << " * " << OpName << "_input = this->fVec_" << OpName << "_input;\n";
       else
          out << SP << fType << " " << OpName << "_input[" << seq_length * batch_size * input_size << "];\n";
       out << SP << "for(size_t seq = 0; seq < " << seq_length << "; seq++) {\n";
       out << SP << SP << "for(size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
       out << SP << SP << SP << "for(size_t i = 0; i < " << input_size << "; i++) {\n";
       out << SP << SP << SP << SP << OpName << "_input[seq * " << batch_size * input_size << " + batch * " << input_size
           << " + i] = " << "tensor_" << fNX << "[batch * " << seq_length * input_size << " + seq * " << input_size
           << " + i];\n";
       out << SP << SP << SP << "}\n";
       out << SP << SP << "}\n";
       out << SP << "}\n";
    }
 
    // Set the initial hidden state
    if (!fNInitial_h.empty()) {
       if (fAttrLayout == 0) {
          out << SP << fType << " *" << OpName << "_initial_hidden_state = " << " tensor_" << fNInitial_h << ";\n";
       } else {
          if (fUseSession)
             out << SP << fType << " * " << OpName << "_initial_hidden_state = this->fVec_" << OpName
                 << "_initial_hidden_state;\n";
          else
             out << fType << " " << OpName << "_initial_hidden_state[" << num_directions * batch_size * fAttrHiddenSize
                 << "] = {0};\n";
 
          for (size_t direction = 0; direction < num_directions; direction++) {
             out << SP << "for(size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
             out << SP << SP << "for(size_t h = 0; h < " << fAttrHiddenSize << "; h++) {\n";
             out << SP << SP << SP << OpName << "_initial_hidden_state[" << direction * batch_size * fAttrHiddenSize
                 << " + batch * " << fAttrHiddenSize << " + h] = tensor_" << fNInitial_h << "[batch * "
                 << num_directions * fAttrHiddenSize << " + " << direction * fAttrHiddenSize << " + h];\n";
             out << SP << SP << "}\n";
             out << SP << "}\n";
          }
       }
    }
 
    if (fUseSession)
       out << SP << fType << " * " << OpName << "_feedforward = this->fVec_" << OpName << "_feedforward;\n";
    else
       out << SP << fType << " " << OpName << "_feedforward[" << seq_length * batch_size * fAttrHiddenSize
           << "] = {0};\n";
 
    // Set the hidden state
    if (fAttrLayout == 0 && !fNY.empty()) {
       out << SP << fType << " *" << OpName << "_hidden_state = tensor_" << fNY << ";\n";
    } else {
       if (fUseSession)
          out << SP << fType << " * " << OpName << "_hidden_state = this->fVec_" << OpName << "_hidden_state;\n";
       else
          out << SP << fType << " " << OpName << "_hidden_state["
              << seq_length * num_directions * batch_size * fAttrHiddenSize << "] = {0};\n";
    }
 
    out << SP << "char " << OpName << "_transA = 'N';\n";
    out << SP << "char " << OpName << "_transB = 'T';\n";
    out << SP << "int " << OpName << "_m = " << seq_length * batch_size << ";\n";
    out << SP << "int " << OpName << "_n = " << fAttrHiddenSize << ";\n";
    out << SP << "int " << OpName << "_k = " << input_size << ";\n";
    if (fType == "float") {
       out << SP << "float " << OpName << "_alpha = 1.;\n";
       out << SP << "float " << OpName << "_beta = .0;\n";
    }
    if (!fNB.empty()) {
       out << SP << "int " << OpName << "_bias_size = " << seq_length * batch_size * fAttrHiddenSize << ";\n";
       out << SP << "int " << OpName << "_incx = 1;\n";
       out << SP << "int " << OpName << "_incy = 1;\n";
    }
 
    for (size_t direction = 0; direction < num_directions; direction++) {
       // feedforward = input * W^T + bias
       if (fType == "float") {
          if (direction == 0) {
             out << SP << "BLAS::sgemm_(&" << OpName << "_transB, &" << OpName << "_transA, &" << OpName << "_n, &"
                 << OpName << "_m, &" << OpName << "_k, &" << OpName << "_alpha, tensor_" << fNW << ", &" << OpName
                 << "_k, " << OpName << "_input, &" << OpName << "_k, &" << OpName << "_beta, " << OpName
                 << "_feedforward, &" << OpName << "_n);\n";
          } else {
             out << SP << "size_t " << OpName << "_w_offset = " << fAttrHiddenSize * input_size << ";\n";
             out << SP << "BLAS::sgemm_(&" << OpName << "_transB, &" << OpName << "_transA, &" << OpName << "_n, &"
                 << OpName << "_m, &" << OpName << "_k, &" << OpName << "_alpha, tensor_" << fNW << " + " << OpName
                 << "_w_offset, &" << OpName << "_k, " << OpName << "_input, &" << OpName << "_k, &" << OpName
                 << "_beta, " << OpName << "_feedforward, &" << OpName << "_n);\n";
          }
       }
       // Add the bias
       if (!fNB.empty()) {
          if (fType == "float") {
             if (direction == 0) {
                out << SP << "BLAS::saxpy_(&" << OpName << "_bias_size, &" << OpName << "_alpha, tensor_" << fNB << ", &"
                    << OpName << "_incx, " << OpName << "_feedforward, &" << OpName << "_incy);\n";
             } else {
                out << SP << "size_t " << OpName << "_bias_offset = " << seq_length * batch_size * fAttrHiddenSize
                    << ";\n";
                out << SP << "BLAS::saxpy_(&" << OpName << "_bias_size, &" << OpName << "_alpha, tensor_" << fNB << " + "
                    << OpName << "_bias_offset, &" << OpName << "_incx, " << OpName << "_feedforward, &" << OpName
                    << "_incy);\n";
             }
          }
       }
 
       // Copy feedforward into hidden state
       out << SP << "for (size_t seq = 0; seq < " << seq_length << "; seq++) {\n";
       out << SP << SP << "size_t offset = seq * " << batch_size * fAttrHiddenSize << ";\n";
       out << SP << SP << "size_t size = " << batch_size * fAttrHiddenSize << ";\n";
       out << SP << SP << "size_t h_offset = seq * " << num_directions * batch_size * fAttrHiddenSize << " + "
           << direction * batch_size * fAttrHiddenSize << ";\n";
       out << SP << SP << "std::copy(" << OpName << "_feedforward + offset, " << OpName
           << "_feedforward + offset + size, " << OpName << "_hidden_state + h_offset);\n";
       out << SP << "}\n";
 
       out << SP << "for (size_t seq = 0; seq < " << seq_length << "; seq++) {\n";
       if (fAttrDirection == "backward" || direction == 1) {
          out << SP << SP << "size_t index = " << seq_length - 1 << " - seq;\n";
       } else {
          out << SP << SP << "size_t index = seq;\n";
       }
 
       out << SP << SP << "int m2 = " << batch_size << ";\n";
       out << SP << SP << "size_t offset = index * " << num_directions * batch_size * fAttrHiddenSize << " + "
           << direction * batch_size * fAttrHiddenSize << ";\n";
       out << SP << SP << "size_t size = " << batch_size * fAttrHiddenSize << ";\n";
       out << SP << SP << "if (seq == 0) {\n";
       if (!fNInitial_h.empty()) {
          // hidden_state = hidden_state + initial_hidden_state * R^T
          out << SP << SP << SP << "size_t r_offset = " << direction * fAttrHiddenSize * fAttrHiddenSize << ";\n";
          out << SP << SP << SP << "size_t initial_hidden_state_offset = " << direction * batch_size * fAttrHiddenSize
              << ";\n";
          if (fType == "float") {
             out << SP << SP << SP << "BLAS::sgemm_(&" << OpName << "_transB, &" << OpName << "_transA, &" << OpName
                 << "_n, &m2, &" << OpName << "_n, &" << OpName << "_alpha, tensor_" << fNR << " + r_offset, &" << OpName
                 << "_n, " << OpName << "_initial_hidden_state + initial_hidden_state_offset, &" << OpName << "_n, &"
                 << OpName << "_alpha, " << OpName << "_hidden_state + offset, &" << OpName << "_n);\n";
          }
       }
       out << SP << SP << "} else {\n";
       // hidden_state = hidden_state + previous_hidden_state * R^T
       out << SP << SP << SP << "size_t r_offset = " << direction * fAttrHiddenSize * fAttrHiddenSize << ";\n";
       if (fAttrDirection == "backward" || direction == 1) {
          out << SP << SP << SP << "size_t previous_offset = (index + 1) * "
              << num_directions * batch_size * fAttrHiddenSize << " + " << direction * batch_size * fAttrHiddenSize
              << ";\n";
       } else {
          out << SP << SP << SP << "size_t previous_offset = (seq - 1) * "
              << num_directions * batch_size * fAttrHiddenSize << " + " << direction * batch_size * fAttrHiddenSize
              << ";\n";
       }
       if (fType == "float") {
          out << SP << SP << SP << "BLAS::sgemm_(&" << OpName << "_transB, &" << OpName << "_transA, &" << OpName
              << "_n, &m2, &" << OpName << "_n, &" << OpName << "_alpha, tensor_" << fNR << " + r_offset, &" << OpName
              << "_n, " << OpName << "_hidden_state + previous_offset, &" << OpName << "_n, &" << OpName << "_alpha, "
              << OpName << "_hidden_state + offset, &" << OpName << "_n);\n";
       }
       out << SP << SP << "}\n";
 
       // Clip the elements of the hidden state into the range [-fAttrClip, fAttrClip]
       if (fAttrClip > .0) {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          if (fType == "float") {
             out << SP << SP << SP << "float x = (" << OpName << "_hidden_state[i] > " << -fAttrClip << ") ? " << OpName
                 << "_hidden_state[i] : " << -fAttrClip << ";\n";
          }
          out << SP << SP << SP << OpName << "_hidden_state[i] = (x < " << fAttrClip << ") ? x : " << fAttrClip << ";\n";
          out << SP << SP << "}\n";
       }
 
       // Apply the activation function to the hidden state
       if (fAttrActivations[direction] == "Relu") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          out << SP << SP << SP << "if (" << OpName << "_hidden_state[i] < 0.)\n";
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = 0.;\n";
          out << SP << SP << "}\n";
       } else if (fAttrActivations[direction] == "Tanh") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          if (fType == "float") {
             out << SP << SP << SP << "float ex = std::exp(-2 * " << OpName << "_hidden_state[i]);\n";
          }
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = (1. - ex) / (1. + ex);\n";
          out << SP << SP << "}\n";
       } else if (fAttrActivations[direction] == "Sigmoid") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = 1. / (1. + std::exp(-" << OpName
              << "_hidden_state[i]));\n";
          out << SP << SP << "}\n";
       } else if (fAttrActivations[direction] == "Affine") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = " << fAttrActivationAlpha[direction] << " * "
              << OpName << "_hidden_state[i] + " << fAttrActivationBeta[direction] << ";\n";
          out << SP << SP << "}\n";
       } else if (fAttrActivations[direction] == "ScaledTanh") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          if (fType == "float") {
             out << SP << SP << SP << "float ex = std::exp(-2 * " << fAttrActivationBeta[direction] << " * " << OpName
                 << "_hidden_state[i]);\n";
          }
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = " << fAttrActivationAlpha[direction]
              << " * (1. - ex) / (1. + ex);\n";
          out << SP << SP << "}\n";
       } else if (fAttrActivations[direction] == "HardSigmoid") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          if (fType == "float") {
             out << SP << SP << SP << "float a = " << fAttrActivationAlpha[direction] << " * " << OpName
                 << "_hidden_state[i] + " << fAttrActivationBeta[direction] << ";\n";
             out << SP << SP << SP << "float b = (a > 0.) ? a : 0.;\n";
          }
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = (b < 1.) ? b : 1.;\n";
          out << SP << SP << "}\n";
       } else if (fAttrActivations[direction] == "LeakyRelu") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          out << SP << SP << SP << "if (" << OpName << "_hidden_state[i] < 0.)\n";
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = " << fAttrActivationAlpha[direction] << " * "
              << OpName << "_hidden_state[i];\n";
          out << SP << SP << "}\n";
       } else if (fAttrActivations[direction] == "ThresholdRelu") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          out << SP << SP << SP << "if (" << OpName << "_hidden_state[i] < " << fAttrActivationAlpha[direction] << ")\n";
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = 0.;\n";
          out << SP << SP << "}";
       } else if (fAttrActivations[direction] == "Elu") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          out << SP << SP << SP << "if (" << OpName << "_hidden_state[i] < 0.)\n";
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = " << fAttrActivationAlpha[direction]
              << " * std::exp(" << OpName << "_hidden_state[i] - 1.);\n";
          out << SP << SP << "}\n";
       } else if (fAttrActivations[direction] == "Softsign") {
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = " << OpName << "_hidden_state[i] / (1. + abs("
              << OpName << "_hidden_state[i]));\n";
          out << SP << SP << "}\n";
       } else { // fAttrActivations[direction] = Softplus
          out << SP << SP << "for (size_t i = offset; i < offset + size; i++) {\n";
          out << SP << SP << SP << SP << OpName << "_hidden_state[i] = log(1. + std::exp(" << OpName
              << "_hidden_state[i]));\n";
          out << SP << SP << "}\n";
          out << SP << "}\n";
       }
       out << SP << "}\n";
    }
 
    // Padding the hidden state for RNN with different sequence lengths
    if (!fNSequence_lens.empty()) {
       out << SP << "for (size_t seq = 0; seq < " << seq_length << "; seq++) {\n";
       out << SP << SP << "for (size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
       out << SP << SP << SP << "if (seq >= tensor_" << fNSequence_lens << "[batch]) {\n";
       out << SP << SP << SP << SP << "for (size_t h = 0; h < " << fAttrHiddenSize << "; h++) {\n";
       if (num_directions == 1) {
          out << SP << SP << SP << SP << SP << OpName << "_hidden_state[seq * "
              << num_directions * batch_size * fAttrHiddenSize << " + batch * " << fAttrHiddenSize << " + h] = 0.;\n";
       } else {
          out << SP << SP << SP << SP << SP << OpName << "_hidden_state[seq * "
              << num_directions * batch_size * fAttrHiddenSize << " + batch * " << fAttrHiddenSize << " + h] = 0.;\n";
          out << SP << SP << SP << SP << SP << OpName << "_hidden_state[seq * "
              << num_directions * batch_size * fAttrHiddenSize << " + " << batch_size * fAttrHiddenSize << " + batch * "
              << fAttrHiddenSize << " + h] = 0.;\n";
       }
       out << SP << SP << SP << SP << "}\n";
       out << SP << SP << SP << "}\n";
       out << SP << SP << "}\n";
       out << SP << "}\n";
    }
 
    // Copy the hidden state into y and y_h
    if (fAttrLayout == 0) {
       if (!fNY_h.empty()) {
          if (fNSequence_lens.empty()) {
             size_t yh_size = batch_size * fAttrHiddenSize;
             if (fAttrDirection == "backward") {
                out << SP << "std::copy(" << OpName << "_hidden_state, " << OpName << "_hidden_state + " << yh_size
                    << ", tensor_" << fNY_h << ");\n";
             } else {
                size_t offset = (seq_length - 1) * num_directions * batch_size * fAttrHiddenSize;
                out << SP << "std::copy(" << OpName << "_hidden_state + " << offset << ", " << OpName
                    << "_hidden_state + " << offset << " + " << yh_size << ", tensor_" << fNY_h << ");\n";
             }
             if (num_directions == 2) {
                out << SP << "std::copy(" << OpName << "_hidden_state + " << yh_size << ", " << OpName
                    << "_hidden_state + " << 2 * yh_size << ", tensor_" << fNY_h << " + " << yh_size << ");\n";
             }
          } else { // RNN with different sequence lengths
             if (fAttrDirection == "backward") {
                out << SP << "for (size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
                out << SP << SP << "size_t offset = batch * " << fAttrHiddenSize << ";\n";
                out << SP << SP << "std::copy(" << OpName << "_hidden_state + offset, " << OpName
                    << "_hidden_state + offset + " << fAttrHiddenSize << ", tensor_" << fNY_h << " + offset);\n";
                out << SP << "}\n";
             } else {
                out << SP << "for (size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
                out << SP << SP << "size_t seq = " << "tensor_" << fNSequence_lens << "[batch] - 1;\n";
                out << SP << SP << "size_t offset = seq * " << num_directions * batch_size * fAttrHiddenSize
                    << " + batch * " << fAttrHiddenSize << ";\n";
                out << SP << SP << "size_t yh_offset = batch * " << fAttrHiddenSize << ";\n";
                out << SP << SP << "std::copy(" << OpName << "_hidden_state + offset, " << OpName
                    << "_hidden_state + offset + " << fAttrHiddenSize << ", tensor_" << fNY_h << " + yh_offset);\n";
                out << SP << "}\n";
             }
             if (num_directions == 2) {
                out << SP << "for (size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
                out << SP << SP << "size_t offset = " << batch_size * fAttrHiddenSize << " + batch * " << fAttrHiddenSize
                    << ";\n";
                out << SP << SP << "size_t yh_offset = " << batch_size * fAttrHiddenSize << " + batch * "
                    << fAttrHiddenSize << ";\n";
                out << SP << SP << "std::copy(" << OpName << "_hidden_state + offset, " << OpName
                    << "_hidden_state + offset + " << fAttrHiddenSize << ", tensor_" << fNY_h << " + yh_offset);\n";
                out << SP << "}\n";
             }
          }
       }
    } else { // fAttrLayout=1
       if (!fNY.empty()) {
          for (size_t direction = 0; direction < num_directions; direction++) {
             out << SP << "for (size_t seq = 0; seq < " << seq_length << "; seq++) {\n";
             out << SP << SP << "for (size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
             out << SP << SP << SP << "size_t offset = seq * " << num_directions * batch_size * fAttrHiddenSize << " + "
                 << direction * batch_size * fAttrHiddenSize << " + batch * " << fAttrHiddenSize << ";\n";
             out << SP << SP << SP << "size_t y_offset = batch * " << seq_length * num_directions * fAttrHiddenSize
                 << " + seq * " << num_directions * fAttrHiddenSize << " + " << direction * fAttrHiddenSize << ";\n";
             out << SP << SP << SP << "std::copy(" << OpName << "_hidden_state + offset, " << OpName
                 << "_hidden_state + offset + " << fAttrHiddenSize << ", tensor_" << fNY << " + y_offset);\n";
             out << SP << SP << "}\n";
             out << SP << "}\n";
          }
       }
       if (!fNY_h.empty()) {
          if (fAttrDirection == "backward") {
             out << SP << "for (size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
             out << SP << SP << "size_t offset = batch * " << fAttrHiddenSize << ";\n";
             out << SP << SP << "size_t yh_offset = batch * " << num_directions * fAttrHiddenSize << ";\n";
             out << SP << SP << "std::copy(" << OpName << "_hidden_state + offset, " << OpName
                 << "_hidden_state + offset + " << fAttrHiddenSize << ", tensor_" << fNY_h << " + yh_offset);\n";
             out << SP << "}\n";
          } else {
             out << SP << "for (size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
             if (fNSequence_lens.empty()) {
                out << SP << SP << "size_t seq = " << seq_length - 1 << ";\n";
             } else {
                out << SP << SP << "size_t seq = " << "tensor_" << fNSequence_lens << "[batch] - 1;\n";
             }
             out << SP << SP << "size_t offset = seq * " << num_directions * batch_size * fAttrHiddenSize
                 << " + batch * " << fAttrHiddenSize << ";\n";
             out << SP << SP << "size_t yh_offset = batch * " << num_directions * fAttrHiddenSize << ";\n";
             out << SP << SP << "std::copy(" << OpName << "_hidden_state + offset, " << OpName
                 << "_hidden_state + offset + " << fAttrHiddenSize << ", tensor_" << fNY_h << " + yh_offset);\n";
             out << SP << "}\n";
          }
          if (num_directions == 2) {
             out << SP << "for (size_t batch = 0; batch < " << batch_size << "; batch++) {\n";
             out << SP << SP << "size_t offset = " << batch_size * fAttrHiddenSize << " + batch * " << fAttrHiddenSize
                 << ";\n";
             out << SP << SP << "size_t yh_offset = batch * " << num_directions * fAttrHiddenSize << " + "
                 << fAttrHiddenSize << ";\n";
             out << SP << SP << "std::copy(" << OpName << "_hidden_state + offset, " << OpName
                 << "_hidden_state + offset + " << fAttrHiddenSize << ", tensor_" << fNY_h << " + yh_offset);\n";
             out << SP << "}\n";
          }
       }
    }
 
    return out.str();
 }
 
 } // namespace TMVA::Experimental::SOFIE
 
 #endif

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