TfLiteStatus PopulateQuantizedLstmParams8x8_16(
    TfLiteContext* context, TfLiteNode* node,
    lstm_eval::IntegerLstmParameter* integer_lstm_param) {
  // Calculate quantized clip for projection and cell.
  const auto* params = static_cast<TfLiteLSTMParams*>(node->builtin_data);
  const float cell_clip = params->cell_clip;
  const float proj_clip = params->proj_clip;

  const TfLiteTensor* cell_state =
      GetVariableInput(context, node, kCellStateTensor);
  TF_LITE_ENSURE(context, cell_state != nullptr);
  const TfLiteTensor* output_tensor = GetOutput(context, node, kOutputTensor);

  auto* cell_state_params =
      static_cast<TfLiteAffineQuantization*>(cell_state->quantization.params);
  auto* proj_params = static_cast<TfLiteAffineQuantization*>(
      output_tensor->quantization.params);
  if (cell_clip > 0.0) {
    integer_lstm_param->quantized_cell_clip = static_cast<int16_t>(std::min(
        std::max(cell_clip / cell_state_params->scale->data[0], -32768.0f),
        32767.0f));
  } else {
    integer_lstm_param->quantized_cell_clip = 0;
  }
  if (proj_clip > 0.0) {
    integer_lstm_param->quantized_proj_clip = static_cast<int8_t>(std::min(
        std::max(proj_clip / proj_params->scale->data[0], -128.0f), 127.0f));
  } else {
    integer_lstm_param->quantized_proj_clip = 0;
  }

  // Calculate effective scales.
  OpData* op_data = static_cast<OpData*>(node->user_data);
  const bool use_layer_norm = op_data->use_layer_norm;

  const TfLiteTensor* input = GetInput(context, node, kInputTensor);

  const TfLiteTensor* input_to_input_weights =
      GetOptionalInputTensor(context, node, kInputToInputWeightsTensor);
  const TfLiteTensor* input_to_forget_weights =
      GetInput(context, node, kInputToForgetWeightsTensor);
  const TfLiteTensor* input_to_cell_weights =
      GetInput(context, node, kInputToCellWeightsTensor);
  const TfLiteTensor* input_to_output_weights =
      GetInput(context, node, kInputToOutputWeightsTensor);

  const TfLiteTensor* recurrent_to_input_weights =
      GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor);
  const TfLiteTensor* recurrent_to_forget_weights =
      GetInput(context, node, kRecurrentToForgetWeightsTensor);
  const TfLiteTensor* recurrent_to_cell_weights =
      GetInput(context, node, kRecurrentToCellWeightsTensor);
  const TfLiteTensor* recurrent_to_output_weights =
      GetInput(context, node, kRecurrentToOutputWeightsTensor);

  const TfLiteTensor* cell_to_input_weights =
      GetOptionalInputTensor(context, node, kCellToInputWeightsTensor);
  const TfLiteTensor* cell_to_forget_weights =
      GetOptionalInputTensor(context, node, kCellToForgetWeightsTensor);
  const TfLiteTensor* cell_to_output_weights =
      GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor);

  const TfLiteTensor* input_layer_norm_coefficients =
      GetOptionalInputTensor(context, node, kInputLayerNormCoefficientsTensor);
  const TfLiteTensor* forget_layer_norm_coefficients =
      GetOptionalInputTensor(context, node, kForgetLayerNormCoefficientsTensor);
  const TfLiteTensor* cell_layer_norm_coefficients =
      GetOptionalInputTensor(context, node, kCellLayerNormCoefficientsTensor);
  const TfLiteTensor* output_layer_norm_coefficients =
      GetOptionalInputTensor(context, node, kOutputLayerNormCoefficientsTensor);

  const TfLiteTensor* projection_weights =
      GetOptionalInputTensor(context, node, kProjectionWeightsTensor);

  TfLiteTensor* output_state =
      GetVariableInput(context, node, kOutputStateTensor);
  TF_LITE_ENSURE(context, output_state != nullptr);

  // Since we have already checked that weights are all there or none, we can
  // check the existence of only one to get the condition.
  const bool use_cifg = (input_to_input_weights == nullptr);
  const bool use_peephole = (cell_to_output_weights != nullptr);
  const bool use_projection = (projection_weights != nullptr);

  // Get intermediate scales and zero points.
  std::vector<float> intermediate_scale;
  std::vector<int32> intermediate_zp;
  for (int i = 0; i < 4; ++i) {
    if (use_layer_norm) {
      const TfLiteTensor* intermediate = GetIntermediates(context, node, i);
      auto* params = static_cast<TfLiteAffineQuantization*>(
          intermediate->quantization.params);
      intermediate_scale.push_back(params->scale->data[0]);
      intermediate_zp.push_back(params->zero_point->data[0]);
    } else {
      // Q3.12 for activation functions.
      intermediate_scale.push_back(std::pow(2, -12));
      intermediate_zp.push_back(0);
    }
  }
  // In the absense of projection, hidden becomes otuput and this intermediate
  // is ignored.
  const TfLiteTensor* hidden = GetIntermediates(context, node, 4);
  auto* hidden_params =
      static_cast<TfLiteAffineQuantization*>(hidden->quantization.params);
  intermediate_scale.push_back(hidden_params->scale->data[0]);
  intermediate_zp.push_back(hidden_params->zero_point->data[0]);

  // Scales.
  const float default_scale = 1.0;
  float input_scale = default_scale;
  float input_to_input_weight_scale = default_scale;
  float recurrent_to_input_weight_scale = default_scale;
  float cell_to_input_weight_scale = default_scale;
  float input_to_forget_weight_scale = default_scale;
  float recurrent_to_forget_weight_scale = default_scale;
  float cell_to_forget_weight_scale = default_scale;
  float input_to_cell_weight_scale = default_scale;
  float recurrent_to_cell_weight_scale = default_scale;
  float input_to_output_weight_scale = default_scale;
  float recurrent_to_output_weight_scale = default_scale;
  float cell_to_output_weight_scale = default_scale;
  float projection_weight_scale = default_scale;
  float layer_norm_input_scale = default_scale;
  float layer_norm_forget_scale = default_scale;
  float layer_norm_cell_scale = default_scale;
  float layer_norm_output_scale = default_scale;
  float output_state_scale = default_scale;
  int cell_scale = 1;

  // Effective scales.
  float effective_input_to_input_scale = default_scale;
  float effective_recurrent_to_input_scale = default_scale;
  float effective_cell_to_input_scale = default_scale;
  float effective_input_to_forget_scale = default_scale;
  float effective_recurrent_to_forget_scale = default_scale;
  float effective_cell_to_forget_scale = default_scale;
  float effective_input_to_cell_scale = default_scale;
  float effective_recurrent_to_cell_scale = default_scale;
  float effective_input_to_output_scale = default_scale;
  float effective_recurrent_to_output_scale = default_scale;
  float effective_cell_to_output_scale = default_scale;
  float effective_proj_scale = default_scale;
  float effective_hidden_scale = default_scale;

  // Populate scales.
  if (!use_cifg) {
    input_to_input_weight_scale = input_to_input_weights->params.scale;
    recurrent_to_input_weight_scale = recurrent_to_input_weights->params.scale;
  }

  if (use_peephole) {
    if (!use_cifg) {
      cell_to_input_weight_scale = cell_to_input_weights->params.scale;
    }
    cell_to_forget_weight_scale = cell_to_forget_weights->params.scale;
    cell_to_output_weight_scale = cell_to_output_weights->params.scale;
  }

  if (use_layer_norm) {
    if (!use_cifg) {
      layer_norm_input_scale = input_layer_norm_coefficients->params.scale;
    }
    layer_norm_forget_scale = forget_layer_norm_coefficients->params.scale;
    layer_norm_cell_scale = cell_layer_norm_coefficients->params.scale;
    layer_norm_output_scale = output_layer_norm_coefficients->params.scale;
  }

  if (use_projection) {
    projection_weight_scale = projection_weights->params.scale;
  }
  output_state_scale = output_state->params.scale;

  input_to_forget_weight_scale = input_to_forget_weights->params.scale;
  input_to_cell_weight_scale = input_to_cell_weights->params.scale;
  input_to_output_weight_scale = input_to_output_weights->params.scale;
  recurrent_to_forget_weight_scale = recurrent_to_forget_weights->params.scale;
  recurrent_to_cell_weight_scale = recurrent_to_cell_weights->params.scale;
  recurrent_to_output_weight_scale = recurrent_to_output_weights->params.scale;

  // Check cell state (already used above)
  TF_LITE_ENSURE(context, CheckedLog2(cell_state->params.scale, &cell_scale));
  TF_LITE_ENSURE(context, cell_scale <= -9);
  integer_lstm_param->cell_scale = cell_scale;
  input_scale = input->params.scale;

  // Calculate effective scales.
  if (!use_cifg) {
    effective_input_to_input_scale =
        input_to_input_weight_scale * input_scale / intermediate_scale[0];
    effective_recurrent_to_input_scale = recurrent_to_input_weight_scale *
                                         output_state_scale /
                                         intermediate_scale[0];
  }
  effective_input_to_forget_scale =
      input_to_forget_weight_scale * input_scale / intermediate_scale[1];
  effective_recurrent_to_forget_scale = recurrent_to_forget_weight_scale *
                                        output_state_scale /
                                        intermediate_scale[1];

  effective_input_to_cell_scale =
      input_to_cell_weight_scale * input_scale / intermediate_scale[2];
  effective_recurrent_to_cell_scale = recurrent_to_cell_weight_scale *
                                      output_state_scale /
                                      intermediate_scale[2];

  effective_input_to_output_scale =
      input_to_output_weight_scale * input_scale / intermediate_scale[3];
  effective_recurrent_to_output_scale = recurrent_to_output_weight_scale *
                                        output_state_scale /
                                        intermediate_scale[3];

  effective_hidden_scale =
      std::pow(2, -15) / intermediate_scale[4] * std::pow(2, -15);

  effective_proj_scale =
      projection_weight_scale * intermediate_scale[4] / output_state_scale;

  if (use_peephole) {
    if (!use_cifg) {
      effective_cell_to_input_scale = std::pow(2, cell_scale) *  // NOLINT
                                      cell_to_input_weight_scale /
                                      intermediate_scale[0];
    }
    effective_cell_to_forget_scale = std::pow(2, cell_scale) *  // NOLINT
                                     cell_to_forget_weight_scale /
                                     intermediate_scale[1];
    effective_cell_to_output_scale = std::pow(2, cell_scale) *  // NOLINT
                                     cell_to_output_weight_scale /
                                     intermediate_scale[3];
  }

  // Decompose scales.
  QuantizeMultiplier(effective_input_to_input_scale,
                     &integer_lstm_param->effective_input_to_input_scale_a,
                     &integer_lstm_param->effective_input_to_input_scale_b);
  QuantizeMultiplier(effective_recurrent_to_input_scale,
                     &integer_lstm_param->effective_recurrent_to_input_scale_a,
                     &integer_lstm_param->effective_recurrent_to_input_scale_b);
  QuantizeMultiplier(effective_cell_to_input_scale,
                     &integer_lstm_param->effective_cell_to_input_scale_a,
                     &integer_lstm_param->effective_cell_to_input_scale_b);
  QuantizeMultiplier(effective_input_to_forget_scale,
                     &integer_lstm_param->effective_input_to_forget_scale_a,
                     &integer_lstm_param->effective_input_to_forget_scale_b);
  QuantizeMultiplier(
      effective_recurrent_to_forget_scale,
      &integer_lstm_param->effective_recurrent_to_forget_scale_a,
      &integer_lstm_param->effective_recurrent_to_forget_scale_b);
  QuantizeMultiplier(effective_cell_to_forget_scale,
                     &integer_lstm_param->effective_cell_to_forget_scale_a,
                     &integer_lstm_param->effective_cell_to_forget_scale_b);
  QuantizeMultiplier(effective_input_to_cell_scale,
                     &integer_lstm_param->effective_input_to_cell_scale_a,
                     &integer_lstm_param->effective_input_to_cell_scale_b);
  QuantizeMultiplier(effective_recurrent_to_cell_scale,
                     &integer_lstm_param->effective_recurrent_to_cell_scale_a,
                     &integer_lstm_param->effective_recurrent_to_cell_scale_b);
  QuantizeMultiplier(effective_input_to_output_scale,
                     &integer_lstm_param->effective_input_to_output_scale_a,
                     &integer_lstm_param->effective_input_to_output_scale_b);
  QuantizeMultiplier(
      effective_recurrent_to_output_scale,
      &integer_lstm_param->effective_recurrent_to_output_scale_a,
      &integer_lstm_param->effective_recurrent_to_output_scale_b);
  QuantizeMultiplier(effective_cell_to_output_scale,
                     &integer_lstm_param->effective_cell_to_output_scale_a,
                     &integer_lstm_param->effective_cell_to_output_scale_b);
  QuantizeMultiplier(effective_proj_scale,
                     &integer_lstm_param->effective_proj_scale_a,
                     &integer_lstm_param->effective_proj_scale_b);
  QuantizeMultiplier(effective_hidden_scale,
                     &integer_lstm_param->effective_hidden_scale_a,
                     &integer_lstm_param->effective_hidden_scale_b);
  QuantizeMultiplier(layer_norm_input_scale,
                     &integer_lstm_param->layer_norm_input_scale_a,
                     &integer_lstm_param->layer_norm_input_scale_b);
  QuantizeMultiplier(layer_norm_forget_scale,
                     &integer_lstm_param->layer_norm_forget_scale_a,
                     &integer_lstm_param->layer_norm_forget_scale_b);
  QuantizeMultiplier(layer_norm_cell_scale,
                     &integer_lstm_param->layer_norm_cell_scale_a,
                     &integer_lstm_param->layer_norm_cell_scale_b);
  QuantizeMultiplier(layer_norm_output_scale,
                     &integer_lstm_param->layer_norm_output_scale_a,
                     &integer_lstm_param->layer_norm_output_scale_b);

  integer_lstm_param->hidden_zp = intermediate_zp[4];

  // 10000 is used to make sure the kernel logic does not overflow.
  if (!use_cifg) {
    integer_lstm_param->input_variance_guard =
        std::max(1, static_cast<int32_t>(10000 * layer_norm_input_scale));
  }
  integer_lstm_param->forget_variance_guard =
      std::max(1, static_cast<int32_t>(10000 * layer_norm_forget_scale));
  integer_lstm_param->cell_variance_guard =
      std::max(1, static_cast<int32_t>(10000 * layer_norm_cell_scale));
  integer_lstm_param->output_variance_guard =
      std::max(1, static_cast<int32_t>(10000 * layer_norm_output_scale));

  return kTfLiteOk;
}
TfLiteStatus PopulateQuantizedLstmParams8x8_16(
    TfLiteContext* context, TfLiteNode* node,
    lstm_eval::IntegerLstmParameter* integer_lstm_param) {
   
  const auto* params = static_cast<TfLiteLSTMParams*>(node->builtin_data);
  const float cell_clip = params->cell_clip;
  const float proj_clip = params->proj_clip;

  const TfLiteTensor* cell_state =
      GetVariableInput(context, node, kCellStateTensor);
  TF_LITE_ENSURE(context, cell_state != nullptr);
  const TfLiteTensor* output_tensor = GetOutput(context, node, kOutputTensor);

  auto* cell_state_params =
      static_cast<TfLiteAffineQuantization*>(cell_state->quantization.params);
  auto* proj_params = static_cast<TfLiteAffineQuantization*>(
      output_tensor->quantization.params);
  if (cell_clip > 0.0) {
    integer_lstm_param->quantized_cell_clip = static_cast<int16_t>(std::min(
        std::max(cell_clip / cell_state_params->scale->data[0], -32768.0f),
        32767.0f));
  } else {
    integer_lstm_param->quantized_cell_clip = 0;
  }
  if (proj_clip > 0.0) {
    integer_lstm_param->quantized_proj_clip = static_cast<int8_t>(std::min(
        std::max(proj_clip / proj_params->scale->data[0], -128.0f), 127.0f));
  } else {
    integer_lstm_param->quantized_proj_clip = 0;
  }

   
  OpData* op_data = static_cast<OpData*>(node->user_data);
  const bool use_layer_norm = op_data->use_layer_norm;

  const TfLiteTensor* input = GetInput(context, node, kInputTensor);

  const TfLiteTensor* input_to_input_weights =
      GetOptionalInputTensor(context, node, kInputToInputWeightsTensor);
  const TfLiteTensor* input_to_forget_weights =
      GetInput(context, node, kInputToForgetWeightsTensor);
  const TfLiteTensor* input_to_cell_weights =
      GetInput(context, node, kInputToCellWeightsTensor);
  const TfLiteTensor* input_to_output_weights =
      GetInput(context, node, kInputToOutputWeightsTensor);

  const TfLiteTensor* recurrent_to_input_weights =
      GetOptionalInputTensor(context, node, kRecurrentToInputWeightsTensor);
  const TfLiteTensor* recurrent_to_forget_weights =
      GetInput(context, node, kRecurrentToForgetWeightsTensor);
  const TfLiteTensor* recurrent_to_cell_weights =
      GetInput(context, node, kRecurrentToCellWeightsTensor);
  const TfLiteTensor* recurrent_to_output_weights =
      GetInput(context, node, kRecurrentToOutputWeightsTensor);

  const TfLiteTensor* cell_to_input_weights =
      GetOptionalInputTensor(context, node, kCellToInputWeightsTensor);
  const TfLiteTensor* cell_to_forget_weights =
      GetOptionalInputTensor(context, node, kCellToForgetWeightsTensor);
  const TfLiteTensor* cell_to_output_weights =
      GetOptionalInputTensor(context, node, kCellToOutputWeightsTensor);

  const TfLiteTensor* input_layer_norm_coefficients =
      GetOptionalInputTensor(context, node, kInputLayerNormCoefficientsTensor);
  const TfLiteTensor* forget_layer_norm_coefficients =
      GetOptionalInputTensor(context, node, kForgetLayerNormCoefficientsTensor);
  const TfLiteTensor* cell_layer_norm_coefficients =
      GetOptionalInputTensor(context, node, kCellLayerNormCoefficientsTensor);
  const TfLiteTensor* output_layer_norm_coefficients =
      GetOptionalInputTensor(context, node, kOutputLayerNormCoefficientsTensor);

  const TfLiteTensor* projection_weights =
      GetOptionalInputTensor(context, node, kProjectionWeightsTensor);

  TfLiteTensor* output_state =
      GetVariableInput(context, node, kOutputStateTensor);
  TF_LITE_ENSURE(context, output_state != nullptr);

   
   
  const bool use_cifg = (input_to_input_weights == nullptr);
  const bool use_peephole = (cell_to_output_weights != nullptr);
  const bool use_projection = (projection_weights != nullptr);

   
  std::vector<float> intermediate_scale;
  std::vector<int32> intermediate_zp;
  for (int i = 0; i < 4; ++i) {
    if (use_layer_norm) {
      const TfLiteTensor* intermediate = GetIntermediates(context, node, i);
      auto* params = static_cast<TfLiteAffineQuantization*>(
          intermediate->quantization.params);
      intermediate_scale.push_back(params->scale->data[0]);
      intermediate_zp.push_back(params->zero_point->data[0]);
    } else {
       
      intermediate_scale.push_back(std::pow(2, -12));
      intermediate_zp.push_back(0);
    }
  }
   
   
  const TfLiteTensor* hidden = GetIntermediates(context, node, 4);
  auto* hidden_params =
      static_cast<TfLiteAffineQuantization*>(hidden->quantization.params);
  intermediate_scale.push_back(hidden_params->scale->data[0]);
  intermediate_zp.push_back(hidden_params->zero_point->data[0]);

   
  const float default_scale = 1.0;
  float input_scale = default_scale;
  float input_to_input_weight_scale = default_scale;
  float recurrent_to_input_weight_scale = default_scale;
  float cell_to_input_weight_scale = default_scale;
  float input_to_forget_weight_scale = default_scale;
  float recurrent_to_forget_weight_scale = default_scale;
  float cell_to_forget_weight_scale = default_scale;
  float input_to_cell_weight_scale = default_scale;
  float recurrent_to_cell_weight_scale = default_scale;
  float input_to_output_weight_scale = default_scale;
  float recurrent_to_output_weight_scale = default_scale;
  float cell_to_output_weight_scale = default_scale;
  float projection_weight_scale = default_scale;
  float layer_norm_input_scale = default_scale;
  float layer_norm_forget_scale = default_scale;
  float layer_norm_cell_scale = default_scale;
  float layer_norm_output_scale = default_scale;
  float output_state_scale = default_scale;
  int cell_scale = 1;

   
  float effective_input_to_input_scale = default_scale;
  float effective_recurrent_to_input_scale = default_scale;
  float effective_cell_to_input_scale = default_scale;
  float effective_input_to_forget_scale = default_scale;
  float effective_recurrent_to_forget_scale = default_scale;
  float effective_cell_to_forget_scale = default_scale;
  float effective_input_to_cell_scale = default_scale;
  float effective_recurrent_to_cell_scale = default_scale;
  float effective_input_to_output_scale = default_scale;
  float effective_recurrent_to_output_scale = default_scale;
  float effective_cell_to_output_scale = default_scale;
  float effective_proj_scale = default_scale;
  float effective_hidden_scale = default_scale;

   
  if (!use_cifg) {
    input_to_input_weight_scale = input_to_input_weights->params.scale;
    recurrent_to_input_weight_scale = recurrent_to_input_weights->params.scale;
  }

  if (use_peephole) {
    if (!use_cifg) {
      cell_to_input_weight_scale = cell_to_input_weights->params.scale;
    }
    cell_to_forget_weight_scale = cell_to_forget_weights->params.scale;
    cell_to_output_weight_scale = cell_to_output_weights->params.scale;
  }

  if (use_layer_norm) {
    if (!use_cifg) {
      layer_norm_input_scale = input_layer_norm_coefficients->params.scale;
    }
    layer_norm_forget_scale = forget_layer_norm_coefficients->params.scale;
    layer_norm_cell_scale = cell_layer_norm_coefficients->params.scale;
    layer_norm_output_scale = output_layer_norm_coefficients->params.scale;
  }

  if (use_projection) {
    projection_weight_scale = projection_weights->params.scale;
  }
  output_state_scale = output_state->params.scale;

  input_to_forget_weight_scale = input_to_forget_weights->params.scale;
  input_to_cell_weight_scale = input_to_cell_weights->params.scale;
  input_to_output_weight_scale = input_to_output_weights->params.scale;
  recurrent_to_forget_weight_scale = recurrent_to_forget_weights->params.scale;
  recurrent_to_cell_weight_scale = recurrent_to_cell_weights->params.scale;
  recurrent_to_output_weight_scale = recurrent_to_output_weights->params.scale;

   
  TF_LITE_ENSURE(context, CheckedLog2(cell_state->params.scale, &cell_scale));
  TF_LITE_ENSURE(context, cell_scale <= -9);
  integer_lstm_param->cell_scale = cell_scale;
  input_scale = input->params.scale;

   
  if (!use_cifg) {
    effective_input_to_input_scale =
        input_to_input_weight_scale * input_scale / intermediate_scale[0];
    effective_recurrent_to_input_scale = recurrent_to_input_weight_scale *
                                         output_state_scale /
                                         intermediate_scale[0];
  }
  effective_input_to_forget_scale =
      input_to_forget_weight_scale * input_scale / intermediate_scale[1];
  effective_recurrent_to_forget_scale = recurrent_to_forget_weight_scale *
                                        output_state_scale /
                                        intermediate_scale[1];

  effective_input_to_cell_scale =
      input_to_cell_weight_scale * input_scale / intermediate_scale[2];
  effective_recurrent_to_cell_scale = recurrent_to_cell_weight_scale *
                                      output_state_scale /
                                      intermediate_scale[2];

  effective_input_to_output_scale =
      input_to_output_weight_scale * input_scale / intermediate_scale[3];
  effective_recurrent_to_output_scale = recurrent_to_output_weight_scale *
                                        output_state_scale /
                                        intermediate_scale[3];

  effective_hidden_scale =
      std::pow(2, -15) / intermediate_scale[4] * std::pow(2, -15);

  effective_proj_scale =
      projection_weight_scale * intermediate_scale[4] / output_state_scale;

  if (use_peephole) {
    if (!use_cifg) {
      effective_cell_to_input_scale = std::pow(2, cell_scale) *   
                                      cell_to_input_weight_scale /
                                      intermediate_scale[0];
    }
    effective_cell_to_forget_scale = std::pow(2, cell_scale) *   
                                     cell_to_forget_weight_scale /
                                     intermediate_scale[1];
    effective_cell_to_output_scale = std::pow(2, cell_scale) *   
                                     cell_to_output_weight_scale /
                                     intermediate_scale[3];
  }

   
  QuantizeMultiplier(effective_input_to_input_scale,
                     &integer_lstm_param->effective_input_to_input_scale_a,
                     &integer_lstm_param->effective_input_to_input_scale_b);
  QuantizeMultiplier(effective_recurrent_to_input_scale,
                     &integer_lstm_param->effective_recurrent_to_input_scale_a,
                     &integer_lstm_param->effective_recurrent_to_input_scale_b);
  QuantizeMultiplier(effective_cell_to_input_scale,
                     &integer_lstm_param->effective_cell_to_input_scale_a,
                     &integer_lstm_param->effective_cell_to_input_scale_b);
  QuantizeMultiplier(effective_input_to_forget_scale,
                     &integer_lstm_param->effective_input_to_forget_scale_a,
                     &integer_lstm_param->effective_input_to_forget_scale_b);
  QuantizeMultiplier(
      effective_recurrent_to_forget_scale,
      &integer_lstm_param->effective_recurrent_to_forget_scale_a,
      &integer_lstm_param->effective_recurrent_to_forget_scale_b);
  QuantizeMultiplier(effective_cell_to_forget_scale,
                     &integer_lstm_param->effective_cell_to_forget_scale_a,
                     &integer_lstm_param->effective_cell_to_forget_scale_b);
  QuantizeMultiplier(effective_input_to_cell_scale,
                     &integer_lstm_param->effective_input_to_cell_scale_a,
                     &integer_lstm_param->effective_input_to_cell_scale_b);
  QuantizeMultiplier(effective_recurrent_to_cell_scale,
                     &integer_lstm_param->effective_recurrent_to_cell_scale_a,
                     &integer_lstm_param->effective_recurrent_to_cell_scale_b);
  QuantizeMultiplier(effective_input_to_output_scale,
                     &integer_lstm_param->effective_input_to_output_scale_a,
                     &integer_lstm_param->effective_input_to_output_scale_b);
  QuantizeMultiplier(
      effective_recurrent_to_output_scale,
      &integer_lstm_param->effective_recurrent_to_output_scale_a,
      &integer_lstm_param->effective_recurrent_to_output_scale_b);
  QuantizeMultiplier(effective_cell_to_output_scale,
                     &integer_lstm_param->effective_cell_to_output_scale_a,
                     &integer_lstm_param->effective_cell_to_output_scale_b);
  QuantizeMultiplier(effective_proj_scale,
                     &integer_lstm_param->effective_proj_scale_a,
                     &integer_lstm_param->effective_proj_scale_b);
  QuantizeMultiplier(effective_hidden_scale,
                     &integer_lstm_param->effective_hidden_scale_a,
                     &integer_lstm_param->effective_hidden_scale_b);
  QuantizeMultiplier(layer_norm_input_scale,
                     &integer_lstm_param->layer_norm_input_scale_a,
                     &integer_lstm_param->layer_norm_input_scale_b);
  QuantizeMultiplier(layer_norm_forget_scale,
                     &integer_lstm_param->layer_norm_forget_scale_a,
                     &integer_lstm_param->layer_norm_forget_scale_b);
  QuantizeMultiplier(layer_norm_cell_scale,
                     &integer_lstm_param->layer_norm_cell_scale_a,
                     &integer_lstm_param->layer_norm_cell_scale_b);
  QuantizeMultiplier(layer_norm_output_scale,
                     &integer_lstm_param->layer_norm_output_scale_a,
                     &integer_lstm_param->layer_norm_output_scale_b);

  integer_lstm_param->hidden_zp = intermediate_zp[4];

   
  if (!use_cifg) {
    integer_lstm_param->input_variance_guard =
        std::max(1, static_cast<int32_t>(10000 * layer_norm_input_scale));
  }
  integer_lstm_param->forget_variance_guard =
      std::max(1, static_cast<int32_t>(10000 * layer_norm_forget_scale));
  integer_lstm_param->cell_variance_guard =
      std::max(1, static_cast<int32_t>(10000 * layer_norm_cell_scale));
  integer_lstm_param->output_variance_guard =
      std::max(1, static_cast<int32_t>(10000 * layer_norm_output_scale));

  return kTfLiteOk;
}