#include "tensorflow/core/framework/types.pb.h"

namespace mlir {
namespace TFL {

double PowerOfTwoBound(double value) {
  return std::pow(2, std::ceil(std::log2(value)));
}

tensorflow::DataType GetQuantizedInferenceType(bool is_signed,
                                               int number_of_bits) {
  if (is_signed && number_of_bits == 8) {
    return tensorflow::DT_QINT8;
  } else if (!is_signed && number_of_bits == 8) {

  func::FuncOp func = getOperation();
  MLIRContext* ctx = func.getContext();
  ScopedTFLQuantOpsToMlirQuantOpsConverter converter(func);
  if (use_quantization_flags_) {
    quant_specs_.inference_type = GetQuantizedInferenceType(
        this->quantize_signed_, this->activation_number_of_bits_);
    if (quant_specs_.inference_type == tensorflow::DT_INVALID) {
      func.emitError() << "prepare-quantize pass failed: unsupported "

    "effective_hidden_scale_intermediate"};

// Calculates the minimum power of two that is not less than the value.
double PowerOfTwoBound(double value);

tensorflow::DataType GetQuantizedInferenceType(bool is_signed,
                                               int activation_number_of_bits);

// Returns the element type of LSTM's intermediate tensor designated by the
// index.
template <typename LstmOp>