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

namespace mlir {
namespace TFL {

bool IsQuantized(const tflite::TensorT& tensor);

absl::StatusOr<mlir::quant::QuantizedType> GetQuantizedType(
    const tflite::TensorT& tensor, mlir::Builder builder,
    bool is_constant = false, mlir::Type storage_type = {});

// Imports float tensor with calibration value into calibrated quantized type.

         !tensor.quantization->zero_point.empty();
}

// Returns the correct type for a quantized tensor.
// We have a special case for constants since they have a higher minimum value.
StatusOr<QuantizedType> GetQuantizedType(const TensorT& tensor, Builder builder,
                                         bool is_constant,
                                         mlir::Type storage_type) {

}

// Returns the quantized type for the
// input_type/min/max/storag_type_width/narrow_range.
// This is entry point to the Quant dialect and used for both quantizing
// activations and weights.
Type GetQuantizedType(Builder builder, const Type input_type,
                      const ArrayRef<double> min, const ArrayRef<double> max,
                      const int quant_dim, const int storage_type_width,

                           SmallVectorImpl<double>& mins,
                           SmallVectorImpl<double>& maxs);

// Returns the quantized type for the
// input_type/min/max/storage_type_width/narrow_range.
Type GetQuantizedType(Builder builder, Type input_type, ArrayRef<double> min,
                      ArrayRef<double> max, int quant_dim,
                      int storage_type_width, bool narrow_range, bool is_signed,

    TF_ASSIGN_OR_RETURN(value,
                        tfl::ConvertIntBuffer(shaped_type, buffer, truncate));
    TF_ASSIGN_OR_RETURN(
        mlir::quant::QuantizedType type,
        tfl::GetQuantizedType(tensor, builder, /*is_constant=*/true,
                              /*storage_type=*/value.getElementType()));
    shaped_type = shaped_type.clone(type);
    auto op = builder.create<tfl::QConstOp>(