case tflite::TensorType_FLOAT16:
      return builder.getF16Type();
    case tflite::TensorType_BFLOAT16:
      return builder.getBF16Type();
    case tflite::TensorType_FLOAT32:
      return builder.getF32Type();
    case tflite::TensorType_FLOAT64:
      return builder.getF64Type();
    case tflite::TensorType_INT32:
      return builder.getIntegerType(32);
    case tflite::TensorType_UINT16:

#include "llvm/Support/raw_ostream.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_utils.h"
#include "tensorflow/lite/model.h"

using llvm::cl::opt;

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s.mlir -o - \
// RUN:   | %p/importer_test_min_max - \
// RUN:   | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - \
// RUN:   | FileCheck %s

                             const tflite::Model* input_model,
                             BufferType quant_type,
                             bool use_updated_hybrid_scheme) {
  tflite::TensorType inference_type;
  switch (quant_type) {
    case BufferType::QUANTIZED_FLOAT16:
      inference_type = tflite::TensorType_FLOAT16;
      break;
    default:
      inference_type = tflite::TensorType_INT8;
  }

// Convert the scalar type of a TFLite tensor to the corresponding
// Tensorflow type
tensorflow::DataType TflTypeToTfType(tflite::TensorType type);

// Convert the Tensorflow scalar type to the corresponding TFLite type
absl::StatusOr<tflite::TensorType> TfTypeToTflType(tensorflow::DataType type);

// Returns element type from attribute Type 'type_attr'.

  flatbuffers::Offset<tflite::Model> input_model_location =
      tflite::Model::Pack(input_builder, &input_model);
  tflite::FinishModelBuffer(input_builder, input_model_location);

  std::string serialized_model(
      reinterpret_cast<const char*>(input_builder.GetBufferPointer()),
      input_builder.GetSize());

  OwningOpRef<mlir::ModuleOp> module = tflite::FlatBufferToMlir(

// variables should be legalized to TFLite or not.
const char kLegalizeTflVariables[] = "tfl._legalize_tfl_variables";

// Returns true if 'op' is TF op that accepts resource type, but is
// supported by TFLite.
bool IsSupportedTFLiteResourceOp(Operation* op) {
  return llvm::isa<TF::ReadVariableOp, TF::AssignVariableOp, TF::VarHandleOp,
                   TF::LookupTableFindV2Op, TF::LookupTableImportV2Op,
                   TF::LookupTableSizeV2Op>(op);

               << ", input_inference_type: "
               << tflite::EnumNameTensorType(input_type)
               << ", output_inference_type: "
               << tflite::EnumNameTensorType(output_type) << "\n";
  mlir::Builder mlir_builder(&context);
  mlir::Type input_mlir_type =
      tflite::ConvertElementType(input_type, mlir_builder);
  mlir::Type output_mlir_type =
      tflite::ConvertElementType(output_type, mlir_builder);

        return tensorflow::PyoOrThrow(
            tflite::MlirSparsifyModel(input_contents_txt_raw.ptr()));
      },
      py::arg("input_contents_txt_raw"),
      R"pbdoc(
      Returns a sparsified model.
    )pbdoc");
  m.def(
      "RegisterCustomOpdefs",
      [](py::object custom_opdefs_txt_raw) {
        return tensorflow::PyoOrThrow(
            tflite::RegisterCustomOpdefs(custom_opdefs_txt_raw.ptr()));
      },

  // Load input model
  auto input_fbm = tflite::FlatBufferModel::BuildFromFile(
      "tensorflow/lite/testdata/sparse_tensor.bin");
  tflite::ModelT input_model;
  input_fbm->GetModel()->UnPackTo(&input_model);

  // Populate input metadata
  auto model_metadata_buffer = std::make_unique<tflite::BufferT>();
  model_metadata_buffer->data =
      std::vector<uint8_t>(expected_value.begin(), expected_value.end());

#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/core/framework/tensor.pb.h"
#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,