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    mlir::StatusScopedDiagnosticHandler& status_handler,
    const toco::TocoFlags& toco_flags, const mlir::TFL::PassConfig& pass_config,
    std::optional<Session*> session, std::string* result,
    const std::unordered_set<std::string>& saved_model_tags) {
  // Currently, TF quantization only support dynamic range quant, as such
  // when toco flag post training quantization is specified with converting to

#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace TFL {
namespace tac {

// TODO(renjieliu): add more patterns.

// This basically:
// Pack => (Concat -> Reshape)
struct LowerPackIntoConcatReshape : public OpRewritePattern<TFL::PackOp> {

// RUN: tac-translate -input-mlir -output-mlir -device-specs=NNAPI %s -o - 2>&1 | FileCheck %s

module {
  // CHECK-LABEL: main
  func.func @main(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
    %0 = "tfl.squared_difference"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
    func.return %0 : tensor<4xf32>
    // CHECK:  [[VAL_0:%.*]] = tfl.sub %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<4xf32>

// ops.
//
// When `legacy_float_scale` is true, the quantizer will use float scale instead
// of double, and call TOCO's quantization routines to maintain bit-exactness of
// the values with the TOCO quantizer.
TfLiteStatus QuantizeModel(
    absl::string_view model_buffer, const tflite::TensorType &input_type,
    const tflite::TensorType &output_type,
    const tflite::TensorType &inference_type,

// of the converted model and some statistics like arithmetic ops count.
// `debug_info_str` contains the `GraphDebugInfo` proto. When
// `enable_mlir_converter` is True, use MLIR-based conversion instead of
// TOCO conversion.
PyObject* Convert(PyObject* model_flags_proto_txt_raw,
                  PyObject* toco_flags_proto_txt_raw,
                  PyObject* input_contents_txt_raw,
                  bool extended_return = false,

#include <pybind11/stl.h>

#include <string>
#include <vector>

#include "pybind11/pybind11.h"  // from @pybind11
#include "tensorflow/compiler/mlir/lite/experimental/tac/py_wrapper/tac_wrapper.h"

// Warning: The API is experimental and subject to change.
PYBIND11_MODULE(_pywrap_tac_wrapper, m) {
  m.def(
      "run_tac",
      [](const std::string& model_file_path,

        "//tensorflow/compiler/mlir/lite/experimental/tac:common",
        "//tensorflow/compiler/mlir/lite/experimental/tac:device_transform_patterns",
        "//tensorflow/compiler/mlir/lite/experimental/tac/hardwares:simple_hardware",
    ],
    alwayslink = 1,
)

cc_library(
    name = "target_hardware",
    srcs = ["target_hardware.cc"],
    hdrs = ["target_hardware.h"],
    deps = [

  // This option only targets `DT_HALF` and `DT_QINT8` inference type.
  bool weight_quantization = false;

  // Whether to use the MLIR dynamic range quantizer instead of TOCO.
  bool enable_mlir_dynamic_range_quantizer = false;

  // Whether to allow weight-only quantization. This scheme quantizes
  // weights but will dequantize them back at runtime which is useful for

// RUN: tac-opt-all-backends -tfl-device-transform-gpu %s -split-input-file -verify-diagnostics | FileCheck %s

func.func @pack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<2x1xf32> {
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
  func.return %0 : tensor<2x1xf32>
}

// CHECK:   func @pack(%[[VAL_0:.*]]: tensor<1xf32>, %[[VAL_1:.*]]: tensor<1xf32>) -> tensor<2x1xf32> {