#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/tac_filter.pb.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/passes.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

using ::third_party::tensorflow::compiler::mlir::lite::experimental::tac::
    FunctionFilter;

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project

namespace mlir {
namespace TFL {
namespace tac {

// Interface for Importing program to TAC (Target Aware Conversion) Module.
// This class is an interface for importing program in TAC.
// See TacModule in how to register it with the module and use it.
class TacImporter {
 public:
  virtual ~TacImporter() = default;

#include "tensorflow/core/platform/types.h"
#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"
#include "tensorflow/lite/toco/types.pb.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

using tensorflow::quantization::PyFunctionLibrary;

Status HandleInputOutputArraysWithModule(
    const toco::ModelFlags& model_flags,

#include "tensorflow/compiler/mlir/lite/experimental/tac/examples/example_hardware.h"

#include <memory>

#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {

constexpr char ExampleHardware::kId[];  // Define kId.

#include "absl/status/status.h"
#include "llvm/Support/SourceMgr.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/tac_importer_exporter.h"

namespace mlir {
namespace TFL {
namespace tac {
// TAC Importer for TFLite.
// This import to MLIR from tflite file or MLIR
class TfLiteImporter : public mlir::TFL::tac::TacImporter {
 public:
  // Options for configuring the importer.
  struct Options {
    std::string file_name;

**FastAPI** cho bạn những tính năng sau:

### Dựa trên những tiêu chuẩn mở

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/utils.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_gpu.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

#include "tensorflow/compiler/mlir/lite/experimental/tac/common/cost.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/targets.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/common/utils.h"
#include "tensorflow/compiler/mlir/lite/experimental/tac/hardwares/target_hardware.h"
#include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h"

namespace mlir {
namespace TFL {
namespace tac {
namespace {

syntax = "proto3";

package third_party.tensorflow.compiler.mlir.lite.experimental.tac;

// A list of filters for TAC users to run ops/functions on ML hardwares. The
// intuition is that, for ops/functions that can be run on ML hardware (e.g.
// EdgeTPU) and TFLite CPU, TAC users give a hint that they're more performant
// to run on TFLite CPU. These filters give the TAC users freedom to specify the
// parts that they want to use other hardware to accelerate.

                + "<field name=\"access\"><list><item></item><item>http://www.taro.com/</item><item>******@****.***</item><item>090-xxxx-xxxx</item></list></field>\n"//
                + "<field name=\"image\"><list><item>taro.png</item><item>jiro.png</item><item>hanako.png</item></list></field>\n"//