// CHECK:           %[[VAL_5:.*]] = tfl.add %[[VAL_0]], %[[VAL_1]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>
// CHECK:           %[[VAL_6:.*]] = tfl.mul %[[VAL_5]], %[[VAL_2]] {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>

// Test for partial folding: only fold i32 constants.
// RUN: tac-opt-all-backends -tfl-fold-constants-to-subgraph='fold-all-constants=false' %s -split-input-file -verify-diagnostics | FileCheck --check-prefix=PARTIAL %s

// Test for fold all constants.
// RUN: tac-opt-all-backends -tfl-fold-constants-to-subgraph='fold-all-constants=true' %s -split-input-file -verify-diagnostics | FileCheck --check-prefix=ALL %s

module {

    default_visibility = [
        "//tensorflow:__pkg__",
        "//tensorflow/compiler/mlir/lite:__subpackages__",
        "//tensorflow/lite/python/converter:__subpackages__",
        "//tensorflow/lite/toco/python:__subpackages__",
    ],
    licenses = ["notice"],
)

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

tf_cc_binary(
    name = "tac-opt-all-backends",
    testonly = True,
    deps = [
        ":tac-opt_lib",
        "//tensorflow/compiler/mlir/lite/experimental/common:outline_operations",
        "//tensorflow/compiler/mlir/lite/experimental/tac/hardwares:all-target-hardwares",
    ],
)

cc_library(
    name = "tac-translate-lib",
    srcs = [
        "tac_translate.cc",
    ],
    deps = [

}

// CHECK: %[[CST:.*]] = arith.constant dense<1> : tensor<4xi32>
// CHECK:  [[VAL_0:%.*]] = "tfl.reshape"(%1, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>
// CHECK:  [[VAL_1:%.*]] = "tfl.reshape"(%2, %[[CST]]) {tac.device = "GPU",  tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<4xi32>) -> tensor<1x1x1x1xf32>

#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 "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,

**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 {