return patterns;
}

std::unique_ptr<TargetHardware> CreateNNAPIHardware() {
  return std::make_unique<NNAPIHardware>();
}

TargetHardwareRegistration<NNAPIHardware> nnapi_hardware(
    "Target device for NNAPI", CreateNNAPIHardware);

// Currently used for these ops:
// tfl.squared_difference
class NNAPIBasicSupportedOpNoCost : public TargetHardwareOperation {

namespace mlir {
namespace TFL {
namespace tac {

class NNAPIHardware : public SimpleHardware {
 public:
  static constexpr char kId[] = "NNAPI";

  mlir::RewritePatternSet GetTransformations(
      MLIRContext* context) const override;

  mlir::TypeID GetTypeId() const override {
    return mlir::TypeID::get<NNAPIHardware>();
  }

  bool IsNotSupportedOp(mlir::Operation* op) const override { return false; }

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

cc_library(
    name = "nnapi_hardware",
    srcs = ["nnapi_hardware.cc"],
    hdrs = ["nnapi_hardware.h"],
    deps = [
        "//tensorflow/compiler/mlir/lite:cost_estimators",
        "//tensorflow/compiler/mlir/lite:tensorflow_lite",

  }
  void runOnOperation() override;
};

void DeviceTransformNNAPIPass::runOnOperation() {
  auto func = getOperation();
  auto* ctx = &getContext();
  NNAPIHardware nnapi_hardware;
  RewritePatternSet patterns = nnapi_hardware.GetTransformations(ctx);
  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}

}  // namespace

static PassRegistration<DeviceTransformNNAPIPass> pass;

## TAC components

### Hardwares

Hardwares are used to modeling target device capabilities & also ops cost for
the target devices.

We have already modeled `cpu_hardware` & `gpu_hardware` as well as the
`nnapi_hardware`.

### Passes

#### Target Annotation Pass
In this pass, every op will be targeted with the user specified targets based on
the device capabilites. For example, If the user specified the desired targets