// is true, the TF::AssertOp will not be removed.
std::unique_ptr<OperationPass<func::FuncOp>> CreateLegalizeTFPass(
    bool run_tfl_runtime_verification, bool preserve_assert_op = false);
std::unique_ptr<OperationPass<func::FuncOp>> CreateLegalizeTFPass();

// Creates an instance of the TensorFlow Lite dialect Optimize pass.
std::unique_ptr<OperationPass<func::FuncOp>> CreateOptimizePass(

CreateDropWhileShapeInvariantInDeviceClusterPass();

// Creates a pass that moves writes to replicate invariant resource variables
// outside tf_device.replicate op.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateHoistReplicateInvariantResourceWritesPass();

// Transforms functional control flow operations in the TensorFlow dialect to
// MLIR Control Flow Graph (CFG) form.
std::unique_ptr<OperationPass<func::FuncOp>>

  pm.addNestedPass<FuncOp>(
      mlir::TFDevice::CreateVerifyNoOutsideCompilationMarkersPass());

  pm.addNestedPass<FuncOp>(mlir::TFDevice::CreateClusterConstantSinkingPass());
  pm.addPass(mlir::TF::CreateResourceDeviceInferencePass());
  pm.addNestedPass<FuncOp>(
      tensorflow::tf2xla::internal::CreateHoistBroadcastReadPass());
  pm.addNestedPass<FuncOp>(

    pm.addNestedPass<mlir::func::FuncOp>(mlir::createCanonicalizerPass());
    if (quantization_options.op_set() == OpSet::XLA) {
      pm.addNestedPass<mlir::func::FuncOp>(
          mlir::quant::CreateReplaceCastHacksWithTFXLAOpsPass());
    }
    pm.addNestedPass<mlir::func::FuncOp>(mlir::createCSEPass());
  }
  pm.addNestedPass<mlir::func::FuncOp>(mlir::quant::CreateOptimizePass());
}

    pass_manager.addPass(mlir::TFL::CreatePrepareQuantizeVariablesPass());
    pass_manager.addNestedPass<mlir::func::FuncOp>(
        mlir::TFL::CreateQuantizePass(quant_specs));
    pass_manager.addNestedPass<mlir::func::FuncOp>(
        mlir::TFL::CreatePostQuantizePass(emit_quant_adaptor_ops));
  }
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreateOptimizeOpOrderPass());

  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::mhlo::createLegalizeEinsumToDotGeneralPass());
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::mhlo::createLegalizeDotToDotGeneralPass());
  // Unfuse mhlo BatchNorm to primitive ops.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createUnfuseBatchNormPass());
  // Fuse Conv + Mul to Conv.
  pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFuseConvolutionPass());

  auto func_op = module_op->lookupSymbol<func::FuncOp>("unknown_rank_arg");
  ASSERT_THAT(func_op, NotNull());
  ASSERT_THAT(func_op.getNumArguments(), Eq(1));

  EXPECT_FALSE(HasRankOf(func_op.getArgument(0), /*rank=*/1));
}

TEST_F(AttrsAndConstraintsTest, TryCastSucceeds) {
  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(kModuleStatic);
  ASSERT_TRUE(module_op);

  func::FuncOp main_fn = FindMainFuncOp(*module_op);

  // It's simply clone the FuncOp and hardware specific transformations.
  func::FuncOp GetAlternativeViewForSpec(
      func::FuncOp func,
      const InferenceDeviceType& current_device_inference_type,
      const InferenceDeviceType& target_device_inference_type, ModuleOp module,
      OpBuilder* builder);

  // Apply any device-specific optimizations.
  void Optimize(func::FuncOp func, const std::string& hardware);

  ];
}

def IdentifyDilatedConvPass : Pass<"tfl-identify-dilated-conv", "mlir::func::FuncOp"> {
  let summary = "Convert dense tensor to sparse format.";
  let constructor = "CreateIdentifyDilatedConvPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
}

def GetArithmeticCountPass : Pass<"tfl-get-arithmetic-count", "mlir::func::FuncOp"> {
  let summary = "Calculate arithmetic count for tfl operations.";

// input and output types by unwrapping quantization parameters.
std::unique_ptr<OperationPass<func::FuncOp>> CreateQuantizePass();

// Overloading of CreateQuantizePass which takes QuantizationSpecs.
std::unique_ptr<OperationPass<func::FuncOp>> CreateQuantizePass(
    QuantizationSpecs quant_specs,
    tensorflow::quantization::OpSet target_opset);