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);

  ];
}

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);

// moved to the main function. Returns failure() iff validation fails.
FailureOr<absl::flat_hash_map<std::string, func::FuncOp>> GetInitFuncOps(
    ModuleOp module_op) {
  absl::flat_hash_map<std::string, func::FuncOp> init_func_ops;

  for (func::FuncOp init_func_op : GetInitializerFunctions(module_op)) {
    if (failed(ValidateInitFunc(init_func_op))) {
      return failure();
    }

};

// Returns true iff func_op has either no Region or the body has no Blocks.
bool IsFuncOpEmpty(func::FuncOp func_op) {
  return func_op->getNumRegions() == 0 || func_op.getBody().empty();
}

// Gets the GraphOp from the function op. Returns an empty op iff it doesn't
// exist.
GraphOp GetGraphOpFromFuncOp(func::FuncOp func_op) {
  if (IsFuncOpEmpty(func_op)) return {};

  auto graph_op_range = func_op.front().without_terminator();