// 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());
}

  pm.addNestedPass<func::FuncOp>(mhlo::createMhloQuantLegalizeToIntPass());
  pm.addNestedPass<func::FuncOp>(createCanonicalizerPass());
  // Integer graph optimization relies on chlo broadcast ops for easier handling
  // of dynamic shapes. Therefore we lower chlo ops after optimization.
  pm.addNestedPass<func::FuncOp>(CreateOptimizeIntGraphPass());
  pm.addNestedPass<func::FuncOp>(mhlo::createChloLegalizeToHloPass());

  SymbolTableCollection table;
  SymbolUserMap symbol_map(table, module);

  // Create map from caller to set of all callee(s).
  llvm::DenseMap<func::FuncOp, llvm::DenseSet<func::FuncOp>> caller_callee_map;

  // Use worklist to populate the set of reachable functions.
  std::queue<func::FuncOp> function_worklist;

  // Iterates over all functions within the module to (1) create caller-callee

}

bool IsEntryFunction(func::FuncOp func) {
  for (const auto &attr : GetEntryFunctionAttributeNames()) {
    if (func->hasAttr(attr)) {
      return true;
    }
  }
  return false;
}

llvm::SmallVector<func::FuncOp> GetEntryFunctions(ModuleOp module) {
  llvm::SmallVector<func::FuncOp> entry_funcs;
  module.walk([&](func::FuncOp func) {

    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());

bool IsEntryFunction(func::FuncOp func);

// Get all the entry functions in an MLIR module.
llvm::SmallVector<func::FuncOp> GetEntryFunctions(ModuleOp module);

// Get all the functions referenced in a symber user op and save them in
// `callees`.
LogicalResult GetCallees(SymbolUserOpInterface op, SymbolTable &symtab,
                         llvm::SmallVector<func::FuncOp> &callees);

  // Optimizes TF graph via cleanups, merges, rewrites, constant folding,
  // and edge case handling where possible.
  pm.addNestedPass<func::FuncOp>(TF::CreateDropWhileShapeInvariantPass());
  pm.addNestedPass<func::FuncOp>(
      tf_executor::CreateTFExecutorGraphPruningPass());
  pm.addNestedPass<func::FuncOp>(
      tf_executor::CreateTFExecutorIslandCoarseningPass());
  pm.addPass(TF::CreateTFFunctionalControlFlowToRegions());