&ctx);

  if (failed(applyPatternsAndFoldGreedily(module_op, std::move(patterns)))) {
    module_op.emitError()
        << "Failed to compose stablehlo uniform quantized types.";
    signalPassFailure();
  }
}

}  // namespace

std::unique_ptr<OperationPass<ModuleOp>>
CreateComposeUniformQuantizedTypePass() {

    return signalPassFailure();
  }
}

}  // namespace

namespace TFDevice {
std::unique_ptr<OperationPass<ModuleOp>> CreateResourceOpLiftingPass() {
  return std::make_unique<ResourceOpLiftingPass>();
}

std::unique_ptr<OperationPass<ModuleOp>>
CreateResourceOpLiftingForMainFunctionPass() {
  return std::make_unique<ResourceOpLiftingForMainFunctionPass>();
}

}  // namespace TFDevice

  }

  ModuleOp module_;
  SymbolTable symbol_table_;
};

static PassRegistration<QuantizeCompositeFunctionsPass> pass;

#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/quantize_composite_functions.inc"

void QuantizeCompositeFunctionsPass::runOnOperation() {
  MLIRContext* ctx = &getContext();
  ModuleOp module = getOperation();

  PassManager pm(ctx);

void EncapsulateFuncAndSerialize(FuncOp func,
                                 std::string* serialized_func_module) {
  // Create a new module to hold func and all referenced functions.
  OwningOpRef<mlir::ModuleOp> module_for_func =
      ModuleOp::create(mlir::UnknownLoc::get(func.getContext()));
  SymbolTable symbol_table(module_for_func.get());

  symbol_table.insert(func);
  *serialized_func_module =

  return_op.erase();
}

// TFL::WhileOp has regions, so we add CallOp to call the FuncOp in the regions
// if we have while ops.
void AddRegionsForTflWhileOp(mlir::ModuleOp module) {
  mlir::SymbolTable symbol_table(module);
  module.walk([&](mlir::TFL::WhileOp while_op) {
    auto cond = symbol_table.lookup<mlir::func::FuncOp>(

  void getDependentDialects(mlir::DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect>();
  }

  void runOnOperation() override;
};

bool UseEmbeddingPipelining(ModuleOp& module) {
  // Enable automated pipelining pass unless:
  // 1. The user disables it via flag, or
  // 2. The graph contains TF.Summary ops. Graphs like this typically only run

/// pass.
std::unique_ptr<OperationPass<ModuleOp>> TFL::CreateLowerStaticTensorListPass(
    bool allow_tensorlist_pass_through, bool default_to_single_batch,
    bool enable_dynamic_update_slice) {
  return std::make_unique<LowerStaticTensorListPass>(
      allow_tensorlist_pass_through, default_to_single_batch,
      enable_dynamic_update_slice);
}

std::unique_ptr<OperationPass<ModuleOp>>
TFL::CreateLowerStaticTensorListPass() {

  ConversionTarget target(*context);
  target.addLegalDialect<arith::ArithDialect>();
  target.addLegalDialect<func::FuncDialect>();
  target.addLegalDialect<TF::TensorFlowDialect>();
  target.addLegalOp<ModuleOp>();
  target.addLegalOp<func::FuncOp>();
  target.addIllegalOp<TF::XlaConvV2Op>();
  target.addIllegalOp<TF::XlaGatherOp>();

  RewritePatternSet patterns(context);
  mhlo::Tf2XlaTypeConverter converter;