<< ", session flag not enabled";
    return absl::OkStatus();
  }

  VLOG(1) << "Run MLIR Graph Optimization Passes";
  PassManager pm(module.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(pm);

  // Run island coarsening before shape inference to allow more exact shape
  // inference using constant folding within islands.
  pm.addNestedPass<func::FuncOp>(

  }

  mlir::StatusScopedDiagnosticHandler diag_handler(module.getContext());

  // Lower MLIR TF Dialect to MLIR TFRT CoreRT dialect.
  mlir::PassManager pm(module.getContext());
  tensorflow::applyTensorflowAndCLOptions(pm);

  tensorflow::TfrtPipelineOptions pass_options;
  if (!options.default_device.empty()) {
    pass_options.default_device = options.default_device;
  }
  if (!options.force_data_format.empty()) {

}

}  // namespace

tensorflow::Status ExportFromTensorflowDialectToExecutor(
    ModuleOp module, llvm::StringRef module_name) {
  PassManager tf_to_executor(module.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(tf_to_executor);
  tf_to_executor.enableVerifier();

  AddTfDialectToExecutorPasses(tf_to_executor);

  mlir::StatusScopedDiagnosticHandler diag_handler(
      module.getContext(), /*propagate=*/false,

// tensorflow specific options.
//
// Note that this function should be in a more appropriate file, but it is
// unclear what a proper file would be as no other functions would currently be
// in the file also.
void applyTensorflowAndCLOptions(mlir::PassManager& pm,
                                 llvm::StringRef dir_path = "");

// Prints the pass pipeline of `pass_manager` to `os`.
void PrintPassPipeline(const mlir::PassManager& pass_manager,

}

}  // namespace

tensorflow::Status ExportFromTensorflowDialectToExecutor(
    ModuleOp module, llvm::StringRef module_name) {
  PassManager tf_to_executor(module.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(tf_to_executor);
  tf_to_executor.enableVerifier();
  AddTfDialectToExecutorPasses(tf_to_executor);

  if (VLOG_IS_ON(1) ||
      DEBUG_DATA_DUMPER()->ShouldDump(module_name.str(), kDebugGroupMain)) {

        "1; this could happen if the binary doesn't link the constant fold "
        "hook registration library.");
  }

  PassManager bridge(module.getContext());
  bridge.enableVerifier();
  ::tensorflow::applyTensorflowAndCLOptions(bridge);

  // Populate a passmanager with the list of passes that implement the bridge.
  pipeline_builder(bridge);

  mlir::StatusScopedDiagnosticHandler diag_handler(

        "1; this could happen if the binary doesn't link the constant fold "
        "hook registration library.");
  }

  PassManager bridge(module.getContext());
  bridge.enableVerifier();
  ::tensorflow::applyTensorflowAndCLOptions(bridge);

  // Populate a passmanager with the list of passes that implement the bridge.
  pipeline_builder(bridge);

  mlir::StatusScopedDiagnosticHandler diag_handler(

absl::Status RunLowerClusterToRuntimeOpsPassPipeline(
    mlir::ModuleOp module, tsl::DeviceType xla_device_type,
    llvm::StringRef module_name) {
  PassManager runtime_lowering(module.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(runtime_lowering);

  if (xla_device_type == DeviceType(DEVICE_TPU_XLA_JIT)) {
    AddTPULowerClusterToRuntimeOpsPassPipeline(runtime_lowering, module_name);
  } else {

        path, std::make_unique<WritableFileRawStream>(std::move(file)));
  };
  pm.enableCrashReproducerGeneration(factory, /*genLocalReproducer=*/false);
}

void applyTensorflowAndCLOptions(mlir::PassManager& pm,
                                 llvm::StringRef dir_path) {
  mlir::registerPassManagerCLOptions();
  if (!mlir::succeeded(mlir::applyPassManagerCLOptions(pm))) {

  // breakup islands passes are common between the export pipeline and here.
  // Reconsider this if there is more commonality in the future with more
  // passes.
  mlir::PassManager manager(module.getContext());
  applyTensorflowAndCLOptions(manager);
  manager.addPass(mlir::TF::CreatePrepareTpuComputationForTfExportPass());
  manager.addPass(mlir::TF::CreateTFRegionControlFlowToFunctional());
  manager.addPass(mlir::TF::CreateTFShapeInferencePass());