void runOnOperation() override;
};

LogicalResult ConvertToLegacyAttributes(func::FuncOp func_op) {
  auto result = func_op->walk([&](mlir::Operation* op) {
    if (failed(TF::HasValidCompilationAndReplicationAttributes(*op)))
      return WalkResult::interrupt();
    if (op->hasAttr(TF::kReplicationInfoAttr)) {
      op->setAttr(TF::kTpuReplicateAttr, op->getAttr(TF::kReplicationInfoAttr));

#include "mlir/IR/Operation.h"  // from @llvm-project
#include "tensorflow/compiler/tf2xla/tf2xla_defs.h"

namespace mlir {
namespace TF {

using ::tensorflow::kValidDeviceTypes;

LogicalResult HasValidCompilationAndReplicationAttributes(Operation& op) {
  auto replicate_attr = op.getAttrOfType<StringAttr>(kReplicationInfoAttr);
  auto compile_attr = op.getAttrOfType<StringAttr>(kCompileDeviceTypeAttr);

  }
  return matchPattern(val, m_Constant(&attr));
}

// Checks if both compilation and replication attributes are present in the
// operation, and if their values are valid.
LogicalResult HasValidCompilationAndReplicationAttributes(Operation &op);

// Checks if the device attribute is valid.
LogicalResult IsValidDeviceTypeOrEmpty(StringAttr attr);

using ParallelExecutionIdPairs =

  LogicalResult result = HasValidDeviceTypeAttribute(block);
  if (failed(result)) return result;

  for (Operation& op : *block) {
    LogicalResult result =
        mlir::TF::HasValidCompilationAndReplicationAttributes(op);
    if (failed(result)) return result;

    // Skip ops with non-TPU device type, they are handled elsewhere.
    auto device_type_attr =

  llvm::SmallVector<tf_device::ClusterFuncOp> to_be_erased;
  OpBuilder builder(&getContext());
  auto result = getOperation().walk([&](tf_device::ClusterFuncOp op) {
    if (failed(TF::HasValidCompilationAndReplicationAttributes(*op)))
      return WalkResult::interrupt();
    // Skip non-tpu device cluster_func.
    auto cluster_id = op->getAttrOfType<StringAttr>(TF::kReplicationInfoAttr);