if (!op_name_or_status.ok()) return false;

  auto op_name = std::move(op_name_or_status).value();
  if (op_name == "NextIteration")
    return mlir::isa<mlir::TF::TensorFlowRefType>(
        mlir::getElementTypeOrSelf(op->getOperand(0).getType()));

  if (op_name == "Enter" || op_name == "Exit" || op_name == "Switch" ||
      op_name == "Merge") {
    return mlir::isa<mlir::TF::TensorFlowRefType>(

    "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_count",
    "Counts the attempts of legalization of ops", "op_name");

auto *mlir_failed_legalization_count = tensorflow::monitoring::Counter<2>::New(
    "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_pass_count",
    "Counts the failure of legalization of ops", "op_name", "legality");

class LegalizeTF : public impl::LegalizeTFBase<LegalizeTF> {
 public:

            KotlinCoreEnvironment.underApplicationLock {
                if (applicationArea.hasExtensionPoint(ClassTypePointerFactory.EP_NAME)) return@underApplicationLock
                CoreApplicationEnvironment.registerApplicationExtensionPoint(
                    ClassTypePointerFactory.EP_NAME,
                    ClassTypePointerFactory::class.java
                )

      return failure();

    Operation *inner_op;

    StringRef op_name = op->getName().stripDialect();
    if (!func_symbols_.contains(op_name)) {
      std::string tf_op_name = llvm::formatv(
          "{0}.{1}", TF::TensorFlowDialect::getDialectNamespace(), op_name);
      OperationState state =
          OperationState(loc, tf_op_name, inner_op_operands, new_types, attrs,

    return std::string("tf.If");
  }

  llvm::StringRef op_name(tflite::EnumNameBuiltinOperator(builtin_code));

  // If the Op name contains stablehlo
  if (IsStablehloOp(op_code)) {
    return llvm::Twine(
               llvm::Twine("vhlo.", op_name.drop_front(10).lower()).str(),
               "_v1")
        .str();
  }
  return llvm::Twine("tfl.", op_name.lower()).str();
}

};

TF_KernelBuilder* TF_NewKernelBuilder(
    const char* op_name, const char* device_name,
    void* (*create_func)(TF_OpKernelConstruction*),
    void (*compute_func)(void*, TF_OpKernelContext*),
    void (*delete_func)(void*)) {
  TF_KernelBuilder* result = new TF_KernelBuilder;
  result->cc_builder = new ::tensorflow::KernelDefBuilder(op_name);
  result->cc_builder->Device(device_name);
  result->create_function = create_func;

    const char* node_name = opdef.name().c_str();
    const char* op_name = opdef.name().c_str();
    const char* device_name = "CPU";
    static auto fake_compute_func = [](void* kernel, TF_OpKernelContext* ctx) {
    };

    TF_KernelBuilder* builder =
        TF_NewKernelBuilder(op_name, device_name, /*create_func=*/nullptr,
                            fake_compute_func, /*delete_func=*/nullptr);

				if !ztunnelPod {
					return fmt.Errorf("workloads command is only supported by Ztunnel proxies: %v", podName)
				}
				podNames = []string{podName}
				podNamespace = ns
			} else {
				var err error
				podNames, podNamespace, err = ctx.InferPodsFromTypedResource("daemonset/ztunnel", ctx.IstioNamespace())
				if err != nil {
					return err
				}
			}

			destLoggerLevels := map[string]Level{}

			}
		}
	}
	return "", false, false
}

// chooseExe finds and returns path to preferred binary. names is a list of
// names to search on both Linux and OSX. osxNames is a list of names specific
// to OSX. names always has a higher priority than osxNames. The order of
// the name within each list decides its priority (e.g. the first name has a
// higher priority than the second name in the list).
//

    } else if (auto island =
                   llvm::dyn_cast<mlir::tf_executor::IslandOp>(inst)) {
      Operation& inner_op = island.GetBody().front();
      auto op_name = GetTensorFlowOpName(inner_op.getName().getStringRef());
      if (llvm::isa<FuncOp>(inner_op) && op_name.ok()) {
        // If it is TF Control dialect specific op, look up custom operation
        // in the module and first convert that, then add it to function