[
            "//third_party/tf_runtime_llvm:tfrt_translate_main",
        ],
        ["@tf_runtime//third_party/llvm_derived:tfrt_translate_main"],
    ),
)

tf_cc_binary(
    name = "bef_executor",
    testonly = True,
    visibility = [":friends"],
    deps = [
        "@tf_runtime//:dtype",
        "@tf_runtime//:simple_tracing_sink",
        "@tf_runtime//tools:bef_executor_expensive_kernels",

    return isa<tf_executor::IslandOp>(region->getParentOp());
  }
};

}  // namespace

TensorFlowExecutorDialect::TensorFlowExecutorDialect(MLIRContext *context)
    : Dialect(/*name=*/"tf_executor", context,
              TypeID::get<TensorFlowExecutorDialect>()) {
  addOperations<
#define GET_OP_LIST
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.cc.inc"
      >();

    %control = tf_executor.island() wraps "tf.TPUReplicateMetadata"() {_tpu_replicate = "cluster", device = "/device:TPU:0", num_replicas = 2, topology = "topology"} : () -> ()
    %ri, %c0 = tf_executor.island wraps "tf.TPUReplicatedInput"(%arg0, %arg1) {index = 1 : i64, is_mirrored_variable = false, is_packed = false} : (tensor<i32>, tensor<i32>) -> tensor<i32>

    auto executor_graph =
        rewriter.create<tf_executor::GraphOp>(loc, func_type.getResults());
    rewriter.inlineRegionBefore(graph.getNodes(), executor_graph.getBody(),
                                executor_graph.getBody().end());

    // Add terminator of tf_executor::graph
    rewriter.setInsertionPointToEnd(&executor_graph.getBody().front());
    rewriter.create<tf_executor::FetchOp>(loc);

    // Add terminator of func

  return llvm::isa<tf_executor::EnterOp, tf_executor::ExitOp,
                   tf_executor::IslandOp, tf_executor::MergeOp,
                   tf_executor::SwitchOp>(op);
}

// Assigns all data results to a specified device.
void PopulateDeviceForOpResults(
    Operation& op, llvm::StringRef device,
    llvm::DenseMap<Value, llvm::StringRef>& value_to_device) {
  Operation* op_to_update = &op;

        tf_executor.graph {
          %control_0 = tf_executor.island wraps "tf.NoOp"() : () -> ()
        }
        return
      }

      func.func private @function_2() -> () attributes {tf._original_func_name = "__func_2"} {
        tf_executor.graph {
          %control_0 = tf_executor.island wraps "tf.NoOp"() : () -> ()
        }
        return
      }

        %0 = tf_executor.graph {
          %outputs, %control = tf_executor.island wraps "tf.XlaSharding"(%arg0) {_XlaSharding = "\08\03\1A\02\02\01\22\02\00\01", sharding = "\08\03\1A\02\02\01\22\02\00\01"} : (tensor<2x2xf32>) -> tensor<2x2xf32>

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/translate/import_model.h"

namespace mlir {
namespace quant {
namespace {

using ::mlir::tf_executor::FetchOp;
using ::mlir::tf_executor::GraphOp;
using ::mlir::tf_executor::IslandOp;

  }
  return absl::OkStatus();
}

// Collects control ret Nodes based on tf_executor.graph's associated
// tf_executor.fetch control inputs.
Status Exporter::GetControlRetNodes(
    mlir::tf_executor::FetchOp fetch,
    absl::flat_hash_set<Node*>* control_ret_nodes) {
  for (Value fetch_operand : fetch.getOperands()) {
    if (mlir::isa<mlir::tf_executor::ControlType>(fetch_operand.getType())) {
      Operation* defining_op =

  }
  return absl::OkStatus();
}

// Collects control ret Nodes based on tf_executor.graph's associated
// tf_executor.fetch control inputs.
Status Exporter::GetControlRetNodes(
    mlir::tf_executor::FetchOp fetch,
    absl::flat_hash_set<Node*>* control_ret_nodes) {
  for (Value fetch_operand : fetch.getOperands()) {
    if (mlir::isa<mlir::tf_executor::ControlType>(fetch_operand.getType())) {
      Operation* defining_op =