// tf_executor.island {_parallel_execution_ids=r0:0,p0:0} wraps "tf.OpC"
// tf_executor.island {_parallel_execution_ids=r0:0,p0:1} wraps "tf.OpD"
// tf_executor.island {_parallel_execution_ids=r0:1} wraps "tf.OpB"
// tf_executor.island {_parallel_execution_ids=r0:1,p0:0} wraps "tf.OpC"
// tf_executor.island {_parallel_execution_ids=r0:1,p0:1} wraps "tf.OpD"
//
// "tf.OpA" will not have `_parallel_execution_ids` attr,

        %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>

    %0:6 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.ReadVariableOp"(%arg2) {device = ""} : (tensor<*x!tf_type.resource>) -> tensor<*xf32>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.ReadVariableOp"(%arg1) {device = ""} : (tensor<*x!tf_type.resource>) -> tensor<*xf32>

#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;

// tf_executor dialect.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ConvertGraphdefToMlir(
    const GraphDef& graphdef, const GraphDebugInfo& debug_info,
    const GraphImportConfig& specs, mlir::MLIRContext* context);

// Given a Graph, returns a MLIR module containing the graph, expressed with
// tf_executor dialect.

    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.VariableV2"() {container = "", device = "/job:localhost/replica:0/task:0/device:CPU:0", shape = #tf_type.shape<>, shared_name = "v_load_44"} : () -> tensor<!tf_type.f32ref>

  }
  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 =

// Creates a pass that converts mlir functions consisting of mlir ops into a
// tf_executor dialect as a single island.
std::unique_ptr<OperationPass<func::FuncOp>>
CreateFunctionalToExecutorDialectConversionPass();

// Creates a pass that lifts inner ops of tf_executor.island ops in
// tf_executor.graph into the same block as the tf_executor.graph.
std::unique_ptr<OperationPass<func::FuncOp>>

// Lowers MLIR module to XLA HLO inside an XlaComputation. The input module
// should only contain operations in tf dialect. If the input module contains
// operation in the tf_executor dialect, for example, returns an error.
// Exception to this are tf_executor dialect ops that are optimized away through
// canonicalization.
//
// Operations in tf dialect are lowered to XLA HLO through the following steps: