for (auto &block : region) {
        // Split tfg.NextIteration to break the cycle.
        SplitNextIteration(block);
        tfg::SortTopologically(&block);
      }
    }
  }

  // Version information is embedded in graph operation in TFG. In TFE, it's
  // embedded in the module operation.
  for (auto &op : module.getBodyRegion().getOps()) {
    auto graph = dyn_cast<tfg::GraphOp>(op);
    if (!graph) continue;

LogicalResult InferReturnTypeComponentsForTFOp(
    std::optional<Location> location, Operation* op, int64_t graph_version,
    tfg::OperandAsConstantFn operand_as_constant_fn,
    tfg::OpResultAsShapeFn op_result_as_shape_fn,
    tfg::ResultElementTypeFn result_element_type_fn,
    SmallVectorImpl<ShapedTypeComponents>& inferred_return_shapes);

}  // namespace TF
}  // namespace mlir

  tfg.func @test_func_name0(%test_func_name0: tensor<*x!tf_type.resource> {tf._output_shapes = #tf_type.shape<*>, tf._resource_arg_unique_id = 0 : i64, tfg.name = "test_func_name0"},
                            %test_func_name01: tensor<*x!tf_type.resource> {tf._output_shapes = #tf_type.shape<*>, tf._resource_arg_unique_id = 0 : i64, tfg.name = "test_func_name01"})

// RUN: tf-opt -tfe-legalize-tfg %s | FileCheck %s

module  {
  tfg.graph #tf_type.version<producer = 62, min_consumer = 12> {
    %Const, %ctl = Const name("Constant") {dtype = i32, value = dense<0> : tensor<i32>} : () -> (tensor<i32>)
    %foo, %ctl_0 = foo(%Const) name("_tf.foo") : (tensor<i32>) -> (tensor<*xi32>)
  }
  tfg.func @foo(%arg: tensor<*xi32> {tfg.name = "arg"})
       -> (tensor<*xi32> {tfg.dtype = i32, tfg.name = "return_value"})
   {

    tfg::OperandAsConstantFn operand_as_constant_fn,
    tfg::OpResultAsShapeFn op_result_as_shape_fn,
    tfg::ResultElementTypeFn result_element_type_fn,
    SmallVectorImpl<ShapedTypeComponents>& inferred_return_shapes) {
  assert(op->getName().getDialectNamespace() ==
         TensorFlowDialect::getDialectNamespace());
  return tfg::InferReturnTypeComponentsForTFOp(

// RUN: tf-opt -tfe-legalize-tfg %s | FileCheck %s

module  {
  tfg.graph #tf_type.version<producer = 27, min_consumer = 0> {
    // CHECK: tf_executor.Enter
    // CHECK: {{%.*}}, %[[TOKEN:.*]], {{%.*}} = tf_executor.NextIteration.Source
    // CHECK: {{%.*}}, {{%.*}}, %[[CONTROL:.*]] = tf_executor.Merge
    // CHECK: tf_executor.island(%[[CONTROL]]) wraps "tf.Const"()
    // CHECK: tf_executor.LoopCond
    // CHECK: tf_executor.Switch

    'tf-mlir-translate',
    'tf-opt',
    'tf-quant-opt',
    'tf-reduce',
    'tf-tfrt-opt',
    'tf_tfjs_translate',
    'tf_tfl_translate',
    'tfcompile',
    'tfg-opt-no-passes',
    'tfg-transforms-opt',
    'tfg-translate',
    'tfjs-opt',
]
tools = [ToolSubst(s, unresolved='ignore') for s in tool_names]
llvm_config.add_tool_substitutions(tools, tool_dirs)

              mlir::tf_device::TensorFlowDeviceDialect,
              mlir::tf_executor::TensorFlowExecutorDialect,
              mlir::tf_saved_model::TensorFlowSavedModelDialect,
              mlir::tfg::TFGraphDialect>();
  if (include_extensions) {
    mlir::func::registerAllExtensions(registry);
  }
}

// Inserts all the TensorFlow dialects in the provided registry. This is

    // specifiable.
    GraphDebugInfo debug_info;
    switch (config.dialect) {
      case MlirDumpConfig::Dialect::kTFG: {
        TF_ASSIGN_OR_RETURN(module,
                            mlir::tfg::ImportGraphAndFunctionsToMlir(
                                &context, debug_info, graph,
                                flib_def ? *flib_def : graph.flib_def()));
        break;
      }
    }

#include "tensorflow/core/ir/importexport/mangling.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/platform/protobuf.h"

namespace tensorflow {
namespace mangling_util {
namespace {

using ::mlir::tfg::mangling_util::PrintShortTextProto;

const char kAttributePrefix[] = "tf.";
const char kDataTypePrefix[] = "tfdtype$";
const char kTensorShapePrefix[] = "tfshape$";
const char kTensorPrefix[] = "tftensor$";