// function and the analysis information for that function.
template <typename DerivedT, typename AnalysisT>
class PerFunctionAggregateAnalysisConsumerPass
    : public PassWrapper<
          PerFunctionAggregateAnalysisConsumerPass<DerivedT, AnalysisT>,
          OperationPass<ModuleOp>> {
 public:
  static ::mlir::TypeID resolveTypeID() {
    static ::mlir::SelfOwningTypeID id;
    return id;

#include "tensorflow/compiler/mlir/tensorflow/utils/error_util.h"

namespace mlir {
namespace tf_test {

namespace {

struct TestSideEffectAnalysisPass
    : public TF::PerFunctionAggregateAnalysisConsumerPass<
          TestSideEffectAnalysisPass, TF::SideEffectAnalysis> {
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TestSideEffectAnalysisPass)

  void runOnFunction(func::FuncOp func,

// aliasing values for each such result. Each value is assigned a unique ID, and
// that ID is used to annotate the operations.
struct TestResourceAliasAnalysis
    : public TF::PerFunctionAggregateAnalysisConsumerPass<
          TestResourceAliasAnalysis, TF::ResourceAliasAnalysis> {
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TestResourceAliasAnalysis)

  StringRef getArgument() const final {

namespace {

constexpr char kDeviceAttr[] = "device";
constexpr char kDeviceCPU[] = "CPU";
constexpr char kFuncDeviceAttr[] = "tf.device";

struct TPUDynamicLayoutPass
    : public TF::PerFunctionAggregateAnalysisConsumerPass<
          TPUDynamicLayoutPass, TF::ResourceAliasAnalysis> {
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TPUDynamicLayoutPass)

  void runOnFunction(
      func::FuncOp func,

//
// We do this as an IR->IR transform to keep the Graph exporter as simple as
// possible.

namespace mlir {

namespace {

class BreakUpIslands : public TF::PerFunctionAggregateAnalysisConsumerPass<
                           BreakUpIslands, TF::SideEffectAnalysis> {
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<tf_executor::TensorFlowExecutorDialect>();
  }