public:
  ConvertGeneralOp(MLIRContext *context,
                   const DenseSet<StringRef> &func_symbols)
      : ConversionPattern(MatchAnyOpTypeTag(), PatternBenefit(1), context),
        func_symbols_(func_symbols) {}

  LogicalResult matchAndRewrite(
      Operation *op, llvm::ArrayRef<Value> operands,
      ConversionPatternRewriter &rewriter) const final {

 public:
  explicit InsertCustomAggregationOpsPass() : test_mode_(true) {
    initializeForTest();
  }

  explicit InsertCustomAggregationOpsPass(const CalibrationOptions &calib_opts)
      : test_mode_(false), calib_opts_(calib_opts) {}

  InsertCustomAggregationOpsPass(const InsertCustomAggregationOpsPass &other) {
    test_mode_ = other.test_mode_;
    test_case_ = other.test_case_;
    calib_opts_ = other.calib_opts_;

// returns std::nullopt on failure.
std::optional<flatbuffers::Offset<tflite::Operator>> CreateFlatBufferOperator(
    Operation *mlir_op, uint32_t opcode_index,
    const std::vector<int32_t> &operands, const std::vector<int32_t> &results,
    const std::vector<int32_t> &intermediates,
    flatbuffers::FlatBufferBuilder *fbb);

// Populates the array of mlir::NamedAttributes corresponding to the given

			LOONG64DWARFRegisters[r] = (r - from) + base
		}
	}
	f(REG_R0, REG_R31, 0)
	f(REG_F0, REG_F31, 32)

}

const (
	BIG = 2046
)

const (
	// mark flags
	LABEL  = 1 << 0
	LEAF   = 1 << 1
	SYNC   = 1 << 2
	BRANCH = 1 << 3
)

const (
	C_NONE = iota
	C_REG
	C_FREG
	C_FCSRREG
	C_FCCREG
	C_ZCON
	C_SCON // 12 bit signed

 public:
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(TF::OptionalFromValueOp op,
                                PatternRewriter& rewriter) const override {
    Value value = nullptr;
    for (auto v : op.getComponents()) {
      value = v;
    }
    if (!value) return failure();
    rewriter.replaceOpWithNewOp<TF::IdentityOp>(op, value.getType(), value);

namespace tensorflow {
namespace {
// Data layout supported by TFLite.
constexpr mlir::StringRef kTFLiteDataLayout = "NHWC";
}  // namespace

void AddQuantizationPasses(const mlir::TFL::PassConfig& pass_config,
                           mlir::OpPassManager& pass_manager) {
  const mlir::quant::QuantizationSpecs& quant_specs = pass_config.quant_specs;
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreatePrepareQuantizePass(quant_specs));

std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsPass(
    const ::stablehlo::quantization::QuantizationSpecs& quantization_specs);

// Creates a pass that inserts CalibrationStatisticsSaverOp.
std::unique_ptr<OperationPass<ModuleOp>>
CreateInsertCalibrationStatisticsSaverPass(
    StringRef calibration_data_dir,
    const std::vector<std::string>& aggregator_ops_to_ignore);

  PrepareLiftingPass(const PrepareLiftingPass& other) {
    op_set_ = other.op_set_;
  }

  StringRef getArgument() const final {
    // This is the argument used to refer to the pass in
    // the textual format (on the commandline for example).
    return "quant-prepare-lifting";
  }

  StringRef getDescription() const final {
    // This is a brief description of the pass.

    if (!input_type) {
      return failure();
    }
    const ArrayRef<int64_t> input_shape = input_type.getShape();

    const int input_size = input_shape.size();

    RankedTensorType begin_type = mlir::cast<RankedTensorType>(begin.getType());
    const ArrayRef<int64_t> begin_shape = begin_type.getShape();
    const int begin_dim = begin_shape.size();

    if (begin_dim != 1) return failure();

	// Metrics v3 endpoints
	metricsV3Path = "/metrics/v3"
)

// Standard env prometheus auth type
const (
	EnvPrometheusAuthType = "MINIO_PROMETHEUS_AUTH_TYPE"
)

type prometheusAuthType string

const (
	prometheusJWT    prometheusAuthType = "jwt"
	prometheusPublic prometheusAuthType = "public"
)

// registerMetricsRouter - add handler functions for metrics.