a.at(i).map_key();
          switch (key.type_case()) {
            case ::tensorflow::proto_splitter::FieldIndex::MapKey::TypeCase::kS:
              if (chunked_key.has_s() && chunked_key.s() == key.s()) {
                matches += 1;
              }
              break;
            case ::tensorflow::proto_splitter::FieldIndex::MapKey::TypeCase::
                kBoolean:

template <>
struct type_caster<::stablehlo::quantization::CalibrationOptions>
    : public internal::SerializedProtobufCaster<
          ::stablehlo::quantization::CalibrationOptions> {};

template <>
struct type_caster<tensorflow::SignatureDef>
    : public internal::SerializedProtobufCaster<tensorflow::SignatureDef> {};

template <>
struct type_caster<tensorflow::GraphDef>

  }

  return {};
}

// Gets the string representation of the type name.
std::string GetTypeName(const Type type) {
  std::string type_name{};
  auto os = llvm::raw_string_ostream{type_name};
  os << type;
  return type_name;
}

// Retrieves the value of `tf_saved_model.initializer_type` attribute from the
// initializer function. Assumes that there exists such an attribute.

namespace mlir {
namespace TFL {

typedef TFLDialect TensorFlowLiteDialect;

// The Control type is a token-like value that models control dependencies
class ControlType : public Type::TypeBase<ControlType, Type, TypeStorage> {
 public:
  using Base::Base;
  static constexpr StringLiteral name = "tfl.control";
};

#include "tensorflow/compiler/mlir/lite/ir/tfl_ops_interface.h.inc"

}

static mlir::Attribute BuildI64ArrayAttr(std::vector<int32_t> value,
                                         mlir::Builder builder) {
  std::vector<int64_t> typecast(value.begin(), value.end());
  return builder.getI64ArrayAttr(typecast);
}

static mlir::Attribute BuildVhloBooleanV1Attr(bool value,
                                              mlir::Builder builder) {

	2, // [2:2] is the sub-list for method output_type
	2, // [2:2] is the sub-list for method input_type
	2, // [2:2] is the sub-list for extension type_name
	2, // [2:2] is the sub-list for extension extendee
	0, // [0:2] is the sub-list for field type_name
}

func init() { file_dns_proto_nds_proto_init() }
func file_dns_proto_nds_proto_init() {
	if File_dns_proto_nds_proto != nil {
		return
	}

  const std::string type_index_name =
      ::tensorflow::port::MaybeAbiDemangle(a.TypeId().name());

  return ::tensorflow::errors::Internal(
      "No unary variant binary_op function found for op ADD Variant "
      "type_name: ",
      type_index_name, " for device type: ", cc_ctx->device()->name());
}

void TF_AddNVariant(TF_OpKernelContext* ctx,
                    void (*binary_add_func)(TF_OpKernelContext* ctx,

	7, // [7:7] is the sub-list for method output_type
	7, // [7:7] is the sub-list for method input_type
	7, // [7:7] is the sub-list for extension type_name
	7, // [7:7] is the sub-list for extension extendee
	0, // [0:7] is the sub-list for field type_name
}

func init() { file_zdsapi_zds_proto_init() }
func file_zdsapi_zds_proto_init() {
	if File_zdsapi_zds_proto != nil {
		return
	}

	14, // [14:14] is the sub-list for method output_type
	14, // [14:14] is the sub-list for method input_type
	14, // [14:14] is the sub-list for extension type_name
	14, // [14:14] is the sub-list for extension extendee
	0,  // [0:14] is the sub-list for field type_name
}

func init() { file_workloadapi_security_authorization_proto_init() }
func file_workloadapi_security_authorization_proto_init() {

	8, // [8:10] is the sub-list for method output_type
	6, // [6:8] is the sub-list for method input_type
	6, // [6:6] is the sub-list for extension type_name
	6, // [6:6] is the sub-list for extension extendee
	0, // [0:6] is the sub-list for field type_name
}

func init() { file_test_echo_proto_echo_proto_init() }
func file_test_echo_proto_echo_proto_init() {
	if File_test_echo_proto_echo_proto != nil {
		return