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>

// The MLIR type represents a C++ mlrt::Future.
class FutureType
    : public mlir::Type::TypeBase<FutureType, mlir::Type, mlir::TypeStorage> {
 public:
  using Base::Base;
  static constexpr mlir::StringLiteral name = "mlrt.compiler.future";
};

// The MLIR type represents a C++ mlrt::Promise.
class PromiseType
    : public mlir::Type::TypeBase<PromiseType, mlir::Type, mlir::TypeStorage> {
 public:
  using Base::Base;

    return {getTrailingObjects<StringAttr>(), num_attrs};
  }

  unsigned num_attrs;
};

template <typename Derived>
class TFRTypeImpl : public Type::TypeBase<Derived, TFRType, TFRTypeStorage> {
 public:
  using Base = Type::TypeBase<Derived, TFRType, TFRTypeStorage>;
  using TFRBase = TFRTypeImpl<Derived>;
  using Base::Base;

  static Derived get(ArrayRef<StringAttr> attrs, MLIRContext* context) {

class TFTensorType
    : public mlir::Type::TypeBase<TFTensorType, mlir::Type, mlir::TypeStorage> {
 public:
  using Base::Base;
  static constexpr mlir::StringLiteral name = "tensorflow.tf_mlrt.tf_tensor";
};

// The MLIR type represents a tensorflow::Device*
class TFDeviceType
    : public mlir::Type::TypeBase<TFDeviceType, mlir::Type, mlir::TypeStorage> {
 public:
  using Base::Base;

};

// The MLIR type represents a tensorflow::Tensor.
class TFTensorType : public Type::TypeBase<TFTensorType, Type, TypeStorage> {
 public:
  using Base::Base;
  static constexpr StringLiteral name = "tfrt.tf_tensor";
};

// The MLIR type represents a tensorflow::Allocator.
class TFAllocatorType
    : public Type::TypeBase<TFAllocatorType, Type, TypeStorage> {
 public:
  using Base::Base;

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

class TokenType : public Type::TypeBase<TokenType, Type, TypeStorage> {
 public:
  using Base::Base;
  static constexpr ::mlir::StringLiteral name = "tf_executor.token";

	1, // [1:1] is the sub-list for method output_type
	1, // [1:1] is the sub-list for method input_type
	1, // [1:1] is the sub-list for extension type_name
	1, // [1:1] is the sub-list for extension extendee
	0, // [0:1] is the sub-list for field type_name
}

func init() { file_pkg_test_config_mock_config_proto_init() }
func file_pkg_test_config_mock_config_proto_init() {

  }

  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"