// 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 tileHash(data[start:end]), nil
}

// tileHash computes the subtree hash corresponding to the (2^K)-1 hashes in data.
func tileHash(data []byte) Hash {
	if len(data) == 0 {
		panic("bad math in tileHash")
	}
	if len(data) == HashSize {
		var h Hash
		copy(h[:], data)
		return h
	}
	n := len(data) / 2
	return NodeHash(tileHash(data[:n]), tileHash(data[n:]))
}

				// bit of tophash to decide which way NaNs go.
				// NOTE: this case is why we need two evacuate tophash
				// values, evacuatedX and evacuatedY, that differ in
				// their low bit.
				if checkBucket>>(it.B-1) != uintptr(b.tophash[offi]&1) {
					continue
				}
			}
		}
		if (b.tophash[offi] != evacuatedX && b.tophash[offi] != evacuatedY) ||

// 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";

// typedarrayclear zeroes the value at ptr of an array of elemType,
// only clears len elem.
//
//go:noescape
func typedarrayclear(elemType *abi.Type, ptr unsafe.Pointer, len int)

//go:noescape
func typehash(t *abi.Type, p unsafe.Pointer, h uintptr) uintptr

func verifyNotInHeapPtr(p uintptr) bool

//go:noescape
func growslice(t *abi.Type, old unsafeheader.Slice, num int) unsafeheader.Slice

//go:noescape

          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:
              if (chunked_key.has_boolean() &&

            return new AnnotationVisitor(types);
        }

        @Override
        public org.objectweb.asm.AnnotationVisitor visitTypeAnnotation(int typeRef, TypePath typePath, String descriptor, boolean visible) {
            maybeAddDependentType(types, Type.getType(descriptor));
            return new AnnotationVisitor(types);
        }
    }

	// 2 bits:   type   0 = literal  1=EOF  2=Match   3=Unused
	// 8 bits:   xlength = length - MIN_MATCH_LENGTH
	// 22 bits   xoffset = offset - MIN_OFFSET_SIZE, or literal
	lengthShift = 22
	offsetMask  = 1<<lengthShift - 1
	typeMask    = 3 << 30
	literalType = 0 << 30
	matchType   = 1 << 30
)

// The length code for length X (MIN_MATCH_LENGTH <= X <= MAX_MATCH_LENGTH)
// is lengthCodes[length - MIN_MATCH_LENGTH]
var lengthCodes = [...]uint32{

	TypeOffset               // signed offset in load/store
	TypeNegOffset            // A negative 16 bit value 0b1111111xxxxx000 encoded as 0bxxxxx (e.g in the hashchk instruction)
	TypeLast                 // must be the last one
)

func (t ArgType) String() string {
	switch t {
	default:
		return fmt.Sprintf("ArgType(%d)", int(t))
	case TypeUnknown:
		return "Unknown"
	case TypeReg:

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"