// watched and not the result of any operation in the tape.
using TensorTape = std::unordered_map<int64_t, int64_t>;

// Map from operation-id to tape entry.
template <typename BackwardFunction, typename TapeTensor>
using OpTape =
    std::unordered_map<int64_t, OpTapeEntry<BackwardFunction, TapeTensor>>;

// Operations the tape needs to perform on tensors to do backpropagation. Named

  explicit TaggedValue(float f32) : type_(FLOAT32), data_(Float32(f32)) {}
  /// TaggedValue constructor for type INT64.
  explicit TaggedValue(int64_t i64) : type_(INT64), data_(Int64(i64)) {}
  /// TaggedValue constructor for type FLOAT32.
  explicit TaggedValue(Float32 f32) : type_(FLOAT32), data_(f32) {}
  /// TaggedValue constructor for type INT64.
  explicit TaggedValue(Int64 i64) : type_(INT64), data_(i64) {}

			return errorf("%s: malformed elf file: %v", pn, err)
		}
		symbols[i] = elfsym.sym
		if elfsym.type_ != elf.STT_FUNC && elfsym.type_ != elf.STT_OBJECT && elfsym.type_ != elf.STT_NOTYPE && elfsym.type_ != elf.STT_COMMON {
			continue
		}
		if elfsym.shndx == elf.SHN_COMMON || elfsym.type_ == elf.STT_COMMON {
			sb := l.MakeSymbolUpdater(elfsym.sym)
			if uint64(sb.Size()) < elfsym.size {

	LdMachoFileFvmlib     = 3
	LdMachoFileCore       = 4
	LdMachoFilePreload    = 5
)

func unpackcmd(p []byte, m *ldMachoObj, c *ldMachoCmd, type_ uint, sz uint) int {
	e4 := m.e.Uint32
	e8 := m.e.Uint64

	c.type_ = int(type_)
	c.size = uint32(sz)
	switch type_ {
	default:
		return -1

	case LdMachoCmdSegment:
		if sz < 56 {
			return -1
		}
		c.seg.name = cstring(p[8:24])

        if not (
            field.shape == SHAPE_SINGLETON  # type: ignore[attr-defined]
            and not lenient_issubclass(field.type_, BaseModel)
            and not lenient_issubclass(field.type_, dict)
            and not field_annotation_is_sequence(field.type_)
            and not is_dataclass(field.type_)
            and not isinstance(field_info, params.Body)
        ):
            return False

void TapeVSpace::DeleteGradient(AbstractTensorHandle* gradient) const {
  gradient->Unref();
}

void Tape::Watch(const AbstractTensorHandle* t) {
  GradientTape::Watch(ToId(t));
}
void Tape::RecordOperation(absl::Span<AbstractTensorHandle* const> inputs,
                           absl::Span<AbstractTensorHandle* const> outputs,

    openIdConnect = "openIdConnect"


class SecurityBase(BaseModelWithConfig):
    type_: SecuritySchemeType = Field(alias="type")
    description: Optional[str] = None


class APIKeyIn(Enum):
    query = "query"
    header = "header"
    cookie = "cookie"


class APIKey(SecurityBase):
    type_: SecuritySchemeType = Field(default=SecuritySchemeType.apiKey, alias="type")

        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "@com_google_absl//absl/strings",
    ],
)

cc_library(
    name = "tape",
    hdrs = ["tape.h"],
    visibility = ["//tensorflow:internal"],
    deps = [
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
        "//tensorflow/core/config:flag_defs",

) -> Dict[Callable[[Any], Any], Tuple[Any, ...]]:
    encoders_by_class_tuples: Dict[Callable[[Any], Any], Tuple[Any, ...]] = defaultdict(
        tuple
    )
    for type_, encoder in type_encoder_map.items():
        encoders_by_class_tuples[encoder] += (type_,)
    return encoders_by_class_tuples


encoders_by_class_tuples = generate_encoders_by_class_tuples(ENCODERS_BY_TYPE)


def jsonable_encoder(
    obj: Annotated[

func getMachoHdr() *MachoHdr {
	return &machohdr
}

func newMachoLoad(arch *sys.Arch, type_ uint32, ndata uint32) *MachoLoad {
	if arch.PtrSize == 8 && (ndata&1 != 0) {
		ndata++
	}

	load = append(load, MachoLoad{})
	l := &load[len(load)-1]
	l.type_ = type_
	l.data = make([]uint32, ndata)
	return l
}

func newMachoSeg(name string, msect int) *MachoSeg {