{as: AMOVFL, a1: C_REG, a6: C_CREG, type_: 69, size: 4},
	{as: AMOVFL, a1: C_REG, a6: C_32CON, type_: 69, size: 4},

	{as: ASYSCALL, type_: 5, size: 4},
	{as: ASYSCALL, a1: C_REG, type_: 77, size: 12},
	{as: ASYSCALL, a1: C_U15CON, type_: 77, size: 12},
	{as: ABEQ, a6: C_BRA, type_: 16, size: 4},
	{as: ABEQ, a1: C_CREG, a6: C_BRA, type_: 16, size: 4},

  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) {}

    mode: Literal["validation", "serialization"] = "validation",
) -> ModelField:
    """
    Create a new response field. Raises if type_ is invalid.
    """
    class_validators = class_validators or {}
    if PYDANTIC_V2:
        field_info = field_info or FieldInfo(
            annotation=type_, default=default, alias=alias
        )
    else:
        field_info = field_info or FieldInfo()

  bool passed() const { return type_ == kSuccess; }

  // Returns true iff the test part failed.
  bool failed() const { return type_ != kSuccess; }

  // Returns true iff the test part non-fatally failed.
  bool nonfatally_failed() const { return type_ == kNonFatalFailure; }

  // Returns true iff the test part fatally failed.
  bool fatally_failed() const { return type_ == kFatalFailure; }

 private:

        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

    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")

		p.want(_Lbrack)
		t := new(MapType)
		t.pos = pos
		t.Key = p.type_()
		p.want(_Rbrack)
		t.Value = p.type_()
		return t

	case _Struct:
		return p.structType()

	case _Interface:
		return p.interfaceType()

	case _Name:
		return p.qualifiedName(nil)

	case _Lparen:
		p.next()
		t := p.type_()
		p.want(_Rparen)
		return t
	}

	return nil
}

        "//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 {