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")

}

type memoryBasicInformation struct {
	baseAddress       uintptr
	allocationBase    uintptr
	allocationProtect uint32
	regionSize        uintptr
	state             uint32
	protect           uint32
	type_             uint32
}

// https://learn.microsoft.com/en-us/windows-hardware/drivers/ddi/wdm/ns-wdm-_osversioninfow
type _OSVERSIONINFOW struct {
	osVersionInfoSize uint32
	majorVersion      uint32

==============================================================================*/
#include "tensorflow/c/eager/tracing_utils.h"

#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/experimental/gradients/tape/tape_operation.h"
#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {
namespace tracing {

		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 {

#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/eager/unified_api_testutil.h"
#include "tensorflow/c/experimental/gradients/grad_test_helper.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"
#include "tensorflow/c/experimental/ops/array_ops.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"