return default

    if len(types_) == 1:
      type_, = types_
    else:
      type_ = types_

    if default is not None and type_ != default:
      print('WARN: type annotation {}({}) does not match {}({})'.format(
          type_, type(type_), default, type(default)))
      self.debug_print(node)

    return type_

  def _pack_tensor_list(self, value):

}

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

    from fastapi._compat import is_pv1_scalar_field

    field_info = FieldInfo()
    field = ModelField(
        name="foo",
        field_info=field_info,
        type_=Union[str, List[int]],
        class_validators={},
        model_config=BaseConfig,
    )
    assert not is_pv1_scalar_field(field)


@needs_pydanticv2
def test_get_model_config():

==============================================================================*/
#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 {

    labels = tf.one_hot(features['label'], num_classes)

    with tf.GradientTape() as tape:
      logits = model(inputs)
      loss_value = tf.reduce_mean(
          tf.nn.softmax_cross_entropy_with_logits(labels, logits))

    grads = tape.gradient(loss_value, model.trainable_variables)
    correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(labels, 1))

		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"