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"

#include "tensorflow/c/eager/c_api_test_util.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/nn_ops.h"
#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/tensor_float_32_utils.h"
#include "tensorflow/core/platform/test.h"

                       TestPartResult::Type type,
                       const string& substr);
  ~SingleFailureChecker();
 private:
  const TestPartResultArray* const results_;
  const TestPartResult::Type type_;
  const string substr_;

  GTEST_DISALLOW_COPY_AND_ASSIGN_(SingleFailureChecker);
};

}  // namespace internal

}  // namespace testing

                       TestPartResult::Type type,
                       const string& substr);
  ~SingleFailureChecker();
 private:
  const TestPartResultArray* const results_;
  const TestPartResult::Type type_;
  const string substr_;

  GTEST_DISALLOW_COPY_AND_ASSIGN_(SingleFailureChecker);
};

}  // namespace internal

}  // namespace testing

        else:
            alias = field_info.alias or param_name
        field_info.alias = alias
        field = create_response_field(
            name=param_name,
            type_=use_annotation_from_field_info,
            default=field_info.default,
            alias=alias,
            required=field_info.default in (Required, Undefined),
            field_info=field_info,
        )

[source,java]
.Resource.java
----
include::{snippetsPath}/plugins/readOnlyNestedProperty/groovy/buildSrc/src/main/java/Resource.java[tags=resource]
----

A _named managed type_ is a managed type that additionally has an abstract property "name" of type `String`.