obj:    objs(int64(10), 10.0, 10.49, 10.5, true, "10", "MTA=", "3723.004s", "1h2m3s4ms", "2011-08-18T19:03:37.01+01:00", "2011-08-18T19:03:37.01+01:00", "2011-08-18T00:00:00Z", "2011-08-18"),
			schema: schemas(integerType, numberType, numberType, numberType, booleanType, stringType, byteType, stringType, durationFormat, stringType, dateTimeFormat, stringType, dateFormat),
			expectCost: map[string]int64{
				"int(self.val2) == self.val1":         6,

namespace tensorflow {
namespace {

REGISTER_OP("MyAddN")
    .Input("inputs: N * T")
    .Output("sum: T")
    .Attr("N: int >= 1")
    .Attr("T: {numbertype, variant}")
    .SetIsCommutative()
    .SetIsAggregate()
    .SetShapeFn(shape_inference::UnchangedShape);

REGISTER_OP("RiscAddDummy")
    .Input("x: T")
    .Input("y: T")
    .Output("z: T")
    .Attr(

// capital letters, the operator expects the user to provide the attribute value
// at operation runtime.
//
// <type> can be:
//   "string", "int", "float", "bool", "type", "shape", or "tensor"
//   "numbertype", "realnumbertype", "quantizedtype"
//       (meaning "type" with a restriction on valid values)
//   "{int32,int64}" or {realnumbertype,quantizedtype,string}"

	}
	f, err := strconv.ParseFloat(s, 64)
	if err != nil {
		return nil, &UnmarshalTypeError{Value: "number " + s, Type: reflect.TypeFor[float64](), Offset: int64(d.off)}
	}
	return f, nil
}

var numberType = reflect.TypeFor[Number]()

// literalStore decodes a literal stored in item into v.
//
// fromQuoted indicates whether this literal came from unwrapping a

			obj:    objs(int64(10), 10.0, 10.49, 10.5, true, "10", "MTA=", "3723.004s", "1h2m3s4ms", "2011-08-18T19:03:37.01+01:00", "2011-08-18T19:03:37.01+01:00", "2011-08-18T00:00:00Z", "2011-08-18"),
			schema: schemas(integerType, numberType, numberType, numberType, booleanType, stringType, byteType, stringType, durationFormat, stringType, dateTimeFormat, stringType, dateFormat),
			valid: []string{
				"int(self.val2) == self.val1",
				"int(self.val3) == self.val1",

	e.Write(b)
}

var (
	float32Encoder = (floatEncoder(32)).encode
	float64Encoder = (floatEncoder(64)).encode
)

func stringEncoder(e *encodeState, v reflect.Value, opts encOpts) {
	if v.Type() == numberType {
		numStr := v.String()
		// In Go1.5 the empty string encodes to "0", while this is not a valid number literal
		// we keep compatibility so check validity after this.
		if numStr == "" {

    .Output("o: float")
    .SetShapeFn(::tensorflow::shape_inference::UnknownShape);

REGISTER_OP("AddNLikeTest")
    .Input("inputs: N * T")
    .Output("sum: T")
    .Attr("N: int >= 1")
    .Attr("T: numbertype")
    .SetIsCommutative()
    .SetIsAggregate();

Node* Sequencer(const GraphDefBuilder::Options& opts,
                const string& call_node_name) {
  if (opts.HaveError()) return nullptr;

private
fun hasClassFlags(kmClassSupplier: Supplier<KmClass>, memberType: MemberType, jvmSignature: String, predicate: FlagsPredicate): Boolean {
    val kmClass = kmClassSupplier.get()
    return when (memberType) {
        MemberType.TYPE -> hasTypeFlags(kmClass, jvmSignature, predicate)
        MemberType.FIELD -> hasPropertyFlags(kmClass::properties, jvmSignature, predicate)

        }
}


internal
enum class MemberType {
    TYPE, CONSTRUCTOR, FIELD, METHOD
}


private
fun memberTypeFor(member: CtMember): MemberType =
    when (member) {
        is CtConstructor -> MemberType.CONSTRUCTOR
        is CtField -> MemberType.FIELD
        is CtMethod -> MemberType.METHOD

                    }
                    String memberType = (String) memberOf.get("@odata.type");
                    if (memberType == null) {
                        logger.warn("@odata.type is null: {}", memberOf);
                        continue;
                    }
                    memberType = memberType.toLowerCase(Locale.ENGLISH);
                    final String id = (String) memberOf.get("id");