Composite = composite.Composite


@composite.Composite(
    'TestNoOp', derived_attrs=['T: numbertype'], outputs=['o1: T'])
def _composite_no_op():
  pass


@Composite(
    'TestCompositeOp',
    inputs=['x: T', 'y: T'],
    attrs=['act: {"", "relu"}', 'trans: bool = true'],
    derived_attrs=['T: numbertype'],
    outputs=['o1: T', 'o2: T'])
def _composite_op(x, y, act, trans):
  return x + act, y + trans

    .Input("input: T")
    .Output("output: T")
    .Attr("T: numbertype");

REGISTER_OP("TestIdentityNOp")
    .Input("input: N * T")
    .Output("output: N * T")
    .Attr("N: int >= 1")
    .Attr("T: numbertype");

REGISTER_OP("TestInputNOp")
    .Input("input: N * T")
    .Output("output: T")
    .Attr("N: int >= 1")
    .Attr("T: numbertype");

REGISTER_OP("TestOutputNOp")
    .Input("input: T")

#include "tensorflow/core/framework/shape_inference.h"

namespace tensorflow {

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

REGISTER_OP("MyBiasedDense")
    .Input("input: T")
    .Input("weight: T")
    .Input("bias: T")
    .Output("out: T")

flags.DEFINE_bool('gen_register_op', True,
                  'Generate register op cc file or tfr mlir file.')

flags.mark_flag_as_required('output')


@Composite('TestRandom', derived_attrs=['T: numbertype'], outputs=['o: T'])
def _composite_random_op():
  pass


def main(_):
  if FLAGS.gen_register_op:
    assert FLAGS.output.endswith('.cc')

			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 == "" {