return
		}

		// if both arguments are constants, the result is a constant
		if x.mode == constant_ && y.mode == constant_ {
			x.val = constant.BinaryOp(constant.ToFloat(x.val), token.ADD, constant.MakeImag(constant.ToFloat(y.val)))
		} else {
			x.mode = value
		}

		if check.recordTypes() && x.mode != constant_ {
			check.recordBuiltinType(call.Fun, makeSig(resTyp, x.typ, x.typ))
		}

	case constant.Float:
		i := constant.ToInt(x)
		if i.Kind() != constant.Int {
			v, _ := constant.Float64Val(x)
			return v
		}
		x = i
		fallthrough
	case constant.Int:
		if v, ok := constant.Int64Val(x); ok {
			return v
		}
		if v, ok := constant.Uint64Val(x); ok {
			return v
		}
	case constant.String:
		return constant.StringVal(x)
	case constant.Bool:

module {
  func.func @main() -> tensor<1xi64> {
  %0 = "tfl.custom"() {custom_code = "stablehlo.constant", custom_option = #tfl<const_bytes : "0x76616C75650001020109010101062C022401">} : () -> tensor<1xi64>
  func.return %0 : tensor<1xi64>
  }
}

// CHECK:  module {
// CHECK-NEXT:    func @main() -> tensor<1xi64> {
// CHECK-NEXT:    %[[c0:.+]] = stablehlo.constant dense<2> : tensor<1xi64>
// CHECK-NEXT:    return %[[c0]] : tensor<1xi64>
// CHECK-NEXT:    }

    private void method() {
        for ($constantType i = Constant1.CONSTANT1 $addition; i < Constant2.CONSTANT2 $addition; i += Constant3.CONSTANT3 $addition) {
        }
        $constantType value = Constant3.CONSTANT3 $addition;
        switch((int) value) {
            case (int) (Constant4.CONSTANT4 $addition):
            default:
                value = (int) (Constant5.CONSTANT5 $addition);
        }

calvin <******@****.***> 1706611761 +0800

// typexpr    <expr> (               <mode>                    )
//
// constant   <expr> (<untyped kind> <mode>                    )
// constant   <expr> (               <mode>       of type <typ>)
// constant   <expr> (<untyped kind> <mode> <val>              )
// constant   <expr> (               <mode> <val> of type <typ>)
//
// variable   <expr> (<untyped kind> <mode>                    )

// CHECK-LABEL: conv
// CHECK-DAG:  %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<16xf32>
// CHECK-DAG:  %[[CONSTANT0:.*]] = arith.constant dense<[3, 0, 1, 2]> : tensor<4xi32>
// CHECK-DAG:  %[[CONSTANT1:.*]] = arith.constant dense<[{{\[}}0, 0], [1, 1], [1, 1], [0, 0]]> : tensor<4x2xi32>
// CHECK:  %0 = "tf.Transpose"(%arg1, %[[CONSTANT0]]) : (tensor<3x3x3x16xf32>, tensor<4xi32>) -> tensor<16x3x3x3xf32>

                        ctx->GetAttr("Tconstants", &constant_types));
  std::vector<int> constants(constant_types.size());
  std::iota(constants.begin(), constants.end(), 0);
  return constants;
}

std::vector<int> ResourcesVector(OpKernelConstruction* ctx) {
  DataTypeVector constant_types;
  OP_REQUIRES_OK_RETURN(ctx, std::vector<int>(),
                        ctx->GetAttr("Tconstants", &constant_types));

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

namespace tensorflow {

using shape_inference::InferenceContext;

REGISTER_OP("XlaLaunch")
    .Input("constants: Tconstants")
    .Attr("Tconstants: list(type) >= 0")
    .Input("args: Targs")
    .Attr("Targs: list(type) >= 0")
    .Input("resources: Nresources * resource")
    .Attr("Nresources: int >= 0")
    .Output("results: Tresults")

  return %0 : tensor<16x4xf32>
}
// Check that the constant is cloned with same value.
// The duplicated constant is the last index of the ConcatV2 op (which
// accepts a variadic arg).
// CHECK-DAG: %[[CST_0:.*]] = "tf.Const"()
// CHECK-SAME: value = dense<1> : tensor<i32>
// CHECK-DAG: %[[CST_1:.*]] = "tf.Const"()
// CHECK-SAME: value = dense<1> : tensor<i32>

// Check that the constants used for tf.ConcatV2 and tf.AddV2 are different.