return false
				}
			}

			yparams = check.subst(nopos, y.params, smap, nil, ctxt).(*Tuple)
			yresults = check.subst(nopos, y.results, smap, nil, ctxt).(*Tuple)
		}

		return x.variadic == y.variadic &&
			c.identical(x.params, yparams, p) &&
			c.identical(x.results, yresults, p)

	case *Union:
		if y, _ := y.(*Union); y != nil {
			// TODO(rfindley): can this be reached during type checking? If so,

   %4 = "quantfork.dcast"(%3) : (tensor<1x2x!quant.uniform<i8:f32, 6.000000e-03:-128>>) -> tensor<1x2xf32>
// expected-error @+2 {{Failed to find a valid entry function}}
// expected-error @+1 {{'tf.XlaCallModule' op operand #0 must be variadic of tensor of tf.dtype values}}

					return false
				}
			}

			yparams = check.subst(nopos, y.params, smap, nil, ctxt).(*Tuple)
			yresults = check.subst(nopos, y.results, smap, nil, ctxt).(*Tuple)
		}

		return x.variadic == y.variadic &&
			c.identical(x.params, yparams, p) &&
			c.identical(x.results, yresults, p)

	case *Union:
		if y, _ := y.(*Union); y != nil {
			// TODO(rfindley): can this be reached during type checking? If so,

}

func f0() (a []int)         { return }
func f1() (a []int, b int)  { return }
func f2() (a, b []int)      { return }

func append_([]int, ...int) {}

func issue9473(a []int, b ...int) {
	// variadic builtin function
	_ = append(f0())
	_ = append(f0(), f0()...)
	_ = append(f1())
	_ = append(f2 /* ERRORx `cannot use .* in argument` */ ())
	_ = append(f2()... /* ERROR "cannot use ..." */ )

			hash += h.Hash(f.Type())
		}
		return hash

	case *types.Pointer:
		return 9067 + 2*h.Hash(t.Elem())

	case *types.Signature:
		var hash uint32 = 9091
		if t.Variadic() {
			hash *= 8863
		}

		// Use a separate hasher for types inside of the signature, where type
		// parameter identity is modified to be (index, constraint). We must use a

        };
    if (tf_op->hasTrait<mlir::OpTrait::AttrSizedOperandSegments>() ||
        tf_op->hasTrait<mlir::OpTrait::AttrSizedResultSegments>()) {
      // The op has multiple variadic operands or results.
      // Calculate operand and result segment sizes using the OpDef.
      tensorflow::NameRangeMap input_ranges, output_ranges;
      // This will fail only if the OpDef is syntactically invalid.

	_Sizeof
	_Slice
	_SliceData
	_String
	_StringData

	// testing support
	_Assert
	_Trace
)

var predeclaredFuncs = [...]struct {
	name     string
	nargs    int
	variadic bool
	kind     exprKind
}{
	_Append:  {"append", 1, true, expression},
	_Cap:     {"cap", 1, false, expression},
	_Clear:   {"clear", 1, false, statement},
	_Close:   {"close", 1, false, statement},

	_Sizeof
	_Slice
	_SliceData
	_String
	_StringData

	// testing support
	_Assert
	_Trace
)

var predeclaredFuncs = [...]struct {
	name     string
	nargs    int
	variadic bool
	kind     exprKind
}{
	_Append:  {"append", 1, true, expression},
	_Cap:     {"cap", 1, false, expression},
	_Clear:   {"clear", 1, false, statement},
	_Close:   {"close", 1, false, statement},

class TFL_VariadicTensorOf<list<Type> allowedRuntimeTypes,
                   list<Type> allowedOpTypes = [AnyType]> :
  Variadic<TensorOf<allowedOpTypes>>,
  TFL_RuntimeType<Variadic<TensorOf<allowedRuntimeTypes>>>;

def TFL_I4 : I<4>;
def TFL_Int32Or64 : SignlessIntOfWidths<[32, 64]>;

def TFL_BoolTensor : TFL_TensorOf<[I1]>;
def TFL_FpTensor : TFL_TensorOf<[F32]>;

		// and result values, corresponding parameter and result types unify,
		// and either both functions are variadic or neither is.
		// Parameter and result names are not required to match.
		// TODO(gri) handle type parameters or document why we can ignore them.
		if y, ok := y.(*Signature); ok {
			return x.variadic == y.variadic &&
				u.nify(x.params, y.params, emode, p) &&
				u.nify(x.results, y.results, emode, p)
		}