if check.recordTypes() {
						sig := makeSig(S, S, y.typ)
						sig.variadic = true
						check.recordBuiltinType(call.Fun, sig)
					}
					x.mode = value
					x.typ = S
					break
				}
			}
		}

		// check general case by creating custom signature
		sig := makeSig(S, S, NewSlice(T)) // []T required for variadic signature
		sig.variadic = true

```mlir {.mlir}
/// This is a regular function, taking inputs by value and returning a new value.
/// The body is a regular CFG.
func some_function(%input : tensor<*xf32>) -> tensor<*xf32> {
  // TensorFlow operations are not variadic: this `tf.add` operation always
  // takes two inputs and returns a single output. This simplifies
  // pattern-matching, verification and rewriting.
  %added = tf.Add %input, %input : tensor<*xf32>

				tparams: t.tparams,
				// instantiated signatures have a nil scope
				recv:     recv,
				params:   params,
				results:  results,
				variadic: t.variadic,
			}
		}

	case *Union:
		terms, copied := subst.termlist(t.terms)
		if copied {
			// term list substitution may introduce duplicate terms (unlikely but possible).

// typesSummary returns a string of the form "(t1, t2, ...)" where the
// ti's are user-friendly string representations for the given types.
// If variadic is set and the last type is a slice, its string is of
// the form "...E" where E is the slice's element type.
func (check *Checker) typesSummary(list []Type, variadic bool) string {
	var res []string
	for i, t := range list {
		var s string
		switch {
		case t == nil:

				tparams: t.tparams,
				// instantiated signatures have a nil scope
				recv:     recv,
				params:   params,
				results:  results,
				variadic: t.variadic,
			}
		}

	case *Union:
		terms, copied := subst.termlist(t.terms)
		if copied {
			// term list substitution may introduce duplicate terms (unlikely but possible).

					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,

					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.