}];
  let arguments = (ins
    Variadic<TFTensorType>:$operands_and_static_shapes,
    DenseI32ArrayAttr:$constant_operand_indices,
    StrAttr:$metadata,
    StrAttr:$mlir_module,
    UI32Attr:$num_operands,
    DenseI32ArrayAttr:$operands_with_static_shape,
    StrAttr:$producer_name
  );

  let results = (outs
    TFTensorType:$rendezvous_key_base,
    Variadic<MlrtFutureType>:$results
  );

//
// The implementation fn can assume that the argument [Value] slice
// has the number and type of arguments given by typ.
// If typ describes a variadic function, the final Value is itself
// a slice representing the variadic arguments, as in the
// body of a variadic function. The result Value slice returned by fn
// must have the number and type of results given by typ.
//

}

// Min returns the minimum element of `nums`.
func Min[T IntLike, NumSlice ~[]T](nums NumSlice) T {
	if len(nums) == 0 {
		return T(0)
	}
	return Reduce(min[T], nums, nums[0])
}

// VarMin is the variadic version of Min.
func VarMin[T IntLike](nums ...T) T {
	return Min(nums)
}

type myInt int

func main() {
	fmt.Println(VarMin(myInt(1), myInt(2)))

	seq := []myInt{1, 2}
	fmt.Println(Min(seq))

>;

// Removes `stablehlo.custom_call @Sharding`. Assumes this call always accepts
// one input. Other attributes are ignored.
def RemoveShardingCustomCall : Pat<
  (StableHLO_CustomCallOp (variadic $input0), $call_target_name, $_, $_, $_, $_, $_, $_, $_),
  (replaceWithValue $input0),

func h3[P any](func(P), func(P), func() P)                   {}
func h4[P, Q any](func(P), func(P, Q), func() Q, func(P, Q)) {}

func r1() int { return 0 }

func _() {
	h3(g1, g1, r1)
	h4(g1, g2, r1, g2)
}

// Variadic cases

func f(func(int))
func g[P any](P) {}

func d[P any](...func(P)) {}

func _() {
	d /* ERROR "cannot infer P" */ ()
	d(f)
	d(f, g)
	d(f, g, g)
	d /* ERROR "cannot infer P" */ (g, g, g)

include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td"
include "mlir/IR/OpBase.td"
include "mlir/IR/PatternBase.td"
include "tensorflow/compiler/mlir/quantization/common/attrs_and_constraints.td"

// Combines the two variadic arguments ($in_tensors and $captured_tensors).
def GetBatchFunctionOpArgOperands:
    NativeCodeCall<"cast<TF::BatchFunctionOp>($0[0].getDefiningOp()).getArgOperands()">;

      } : (!tfrt.chain, !tfd.tf_tensor, !tfd.tf_tensor) -> (
        !tfrt.chain, !tfd.tf_tensor)
  }];

  let arguments = (ins TFRT_ChainType, Variadic<AnyType>);
  let results = (outs TFRT_ChainType, Variadic<AnyType>);
}

def PrintTftOp : RuntimeFallbackDialect_Op<"print_tft"> {
  let summary = "print TF tensor operation";
  let description = [{

  );
}

def TF_CalibrationStatisticsSaverOp : TF_Op<"CalibrationStatisticsSaver", []> {
  let summary = "Aggregates and saves calibration statistics.";

  let arguments = (ins
    Variadic<TensorOf<[TF_Float32, TF_Int64]>>:$inputs,

    StrAttr:$output_file_path,
    StrArrayAttr:$ids,
    I32ArrayAttr:$calibration_methods
  );

  TF_DerivedOperandTypeListAttr Tin = TF_DerivedOperandTypeListAttr<0>;

struct AssertTypeEq;

template <typename T>
struct AssertTypeEq<T, T> {
  typedef bool type;
};

// A unique type used as the default value for the arguments of class
// template Types.  This allows us to simulate variadic templates
// (e.g. Types<int>, Type<int, double>, and etc), which C++ doesn't
// support directly.
struct None {};

// The following family of struct and struct templates are used to

struct AssertTypeEq;

template <typename T>
struct AssertTypeEq<T, T> {
  typedef bool type;
};

// A unique type used as the default value for the arguments of class
// template Types.  This allows us to simulate variadic templates
// (e.g. Types<int>, Type<int, double>, and etc), which C++ doesn't
// support directly.
struct None {};

// The following family of struct and struct templates are used to