class OperandsSameElementTypeConstraintBase<string op> :
  PredOpTrait<op # " operands have same element type",
    OperandsSameElementTypeConstraintBasePred>;

// This is a constraint for most of the binary ops, e.g., add, mul, div, etc.
// Binary ops lhs & rhs should have the same value type, and is capable to
// compare quantization types as well.
def BinaryOpSameElementTypeConstraint :
  OperandsSameElementTypeConstraintBase<"binary op">;

  }];

  let arguments = (ins
    TF_FpOrI32OrI64Tensor:$input,

    I64Attr:$group_size,
    I64Attr:$group_key,
    I64Attr:$instance_key,
    TF_AnyStrAttrOf<["Min", "Max", "Mul", "Add"]>:$merge_op,
    TF_AnyStrAttrOf<["Id", "Div"]>:$final_op,
    I64ArrayAttr:$subdiv_offsets,
    DefaultValuedOptionalAttr<I64ArrayAttr, "{}">:$wait_for,

    *   Unary GPU kernels: Abs, Atanh, Acos, Acosh, Asin, Asinh, Atan, Cos, Cosh, Sin, Sinh, Tan, Tanh.
    *   Binary GPU kernels: AddV2, Sub, Div, DivNoNan, Mul, MulNoNan, FloorDiv, Equal, NotEqual, Greater, GreaterEqual, LessEqual, Less.

* `tf.lite`
    * Add experimental supports conversion of models that may be larger than 2GB before buffer deduplication

			expected: []metav1.TableRow{{Cells: []interface{}{"test5", "LoadBalancer", "10.9.8.7", "3.4.5.6,80.11.12.10", "8888/tcp", "<unknown>"}}},
		},
		{
			// Test LoadBalancer service with mul ExternalIPs
			service: api.Service{
				ObjectMeta: metav1.ObjectMeta{Name: "test6"},
				Spec: api.ServiceSpec{
					Type: api.ServiceTypeLoadBalancer,
					Ports: []api.ServicePort{
						{

    StringRef reduce_op = op.getReduceOp();
    if (reduce_op == "Add") {
      BuildReduceBody<AddOp>(element_type, &reduce_scatter.getComputation(),
                             &rewriter);
    } else if (reduce_op == "Mul") {
      BuildReduceBody<MulOp>(element_type, &reduce_scatter.getComputation(),
                             &rewriter);
    } else if (reduce_op == "Min") {