if (!backend_config) {
      return op.emitOpError() << "Missing backend_config attribute";
    }

    for (const auto& attr : backend_config) {
      auto name = attr.getName();
      if (!(name == "top_k" || name == "reduction_dim" ||
            name == "recall_target" || name == "aggregate_to_topk" ||
            name == "reduction_input_size_override" || name == "is_fallback")) {
        return op.emitOpError()

      )
      def multiple_output_ops(
          self, input_tensor: core.Tensor
      ) -> Mapping[str, core.Tensor]:
        k = array_ops.constant(4, dtype=dtypes.int32)
        values, indices = nn_ops.top_k(input_tensor, k, name='TopK')
        adj_values = values + 2
        return {'indices': indices, 'adj_values': adj_values, 'values': values}

      @def_function.function(
          input_signature=[

}

// -----

// CHECK-LABEL: topk
func.func @topk(%arg0: tensor<8xf32>, %arg1: tensor<i32>) -> (tensor<?xf32>, tensor<?xi32>) {
  %0, %1 = "tfl.topk_v2"(%arg0, %arg1) : (tensor<8xf32>, tensor<i32>) -> (tensor<?xf32>, tensor<?xi32>)
  func.return %0, %1: tensor<?xf32>, tensor<?xi32>
}

// -----

// CHECK-LABEL: topk

// CHECK-LABEL:sin
// CHECK:  "tfl.sin"(%arg0) : (tensor<f32>) -> tensor<f32>
}

func.func @topk(%arg0: tensor<8xf32>, %arg1: tensor<i32>) -> (tensor<?xf32>, tensor<?xi32>) {
  %0, %1 = "tf.TopKV2"(%arg0, %arg1) : (tensor<8xf32>, tensor<i32>) -> (tensor<?xf32>, tensor<?xi32>)
  func.return %0, %1: tensor<?xf32>, tensor<?xi32>

// CHECK-LABEL: topk
// CHECK:  "tfl.topk_v2"(%arg0, %arg1)
// CHECK:  return
}

    TFL::TopKV2Op top_k_op = rewriter.create<TFL::TopKV2Op>(
        op.getLoc(), new_values_ty, new_indices_ty, op->getOperand(0), k_cst);

    // Remove original ops (topk, Slice, Slice).
    if (!values.use_empty()) {
      auto values_slice_op = llvm::dyn_cast_or_null<TFL::SliceOp>(
          values.getUses().begin().getUser());

    TFL_OperandHasRankAtLeast<0, 1>,
    TFL_OperandHasRank<1, 0>,
    PredOpTrait<"result and input element type match",
      TFL_TCresVTEtIsSameAsOp<0,0>>,
    SameOperandsAndResultsScale]> {
  let summary = "TopK operator";

  let description = [{
    Returns the top `k` largest element along each last dimensional slice of
    `input` and the indices of values within the last dimension of the input
    tensor.