// CHECK: "RELU"
  %1 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU"} : tensor<4xi32>
  // CHECK: "RELU_N1_TO_1"
  %2 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU_N1_TO_1"} : tensor<4xi32>
  // CHECK: "RELU6"
  %3 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU6"} : tensor<4xi32>
  // CHECK: "TANH"

namespace {
#define GEN_PASS_DEF_OPTIMIZEPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

constexpr char kRelu[] = "RELU";
constexpr char kRelu6[] = "RELU6";
constexpr char kRelu1[] = "RELU_N1_TO_1";

ElementsAttr FlattenTo1D(Attribute a) {
  auto elements = mlir::cast<DenseElementsAttr>(a);
  const std::array<int64_t, 1> flattened_shape = {elements.getNumElements()};

      x -> max(0, min(1, x))
  }];

  let arguments = (ins TFL_TensorOf<[F32, QUI8, QI8]>:$x);

  let results = (outs TFL_TensorOf<[F32, QUI8, QI8]>:$y);
}

def TFL_Relu1Op: TFL_Op<"relu_n1_to_1", [
    PredOpTrait<"x and y must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    Pure,
    QuantizableResult,
    SameOperandsAndResultShape]> {
  let summary = "Relu1 operator";