'input_shape_dynamic': False,
          'enable_per_channel_quantization': False,
          'dilations': [1, 2, 2, 1],
      },
      {
          'testcase_name': 'relu6',
          'activation_fn': nn_ops.relu6,
          'has_bias': False,
          'has_batch_norm': False,
          'target_opset': quant_opts_pb2.TF,
          'input_shape_dynamic': False,

  %0 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU6"} : tensor<? x i16>
  func.return %0#0 : tensor<? x i16>
}

// CHECK-LABEL: testSub
func.func @testSub(tensor<? x i32>, tensor<? x i32>) -> tensor<? x i32> {
^bb0(%arg0: tensor<? x i32>, %arg1: tensor<? x i32>):
  // CHECK: tfl.sub %arg0, %arg1 {fused_activation_function = "RELU6"}

// The actual Optimize Pass.
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()};

    }]>
  ];
}

def TFL_Relu6Op: TFL_Op<"relu6", [
    PredOpTrait<"x and y must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    Pure,
    QuantizableResult,
    SameOperandsAndResultShape]> {
  let summary = "Relu6 operator";

  let description = [{
    Element-wise Relu6 operator
      x -> max(0, min(6, x))
  }];

  func.return %0 : tensor<4xi64>
}

// CHECK-LABEL: testReluOfMinimum6ToRelu6Float
func.func @testReluOfMinimum6ToRelu6Float(%arg0: tensor<4xf32>) -> tensor<4xf32> {
  // CHECK: %0 = "tf.Relu6"(%arg0) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<4xf32>) -> tensor<4xf32>
  // CHECK: return %0
  %cst_6 = arith.constant dense<6.000000e+00> : tensor<f32>

  %0 = "tf.SquaredDifference"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %1 = "tf.Relu6"(%0) : (tensor<1xf32>) -> tensor<1xf32>
  func.return %1: tensor<1xf32>

// CHECK-LABEL: squaredDifferenceRelu
// CHECK:  tfl.squared_difference %arg0, %arg1 : tensor<1xf32>
// CHECK:  %1 = "tfl.relu6"(%0) : (tensor<1xf32>) -> tensor<1xf32>
// CHECK:  return
}