'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"}

    }]>
  ];
}

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))
  }];

  %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
}