%0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6",  per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %1 = "tfl.mul"(%0, %arg2) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

          '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,

    %1 = "tf.BiasAdd"(%0, %cst) {data_format = "NHWC", device = ""} : (tensor<1x2x2x3xf32>, tensor<3xf32>) -> tensor<1x2x2x3xf32>
    %2 = "tf.Relu6"(%1) {device = ""} : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32>
    %3 = "tf.Identity"(%2) {device = ""} : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32>
    return %3 : tensor<1x2x2x3xf32>
  }
}

  // CHECK: %[[VAL_0:.*]] = "tf._FusedConv2D"(%arg2, %arg1, %arg0) <{data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 0.000000e+00 : f32, explicit_paddings = [], fused_ops = ["BiasAdd", "Relu6"], num_args = 1 : i64, operandSegmentSizes = array<i32: 1, 1, 1, 0>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> {TArgs = [f32]} : (tensor<8x32x32x3xf32>, tensor<1x1x3x128xf32>, tensor<128xf32>) -> tensor<*xf32>

  %1 = "tfl.mul"(%0, %arg2) {tac.device = "GPU", fused_activation_function = "RELU6", tac.inference_type = "FLOAT"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

    )

    if merge_fusion_with_dequantize:
      # Check activation functions are explicitly present.
      # If present the last op before return should be stablehlo.clamp for relu6
      # and stablehlo.maximum for relu.
      if activation_fn is nn_ops.relu6:
        self.assertRegex(module_str, r'stablehlo.clamp.*\n.*return')
      elif activation_fn is nn_ops.relu:
        self.assertRegex(module_str, r'stablehlo.maximum.*\n.*return')

    %1 = "tf.Relu6"(%0) : (tensor<*xf32>) -> tensor<*xf32>
    func.return %1 : tensor<*xf32>
  }
}

// CalibrationOptions(calibration_method=CALIBRATION_METHOD_MIN_MAX)
// MIN-MAX-CHECK: func @wrap_composite_func

  %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"}

      {"quantized_ops": ["${main_op}", "BiasAdd", "Relu"], "act_func": "internal_requantize_and_relu_fn", "output_type": "!tf_type.qint8"},
      {"quantized_ops": ["${main_op}", "BiasAdd", "Relu6"], "act_func": "internal_requantize_and_relu6_fn", "output_type": "!tf_type.qint8"},
    ]
    func.func @GenerateQuantizedFunctionName(${quantized_ops})(%input : tensor<*x!tf_type.qint8>,

  %relu_attr = tfr.constant "RELU" -> !tfr.attr
  %relu6_attr = tfr.constant "RELU6" -> !tfr.attr
  %reluN1_1_attr = tfr.constant "RELU_N1_TO_1" -> !tfr.attr
  %none:2 = "tfr.quant_act_range"(%none_attr, %scale, %zp) : (!tfr.attr, f32, i64) -> (!tfr.tensor, !tfr.tensor)
  %relu:2 = "tfr.quant_act_range"(%relu_attr, %scale, %zp) : (!tfr.attr, f32, i64) -> (!tfr.tensor, !tfr.tensor)