%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)

    %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

      {"quantized_ops": ["${main_op}", "Relu"], "act_func": "internal_requantize_and_relu_fn", "output_type": "i8"},
      {"quantized_ops": ["${main_op}", "Relu6"], "act_func": "internal_requantize_and_relu6_fn", "output_type": "i8"},
      {"quantized_ops": ["${main_op}"], "act_func": "internal_dequantize_no_activation_fn", "output_type": "f32"},

// Fusing:  %[[relu:[0-9].*]] = "tfl.relu"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>
// Fusing:  %[[add2:[0-9].*]] = tfl.add %[[relu]], %[[add1]] {fused_activation_function = "RELU6"} : tensor<1xf32>
// Fusing:  %[[add3:[0-9].*]] = tfl.add %[[add2]], %[[relu]] {fused_activation_function = "RELU6"} : tensor<1xf32>
// Fusing:  return

        'input_': input_,
        'filter_': filter_,
        'bias': bias,
        'stride_w': 2,
        'stride_h': 2,
        'dilation_w': 1,
        'dilation_h': 1,
        'padding': 'SAME',
        'act': 'RELU6'
    }

    self._assertOpAndComposite([input_, filter_, bias],
                               tf.function(gen_mnist_ops.new_conv2d),
                               ops_defs._composite_conv_add_relu, kwargs)

    %6 = "quantfork.stats"(%5) {layerStats = dense<[-2788.31055, 4616.62842]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
    %7 = "tf.Relu6"(%6) {device = ""} : (tensor<*xf32>) -> tensor<*xf32>
    %8 = "quantfork.stats"(%7) {layerStats = dense<[0.000000e+00, 6.000000e+00]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
    return %8 : tensor<*xf32>
  }

// These should match the ActivationFunctionType enum in TFLite schema.
def TFL_AFEnum_None  : I32EnumAttrCase<"NONE", 0>;
def TFL_AFEnum_Relu  : I32EnumAttrCase<"RELU", 1>;
def TFL_AFEnum_Relu1 : I32EnumAttrCase<"RELU_N1_TO_1", 2>;
def TFL_AFEnum_Relu6 : I32EnumAttrCase<"RELU6", 3>;
def TFL_AFEnum_Tanh  : I32EnumAttrCase<"TANH", 4>;
def TFL_AFEnum_Sign  : I32EnumAttrCase<"SIGN_BIT", 5>;

def TFL_AFAttr : TFL_AnyStrAttrOf<[

  %biasadd = "tf.BiasAdd"(%conv, %dq_bias) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
  %res = "tf.Relu6"(%biasadd) : (tensor<*xf32>) -> tensor<*xf32>
  %q_res = "quantfork.qcast"(%res) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>

  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
   // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
  %1 = "tfl.add"(%arg0, %0) {fused_activation_function = "RELU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
   // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
  %2 = "tfl.relu"(%arg0) : (tensor<1xf32>) -> tensor<1xf32>

// tfl.log / tfl.logistic / tfl.max_pool_2d / tfl.mirror_pad / tfl.maximum /
// tfl.custom / tfl.mean / tfl.minimum / tfl.pad / tfl.pow / tfl.prelu /
// tfl.relu / tfl.relu6 / tfl.rsqrt / tfl.sin / tfl.slice / tfl.softmax /
// tfl.space_to_depth / tfl.sqrt / tfl.square / tfl.squared_difference /
// tfl.strided_slice / tfl.tanh / tfl.transpose / tfl.transpose_conv