func.return %0 : tensor<1x64x84x32xf32>
}

func.func @testL2Norm(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 300 : i64
  %0 = "tfl.l2_normalization"(%arg0) {fused_activation_function = "NONE"} : (tensor<10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>

  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[-1.0, 1.0]> : tensor<2xf32>} : (tensor<1x1xf32>) -> tensor<1x1xf32>
  %1 = "tfl.l2_normalization"(%0) {fused_activation_function = "NONE"} : (tensor<1x1xf32>) -> tensor<1x1xf32>
  %2 = "quantfork.stats"(%1) {layerStats = dense<[-1.0, 1.0]> : tensor<2xf32>} : (tensor<1x1xf32>) -> tensor<1x1xf32>

  CONCATENATION = 2,
  CONV_2D = 3,
  DEPTHWISE_CONV_2D = 4,
  DEPTH_TO_SPACE = 5,
  DEQUANTIZE = 6,
  EMBEDDING_LOOKUP = 7,
  FLOOR = 8,
  FULLY_CONNECTED = 9,
  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,
  LSH_PROJECTION = 15,
  LSTM = 16,
  MAX_POOL_2D = 17,
  MUL = 18,
  RELU = 19,

  CONCATENATION = 2,
  CONV_2D = 3,
  DEPTHWISE_CONV_2D = 4,
  DEPTH_TO_SPACE = 5,
  DEQUANTIZE = 6,
  EMBEDDING_LOOKUP = 7,
  FLOOR = 8,
  FULLY_CONNECTED = 9,
  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,
  LSH_PROJECTION = 15,
  LSTM = 16,
  MAX_POOL_2D = 17,
  MUL = 18,
  RELU = 19,

  %1 = "tfl.l2_normalization"(%0) {fused_activation_function = "NONE"} : (tensor<1x6x6x16xf32>) -> tensor<1x6x6x16xf32>
  func.return %1 : tensor<1x6x6x16xf32>

// CHECK: %[[in:.*]] = "tfl.dequantize"(%arg0)
// CHECK: %[[l2:.*]] = "tfl.l2_normalization"(%[[in]])

  CONCATENATION = 2,
  CONV_2D = 3,
  DEPTHWISE_CONV_2D = 4,
  DEPTH_TO_SPACE = 5,
  DEQUANTIZE = 6,
  EMBEDDING_LOOKUP = 7,
  FLOOR = 8,
  FULLY_CONNECTED = 9,
  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,
  LSH_PROJECTION = 15,
  LSTM = 16,
  MAX_POOL_2D = 17,
  MUL = 18,
  RELU = 19,

  %3 = "tfl.mul"(%arg0, %2) {fused_activation_function = "NONE"} : (tensor<2xf32>, tensor<f32>) -> tensor<2xf32>
  func.return %3: tensor<2xf32>
  // CHECK: %[[RES:[0-9].*]] = "tfl.l2_normalization"([[INPUT:%.*]]) <{fused_activation_function = "NONE"}> : (tensor<2xf32>) -> tensor<2xf32>
  // CHECK: return %[[RES]]
}

// CHECK-LABEL: @L2NormalizePattern1

  let hasOptions = 0;
}

def TFL_L2NormalizationOp : TFL_Op<"l2_normalization", [Pure,
    FixedOutputRangeInterface,
    QuantizableResult,
    DeclareOpInterfaceMethods<TFL_ArithmeticCount>]> {
  let summary = "L2 Normalize Operator";

  let description = [{
    L2Normalization Op
  }];

  let arguments = (ins
    TFL_TensorOf<[F32, QUI8, QI8, QUI16, QI16, I8]>:$input,

  "$0.cast<DenseElementsAttr>().getValues<APFloat>()[0].isNegInfinity())">>;

def L2NormValidReduceIndex : Constraint<CPred<
  "L2NormalizeReduceAxis($0, $1.cast<DenseElementsAttr>())">>;

// Currently L2Normalization doesn't support activation function
// in TFLite.
// TODO(karimnosseir): Add constraints that the kernel code assumes.
// constraint on axis and depth.
multiclass L2NormalizePatterns<Op FirstOp, Op SecondOp> {

    "CONV_2D",
    "DEPTHWISE_CONV_2D",
    "DEPTH_TO_SPACE",
    "DEQUANTIZE",
    "EMBEDDING_LOOKUP",
    "FLOOR",
    "FULLY_CONNECTED",
    "HASHTABLE_LOOKUP",
    "L2_NORMALIZATION",
    "L2_POOL_2D",
    "LOCAL_RESPONSE_NORMALIZATION",
    "LOGISTIC",
    "LSH_PROJECTION",
    "LSTM",
    "MAX_POOL_2D",
    "MUL",
    "RELU",
    "RELU_N1_TO_1",
    "RELU6",