// CHECK-ROUNDTRIP:  %0 = stablehlo.composite "stablehlo.add_n" %arg0 {composite_attributes = {test_bool = false, test_int = 2 : i64, test_string = "test"}, decomposition = @add_n.impl} : (tensor<i64>) -> tensor<i64>
    %0 = stablehlo.composite "stablehlo.add_n" %arg0 { composite_attributes = { test_int = 2 : i64, test_bool = 0 : i1, test_string = "test"}, decomposition = @add_n.impl } : (tensor<i64>) -> tensor<i64>
    return %0 : tensor<i64>
  }

  func.return %0 : tensor<10x10xf32>
}

func.func @testAddN(%arg0: tensor<10x10xf32>, %arg1: tensor<10x10xf32>, %arg2: tensor<10x10xf32>) -> tensor<10x10xf32> {
  // CHECK: _arithmetic_count = 200 : i64
  %0 = "tfl.add_n"(%arg0, %arg1, %arg2) {fused_activation_function = "NONE"} : (tensor<10x10xf32>, tensor<10x10xf32>, tensor<10x10xf32>) -> tensor<10x10xf32>
  func.return %0 : tensor<10x10xf32>
}

  // CHECK-DAG: [[ONE:%.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<2xf32>}>
  // CHECK: [[ADD_N:%.*]] = "tf.AddN"(%arg0, [[ZERO]], [[ONE]])
  // CHECK: return %arg0, %arg0, [[ZERO]], [[ADD_N]]
  %2 = "tf.AddN"(%arg0, %1, %1) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>

  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,
  MATRIX_SET_DIAG = 115,
  ROUND = 116,

  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,
  MATRIX_SET_DIAG = 115,
  ROUND = 116,

  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,
  MATRIX_SET_DIAG = 115,
  ROUND = 116,

// CHECK-LABEL: testAddN
func.func @testAddN(tensor<? x f32>, tensor<? x f32>, tensor<? x f32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<? x f32>, %arg1: tensor<? x f32>, %arg2: tensor<? x f32>):
  // CHECK: "tfl.add_n"(%arg0, %arg1, %arg2)
  %0 = "tfl.add_n"(%arg0, %arg1, %arg2): (tensor<? x f32>, tensor<? x f32>, tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// -----

// test invalid AddN

      return printOneResultOp(getOperation(), p);
    }
  }];

  let hasOptions = 1;
}

def TFL_AddNOp : TFL_Op<"add_n", [
    Commutative,
    Pure,
    SameOperandsAndResultsScale,
    DeclareOpInterfaceMethods<TFL_ArithmeticCount>]> {
  let summary = "add_n operator";

  let description = [{
    Adds all input tensors element-wise.
  }];

  let arguments = (ins

  %0 = "tf.AddN"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
  func.return %0 : tensor<2x3xi32>

// CHECK-LABEL: addN
// CHECK:  "tfl.add_n"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:  return
}

func.func @reverse_v2(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1xi32>) -> tensor<1x2x3x4xf32> {

    "RANGE",
    "RESIZE_NEAREST_NEIGHBOR",
    "LEAKY_RELU",
    "SQUARED_DIFFERENCE",
    "MIRROR_PAD",
    "ABS",
    "SPLIT_V",
    "UNIQUE",
    "CEIL",
    "REVERSE_V2",
    "ADD_N",
    "GATHER_ND",
    "COS",
    "WHERE",
    "RANK",
    "ELU",
    "REVERSE_SEQUENCE",
    "MATRIX_DIAG",
    "QUANTIZE",
    "MATRIX_SET_DIAG",
    "ROUND",
    "HARD_SWISH",