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,
  HARD_SWISH = 117,
  IF = 118,
  WHILE = 119,
  NON_MAX_SUPPRESSION_V4 = 120,

// CHECK-LABEL:cos
// CHECK:  "tfl.cos"(%arg0) : (tensor<f32>) -> tensor<f32>
}

func.func @elu(%arg0: tensor<11x16xf32>) -> tensor<11x16xf32> {
  %0 = "tf.Elu"(%arg0) : (tensor<11x16xf32>) -> tensor<11x16xf32>
  func.return %0 : tensor<11x16xf32>

// CHECK-LABEL:elu
// CHECK:  "tfl.elu"(%arg0) : (tensor<11x16xf32>) -> tensor<11x16xf32>
}

}

//===----------------------------------------------------------------------===//
// Elu op legalizations.
//===----------------------------------------------------------------------===//

// -----

// CHECK-LABEL: func @elu
func.func @elu(%arg0: tensor<1xf32>) -> tensor<1xf32> {

  func.return %0 : tensor<? x i32>
}

// -----

// CHECK-LABEL: testEluF32
func.func @testEluF32(%arg0: tensor<? x f32>) -> tensor<? x f32> {
  // CHECK: "tfl.elu"(%arg0)
  %0 = "tfl.elu"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>
}

// -----

// CHECK-LABEL: testTileF32

            // Others
            "AddN", "Bitcast", "Cast", "ClipByValue", "Const", "Empty",
            "Identity", "IdentityN", "Relu", "Relu6", "ReluGrad", "Relu6Grad",
            "LeakyReluGrad", "Elu", "EluGrad", "Selu", "SeluGrad", "Select",
            "SelectV2", "Transpose", "ConjugateTranspose",
            "_UnaryOpsComposition", "CollectiveReduceV2",
            "CollectiveAssignGroupV2",

}

def TF_EluOp : TF_Op<"Elu", [Pure, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes the exponential linear function.";

  let description = [{
The ELU function is defined as:

 * $ e ^ x - 1 $ if $ x < 0 $
 * $ x $ if $ x >= 0 $

Examples:

>>> tf.nn.elu(1.0)
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
>>> tf.nn.elu(0.0)

    void $cppClass::print(OpAsmPrinter &p) {
      return printOneResultOp(getOperation(), p);
    }
  }];

  let hasOptions = 1;

  let hasFolder = 1;
}

def TFL_EluOp: TFL_Op<"elu", [
    Pure,
    SameOperandsAndResultShape,
    TFL_SameFirstOperandAndFirstResultElementType]> {
  let summary = "Exponential Linear Unit operator";
  let description = [{
    Computes the exponential linear

}
// CHECK-LABEL:   func @fuseElu
func.func @fuseElu(%arg0: tensor<10xf32>) -> tensor<10xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "args_tf_0", outputs = "Identity_1"}} {
  // CHECK: "tfl.elu"
  %cst = arith.constant dense<1.000000e+00> : tensor<f32>
  %cst_0 = arith.constant dense<0.000000e+00> : tensor<10xf32>
  %0 = tfl.greater(%arg0, %cst_0) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xi1>

    "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",
    "IF",
    "WHILE",
    "NON_MAX_SUPPRESSION_V4",