RSQRT = 76,
  SHAPE = 77,
  POW = 78,
  ARG_MIN = 79,
  FAKE_QUANT = 80,
  REDUCE_PROD = 81,
  REDUCE_MAX = 82,
  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,

  RSQRT = 76,
  SHAPE = 77,
  POW = 78,
  ARG_MIN = 79,
  FAKE_QUANT = 80,
  REDUCE_PROD = 81,
  REDUCE_MAX = 82,
  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,

  RSQRT = 76,
  SHAPE = 77,
  POW = 78,
  ARG_MIN = 79,
  FAKE_QUANT = 80,
  REDUCE_PROD = 81,
  REDUCE_MAX = 82,
  PACK = 83,
  LOGICAL_OR = 84,
  ONE_HOT = 85,
  LOGICAL_AND = 86,
  LOGICAL_NOT = 87,
  UNPACK = 88,
  REDUCE_MIN = 89,
  FLOOR_DIV = 90,
  REDUCE_ANY = 91,
  SQUARE = 92,
  ZEROS_LIKE = 93,
  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,

  func.return %0 : tensor<? x f32>
}

// CHECK-LABEL: testLogicalNot
func.func @testLogicalNot(tensor<? x i1>) -> tensor<? x i1> {
^bb0(%arg0: tensor<? x i1>):
  // CHECK: "tfl.logical_not"(%arg0)
  %0 = "tfl.logical_not"(%arg0): (tensor<? x i1>) -> tensor<? x i1>
  func.return %0 : tensor<? x i1>
}

// CHECK-LABEL: testSign
func.func @testSign(%arg0: tensor<? x f32>) -> tensor<? x f32> {

                      $input1,
                      $input2),
    (replaceWithValue $input2),
    [(HaveSameType $input2, $result),
     (AllElementsAreBool<"false"> $constant)]>;
  // select(logical_not(C), A, B) -> select(C, B, A)
  def Optimize#SelectOp#Not : Pat<
    (SelectOp (TFL_LogicalNotOp $condition), $input1, $input2),
    (SelectOp $condition, $input2, $input1)>;

/// possible.
/// The function currently asserts that the `result_type` to be a f32 tensor
/// type.
/// TODO: Extend this function to handle integral tensor for ops like
/// "tfl.logical_not".
Attribute ConstFoldUnaryOp(Type result_type, Attribute operand,
                           llvm::function_ref<APFloat(APFloat)> calculate) {
  assert(IsF32ShapedType(result_type) || IsBF16ShapedType(result_type));

}

func.func @logicalNot(%arg0: tensor<8xi1>) -> tensor<8xi1> {
  %0 = "tf.LogicalNot"(%arg0) : (tensor<8xi1>) -> tensor<8xi1>
  func.return %0 : tensor<8xi1>
// CHECK-LABEL: logicalNot
// CHECK:  "tfl.logical_not"(%arg0) : (tensor<8xi1>) -> tensor<8xi1>
}

func.func @logicalOr(%arg0: tensor<8xi1>, %arg1: tensor<8xi1>) -> tensor<8xi1> {
  %0 = "tf.LogicalOr"(%arg0, %arg1) : (tensor<8xi1>, tensor<8xi1>) -> tensor<8xi1>

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

def TFL_LogicalNotOp : TFL_Op<"logical_not", [
    Pure,
    SameOperandsAndResultShape]> {
  let summary = "Logical NOT operator";

  let description = [{
    Element-wise logical NOT operation.
  }];

}

// CHECK-LABEL: CheckSelectNegated
func.func @CheckSelectNegated(%arg0: tensor<1x2x3x4xi1>, %arg1: tensor<1x2x3x4xf32>, %arg2: tensor<1x2x3x4xf32>) -> (tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>) {
  %not = "tfl.logical_not"(%arg0) : (tensor<1x2x3x4xi1>) -> tensor<1x2x3x4xi1>
  %sel_v1 = "tfl.select"(%not, %arg1, %arg2) : (tensor<1x2x3x4xi1>, tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32>

        `tf.atan2`, `tf.atanh`, `tf.cos`, `tf.cosh`, `tf.equal`, `tf.exp`,
        `tf.floor`, `tf.greater`, `tf.greater_equal`, `tf.less`,
        `tf.less_equal`, `tf.log`, `tf.logp1`, `tf.logical_and`,
        `tf.logical_not`, `tf.logical_or`, `tf.maximum`, `tf.minimum`,
        `tf.not_equal`, `tf.sin`, `tf.sinh`, `tf.tan`
    *   Deprecate `tf.data.Dataset.shard`.
    *   Deprecate `saved_model.loader.load` which is replaced by