//===----------------------------------------------------------------------===//

int64_t AddNOp::GetArithmeticCount(Operation* op) {
  int64_t count;
  if (ArithmeticCountUtilHelper::GetFirstOutputCount(op, &count)) {
    // AddN cost is roughly the same cost as N-1 Adds.
    const int64_t num_adds = op->getNumOperands() - 1;
    return num_adds * count;
  }

  return -1;
}

  %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
func.func @testAddNWrongOperandResultType(tensor<? x f16>, tensor<? x f16>, tensor<? x f16>) -> tensor<? x f16> {
^bb0(%arg0: tensor<? x f16>, %arg1: tensor<? x f16>, %arg2: tensor<? x f16>):

               WithBroadcastableBinOpBuilder {
  let summary = "Returns x + y element-wise.";

  let description = [{
*NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
[here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)

Given two input tensors, the `tf.add` operation computes the sum for every element in the tensor.