tf.math.add_n performs the same operation as tf.math.accumulate_n, but it
waits for all of its inputs to be ready before beginning to sum.
This buffering can result in higher memory consumption when inputs are ready
at different times, since the minimum temporary storage required is
proportional to the input size rather than the output size.
This op does not broadcast
its inputs. If you need broadcasting, use tf.math.add (or the + operator)