code: def add_position_timing_signal(x, step, hparams):
    
  
    if not hparams.position_start_index:
      index = 0
  
    elif hparams.position_start_index == "random":
      # Shift all positions randomly
      # TODO(dehghani): What would be reasonable for max number of shift?
      index = tf.random_uniform(
          [], maxval=common_layers.shape_list(x)[1], dtype=tf.int32)
  
    elif hparams.position_start_index == "step":
      # Shift positions based on the step
      if hparams.recurrence_type == "act":
        num_steps = hparams.act_max_steps
      else:
        num_steps = hparams.num_rec_steps
      index = tf.cast(
          common_layers.shape_list(x)[1] * step / num_steps, dtype=tf.int32)
  
    # No need for the timing signal in the encoder/decoder input preparation
    assert hparams.pos is None
  
    length = common_layers.shape_list(x)[1]
    channels = common_layers.shape_list(x)[2]
    signal = common_attention.get_timing_signal_1d(
        length, channels, start_index=index)
  
    if hparams.add_or_concat_timing_signal == "add":
      x_with_timing = x + common_layers.cast_like(signal, x)
  
    elif hparams.add_or_concat_timing_signal == "concat":
      batch_size = common_layers.shape_list(x)[0]
      signal_tiled = tf.tile(signal, [batch_size, 1, 1])
      x_with_timing = tf.concat((x, signal_tiled), axis=-1)
  
    return x_with_timing
docs: Add n-dimensional embedding as the position (horizontal) timing signal.
  
    Args:
      x: a tensor with shape [batch, length, depth]
      step: step
      hparams: model hyper parameters
  
    Returns:
      a Tensor with the same shape as x.