code: def _relative_position_to_absolute_position_unmasked(x):
    
    x_shape = common_layers.shape_list(x)
    batch = x_shape[0]
    heads = x_shape[1]
    length = x_shape[2]
    # Concat columns of pad to shift from relative to absolute indexing.
    col_pad = tf.zeros((batch, heads, length, 1))
    x = tf.concat([x, col_pad], axis=3)
  
    # Concat extra elements so to add up to shape (len+1, 2*len-1).
    flat_x = tf.reshape(x, [batch, heads, length * 2 * length])
    flat_pad = tf.zeros((batch, heads, length-1))
    flat_x_padded = tf.concat([flat_x, flat_pad], axis=2)
  
    # Reshape and slice out the padded elements.
    final_x = tf.reshape(flat_x_padded, [batch, heads, length+1, 2*length-1])
    final_x = final_x[:, :, :, length-1:]
    final_x = final_x[:, :, :length, :]
    return final_x
docs: Converts tensor from relative to aboslute indexing for local attention.
  
    Args:
      x: a Tensor of shape [batch (or batch*num_blocks), heads,
                            length, 2 * length - 1]
  
    Returns:
      A Tensor of shape [batch (or batch*num_blocks), heads, length, length-1]