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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _generate_relative_positions_matrix(length_q, length_k, max_relative_position, cache=False):
 """Generates matrix of relative positions between inputs."""

  if not cache:
    if length_q == length_k:
      range_vec_q = range_vec_k = tf.range(length_q)
    else:
      range_vec_k = tf.range(length_k)
      range_vec_q = range_vec_k[-length_q:]
    distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
  else:
    distance_mat = tf.expand_dims(tf.range(-length_k+1, 1, 1), 0)
  distance_mat_clipped = tf.clip_by_value(distance_mat, -max_relative_position,
                                          max_relative_position)
  # Shift values to be >= 0. Each integer still uniquely identifies a relative
  # position difference.
  final_mat = distance_mat_clipped + max_relative_position
  return final_mat