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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 discounted_return(reward, length, discount):
 """Discounted Monte-Carlo returns."""

  timestep = tf.range(reward.shape[1].value)
  mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
  return_ = tf.reverse(tf.transpose(tf.scan(
      lambda agg, cur: cur + discount * agg,
      tf.transpose(tf.reverse(mask * reward, [1]), [1, 0]),
      tf.zeros_like(reward[:, -1]), 1, False), [1, 0]), [1])
  return tf.check_numerics(tf.stop_gradient(return_), 'return')