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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 fixed_step_return(reward, value, length, discount, window):
 """N-step discounted return."""

  timestep = tf.range(reward.shape[1].value)
  mask = tf.cast(timestep[None, :] < length[:, None], tf.float32)
  return_ = tf.zeros_like(reward)
  for _ in range(window):
    return_ += reward
    reward = discount * tf.concat(
        [reward[:, 1:], tf.zeros_like(reward[:, -1:])], 1)
  return_ += discount ** window * tf.concat(
      [value[:, window:], tf.zeros_like(value[:, -window:])], 1)
  return tf.check_numerics(tf.stop_gradient(mask * return_), 'return')