<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def replace(self, episodes, length, rows=None):
 """Replace full episodes. Args: episodes: Tuple of transition quantities with batch and time dimensions. length: Batch of sequence lengths. rows: Episodes to replace, defaults to all. Returns: Operation. """

    rows = tf.range(self._capacity) if rows is None else rows
    assert rows.shape.ndims == 1
    assert_capacity = tf.assert_less(
        rows, self._capacity, message='capacity exceeded')
    with tf.control_dependencies([assert_capacity]):
      assert_max_length = tf.assert_less_equal(
          length, self._max_length, message='max length exceeded')
    with tf.control_dependencies([assert_max_length]):
      replace_ops = tools.nested.map(
          lambda var, val: tf.scatter_update(var, rows, val),
          self._buffers, episodes, flatten=True)
    with tf.control_dependencies(replace_ops):
      return tf.scatter_update(self._length, rows, length)