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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 append(self, transitions, rows=None):
 """Append a batch of transitions to rows of the memory. Args: transitions: Tuple of transition quantities with batch dimension. rows: Episodes to append to, 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(
          tf.gather(self._length, rows), self._max_length,
          message='max length exceeded')
    with tf.control_dependencies([assert_max_length]):
      timestep = tf.gather(self._length, rows)
      indices = tf.stack([rows, timestep], 1)
      append_ops = tools.nested.map(
          lambda var, val: tf.scatter_nd_update(var, indices, val),
          self._buffers, transitions, flatten=True)
    with tf.control_dependencies(append_ops):
      episode_mask = tf.reduce_sum(tf.one_hot(
          rows, self._capacity, dtype=tf.int32), 0)
      return self._length.assign_add(episode_mask)