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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 data(self, rows=None):
 """Access a batch of episodes from the memory. Padding elements after the length of each episode are unspecified and might contain old data. Args: rows: Episodes to select, defaults to all. Returns: Tuple containing a tuple of transition quantities with batch and time dimensions, and a batch of sequence lengths. """

    rows = tf.range(self._capacity) if rows is None else rows
    assert rows.shape.ndims == 1
    episode = tools.nested.map(lambda var: tf.gather(var, rows), self._buffers)
    length = tf.gather(self._length, rows)
    return episode, length