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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 get_decoder_self_attention_bias(length):
 """Calculate bias for decoder that maintains model's autoregressive property. Creates a tensor that masks out locations that correspond to illegal connections, so prediction at position i cannot draw information from future positions. Args: length: int length of sequences in batch. Returns: float tensor of shape [1, 1, length, length] """

  with tf.name_scope("decoder_self_attention_bias"):
    valid_locs = tf.matrix_band_part(tf.ones([length, length]), -1, 0)
    valid_locs = tf.reshape(valid_locs, [1, 1, length, length])
    decoder_bias = _NEG_INF * (1.0 - valid_locs)
  return decoder_bias