<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def get_padding_bias(x):
 """Calculate bias tensor from padding values in tensor. Bias tensor that is added to the pre-softmax multi-headed attention logits, which has shape [batch_size, num_heads, length, length]. The tensor is zero at non-padding locations, and -1e9 (negative infinity) at padding locations. Args: x: int tensor with shape [batch_size, length] Returns: Attention bias tensor of shape [batch_size, 1, 1, length]. """

  with tf.name_scope("attention_bias"):
    padding = get_padding(x)
    attention_bias = padding * _NEG_INF
    attention_bias = tf.expand_dims(
        tf.expand_dims(attention_bias, axis=1), axis=1)
  return attention_bias