code: def get_padding_bias(x):
    
    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
docs: 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].