code: def local_within_block_attention(x,
                                   self_attention_bias,
                                   hparams,
                                   attention_type="local_within_block_mask_right",
                                   q_padding="VALID",
                                   kv_padding="VALID"):
    
    x_new, x_shape, is_4d = maybe_reshape_4d_to_3d(x)
    with tf.variable_scope("local_within_block"):
      y = common_attention.multihead_attention(
          common_layers.layer_preprocess(x_new, hparams),
          None,
          self_attention_bias,
          hparams.attention_key_channels or hparams.hidden_size,
          hparams.attention_value_channels or hparams.hidden_size,
          hparams.hidden_size,
          hparams.num_heads,
          hparams.attention_dropout,
          attention_type=attention_type,
          block_width=hparams.block_width,
          block_length=hparams.block_length,
          q_padding=q_padding,
          kv_padding=kv_padding,
          q_filter_width=hparams.q_filter_width,
          kv_filter_width=hparams.kv_filter_width,
          name="local_within_block")
      if is_4d:
        y = tf.reshape(y, x_shape)
      return y
docs: Local within block self attention.