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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 local_global_attention(x, self_attention_bias, hparams, q_padding="LEFT", kv_padding="LEFT"):
 """Local and global 1d self attention."""

  with tf.variable_scope("self_local_global_att"):
    [x_global, x_local] = tf.split(x, 2, axis=-1)
    split_hidden_size = int(hparams.hidden_size / 2)
    split_heads = int(hparams.num_heads / 2)
    if self_attention_bias is not None:
      self_attention_bias = get_self_attention_bias(x)
    y_global = common_attention.multihead_attention(
        x_global,
        None,
        self_attention_bias,
        hparams.attention_key_channels or split_hidden_size,
        hparams.attention_value_channels or split_hidden_size,
        split_hidden_size,
        split_heads,
        hparams.attention_dropout,
        q_filter_width=hparams.q_filter_width,
        kv_filter_width=hparams.kv_filter_width,
        q_padding=q_padding,
        kv_padding=kv_padding,
        name="global_self_att")
    y_local = common_attention.multihead_attention(
        x_local,
        None,
        None,
        hparams.attention_key_channels or split_hidden_size,
        hparams.attention_value_channels or split_hidden_size,
        split_hidden_size,
        split_heads,
        hparams.attention_dropout,
        attention_type="local_masked",
        block_length=hparams.block_length,
        block_width=hparams.block_width,
        q_filter_width=hparams.q_filter_width,
        kv_filter_width=hparams.kv_filter_width,
        q_padding=q_padding,
        kv_padding=kv_padding,
        name="local_self_att")
    y = tf.concat([y_global, y_local], axis=-1)
    return y