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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 dilated_attention_1d(x, hparams, attention_type="masked_dilated_1d", q_padding="VALID", kv_padding="VALID", gap_size=2):
 """Dilated 1d self attention."""

  # self-attention
  x, x_shape, is_4d = maybe_reshape_4d_to_3d(x)
  with tf.variable_scope("masked_dilated_1d"):
    y = common_attention.multihead_attention(
        x,
        None,
        None,
        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,
        gap_size=gap_size,
        num_memory_blocks=hparams.num_memory_blocks,
        name="self_attention")
    if is_4d:
      y = tf.reshape(y, x_shape)
      y.set_shape([None, None, None, hparams.hidden_size])
    return y