class_docstr: The attention layers custom to the evolved transformer decoder.
  
  This layer consists of applying both a self attention and enc-dec attention to
  the input and then summing their outputs.
docstr: Create an DecoderAttentionLayer.
  
  Args:
    base_num_heads: a positive integer, the base number of heads the attention
      layers are using.
docstr_tok: ["Create","an","DecoderAttentionLayer",".","Args",":","base_num_heads",":","a","positive","integer",",","the","base","number","of","heads","the","attention","layers","are","using","."]
code: def __init__(self, base_num_heads):
      """Create an DecoderAttentionLayer.
  
      Args:
        base_num_heads: a positive integer, the base number of heads the attention
          layers are using.
      """
      self._self_attention = transformer_layers.SelfAttention(
          num_heads=2 * base_num_heads
      )
      self._enc_dec_attention = transformer_layers.EncDecAttention(
          num_heads=base_num_heads
      )
  
imports: import gin
  import mesh_tensorflow
  from mesh_tensorflow.transformer import transformer
  from mesh_tensorflow.transformer import transformer_layers
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new DecoderAttentionLayer object from inherited base classes: transformer with example: obj = DecoderAttentionLayer(base_num_heads)