tensorflow AttentionCellWrapper 实例 和 使用

  cell = multilayer_dropout_cell(
    RNN_CELL_DICT[cell_type],
    hidden_size, n_layers, rnn_dropout)

  if bidirectional:
    cell_bw = multilayer_dropout_cell(
      RNN_CELL_DICT[cell_type],
      hidden_size, n_layers, rnn_dropout)
  else:
    cell_bw = None

  # Add attention.
  if attn_inputs is not None:
    cell = tf_utils.SeqAttentionCellWrapper(
      cell, attn_inputs=attn_inputs,
      attn_size=attn_size, attn_vec_size=attn_vec_size,
      output_size=hidden_size, attn_masks=attn_masks)
    if bidirectional:
      cell_bw = tf_utils.SeqAttentionCellWrapper(
        cell_bw, attn_inputs=attn_inputs,
        attn_size=attn_size, attn_vec_size=attn_vec_size,
        output_size=hidden_size, attn_masks=attn_masks)
    
  if initial_state is None:
    # Create zero state.
    zero_state = cell.zero_state(batch_size, tf.float32)
    
    if bidirectional:
      zero_state_bw = cell_bw.zero_state(batch_size, tf.float32)
      zero_state = (zero_state, zero_state_bw)

    initial_state = zero_state
      
  # Create RNN.
  outputs, final_state = create_rnn(
    cell, initial_state, embedded_seq_inputs.tensor,
    embedded_seq_inputs.sequence_length,
    hidden_size=hidden_size,
    bidirectional=bidirectional, cell_bw=cell_bw)
  rnn_outputs = outputs

SeqAttentionCellWrapper 和 AttentionCellWrapper 类似，