code: def _EncoderDecoderBlock(
      d_model,
      d_ff,
      n_heads,
      dropout,
      dropout_shared_axes,
      mode,
      ff_activation,
  ):
      """Returns a list of layers implementing a Transformer encoder-decoder block.
  
      The input is a triple (decoder_input, mask, encoder) where the mask is
      created from the original source to prevent attending to the padding part
      of the encoder.
  
      Args:
        d_model: int:  depth of embedding
        d_ff: int: depth of feed-forward layer
        n_heads: int: number of attention heads
        dropout: float: dropout rate (how much to drop out)
        dropout_shared_axes: axes on which to share dropout mask
        mode: str: 'train' or 'eval'
        ff_activation: the non-linearity in feed-forward layer
  
      Returns:
        A list of layers which maps triples (decoder_activations, mask,
        encoder_activations) to triples of the same sort.
      """
  
      def _Dropout():
          return tl.Dropout(
              rate=dropout, shared_axes=dropout_shared_axes, mode=mode
          )
  
      attention_qkv = tl.AttentionQKV(
          d_model,
          n_heads=n_heads,
          dropout=dropout,
          mode=mode,
          cache_KV_in_predict=True,
      )
  
      causal_attention = tl.CausalAttention(
          d_model, n_heads=n_heads, mode=mode
      )
  
      feed_forward = _FeedForwardBlock(
          d_model,
          d_ff,
          dropout,
          dropout_shared_axes,
          mode,
          ff_activation,
      )
  
      return [  # vec_d masks vec_e
          ResidualZero(
              tl.LayerNorm(),  # vec_d ..... .....
              causal_attention,  # vec_d ..... .....
              _Dropout(),  # vec_d ..... .....
          ),
          ResidualZero(
              tl.LayerNorm(),  # vec_d ..... .....
              tl.Select(
                  [0, 2, 2, 1, 2]
              ),  # vec_d vec_e vec_e masks vec_e
              attention_qkv,  # vec_d masks vec_e
              _Dropout(),  # vec_d masks vec_e
          ),
          ResidualZero(
              tl.LayerNorm(),
              feed_forward,  # vec_d masks vec_e
              _Dropout(),
          ),
      ]
  
file_path: trax/trax/models/research/rezero.py
imports: from trax import layers
  from trax.fastmath import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: _EncoderDecoderBlock(d_model, d_ff, n_heads, dropout, dropout_shared_axes, mode, ff_activation) and returns: list, tl