code: def EncoderDecoderBlock(
      d_model,
      d_ff,
      n_heads,
      dropout,
      ff_activation,
      ff_dropout,
      mode,
      ff_use_sru=0,
      ff_chunk_size=0,
      ff_sparsity=0,
  ):
      """Reversible transformer decoder layer.
  
      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)
        ff_activation: the non-linearity in feed-forward layer
        ff_dropout: float: (optional) separate dropout rate for feed-forward layer
        mode: str: 'train' or 'eval'
        ff_use_sru: int; if > 0, we use this many SRU layers instead of feed-forward
        ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
        ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
  
      Returns:
        the layer.
      """
      enc_dec_attention = tl.EncDecAttention(
          n_heads=n_heads,
          d_qk=d_model // n_heads,
          d_v=d_model // n_heads,
          attention_dropout=dropout,
          output_dropout=dropout,
          mode=mode,
      )
      enc_dec_attention_half_residual = tl.ReversibleHalfResidual(
          tl.LayerNorm(),
          attention_layer=enc_dec_attention,
      )
  
      causal_attention = tl.SelfAttention(
          n_heads=n_heads,
          d_qk=d_model // n_heads,
          d_v=d_model // n_heads,
          causal=True,
          attention_dropout=dropout,
          output_dropout=dropout,
          mode=mode,
      )
      causal_attention_half_residual = tl.ReversibleHalfResidual(
          tl.LayerNorm(),
          attention_layer=causal_attention,
      )
  
      feed_forward = ct.FeedForwardWithOptions(
          d_model,
          d_ff,
          dropout,
          [-2],
          ff_activation,
          ff_dropout,
          ff_chunk_size,
          ff_use_sru,
          ff_sparsity,
          True,
          mode,
      )
  
      return [  # vec_d1 vec_d2 vec_e masks
          causal_attention_half_residual,
          tl.ReversibleSwap(),
          enc_dec_attention_half_residual,
          tl.ReversibleSwap(),
          tl.ReversibleHalfResidual(feed_forward),
          tl.ReversibleSwap(),
      ]
  
file_path: trax/trax/models/reformer/reformer.py
imports: from trax import layers
  from trax.fastmath import numpy
  from trax.models.research import configurable_transformer
label_desc: Use image node_id 5 for calling a global function with example usage: EncoderDecoderBlock(d_model, d_ff, n_heads, dropout, ff_activation, ff_dropout, mode, ff_use_sru, ff_chunk_size, ff_sparsity) and returns: list