code: def DecoderBlock(
      d_model,
      d_ff,
      d_attention_key,
      d_attention_value,
      n_heads,
      attention_type,
      dropout,
      ff_activation,
      ff_dropout,
      ff_use_sru,
      ff_chunk_size,
      ff_sparsity,
      attention_chunk_size,
      n_attention_layers=1,
      n_feedforward_layers=1,
      center_layernorm=True,
      use_bfloat16=False,
      mode="train",
  ):
      """Reversible transformer decoder layer.
  
      Args:
        d_model: int:  depth of embedding
        d_ff: int: depth of feed-forward layer
        d_attention_key: int: depth of key vector for each attention head
        d_attention_value: int: depth of value vector for each attention head
        n_heads: int: number of attention heads
        attention_type: subclass of tl.BaseCausalAttention: attention class to use
        dropout: float: dropout rate (how much to drop out)
        ff_activation: the non-linearity in feed-forward layer
        ff_dropout: the dropout rate in feed-forward layer
        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
        attention_chunk_size: int, if > 0 run attention chunked at this size
        n_attention_layers: how many residual causal attention layers should we
          have before the feed-forward block (default: 1, the standard block)
        n_feedforward_layers: how many FFNN layers should we have (default 1).
        center_layernorm: whether to use centering in LayerNorm (default) or if
          to skip it, which is known as RMS normalization.
        use_bfloat16: whether to use bfloat16 for weights (default: False).
        mode: str: 'train' or 'eval'
  
  
      Returns:
        the layer.
      """
  
      # pylint: disable=g-complex-comprehension
      def _Attn():
          return ct.ApplyAttentionLayer(
              attention_type,
              d_model,
              n_heads,
              d_attention_key,
              d_attention_value,
              True,
              False,
              dropout,
              dropout,
              attention_chunk_size,
              mode,
          )
  
      def _FF():
          return ct.FeedForwardWithOptions(
              d_model,
              d_ff,
              dropout,
              [-2],
              ff_activation,
              ff_dropout,
              ff_chunk_size,
              ff_use_sru,
              ff_sparsity,
              center_layernorm,
              mode,
              use_bfloat16,
          )
  
      def _attention_half_residual():
          return [
              tl.ReversibleHalfResidual(
                  tl.LayerNorm(center=center_layernorm),
                  attention_layer=_Attn(),
                  name="ReversibleHalfResidualDecoderAttn",
              ),
              tl.ReversibleSwap(),
          ]
  
      def _feed_forward():
          return [
              tl.ReversibleHalfResidual(
                  _FF(), name="ReversibleHalfResidualDecoderFF"
              ),
              tl.ReversibleSwap(),
          ]
  
      return [
          _attention_half_residual() for _ in range(n_attention_layers)
      ] + [_feed_forward() for _ in range(n_feedforward_layers)]
  
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 1 for calling a global function with example usage: DecoderBlock(d_model, d_ff, d_attention_key, d_attention_value, n_heads, attention_type, dropout, ff_activation, ff_dropout, ff_use_sru, ff_chunk_size, ff_sparsity, attention_chunk_size, n_attention_layers, n_feedforward_layers, center_layernorm, use_bfloat16, mode) and returns: unknown, ct, ct, list, list