def EncoderBlock(d_model, d_ff, n_heads, attention_type, dropout, ff_activation,
                 ff_dropout, ff_use_sru=0, ff_chunk_size=0, ff_sparsity=0,
                 attention_chunk_size=0, center_layernorm=True,
                 use_bfloat16=False, use_two_swaps_per_block=True,
                 mode='train'):
  """Returns a list of layers that implements a Reformer encoder block.

  The input to the layer is a pair, (activations, mask), where the mask was
  created from the original source tokens to prevent attending to the padding
  part of the input.

  Args:
    d_model: int:  depth of embedding
    d_ff: int: depth of feed-forward layer
    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
    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)
    use_two_swaps_per_block: bool, if True use two reversible swaps in Encoder
      block, otherwise use only one swap.
    mode: str: 'train' or 'eval'

  Returns:
    A list of layers that maps (activations, mask) to (activations, mask).
  """
  if mode == 'predict':
    # Mode 'predict' means that the decoder should be run one token at a time.
    # The encoder only ever runs over full sequences, which is why it's switched
    # to 'eval' mode instead.
    mode = 'eval'

  def _Attn():
    return ct.ApplyAttentionLayer(
        attention_type=attention_type, d_model=d_model, n_heads=n_heads,
        d_qk=d_model//n_heads, d_v=d_model//n_heads, masked=True, causal=False,
        attention_dropout=dropout, output_dropout=dropout,
        attention_chunk_size=attention_chunk_size, mode=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)

  # TODO(lukaszkaiser): refactor efficient attention layers to unify the API
  # If we're using standard attention, we need to pass reshaped mask and not
  # return the mask to be compatible with the EfficientAttention API.
  attention = _Attn()
  if attention.n_out == 2:
    attention = tl.Serial(
        tl.Parallel([], _InsertAxes12()),
        attention,
        tl.Select([0], n_in=2)
    )

  def _attention_half_residual():
    return [
        tl.ReversibleHalfResidual(tl.LayerNorm(center=center_layernorm),
                                  attention_layer=attention,
                                  name='ReversibleHalfResidualEncoderAttn'),
        tl.ReversibleSwap()
    ]

  def _feed_forward():
    layers = [
        tl.ReversibleHalfResidual(_FF(),
                                  name='ReversibleHalfResidualEncoderFF')
    ]
    if use_two_swaps_per_block:
      layers.append(tl.ReversibleSwap())
    return layers

  return _attention_half_residual() + _feed_forward()
Returns a list of layers that implements a Reformer encoder block.

  The input to the layer is a pair, (activations, mask), where the mask was
  created from the original source tokens to prevent attending to the padding
  part of the input.

  Args:
    d_model: int:  depth of embedding
    d_ff: int: depth of feed-forward layer
    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
    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)
    use_two_swaps_per_block: bool, if True use two reversible swaps in Encoder
      block, otherwise use only one swap.
    mode: str: 'train' or 'eval'

  Returns:
    A list of layers that maps (activations, mask) to (activations, mask).
  
trax/models/reformer/reformer.py