code: def Reformer(
      input_vocab_size,
      output_vocab_size=None,
      d_model=512,
      d_ff=2048,
      n_encoder_layers=6,
      n_decoder_layers=6,
      n_heads=8,
      dropout=0.1,
      max_len=2048,
      ff_activation=tl.Relu,
      ff_dropout=None,
      mode="train",
      pos_type=None,
      pos_axial_shape=None,
      pos_d_axial_embs=None,
      ff_use_sru=0,
      ff_chunk_size=0,
      ff_sparsity=0,
  ):
      """Reversible transformer encoder-decoder model.
  
      This model expects an input pair: target, source.
  
      At the moment, this model supports dot-product attention only. For the
      attention types in the Reformer paper, see ReformerLM.
  
      Args:
        input_vocab_size: int: vocab size of the source.
        output_vocab_size: int (optional): vocab size of the target. If None, the
          source and target are assumed to have the same vocab.
        d_model: int:  depth of embedding
        d_ff: int: depth of feed-forward layer
        n_encoder_layers: int: number of encoder layers
        n_decoder_layers: int: number of decoder layers
        n_heads: int: number of attention heads
        dropout: float: dropout rate (how much to drop out)
        max_len: int: maximum symbol length for positional encoding
        ff_activation: the non-linearity in feed-forward layer
        ff_dropout: float: (optional) separate dropout rate at feed-forward
          nonlinearity. This is called relu_dropout in T2T.
        mode: str: 'train' or 'eval'
        pos_type: string, the type of positional embeddings to use.
        pos_axial_shape: tuple of ints: input shape to use for the axial position
          encoding. If unset, axial position encoding is disabled.
        pos_d_axial_embs: tuple of ints: depth of position embedding for each axis.
          Tuple length must match pos_axial_shape, and values must sum to d_model.
        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:
        A Reformer model as a layer that maps from a target, source pair to
        activations over a vocab set.
      """
      (
          in_encoder,
          out_encoder,
          output_vocab_size,
      ) = ct.EmbeddingAndPositionalEncodings(
          input_vocab_size,
          d_model,
          mode,
          dropout,
          [-2],  # dropout_shared_axes
          max_len,
          output_vocab_size=output_vocab_size,
          pos_type=pos_type,
          pos_axial_shape=pos_axial_shape,
          pos_d_axial_embs=pos_d_axial_embs,
      )
  
      # pylint: disable=g-complex-comprehension
      encoder_blocks = [
          EncoderBlock(
              d_model,
              d_ff,
              n_heads,
              tl.SelfAttention,
              dropout,
              ff_activation,
              ff_dropout,
              mode=mode,
              ff_use_sru=ff_use_sru,
              ff_chunk_size=ff_chunk_size,
              ff_sparsity=ff_sparsity,
          )
          for _ in range(n_encoder_layers)
      ]
      # pylint: enable=g-complex-comprehension
  
      encoder = tl.Serial(
          [
              in_encoder,
              tl.Dup(),
              tl.ReversibleSerial(encoder_blocks),
              _XYAvg(),
              tl.LayerNorm(),
          ]
      )
      if mode == "predict":
          encoder = tl.Cache(encoder)
  
      # pylint: disable=g-complex-comprehension
      encoder_decoder_blocks = [
          EncoderDecoderBlock(
              d_model,
              d_ff,
              n_heads,
              dropout,
              ff_activation,
              ff_dropout,
              mode,
              ff_use_sru=ff_use_sru,
              ff_chunk_size=ff_chunk_size,
              ff_sparsity=ff_sparsity,
          )
          for _ in range(n_decoder_layers)
      ]
      # pylint: enable=g-complex-comprehension
  
      # Assemble and return the model.
      return tl.Serial(
          # Input: encoder_side_tokens, decoder_side_tokens
          # Copy decoder tokens for use in loss.
          tl.Select([0, 1, 1]),  # tok_e tok_d tok_d
          tl.Branch(
              [], [tl.PaddingMask(), _RemoveAxes12()]
          ),  # tok_e mask  tok_d .....
          # Encode.
          encoder,  # vec_e  mask tok_d .....
          # Decode.
          tl.Select([2, 0, 1]),  # tok_d vec_e mask .....
          tl.ShiftRight(mode=mode),  # tok_d vec_e mask .....
          out_encoder,  # vec_d vec_e mask .....
          tl.Dup(),  # vec_d1 vec_d2 vec_e mask .....
          tl.ReversibleSerial(encoder_decoder_blocks),
          _XYAvg(),  # vec_d vec_e mask .....
          tl.LayerNorm(),  # vec_d vec_e mask .....
          # Map to output vocab.
          tl.Select([0], n_in=3),  # vec_d .....
          tl.Dense(output_vocab_size),  # vec_d .....
      )
  
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 6 for calling a global function with example usage: Reformer(input_vocab_size, output_vocab_size, d_model, d_ff, n_encoder_layers, n_decoder_layers, n_heads, dropout, max_len, ff_activation, ff_dropout, mode, pos_type, pos_axial_shape, pos_d_axial_embs, ff_use_sru, ff_chunk_size, ff_sparsity) and returns: tl