code: def Transformer2(
      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,
      dropout_shared_axes=None,
      max_len=2048,
      mode="train",
      ff_activation=tl.Relu,
      ff_dropout=0.1,
      ff_chunk_size=0,
      ff_use_sru=0,
      ff_sparsity=0,
      ff_sparsity_type="1inN",
      attention_chunk_size=0,
      encoder_attention_type=tl.Attention,
      n_encoder_attention_layers=1,
      decoder_attention_type=tl.CausalAttention,
      n_decoder_attention_layers=2,
      pos_type=None,
      pos_axial_shape=None,
      pos_d_axial_embs=None,
  ):
      """Returns a Transformer model.
  
      This model expects an input pair: target, source.
  
      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)
        dropout_shared_axes: axes on which to share dropout mask
        max_len: int: maximum symbol length for positional encoding
        mode: str: 'train' or 'eval'
        ff_activation: the non-linearity in feed-forward layer
        ff_dropout: Stochastic rate (probability) for dropping an activation value
          when applying dropout after the FF dense layer.
        ff_chunk_size: int; if > 0, chunk feed-forward into this-sized chunks
        ff_use_sru: int; if > 0, we use this many SRU layers instead of feed-forward
        ff_sparsity: int, if > 0 use sparse feed-forward block with this sparsity
        ff_sparsity_type: string, if ff_sparsity >0,
          use SparseFF if ff_sparsity_type=`'1inN'` and
          use BlockSparseFF if ff_sparsity_type=`'Block'`
        attention_chunk_size: int, if > 0 run attention chunked at this size
        encoder_attention_type: The attention layer to use for the encoder part.
        n_encoder_attention_layers: int, within each encoder block, how many
          attention layers to have.
        decoder_attention_type: The attention layer to use for the
          encoder-decoder attention.
        n_decoder_attention_layers: int, within each decoder block, how many
          attention layers to have.
        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.
  
      Returns:
        A Transformer 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,
          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 = [
          ct.EncoderBlock(
              d_model,
              d_ff,
              n_heads,
              dropout,
              dropout_shared_axes,
              mode,
              ff_activation,
              ff_dropout,
              ff_chunk_size,
              ff_use_sru,
              ff_sparsity,
              ff_sparsity_type,
              attention_chunk_size,
              encoder_attention_type,
              n_encoder_attention_layers,
          )
          for i in range(n_encoder_layers)
      ]
      # pylint: enable=g-complex-comprehension
  
      encoder = tl.Serial(in_encoder, encoder_blocks, tl.LayerNorm())
      if mode == "predict":
          encoder = tl.Cache(encoder)
  
      # pylint: disable=g-complex-comprehension
      decoder_blocks = [
          ct.DecoderBlock(
              d_model,
              d_ff,
              n_heads,
              dropout,
              dropout_shared_axes,
              mode,
              ff_activation,
              ff_dropout,
              ff_chunk_size,
              ff_use_sru,
              ff_sparsity,
              ff_sparsity_type,
              attention_chunk_size,
              decoder_attention_type,
              n_decoder_attention_layers,
          )
          for i 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, 0, 1, 1]),  # tok_e tok_e tok_d tok_d
          # Encode.
          tl.Branch(
              [], tl.PaddingMask()
          ),  # tok_e mask_e tok_e tok_d tok_d
          encoder,  # vec_e mask_e tok_e tok_d tok_d
          # Simple encoder mask, doesn't contain extra dims.
          tl.Select(
              [2, 0, 2], n_in=3
          ),  #  tok_e vec_e tok_e tok_d tok_d
          tl.Fn(
              "EncoderMask",  # mask_e vec_e tok_e tok_d tok_d
              lambda x: x != 0,
              n_out=1,
          ),
          # Decode.
          tl.Select([3, 1, 0, 2]),  #  tok_d vec_e mask_e tok_e tok_d
          tl.ShiftRight(mode=mode),  # stok_d vec_e mask_e tok_e tok_d
          out_encoder,  # svec_d vec_e mask_e tok_e tok_d
          # Concat encoder and decoder.
          tl.Select([1, 0]),  # vec_e svec_d mask_e tok_e tok_d
          ConcatWithPadding(mode=mode),  # vec_ed tok_e tok_d
          # Decoder blocks with causal attention
          decoder_blocks,  # vec_ed tok_e tok_d
          tl.LayerNorm(),  # vec_ed tok_e tok_d
          # Separate out the encoder part from the concatenated vector.
          tl.Select([0, 1, 2, 2]),  # vec_ed tok_e tok_d tok_d
          StripFromConcatenateWithPadding(mode=mode),  # vec_d tok_d
          # Map to output vocab.
          tl.Dense(output_vocab_size),  # vec_d tok_d
      )
  
imports: from trax import fastmath
  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: Transformer2(input_vocab_size, output_vocab_size, d_model, d_ff, n_encoder_layers, n_decoder_layers, n_heads, dropout, dropout_shared_axes, max_len, mode, ff_activation, ff_dropout, ff_chunk_size, ff_use_sru, ff_sparsity, ff_sparsity_type, attention_chunk_size, encoder_attention_type, n_encoder_attention_layers, decoder_attention_type, n_decoder_attention_layers, pos_type, pos_axial_shape, pos_d_axial_embs) and returns: tl