code: def transformer_moe_2k():
    
    hparams = transformer_moe_8k()
    hparams.batch_size = 2048
  
    hparams.default_ff = "sep"
  
    # hparams.layer_types contains the network architecture:
    encoder_archi = "a/a/a/a/a"
    decoder_archi = "a-sepm/a-sepm/a-moe/a-sepm/a-sepm"
    hparams.layer_types = "{}#{}".format(encoder_archi, decoder_archi)
  
    return hparams
docs: Base transformers model with moe.
  
    Will have the following architecture:
    * No encoder.
      * Layer 0: a - sep  (self-attention - unmasked separable convolutions)
      * Layer 1: a - sep
      * Layer 2: a - sep
      * Layer 3: a - sep
      * Layer 4: a - sep
    * Decoder architecture:
      * Layer 0: a - a - sepm  (self-attention - enco/deco-attention - masked sep)
      * Layer 1: a - a - sepm
      * Layer 2: a - a - moe  (mixture of expert layers in the middle)
      * Layer 3: a - a - sepm
      * Layer 4: a - a - sepm
  
    Returns:
      hparams