code: def mtf_transformer_paper_tr(size):
    
    n = 2 ** size
    hparams = mtf_transformer_base()
    hparams.label_smoothing = 0.1
    hparams.batch_size = 128
    hparams.d_model = 1024
    hparams.d_ff = int(4096 * n)
    hparams.num_heads = int(8 * n)
    hparams.shared_embedding_and_softmax_weights = False
    # one epoch for translate_enfr_wmt32k_packed = 51400 steps
    hparams.learning_rate_decay_steps = 51400
    return hparams
docs: Config for translation experiments.
  
    Train these on translate_enfr_wmt32k_packed for 154000 steps (3 epochs)
  
    The size parameter is an integer that controls the number of heads and the
    size of the size of the feedforward hidden layers.  Increasing size by 1
    doubles each of these.
  
    Args:
      size: an integer
    Returns:
      a hparams object