code: def universal_transformer_base():
    
    hparams = transformer.transformer_base()
    # To have a similar capacity to the transformer_base with 6 layers,
    # we need to increase the size of the UT's layer
    # since, in fact, UT has a single layer repeating multiple times.
    hparams.hidden_size = 1024
    hparams.filter_size = 4096
    hparams.num_heads = 16
    hparams.layer_prepostprocess_dropout = 0.3
    hparams = update_hparams_for_universal_transformer(hparams)
    return hparams
docs: Base parameters for Universal Transformer.