code: def transformer_nat_small():
    
    hparams = transformer.transformer_small()
    hparams.batch_size = 2048
    hparams.learning_rate = 0.2
    hparams.learning_rate_warmup_steps = 4000
    hparams.num_hidden_layers = 3
    hparams.hidden_size = 384
    hparams.filter_size = 2048
    hparams.label_smoothing = 0.0
    hparams.force_full_predict = True
    hparams.optimizer = "adam"
    hparams.optimizer_adam_epsilon = 1e-9
    hparams.optimizer_adam_beta1 = 0.9
    hparams.optimizer_adam_beta2 = 0.997
    hparams.add_hparam("bottleneck_kind", "vq")
    hparams.add_hparam("bottleneck_bits", 12)
    hparams.add_hparam("num_compress_steps", 3)
    hparams.add_hparam("beta", 0.25)
    hparams.add_hparam("epsilon", 1e-5)
    hparams.add_hparam("decay", 0.999)
    hparams.add_hparam("num_samples", 10)
    hparams.add_hparam("mask_startup_steps", 50000)
    return hparams
docs: Set of hyperparameters.