code: def transformer_tall_finetune_tied():
    
    hparams = transformer_tall()
    hparams.multiproblem_max_input_length = 750
    hparams.multiproblem_max_target_length = 100
    hparams.multiproblem_schedule_max_examples = 0
    hparams.learning_rate_schedule = ("linear_warmup*constant*cosdecay")
    hparams.learning_rate_constant = 5e-5
    hparams.learning_rate_warmup_steps = 100
    # Set train steps to learning_rate_decay_steps or less
    hparams.learning_rate_decay_steps = 80000
    hparams.multiproblem_target_eval_only = True
    hparams.multiproblem_reweight_label_loss = True
    hparams.multiproblem_label_weight = 1.0
    hparams.optimizer = "true_adam"
    return hparams
docs: Tied means fine-tune CNN/DM summarization as LM.